Genome-wide complex trait analysis: Difference between revisions

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{{short description|Statistical method for genetic variance component estimation}}

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'''Genome-wide complex trait analysis (GCTA) Genome-based [[restricted maximum likelihood]] (GREML)''' is a statistical method for [[variance]] component estimation in genetics which quantifies the total narrow-sense (additive) contribution to a trait's [[heritability]] of a particular subset of genetic variants (typically limited to [[Single-nucleotide polymorphism|SNPs]] with [[Minor allele frequency|MAF]] >1%, hence terms such as "chip heritability"/"SNP heritability"). This is done by directly quantifying the chance genetic similarity of unrelated strangers and comparing it to their measured similarity on a trait; if two strangers are relatively similar genetically and also have similar trait measurements, then this indicates that the measured genetics causally influence that trait, and how much. This can be seen as plotting prediction error against relatedness.<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232052/figure/F3/ Figure 3] of Yang et al 2010, or Figure 3 of Ritland & Ritland 1996</ref> The GCTA framework extends to bivariate [[genetic correlation]]s between traits;<ref name="Lee2012">Lee et al 2012, [http://bioinformatics.oxfordjournals.org/content/28/19/2540.full "Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood"]</ref> it can also be done on a per-[[chromosome]] basis comparing against chromosome length; and it can also examine changes in heritability over aging and development.<ref name="Deary2012"/> There is an ongoing debate about whether GCTA generates reliable or stable estimates of heritability when used on current SNP data.<ref>{{Cite journal |last=Krishna Kumar |first=Siddharth |last2=Feldman |first2=Marcus W. |last3=Rehkopf |first3=David H. |last4=Tuljapurkar |first4=Shripad |date=2016-01-05 |title=Limitations of GCTA as a solution to the missing heritability problem |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=113 |issue=1 |pages=E61–70 |doi=10.1073/pnas.1520109113 |issn=1091-6490 |pmc=4711841 |pmid=26699465}}</ref>

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'''Genome-wide complex trait analysis''' ('''GCTA''') '''Genome-based [[restricted maximum likelihood]]''' ('''GREML''') is a statistical method for [[heritability]] estimation in genetics, which quantifies the total additive contribution of a set of genetic variants to a trait. GCTA is typically applied to common single nucleotide polymorphisms ([[SNPs]]) on a genotyping array (or "chip") and thus termed "chip" or "SNP" heritability.

GCTA operates by directly quantifying the chance genetic similarity of unrelated individuals and comparing it to their measured similarity on a trait; if two unrelated individuals are relatively similar genetically and also have similar trait measurements, then the measured genetics are likely to causally influence that trait, and the correlation can to some degree tell how much. This can be illustrated by plotting the squared pairwise trait differences between individuals against their estimated degree of relatedness.<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232052/figure/F3/ Figure 3] of Yang et al 2010, or Figure 3 of Ritland & Ritland 1996</ref> GCTA makes a number of modeling assumptions and whether/when these assumptions are satisfied continues to be debated.

The GCTA framework has also been extended in a number of ways: quantifying the contribution from multiple SNP categories (i.e. functional partitioning); quantifying the contribution of Gene-Environment interactions; quantifying the contribution of non-additive/non-linear effects of SNPs; and bivariate analyses of multiple phenotypes to quantify their genetic covariance (co-heritability or [[genetic correlation]]).

GCTA estimates have implications for the potential for discovery from [[Genome-wide association study|Genome-wide Association Studies (GWAS)]] as well as the design and accuracy of [[polygenic scores]]. GCTA estimates from common variants are typically substantially lower than other estimates of total or narrow-sense heritability (such as from twin or kinship studies), which has contributed to the debate over the [[Missing heritability problem]].

GCTA heritability estimates are useful because they can lower bound<ref>"A common misconception about SNP-chip heritability estimates calculated with GCTA and LDSC is that they should be similar to twin study estimates, when in reality twin studies have the advantage of capturing all genetic effects—common, rare and those not genotyped by available methods. Thus, the assumption should be that h2SNP < h2TWIN when using GCTA and LDSC, and this is what we observe for PTSD, as has been observed for many other phenotypes.54" --Duncan et al 2017</ref> the genetic contributions to traits such as [[Heritability of IQ|intelligence]] without relying on the assumptions used in [[twin study|twin studies]] and other family studies and [[Genealogy|pedigree]] analyses, thereby corroborating<ref>Eric Turkheimer ([http://people.virginia.edu/~ent3c/papers2/StillMissingFinal.pdf "Still Missing"], Turkheimer 2011) discusses the GCTA results in the context of the twin study debate: "Of the three reservations about quantitative genetic heritability that were outlined at the outset—the assumptions of twin and family studies, the universality of heritability, and the absence of mechanism—the new paradigm has put the first to rest, and before continuing to explain my skepticism about whether the most important problems have been solved, it is worth appreciating what a significant accomplishment this is. Thanks to the Visscher program of research, it should now be impossible to argue that the whole body of quantitative genetic research showing the universal importance of genes for human development was somehow based on a sanguine view of the equal environments assumption in twin studies, putting an end to an entire misguided school of thought among traditional opponents of classical quantitative (and by association behavioral) genetics (e.g., Joseph, 2010; Kamin & Goldberger, 2002)"; see also [https://www.vox.com/the-big-idea/2017/5/18/15655638/charles-murray-race-iq-sam-harris-science-free-speech Turkheimer, Harden, & Nisbett]: "These methods have given scientists a new way to compute heritability: Studies that measure DNA sequence variation directly have shown that pairs of people who are not relatives, but who are slightly more similar genetically, also have more similar IQs than other pairs of people who happen to be more different genetically. These “DNA-based” heritability studies don’t tell you much more than the classical twin studies did, but they put to bed many of the lingering suspicions that twin studies were fundamentally flawed in some way. Like the validity of intelligence testing, the heritability of intelligence is no longer scientifically contentious."</ref><ref>"This finding of strong genome-wide pleiotropy across diverse cognitive and learning abilities, indexed by general intelligence, is a major finding about the origins of individual differences in intelligence. Nonetheless, this finding seems to have had little impact in related fields such as cognitive neuroscience or experimental cognitive psychology. We suggest that part of the reason for this neglect is that these fields generally ignore individual differences.65,66 Another reason might be that the evidence for this finding rested largely on the twin design, for which there have always been concerns about some of its assumptions;6 we judge that this will change now that GCTA is beginning to confirm the twin results." --[http://www.nature.com/mp/journal/vaop/ncurrent/full/mp2014105a.html "Genetics and intelligence differences: five special findings"], Plomin & Deary 2015</ref><ref>[http://www.gwern.net/docs/genetics/2016-plomin.pdf "Top 10 Replicated Findings From Behavioral Genetics"], Plomin et al 2016: "This research has primarily relied on the twin design in which the resemblance of identical and fraternal twins is compared and the adoption design in which the resemblance of relatives separated by adoption is compared. Although the twin and adoption designs have been criticized separately (Plomin et al., 2013), these two designs generally converge on the same conclusion despite being based on very different assumptions, which adds strength to these conclusions...GCTA underestimates genetic influence for several reasons and requires samples of several thousand individuals to reveal the tiny signal of chance genetic similarity from the noise of DNA differences across the genome (Vinkhuyzen, Wray, Yang, Goddard, & Visscher, 2013). Nonetheless, GCTA has consistently yielded evidence for significant genetic influence for cognitive abilities (Benyamin et al., 2014; Davies et al., 2015; St. Pourcain et al., 2014), psychopathology (L. K. Davis et al., 2013; Gaugler et al., 2014; Klei et al., 2012; Lubke et al., 2012, 2014; McGue et al., 2013; Ripke et al., 2013; Wray et al., 2014), personality (C. A. Rietveld, Cesarini, et al., 2013; Verweij et al., 2012; Vinkhuyzen et al., 2012), and substance use or drug dependence (Palmer et al., 2015; Vrieze, McGue, Miller, Hicks, & Iacono, 2013), thus supporting the results of twin and adoption studies."</ref> them, and enabling the design of well-[[statistical power|powered]] [[Genome-wide association study]] (GWAS) designs to find the specific genetic variants. For example, a GCTA estimate of 30% SNP heritability is consistent with a larger total genetic heritability of 70%. However, if the GCTA estimate was ~0%, then that would imply one of three things: a) there is no genetic contribution, b) the genetic contribution is entirely in the form of genetic variants not included, or c) the genetic contribution is entirely in the form of non-additive effects such as [[epistasis]]/[[Dominance (genetics)|dominance]]. The ability to run GCTA on subsets of chromosomes and regress against chromosome length can reveal whether the responsible genetic variants cluster or are distributed evenly across the genome or are [[sex-linked]]. Examining genetic correlations can reveal to what extent observed correlations, such as between intelligence and socioeconomic status, are due to the same genetic traits, and in the case of diseases, can indicate shared causal pathways such as the overlap of schizophrenia with other mental diseases and intelligence-reducing variants.

== History ==

Estimation in biology/animal breeding using standard [[Analysis of variance|ANOVA]]/[[Restricted maximum likelihood|REML]] methods of variance components such as heritability, shared-environment, maternal effects etc. typically requires individuals of known relatedness such as parent/child; this is often unavailable or the pedigree data unreliable, leading to inability to apply the methods or requiring strict laboratory control of all breeding (which threatens the [[external validity]] of all estimates), and several authors have noted that relatedness could be measured directly from genetic markers (and if individuals were reasonably related, economically few markers would have to be obtained for statistical power), leading Kermit Ritland to propose in 1996 that directly measured pairwise relatedness could be compared to pairwise phenotype measurements (Ritland 1996, [http://www.genetics.forestry.ubc.ca/ritland/reprints/1996_Evolution_HeritInFieldModel.pdf "A Marker-based Method for Inferences About Quantitative Inheritance in Natural Populations"] {{Webarchive|url=https://web.archive.org/web/20090611224719/http://genetics.forestry.ubc.ca/ritland/reprints/1996_Evolution_HeritInFieldModel.pdf |date=2009-06-11 }}<ref>see also Ritland 1996b, [http://genetics.forestry.ubc.ca/ritland/reprints/1996_GenetResearch_r.pdf "Estimators for pairwise relatedness and individual inbreeding coefficients"] {{Webarchive|url=https://web.archive.org/web/20170116084901/http://genetics.forestry.ubc.ca/ritland/reprints/1996_GenetResearch_r.pdf |date=2017-01-16 }}; Ritland & Ritland 1996, [http://genetics.forestry.ubc.ca/ritland/reprints/1996_Evolution_HeritInFieldMimulus.pdf "Inferences about quantitative inheritance based on natural population structure in the yellow monkeyflower, ''Mimulus guttatus''"] {{Webarchive|url=https://web.archive.org/web/20160924204921/http://www.genetics.forestry.ubc.ca/ritland/reprints/1996_Evolution_HeritInFieldMimulus.pdf |date=2016-09-24 }}; Lynch & Ritland 1999, [http://www.genetics.org/content/152/4/1753.full "Estimation of Pairwise Relatedness With Molecular Markers"]; Ritland 2000, [http://www.genetics.forestry.ubc.ca/RITLAND/reprints/2000_ME_Review.pdf "Marker-inferred relatedness as a tool for detecting heritability in nature"]; Thomas 2005, [{{Webarchive|url=https://wwwweb.dropboxarchive.comorg/sweb/45kxuo2p00lii6k20160925061647/2005-thomas.pdf "The estimation of genetic relationships using molecular markers and their efficiency in estimating heritability in natural populations"]</ref>) to combine estimated genetic relatedness with phenotypic measurements to estimate variance components such as heritability or genetic correlations.<ref>pg800-803, ch27 [httpshttp://www.dropboxgenetics.comforestry.ubc.ca/sRITLAND/3bfpufmv9zxqldwreprints/1998-lynchwalsh-geneticsquantitativetraits-ch27-reml2000_ME_Review.pdf "REML Estimation of Genetic Variances"], [https://gwern|date=2016-gainax.s3.amazonaws.com/199809-lynchwalsh-geneticsquantitativetraits.djvu25 ''Genetics and Analysis of Quantitative Traits''], Lynch & Walsh 1998; {{ISBN|0878934812}}</ref>; andThomas subsequently applied to plants/animals<ref>Mousseau et al 19982005, [httphttps://www.naturedropbox.com/hdys/journal45kxuo2p00lii6k/v80/n2/full/6882690a.html "A novel method for estimating heritability using molecular markers"]</ref><ref>Thomas et al 2002, [http://onlinelibrary.wiley.com/doi/10.1046/j.14202005-9101thomas.2002.00372.x/fullpdf "The use of marker-based relationship information to estimate the heritability of body weight in a natural population: a cautionary tale"]</ref><ref>Wilson et al 2003, [https://www.academia.edu/download/44111676/Wilson_20et_20al_202003_GEN_20RES_Quantitative_20genetic_20parameters_20in_20rainbow_20trout.pdf "Marker-assisted estimation of quantitative genetic parametersrelationships inusing rainbowmolecular troutmarkers ''Oncorhynchusand mykiss''"]</ref><ref>Klapertheir et al 2001, [http://jhered.oxfordjournals.org/content/92/5/421.long "Heritability of Phenolicsefficiency in ''Quercusestimating laevis'' Inferred Using Molecular Markers"]</ref><ref>van Kleunen & Ritland 2004, [http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2004.00787.x/full "Predicting evolution of floral traits associated with mating systemheritability in a natural plant populationpopulations"]</ref><ref>van Kleunen & Ritland 2005, [http://jhered).oxfordjournals.org/content/96/4/368.full "Estimating Heritabilities and Genetic Correlations with Marker-Based Methods: An Experimental Test in ''Mimulus guttatus''"]</ref><ref>Shikano 2005, [https://www.dropbox.com/s/d2fhtokdnx8twdb/2005-shikano.pdf "Marker-based estimation of heritability for body color variation in Japanese flounder ''Paralichthys olivaceus''"]</ref>

As genome sequencing costs dropped steeply over the 2000s, acquiring enough markers on enough subjects for reliable estimates using very distantly related individuals became possible. An early application of the method to humans came with Visscher et al. 2006<ref>Visscher et al 2006, [http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.0020041 "Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings"]</ref>/2007,<ref>Visscher et al 2007, [http://www.sciencedirect.com/science/article/pii/S0002929707638841 "Genome partitioning of genetic variation for height from 11,214 sibling pairs"]</ref> which used SNP markers to estimate the actual relatedness of siblings and estimate heritability from the direct genetics. In humans, unlike the original animal/plant applications, relatedness is usually known with high confidence in the 'wild population', and the benefit of GCTA is connected more to avoiding assumptions of classic behavioral genetics designs and verifying their results, and partitioning heritability by SNP class and chromosomes. The first use of GCTA proper in humans was published in 2010, finding 45% of variance in human height can be explained by the included SNPs.<ref name="Yang2010">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232052/ "Common SNPs explain a large proportion of heritability for human height"], Yang et al 2010</ref><ref>"[httphttps://emilkirkegaardwww.dkncbi.nlm.nih.gov/enpubmed/wp-content/uploads/A-Commentary-on-Common-SNPs-Explain-a-Large-Proportion-of-the-Heritability-for-Human-Height-by-Yang-et-al.-2010.pdf21142928 "A Commentary on ‘Common SNPs Explain a Large Proportion of the Heritability for Human Height’ by Yang et al. (2010)"], Visscher et al 2010</ref> (Large GWASes on height have since confirmed the estimate.<ref name="Wood2014">[http://neurogenetics.qimrberghofer.edu.au/papers/Wood2014NatGenet.pdf "Defining the role of common variation in the genomic and biological architecture of adult human height"], Wood et al 2014</ref>) The GCTA algorithm was then described and a software implementation published in 2011.<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3014363/ "GCTA: A Tool for Genome-wide Complex Trait Analysis"], Yang et al 2011</ref> It has since been used to study a wide variety of biological, medical, psychiatric, and psychological traits in humans, and inspired many variant approaches.

== Benefits ==

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{{main|Twin study#Criticism}}

Twin and family studies have long been used to estimate variance explained by particular categories of genetic and environmental causes. Across a wide variety of human traits studied, there is typically minimal shared-environment influence, considerable non-shared environment influence, and a large genetic component (mostly additive), which is on average ~50% and sometimes much higher for some traits such as height or intelligence.<ref>[http://www.gwern.net/docs/genetics/2015-polderman.pdf "Meta-analysis of the heritability of human traits based on fifty years of twin studies"], Polderman et al 2015</ref> However, the twin and family studies have been criticized for their reliance on a number of assumptions that are difficult or impossible to verify, such as the equal environments assumption (that the environments of [[monozygotic]] and [[dizygotic]] twins are equally similar), that there is no misclassification of zygosity (mistaking identical for fraternal & vice versa), that twins are unrepresentative of the general population, and that there is no [[assortative mating]]. Violations of these assumptions can result in both upwards and downwards bias of the parameter estimates.<ref>{{Cite journal|lastlast1=Barnes|firstfirst1=J. C.|last2=Wright|first2=John Paul|last3=Boutwell|first3=Brian B.|last4=Schwartz|first4=Joseph A.|last5=Connolly|first5=Eric J.|last6=Nedelec|first6=Joseph L.|last7=Beaver|first7=Kevin M.|date=2014-11-01|title=Demonstrating the Validity of Twin Research in Criminology|url=https://www.researchgate.net/profile/Brian_Boutwell/publication/267158254_Demonstrating_the_validity_of_twin_research_in_criminology/links/545d24af0cf27487b44d4ae3.pdf267158254|journal=Criminology|language=en|volume=52|issue=4|pages=588–626|doi=10.1111/1745-9125.12049|issn=1745-9125}}</ref> (This debate & criticism have particularly focused on the [[heritability of IQ]].)

The use of SNP or whole-genome data from unrelated subject participants (with participants too related, typically >0.025 or ~fourth cousins levels of similarity, being removed, and several [[Principal component analysis|principal components]] included in the regression to avoid & control for [[population stratification]]) bypasses many heritability criticisms: twins are often entirely uninvolved, there are no questions of equal treatment, relatedness is estimated precisely, and the samples are drawn from a broad variety of subjects.

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=== GWAS power ===

GCTA estimates can be used to resolve the [[missing heritability problem]] and [[Design of experiments|design]] GWASes which will yield genome-wide statistically-significant hits. This is done by comparing the GCTA estimate with the results of smaller GWASes. If a GWAS of n=10k using SNP data fails to turn up any hits, but the GCTA indicates a high heritability accounted for by SNPs, then that implies that there are a large number of variants are involved ([[polygenic|polygenicity]] variants) and thus that much larger GWASes will be required to accurately estimate each SNP's effectseffect and directly account for a fraction of the GCTA heritability.

== Disadvantages ==

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# Limited inference: GCTA estimates are inherently limited in that they cannot estimate broadsense heritability like twin/family studies as they only estimate the heritability due to SNPs. Hence, while they serve as a critical check on the unbiasedness of the twin/family studies, GCTAs cannot replace them for estimating total genetic contributions to a trait.

# Substantial data requirements: the number of SNPs genotyped per person should be in the thousands and ideally the hundreds of thousands for reasonable estimates of genetic similarity (although this is no longer such an issue for current commercial chips which default to hundreds of thousands or millions of markers); and the number of persons, for somewhat stable estimates of plausible SNP heritability, should be at least ''n''>1000 and ideally ''n''>10000.<ref>"GCTA will eventually provide direct DNA tests of quantitative genetic results based on twin and adoption studies. One problem is that many thousands of individuals are required to provide reliable estimates. Another problem is that more SNPs are needed than even the million SNPs genotyped on current SNP microarrays because there is much DNA variation not captured by these SNPs. As a result, GCTA cannot estimate all heritability, perhaps only about half of the heritability. The first reports of GCTA analyses estimate heritability to be about half the heritability estimates from twin and adoption studies for height (Lee, Wray, Goddard, & Visscher, 2011; Yang et al., 2010; Yang, Manolio, et al" 2011), and intelligence (Davies et al., 2011)." pg110, [https://www.dropbox.com/s/1iz7o1hqb8isas2/2012-plomin-behavioralgenetics.pdf ''Behavioral Genetics''], Plomin et al 2012</ref> In contrast, twin studies can offer precise estimates with a fraction of the sample size.

# Computational inefficiency: The original GCTA implementation scales poorly with increasing data size (<math>\mathcal{O}(\text{SNPs} \cdot n^2)</math>), so even if enough data is available for precise GCTA estimates, the computational burden may be unfeasible. GCTA can be meta-analyzed as a standard precision-weighted fixed-effect meta-analysis,<ref>[http://gcta.freeforums.net/thread/213/analysis-greml-results-multiple-cohorts "Meta-analysis of GREML results from multiple cohorts"], Yang 2015</ref> so research groups sometimes estimate cohorts or subsets and then pool them meta-analytically (at the cost of additional complexity and some loss of precision). This has motivated the creation of faster implementations and variant algorithms which make different assumptions, such as using [[Method of moments (statistics)|moment matching]].<ref>[http://{{Cite bioRxiv |biorxiv=10.org/content/early/2016/08/181101/070177 "|first1=Tian |last1=Ge |first2=Chia-Yen |last2=Chen |title=Phenome-wide Heritability Analysis of the UK Biobank"], Ge|date=2016 et|last3=Neale al|first3=Benjamin 2016M. |last4=Sabuncu |first4=Mert R. |last5=Smoller |first5=Jordan W.}}</ref>

# Need for raw data: GCTA requires genetic similarity of all subjects and thus their raw genetic information; due to privacy concerns, individual patient data is rarely shared. GCTA cannot be run on the summary statistics reported publicly by many GWAS projects, and if pooling multiple GCTA estimates, a [[meta-analysis]] must be doneperformed. <br> In contrast, there are alternative techniques which operate on summaries reported by GWASes without requiring the raw data<ref>Pasaniuc & Price 2016, [https://www.dropbox.com/s/4mgmun29xbund7z/2016-pasaniuc.pdf "Dissecting the genetics of complex traits using summary association statistics"]</ref> e.g. "[[linkageLinkage disequilibrium score regression|LD]] score regression]]"<ref>[https://www{{cite journal | pmc=4495769 | date=2015 | last1=Bulik-Sullivan | first1=B.ncbi K.nlm | last2=Loh | first2=P.nih R.gov/pmc/articles/PMC4495769/ "| last3=Finucane | first3=H. | last4=Ripke | first4=S. | last5=Yang | first5=J. | author6=Schizophrenia Working Group of the Psychiatric Genomics Consortium | last7=Patterson | first7=N. | last8=Daly | first8=M. J. | last9=Price | first9=A. L. | last10=Neale | first10=B. M. | title=LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies"], Bulik-Sullivan| etjournal=Nature alGenetics 2015| volume=47 | issue=3 | pages=291–295 | doi=10.1038/ng.3211 | pmid=25642630 }}</ref> contrasts [[linkage disequilibrium]] statistics (available from public datasets like [[1000 Genomes]]) with the public summary effect-sizes to infer heritability and estimate genetic correlations/overlaps of multiple traits. The [[Broad Institute]] runs [http://ldsc.broadinstitute.org/about/ LD Hub] {{Webarchive|url=https://web.archive.org/web/20160511100955/http://ldsc.broadinstitute.org/about/ |date=2016-05-11 }} which provides a public web interface to >=177 traits with LD score regression.<ref>[http://biorxiv.org/content/biorxiv/early/2016/05/03/051094.full.pdf "LD Hub: a centralized database and web interface to LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis"], Zheng et al 2016</ref> Another method using summary data is HESS.<ref>[http://biorxiv.org/content/early/2016/01/14/035907 "Contrasting the genetic architecture of 30 complex traits from summary association data"], Shi et al 2016</ref>

# Confidence intervals may be incorrect, or outside the 0-1 range of heritability, and highly imprecise due to asymptotics.<ref>[http:/{{cite journal | doi=10.1016/wwwj.sciencedirectajhg.com/science/article/pii/S00029297163014342016.04.016 | "title=Fast and Accurate Construction of Confidence Intervals for Heritability"], Schweiger| etjournal=The alAmerican Journal of Human Genetics | date=2 June 2016 | volume=98 | issue=6 | pages=1181–1192 | last1=Schweiger | first1=Regev | last2=Kaufman | first2=Shachar | last3=Laaksonen | first3=Reijo | last4=Kleber | first4=Marcus E. | last5=März | first5=Winfried | last6=Eskin | first6=Eleazar | last7=Rosset | first7=Saharon | last8=Halperin | first8=Eran | pmid=27259052 | pmc=4908190 }}</ref>

# Underestimation of SNP heritability: GCTA implicitly assumes all classes of SNPs, rarer or commoner, newer or older, more or less in linkage disequilibrium, have the same effects on average; in humans, rarer and newer variants tend to have larger and more negative effects<ref>[https://www.dropbox.com/s/idh2vm1dkar3qho/2017-gazal.pdf "Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection"], Gazal et al 2017</ref> as they represent [[mutation load]] being purged by [[Negative selection (natural selection)|negative selection]]. As with measurement error, this will bias GCTA estimates towards underestimating heritability.

== Interpretation ==

GCTA provides an unbiased estimate of the total variance in phenotype explained by all variants included in the relatedness matrix (and any variation correlated with those SNPs). This estimate can also be interpreted as the maximum prediction accuracy (R^2) that could be achieved from a linear predictor using all SNPs in the relatedness matrix. The latter interpretation is particularly relevant to the development of Polygenic Risk Scores, as it defines their maximum accuracy. GCTA estimates are sometimes misinterpreted as estimates of total (or narrow-sense, i.e. additive) heritability, but this is not a guarantee of the method. GCTA estimates are likewise sometimes misinterpreted as "lower bounds" on the narrow-sense heritability but this is also incorrect: first because GCTA estimates can be biased (including biased upwards) if the model assumptions are violated, and second because, by definition (and when model assumptions are met), GCTA can provide an unbiased estimate of the narrow-sense heritability if all causal variants are included in the relatedness matrix. The interpretation of the GCTA estimate in relation to the narrow-sense heritability thus depends on the variants used to construct the relatedness matrix.

GCTA estimates are often misinterpreted as "the total genetic contribution", and since they are often much less than the twin study estimates, the twin studies are presumed to be biased and the genetic contribution to a particular trait is minor.<ref>[https://www.independentsciencenews.org/health/still-chasing-ghosts-a-new-genetic-methodology-will-not-find-the-missing-heritability/ "Still Chasing Ghosts: A New Genetic Methodology Will Not Find the 'Missing Heritability'"], Charney 2013</ref> This is incorrect, as GCTA estimates are lower bounds.

Most frequently, GCTA is run with a single relatedness matrix constructed from common SNPs and will not capture (or not fully capture) the contribution of the following factors:

A more correct interpretation would be that: GCTA estimates are the expected amount of variance that could be predicted by an indefinitely large GWAS using a simple additive linear model (without any interactions or higher-order effects) in a particular population at a particular time given the limited selection of SNPs and a trait measured with a particular amount of precision. Hence, there are many ways to exceed GCTA estimates:

# Any rare or low-frequency variants that are not directly genotyped/imputed.

# SNP genotyping data is typically limited to 200k-1m of the most common or scientifically interesting SNPs, though 150 million+ have been documented by genome sequencing;<ref>[http://biorxiv.org/content/early/2016/07/01/061663 "Deep Sequencing of 10,000 Human Genomes"], Telenti 2015</ref> as SNP prices drop and arrays become more comprehensive or whole-genome sequencing replaces SNP genotyping entirely, the expected narrowsense heritability will increase as more genetic variants are included in the analysis. The selection can also be expanded considerably using [[haplotype]]s<ref>[http://biorxiv.org/content/biorxiv/early/2015/07/12/022418.full.pdf "Haplotypes of common SNPs can explain missing heritability of complex diseases"], Bhatia et al 2015</ref> and [[Imputation (genetics)|imputation]] (SNPs can proxy for unobserved genetic variants which they tend to be inherited with); e.g. Yang et al. 2015<ref name="Yang2015">[http://www.gwern.net/docs/genetics/2015-yang.pdf "Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index"], Yang et al 2015</ref> finds that with more aggressive use of imputation to infer unobserved variants, the height GCTA estimate expands to 56% from 45%, and Hill et al. 2017 finds that expanding GCTA to cover rarer variants raises the intelligence estimates from ~30% to ~53% and explains all the heritability in their sample;<ref name="Hill2017">Hill et al 2017, [http://biorxiv.org/content/early/2017/02/06/106203 "Genomic analysis of family data reveals additional genetic effects on intelligence and personality"]</ref> for 4 traits in the UK Biobank, imputing raised the SNP heritability estimates.<ref name="Evans2017">Evans et al 2017, [http://biorxiv.org/content/early/2017/03/10/115527 "Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits"]</ref> Additional genetic variants include ''de novo'' [[mutations]]/[[mutation load]] & [[structural variation]]s such as [[copy-number variations]].

# Any non-linear, dominance, or epistatic genetic effects. Note that GCTA can be extended to estimate the contribution of these effects through more complex relatedness matrices.

# narrowsense heritability estimates assume simple additivity of effects, ignoring interactions. As some trait values will be due to these more complicated effects, the total genetic effect will exceed that of the subset measured by GCTA, and as the additive SNPs are found and measured, it will become possible to find interactions as well using more sophisticated statistical models.

# The effects of Gene-Environment interactions. Note that GCTA can be extended to estimate the contribution of GxE interactions when the E is known, by including additional variance components.

# all correlation & heritability estimates are biased downwards to zero by the presence of [[measurement error]]; the need for adjusting this leads to techniques such as [[Spearman's correction for measurement error]], as the underestimate can be quite severe for traits where large-scale and accurate measurement is difficult and expensive,<ref>[https://www.dropbox.com/s/s1yax9n9jgkpmb1/2004-hunterschmidt-methodsofmetaanalysis.pdf ''Methods of Meta-Analysis: Correcting Error and Bias in Research Findings''], Hunter & Schmidt 2004</ref> such as intelligence. For example, an intelligence GCTA estimate of 0.31, based on an intelligence measurement with [[test-retest reliability]] <math>r=0.65</math>, would after correction (<math>\frac{0.31}{0.65}</math>), be a true estimate of ~0.48, indicating that common SNPs alone explain half of variance. Hence, a GWAS with a better measurement of intelligence can expect to find more intelligence hits than indicated by a GCTA based on a noisier measurement.

# Structural variants, which are typically not genotyped or imputed.

# Measurement error: GCTA does not model any uncertainty or error on the measured trait.

GCTA makes several model assumptions and may produce biased estimates under the following conditions:

# The distribution of causal variants is systematically different from the distribution of variants included in the relatedness matrix (even if all causal variants are included in the relatedness matrix). For example, if causal variants are systematically at a higher/lower frequency or in higher/lower correlation than all genotyped variants. This can produce either an upwards or downwards bias depending on the relationship between the causal variants and variants used. Various extensions to GCTA have been proposed (for example, GREML-LDMS) to account for these distributional shifts.

# Population stratification is not fully accounted for by covariates. GCTA (specifically GREML) accounts for stratification through the inclusion of fixed effect covariates, typically principal components. If these covariates do not fully capture the stratification the GCTA estimate will be biased, generally upwards. Accounting for recent population structure is particularly challenging for studies of rare variants.

# Residual genetic or environmental relatedness present in the data. GCTA assumes a homogenous population with an independent and identically distributed environmental term. This assumption is violated if related individuals and/or individuals with substantially shared environments are included in the data. In this case, the GCTA estimate will additionally capture the contribution of any genetic variation correlated with the genetic relationship: either direct genetic effects or correlated environment.

# The presence of "indirect" genetic effects. When genetic variants present in the relatedness matrix are correlated with variants present in other individuals that influence the participant's environment, those effects will also be captured in the GCTA estimate. For example, if variants inherited by a participant from their mother influenced their phenotype through their maternal environment, then the effect of those variants will be included in the GCTA estimate even though it is "indirect" (i.e. mediated by parental genetics). This may be interpreted as an upward bias as such "indirect" effects are not strictly causal (altering them in the participant would not lead to a change in phenotype in expectation).

== Implementations ==

Line 55 ⟶ 67:

{{Infobox software

| name = GCTA

| author = [[Jian Yang (geneticist)|Jian Yang]]

| released = 30{{start Augustdate and age|2010|08|30}}<ref name="version history"/>

| ver layout = stacked

| latest release version = 1.25.2

| latest release date version = 22 December 20151.26.0

| latest release date = {{start date and age|2016|06|22}}<ref name="version history">{{cite web

| status = Maintained

| url = https://cnsgenomics.com/software/gcta/#Download

| title = GCTA document

| website = cnsgenomics.com

| access-date = 2021-04-08

}}</ref>

| latest preview version = 1.93.2beta

| latest preview date = {{start date and age|2020|05|08}}<ref name="version history"/>

| programming language = C++

| operating system = [[Linux]]<br/> [[macOS]] (Macnot fully tested)<br/Windows> support[[Microsoft droppedWindows|Windows]] at(not v1.02fully tested)<ref name="version history"/>

| platform = [[x86_64]]

| language = English

| genre = geneticsGenetics

| license = [[GNU_General_Public_License#Version_3|GPL v3]] (source code)<br/>[[MIT License|MIT]] (executable files)<ref name="version history"/>

| website = {{URL|https://cnsgenomics.com/software/gcta/}}; forums: {{URL|gcta.freeforums.net}}

| AsOf = 228 MayApril 20162021

}}

Line 90 ⟶ 110:

* FAST-LMM<ref>[https://www.researchgate.net/profile/David_Heckerman/publication/51618535_FaST_linear_mixed_models_for_genome-wide_association_studies/links/5485d3a70cf268d28f00456a.pdf "Fast linear mixed models for genome-wide association studies"], Lippert 2011</ref>

* FAST-LMM-Select:<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3597090/ "Improved linear mixed models for genome-wide association studies"], Listgarten et al 2012</ref> like GCTA in using [[ridge regression]]<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3989144/ "Advantages and pitfalls in the application of mixed-model association methods"], Yang et al 2014</ref> but including [[feature selection]] to try to exclude irrelevant SNPs which only add noise to the relatedness estimates

* LMM-[[Lasso regression|Lasso]]<ref>[https://web.archive.org/web/20151204193223/http://bioinformatics.oxfordjournals.org/content/29/2/206.full#aff-1 "A lasso multi-marker mixed model for association mapping with population structure correction"], Rakitsch et al 2012</ref>

* GEMMA<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3386377/ "Genome-wide efficient mixed-model analysis for association studies"], Zhou & Stephens 2012</ref>

* EMMAX<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3092069/ "Variance component model to account for sample structure in genome-wide association studies"], Kang et al 2012</ref>

* [http://www.epcc.ed.ac.uk/projects-portfolio/reacta REACTA (formerly ACTA)] {{Webarchive|url=https://web.archive.org/web/20160523120255/http://www.epcc.ed.ac.uk/projects-portfolio/reacta |date=2016-05-23 }} claims order of magnitude runtime reductions<ref>[https://web.archive.org/web/20160522235202/http://bioinformatics.oxfordjournals.org/content/early/2012/09/27/bioinformatics.bts571.full.pdf "Advanced Complex Trait Analysis"], Gray et al 2012</ref><ref>[https://www.semanticscholar.org/paper/Regional-heritability-advanced-complex-trait-Cebamanos-Gray/c340835e1baf4b9fcafbfb001841bbd4793f598f/pdf "Regional Heritability Advanced Complex Trait Analysis for GPU and Traditional Parallel Architecture"], Cebamanos et al 2012</ref>

* [http://www.hsph.harvard.edu/alkes-price/software/ BOLT-REML]/BOLT-LMM<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342297/ "Efficient Bayesian mixed model analysis increases association power in large cohorts"], Loh et al 2012</ref> ([https://data.broadinstitute.org/alkesgroup/BOLT-LMM/ manual] {{Webarchive|url=https://web.archive.org/web/20160611003139/https://data.broadinstitute.org/alkesgroup/BOLT-LMM/ |date=2016-06-11 }}), faster & better scaling;<ref name="Loh2015">[http://biorxiv.org/content/biorxiv/early/2015/06/05/016527.full.pdf "Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis"], Loh et al 2015; see also [http://biorxiv.org/content/early/2015/06/05/016527 "Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis"], Loh et al 2015</ref> with potentially better efficiency in the meta-analysis scenario<ref>[http://biorxiv.org/content/early/2015/05/29/020115 "Mixed Models for Meta-Analysis and Sequencing"], Bulik-Sullivan 2015</ref>

* [http://scholar.harvard.edu/tge/software/megha MEGHA]<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4345618/ "Massively expedited genome-wide heritability analysis (MEGHA)"], Ge et al 2015</ref>

* PLINK >1.9 (December 2013) supports [https://www.cog-genomics.org/plink2/ "the use of genetic relationship matrices in mixed model association analysis and other calculations"]

* LDAK:<ref>Speed et al 2016, [http://biorxiv.org/content/early/2017/01/15/074310 "Re-evaluation of SNP heritability in complex human traits"]</ref> loosens the GCTA assumption that all SNPs, regardless of genotyping quality or frequency, have same averaged expected effect, allowing for potentially finding much more SNP heritability

* GREML-IBD:<ref name="Evans2017B">Evans et al 2017, [http://www.biorxiv.org/content/early/2017/07/17/164848 "Narrow-sense heritability estimation of complex traits using identity-by-descent information."]</ref> GCTA for [[identity by descent]], attempting to estimate heritability based on shared genome segments in distant otherwise-unrelated relatives, in order to capture the effect of rarer variants which are not measured by SNP panels or otherwise imputed

== Traits ==

GCTA estimates frequently find estimates 0.1-0.5, consistent with broadsense heritability estimates (with the exception of personality traits, for which theory & current GWAS results suggest non-additive genetics driven by [[frequency-dependent selection]]<ref name="Verweij2012"/><ref>[http://www.unm.edu/~gfmiller/newpapers_sept6/penke%202007%20targetarticle.pdf "The Evolutionary Genetics of Personality"], Penke et al 2007; [http://www.larspenke.eu/pdfs/Penke_&_Jokela_in_press_-_Evolutionary_Genetics_of_Personality_Revisited.pdf "The Evolutionary Genetics of Personality Revisited"], Penke & Jokela 2016</ref>). Traits univariate GCTA has been used on (excluding SNP heritability estimates computed using other algorithms such as LD score regression, and bivariate GCTAs which are listed in [[genetic correlation]]) include (point-estimate format: "<math>h^2_{SNP}</math>([[standard error]])"):

=== Human ===

==== Anthropometric ====

* [[Height]]: 0.544(0.101),<ref name="Yang2010"/> 0.498(0.04),<ref name="Wood2014"/> 0.56(0.023),<ref name="Yang2015"/> 0.448(0.029),<ref name="Yang2011"/> 0.42(0.052),<ref name="Lubke2012"/> 0.69(0.14),<ref name="Hemani2013">[https://genepi.qimr.edu.au/contents/p/staff/HemaniG_AJHG_865-875.pdf "Inference of the Genetic Architecture Underlying BMI and Height with the Use of 20,240 Sibling Pairs"], Hemani et al 2013</ref> 0.48(0.17)<ref name="Guggenheim2013"/> 0.37(0.14):<ref name="Trzaskowski2013"/> 0.32(0.06),<ref name="Conley2014"/> 0.35(0.12),<ref name="Plomin2013"/> 0.44(0.09),<ref name="Speed2012"/> 0.40(0.09)/0.33(0.09),<ref name="Domingue2016"/> 0.62(0.061),<ref name="Chen2015"/> 0.687(0.016),<ref name="Zaitlen2013"/> 0.56(0.23),<ref name="Toro2014"/> 0.51(0.01),<ref name="Pierson2014">[https://i.imgur.com/mX6LFF6.png Figure 4], [https://blog.23andme.com/wp-content/uploads/2014/10/ASHG_pierson_kleinman_eriksson_hinds_8-12.pdf "Like Mother, Like Daughter: Analysis of Parent-Child Phenotypic Correlations for Hundreds of Phenotypic Traits"], Pierson et al 2014<!-- Note: despite saying "We ran GCTA on a cohort of more than 30,000 unrelated individuals of European ancestry to estimate the narrow-sense heritability of more than 100 phenotype", only 17 of them have ever been published --></ref> 0.47(0.15)/0.69(0.08)<ref name="Trzaskowski2016">[http://ije.oxfordjournals.org/content/45/2/417.full "Application of linear mixed models to study genetic stability of height and body mass index across countries and time"], Trzaskowski et al 2016</ref>

* weight: 0.48(0.14),<ref name="Trzaskowski2013"/> 0.41(0.12),<ref name="Plomin2013"/> 0.25(0.09),<ref name="Domingue2016">[https://www.dropbox.com/s/wszkx5abzlptgd8/2016-domingue.pdf "Genome-Wide Estimates of Heritability for Social Demographic Outcomes"], Domingue et al 2016</ref> 0.26(0.061),<ref name="Chen2015"/> 0.394(0.174),<ref name="Zaidi2017">Zaidi et al 2017, [http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006616 "Investigating the case of human nose shape and climate adaptation"]</ref> 0.224(0.091)<ref name="Zaidi2017"/>

* [[Body mass index]] (BMI): 0.42(0.17)<ref name="Hemani2013"/> 0.14(0.05),<ref name="Vattikuti2012">[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002637 "Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits"], Vattikuti et al 2012</ref> 0.50(0.05)<ref>[http://paa2013.princeton.edu/papers/130559 "Using Genome Wide Estimates of Heritability to Examine the Relevance of Gene-Environment Interplay"], Domingue & Boardman 2013</ref> 0.31(0.07),<ref name="Conley2014"/> 0.43(0.10),<ref name="Boardman2015"/> 0.21(0.061),<ref name="Chen2015"/> 0.424(0.018),<ref name="Zaitlen2013"/> 0.27(0.025),<ref name="Yang2015"/> 0.165(0.029),<ref name="Yang2011">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295936/ "Genome partitioning of genetic variation for complex traits using common SNPs"], Yang et al 2011</ref> 0.24(0.01),<ref name="Pierson2014"/> 0.26 (0.08)<ref name="Trzaskowski2016"/> 0.298(0.034)<ref name="Akiyama2017">Akiyama et al 2017, [https://www.dropbox.com/s/0jhl1lh3780ef6p/2017-akiyama.pdf?dl=0 "Genome-wide association study identifies 112 new loci for body mass index in the Japanese population"]</ref>

** in children: 0.37(0.15)<ref>[https://www.researchgate.net/profile/Maciej_Trzaskowski/publication/236080492_Finding_the_missing_heritability_in_pediatric_obesity_the_contribution_of_genome-wide_complex_trait_analysis/links/02e7e5162d6dd46f56000000.pdf "Finding the missing heritability in pediatric obesity: the contribution of genome-wide complex trait analysis"], Llewellyn et al 2013</ref>

* grip strength: 0.239(0.027)<ref name="Willems2017">Willems et al 2017, [https://www.nature.com/articles/ncomms16015 "Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness"]</ref>

* gestational (pregnancy) weight gain: maternal genome, 0.239(0.055);<ref name="Warrington2017"/> fetal genome, 0.121(0.053)<ref name="Warrington2017">Warrington et al 2017, [http://biorxiv.org/content/early/2017/03/14/116434 "Maternal and fetal genetic contribution to gestational weight gain"]</ref>

* birthweight: maternal genome, 0.13(0.06);<ref name="Warrington2017"/> fetal genome, 0.18(0.06)

* waist-to-hip ratio (WHR): 0.13(0.05)<ref name="Vattikuti2012"/> 0.188(0.037)<ref name="Zaitlen2013"/>

* waist circumference: 0.16(0.061)<ref name="Chen2015"/>

* Breast size: 0.31(0.16)/0.47(0.25)<ref name="Li2013">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4159740/ "Large-scale genotyping identifies a new locus at 22q13.2 associated with female breast size"], Li et al 2013</ref>

* Health (self-rated): 0.177(0.089),<ref name="Boardman2015"/> 0.13(0.006)<ref>[http://biorxiv.org/content/early/2016/04/12/029504 "Molecular genetic contributions to self-rated health"], Harris et al 2016</ref>

* [[Hair color]]:

** Blond: 0.165(0.081)<ref name="Lin2015">[http://www.mdpi.com/2073-4425/6/3/559/pdf "Heritability and Genome-Wide Association Studies for Hair Color in a Dutch Twin Family Based Sample"], Lin et al 2015</ref>

** Brown: 0.095(0.079)<ref name="Lin2015"/>

** Red: 0.246(0.087)<ref name="Lin2015"/>

** Black: 0.00(0.083)<ref name="Lin2015"/>

** Light versus dark: 0.140(0.080)<ref name="Lin2015"/>

* [[unibrow]]: 0.28(0.02)<ref name="Pierson2014"/>

* [[Male pattern hair loss]] (balding): autosomal SNPs, 0.473(0.013), [[X chromosome]], 0.046(0.03), 0.519 total,<ref>[http://biorxiv.org/content/early/2016/08/30/072306 "Genetic Prediction of Male Pattern Baldness"], Hagenaars et al 2016</ref> 0.94<ref>Pirastu et al 2017, [https://www.nature.com/articles/s41467-017-01490-8 "GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk"]</ref>

* melanin index: 0.191(0.263)<ref name="Zaidi2017"/>

* facial features:

** Nares width: 0.504(0.187),<ref name="Zaidi2017"/> 0.226(0.094)<ref name="Zaidi2017"/>

** Alar base width: 0.481(0.188),<ref name="Zaidi2017"/> 0.212(0.093)<ref name="Zaidi2017"/>

** Nasal height: 0.441(0.186),<ref name="Zaidi2017"/> 0.03(0.076)<ref name="Zaidi2017"/>

** Nasal ridge length: 0.524(0.188),<ref name="Zaidi2017"/> 0.059(0.078)<ref name="Zaidi2017"/>

** Nasal tip protrusion: 0.401(0.191),<ref name="Zaidi2017"/> 0.177(0.088)<ref name="Zaidi2017"/>

** External surface area: 0.449(0.187),<ref name="Zaidi2017"/> 0.121(0.086)<ref name="Zaidi2017"/>

** Nostril area: 0.657(0.187),<ref name="Zaidi2017"/> 0.059(0.088)<ref name="Zaidi2017"/>

** nasal root shape, mouth width: 0.669(0.138)<ref name="Cole2017">Cole et al 2017, [https://klein.ucsf.edu/sites/kleinlab.ucsf.edu/files/cole_2017.pdf "Human Facial Shape and Size Heritability and Genetic Correlations"]</ref>

** facial width: 0.521(0.138)<ref name="Cole2017"/>

** Allometry variation in shape due to size: 0.643(0.132)<ref name="Cole2017"/>

** Centroid Size (facial size): 0.277(0.134)<ref name="Cole2017"/>

** nasion to midendocanthion: 0.260(0.134)<ref name="Cole2017"/>

** nasal width: 0.623(0.131)<ref name="Cole2017"/>

** width of the nose, mandible height: 0.604(0.131)<ref name="Cole2017"/>

** overall facial height, lower facial height: 0.579(0.139)<ref name="Cole2017"/>

** outer canthal width: 0.421(0.141)<ref name="Cole2017"/>

** nasal bridge length: 0.456(0.142)<ref name="Cole2017"/>

** palpebral fissure length (average): 0.208(0.140)<ref name="Cole2017"/>

** upper facial depth (average): 0.419(0.136)<ref name="Cole2017"/>

** nose shape, height of the mouth: 0.211(0.138)<ref name="Cole2017"/>

** upper facial height: 0.443(0.140)<ref name="Cole2017"/>

** lower facial depth (average): 0.487(0.140)<ref name="Cole2017"/>

** philtrum length: 0.486(0.130)<ref name="Cole2017"/>

** midfacial depth (average): 0.469(0.139)<ref name="Cole2017"/>

** upper and middle facial width: 0.308(0.139)<ref name="Cole2017"/>

** upper facial height, midfacial width: 0.477(0.140)<ref name="Cole2017"/>

** cheek protrusion: 0.074(0.137)<ref name="Cole2017"/>

** nasal height: 0.244(0.137)<ref name="Cole2017"/>

** midface protrusion, upper facial height: 0.431(0.125)<ref name="Cole2017"/>

** midfacial landmark network around the nose and mouth: 0.433(0.138)<ref name="Cole2017"/>

** morphological facial height: 0.159(0.137)<ref name="Cole2017"/>

** inner canthal width: 0.392(0.142)<ref name="Cole2017"/>

** nasal ala length (average): 0.311(0.140)<ref name="Cole2017"/>

** lower facial height: 0.239(0.139)<ref name="Cole2017"/>

** mouth width: 0.378(0.137)<ref name="Cole2017"/>

** subnasal width: 0.373(0.134)<ref name="Cole2017"/>

** cutaneous lower lip height: 0.177(0.134)<ref name="Cole2017"/>

** nasal protrusion: 0.242(0.139)<ref name="Cole2017"/>

** philtrum width: 0.337(0.126)<ref name="Cole2017"/>

** lower vermilion height: 0.324(0.139)<ref name="Cole2017"/>

** upper lip height: 0.314(0.131)<ref name="Cole2017"/>

** chin height, nasion protrusion: 0.291(0.140)<ref name="Cole2017"/>

** lower lip height: 0.283(0.134)<ref name="Cole2017"/>

** nasal width, maxillary prognathism: 0.169(0.131)<ref name="Cole2017"/>

* skin [[nevus]] (mole/lesion) density count: 0.58(0.025)<ref>Duffy et al 2017, [http://www.biorxiv.org/content/early/2017/08/07/173112 "Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways"]</ref>

* age at [[menarche]]: 0.451(0.022)<ref name="Zaitlen2013"/>

* age at first birth: 0.15(0.04),<ref name="Tropf2015">[http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0126821 "Human Fertility, Molecular Genetics, and Natural Selection in Modern Societies"], Tropf et al 2015</ref> 0.19(0.039)<ref name="Tropf2016">[http://biorxiv.org/content/early/2016/05/02/049163 "Mega-analysis of 31,396 individuals from 6 countries uncovers strong gene-environment interaction for human fertility"], Tropf et al 2016</ref>

* age at [[menopause]]: 0.409(0.048)<ref name="Zaitlen2013"/>

* sex ratio of offspring: 0.026(0.017)<ref name="Zaitlen2013"/>

* number of offspring: 0.073(0.068)/0.102(0.028),<ref name="Zaitlen2013"/> 0.10(0.05),<ref name="Tropf2015"/> 0.22(0.026),<ref name="Tropf2016"/> 0.21(0.05),<ref name="Conley2016">[http://www.pnas.org/content/early/2016/05/25/1523592113.full "Assortative mating and differential fertility by phenotype and genotype across the 20th century"], Conley et al 2016 ([http://www.pnas.org/content/suppl/2016/05/25/1523592113.DCSupplemental/pnas.1523592113.sapp.pdf supplement])</ref> 0.20(0.10),<ref name="Conley2016"/> 0.19(0.09)<ref name="Conley2016"/>

* [[left handedness]]: 0.004(0.145)<ref name="Zaitlen2013"/>

* [[Eye color]]: 0.59(0.01)<ref name="Pierson2014"/>

* Eye dimensions (axial length & [[cornea]]l curvature): 0.46(0.16)/0.42(0.16)<ref name="Guggenheim2013">[http://iovs.arvojournals.org/article.aspx?articleid=2165969 "Coordinated Genetic Scaling of the Human Eye: Shared Determination of Axial Eye Length and Corneal Curvature"], Guggenheim et al 2013</ref>

* [[Coriander#Taste and smell|Cilantro tasting]]: 0.087<ref>[https://blog.23andme.com/wp-content/uploads/2012/11/ASHG2012poster-SW_cilantro-final.pdf "A Genetic Variant Near Olfactory Receptor Genes Associates With Cilantro Preference"], Wu et al 2012</ref>

* cry cutting onions: 0.12(0.02)<ref name="Pierson2014"/>

* sweet vs salty: 0.35(0.03)<ref name="Pierson2014"/>-

==== Social/behavioral ====

* [[Education]]: 0.224(0.042),<ref>[http://www.gwern.net/docs/iq/2013-rietveld.pdf "GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment"], Rietveld et al 2013</ref> 0.21(0.06),<ref name="Davies2016"/> 0.158(0.061),<ref name="Benjamin2012"/> 0.21(0.05),<ref name="Marioni2014"/> 0.17(0.07),<ref name="Conley2014">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4126504/ "Testing the key assumption of heritability estimates based on genome-wide genetic relatedness"], Conley et al 2014</ref> 0.33 (0.10),<ref name="Boardman2015"/> 0.23(0.09),<ref name="Domingue2016"/> 0.156(0.021)<ref name="Hill2017"/>

** rare/family variants: 0.281(0.03)<ref name="Hill2017"/>

** test scores: 0.31(0.12)<ref name="Krapohl2016"/>

** reading scores: 0.27(0.128)<ref name="Davis2014">Davis et al 2014, [http://www.nature.com/articles/ncomms5204 "The correlation between reading and mathematics ability at age twelve has a substantial genetic component"]</ref>

** mathematics scores: 0.52 (0.163)<ref name="Davis2014"/>

* [[Socioeconomic status]] (SES): 0.18(0.05),<ref name="Marioni2014"/> 0.18(0.12)/0.19(0.12),<ref name="Krapohl2016">[http://www.nature.com/mp/journal/v21/n3/pdf/mp20152a.pdf "Genetic link between family socioeconomic status and children's educational achievement estimated from genome-wide SNPs"], Krapohl & Plomin 2016</ref> 0.18(0.12)/0.19(0.12)<ref name="Trzaskowski2014b"/>

** social deprivation: 0.21(0.005)<ref name="Hill2016">[http://biorxiv.org/content/early/2016/03/09/043000 "Molecular genetic contributions to social deprivation and household income in UK Biobank (''n''=112,151)"], Hill et al 2016</ref>

** household income: 0.11(0.007)<ref name="Hill2016"/>

* Exercise:

** Moderate to Vigorous Activity: 0.17(0.09)<ref name="Richmond2014">[http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001618 "Assessing causality in the association between child adiposity and physical activity levels: A Mendelian randomization analysis"], Richmond et al 2014</ref>

** Sedentary Time: 0.25(0.09)<ref name="Richmond2014"/>

** Total Physical Activity: 0.21(0.10)<ref name="Richmond2014"/>

* ability to delay gratification/delay discounting (Monetary Choice Questionnaire): 0.122(0.017)<ref name="Sanchez_Roige2017">Sanchez-Roige et al 2017, [http://biorxiv.org/content/early/2017/06/07/146936 "Genetics of the Research Domain Criteria (RDoC): genome-wide association study of delay discounting"]</ref>

* Tiredness: 0.084(0.006)<ref>[http://biorxiv.org/content/early/2016/04/05/047290 "Genetic contributions to self-reported tiredness"], Deary et al 2016</ref>

* Insomnia: 0.08(0.02)<ref name="Pierson2014"/>

* [[Chronotype]]/morningness: 0.25(0.03),<ref name="Pierson2014"/> 0.194(?),<ref name="Lane2016"/> 0.377(?)<ref name="Lane2016">[http://biorxiv.org/content/early/2016/02/02/038620 "Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UKBiobank"], Lane et al 2016 ([http://biorxiv.org/content/biorxiv/suppl/2016/02/02/038620.DC1/038620-1.pdf supplement])</ref>

* Adult antisocial behavior: 0.55(0.41)<ref>[http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0045086 "Unraveling the genetic etiology of adult antisocial behavior: A genome-wide association study"], Tielbeek et al 2012</ref>

* trust

** trust in people: 0.07(0.17)<ref name="Wootton2016">Wootton et al 2016, [https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/exploring-the-genetic-etiology-of-trust-in-adolescents-combined-twin-and-dna-analyses/5BF8A9F5E74796F1903A0E8878C70825/core-reader"Exploring the Genetic Etiology of Trust in Adolescents: Combined Twin and DNA Analyses"]</ref>

** trust in friends: 0.06(0.24)<ref name="Wootton2016"/>

* loneliness: 0.27(0.12)<ref>Gao et al 2016, [https://www.dropbox.com/s/3samba012ru70p9/2016-gao.pdf "Genome-Wide Association Study of Loneliness Demonstrates a Role for Common Variation"]</ref>

* family relationship satisfaction: 0.053(0.014)<ref name="Warrier2017">Warrier et al 2017, [https://www.biorxiv.org/content/early/2017/10/05/196071 "Genome-wide association study of social relationship satisfaction: significant loci and correlations with psychiatric conditions"]</ref>

* friendship satisfaction: 0.056(0.014)<ref name="Warrier2017"/>

* Non-substance related Behavioral Disinhibition: 0.28(0.102),<ref name="McGue2013"/> 0.19(0.16)<ref name="Vrieze2013"/>

* Stressful life events: 0.3(0.15)<ref>[http://hub.hku.hk/bitstream/10722/189366/1/Content.pdf?accept=1 "Estimating the heritability of reporting stressful life events captured by common genetic variants"], Power et al 2013</ref>

* [[carsickness]]: 0.2(0.01)<ref name="Pierson2014"/>

==== Psychological ====

* Overall brain size: 0.845(0.457)/0.00(0.476)/0.00(0.483)/0.574(0.468),<ref>[https://ncrad.iu.edu/docs/Publications/149_Bryant_2013.pdf "Mapping the Genetic Variation of Regional Brain Volumes as Explained by All Common SNPs from the ADNI Study"], Bryant et al 2013</ref> 0.54(0.23)/0.44(0.23)/0.53(0.23)/0.22(0.24)/0.16(0.23)/0.31(0.23)/0.54(0.23)/0.45(0.23)/0.52(0.23)<ref name="Toro2014">[http://www.fondation-fondamental.org/upload/editeur/files/ToroMolPsy2014.pdf "Genomic architecture of human neuroanatomical diversity"], Toro et al 2014 ([https://neuroanatomy.github.io/pdfs/2014Toro-Bourgeron;GenomicArchitecture;Supplement.pdf supplement])</ref>

* Volume of neuroanatomical structures: 100 brain volumes & latent factors thereof, median 0.348<ref>Zhao et al 2018, [https://www.dropbox.com/s/csfqhc5zx1kgl6s/2018-zhao.pdf?dl=0 "Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies"]</ref>

*** [[Accumbens Area]]: 0.001(0.279)<ref name="Ge2015">[http://biorxiv.org/content/early/2015/12/01/033407 "Heritability of Neuroanatomical Shape"], Ge et al 2015</ref>

*** [[Amygdala]]: 0.096(0.279)<ref name="Ge2015"/>

*** [[Caudate nucleus|Caudate]]: 0.620(0.279)<ref name="Ge2015"/>

*** [[Cerebellum]]: 0.002(0.279)<ref name="Ge2015"/>

*** [[Corpus Callosum]]: 0.521(0.279)<ref name="Ge2015"/>

*** [[Hippocampus]]: 0.001(0.279)<ref name="Ge2015"/>

*** [[Lateral Ventricle]]: 0.266(0.279)<ref name="Ge2015"/>

*** 3rd Ventricle: 0.534(0.279)<ref name="Ge2015"/>

*** 4th Ventricle: 0.392(0.279)<ref name="Ge2015"/>

*** [[Pallidum]]: 0.259(0.279)<ref name="Ge2015"/>

*** [[Putamen]]: 0.310(0.279)<ref name="Ge2015"/>

*** [[Thalamus]]: 0.227(0.279)<ref name="Ge2015"/>

** Global:

*** Intracranial volume: 0.880(0.238)<ref name="Lee2016">[https://www.dropbox.com/s/bfflgmrfd7473hn/2016-lee.pdf "Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia"], Lee et al 2016</ref>

*** Overall mean cortical thickness: 0.796(0.244)<ref name="Lee2016"/>

** Frontal:

*** Left precentral gyrus thickness: 0.718(0.249)<ref name="Lee2016"/>

*** Left rostral anterior cingulate cortex thickness: 0.737(0.243)<ref name="Lee2016"/>

*** Left superior frontal gyrus thickness: 0.597(0.246)<ref name="Lee2016"/>

*** Right lateral orbital frontal cortex thickness: 0.483(0.240)<ref name="Lee2016"/>

*** Right pars opercularis surface area: 0.545(0.252)<ref name="Lee2016"/>

*** Right paracentral lobule thickness: 0.494(0.252)<ref name="Lee2016"/>

*** Right precentral gyrus thickness: 0.731(0.244)<ref name="Lee2016"/>

** Occipital:

*** Left cuneus cortex thickness: 0.550(0.244)<ref name="Lee2016"/>

*** Left lateral occipital cortex thickness: 0.498(0.248)<ref name="Lee2016"/>

*** Right cuneus cortex thickness: 0.723(0.251)<ref name="Lee2016"/>

** Parietal:

*** Left inferior parietal cortex thickness: 0.566(0.248)<ref name="Lee2016"/>

*** Left postcentral gyrus thickness: 0.501(0.249)<ref name="Lee2016"/>

*** Left posterior-cingulate cortex thickness: 0.601(0.246)<ref name="Lee2016"/>

*** Left precuneus cortex surface area: 0.555(0.262)<ref name="Lee2016"/>

*** Left precuneus cortex thickness: 0.896(0.245)<ref name="Lee2016"/>

*** Left superior parietal gyrus surface area: 0.558(0.251)<ref name="Lee2016"/>

*** Left superior parietal gyrus thickness: 0.903(0.241)<ref name="Lee2016"/>

*** Right postcentral gyrus thickness: 0.760(0.246)<ref name="Lee2016"/>

*** Right precuneus cortex surface area: 0.547(0.246)<ref name="Lee2016"/>

*** Right precuneus cortex thickness: 0.965(0.243)<ref name="Lee2016"/>

*** Right superior parietal gyrus thickness: 0.941(0.239)<ref name="Lee2016"/>

*** Right supramarginal gyrus thickness: 0.769(0.240)<ref name="Lee2016"/>

** Temporal:

*** Left banks superior temporal sulcus thickness: 0.680(0.242)<ref name="Lee2016"/>

*** Left entorhinal cortex thickness: 0.587(0.249)<ref name="Lee2016"/>

*** Left fusiform gyrus surface area: 0.566(0.259)<ref name="Lee2016"/>

*** Left insula cortex surface area: 0.561(0.251)<ref name="Lee2016"/>

*** Left superior temporal gyrus surface area: 0.658(0.244)<ref name="Lee2016"/>

*** Left transverse temporal cortex thickness: 0.555(0.245)<ref name="Lee2016"/>

*** Right entorhinal cortex surface area: 0.651(0.251)<ref name="Lee2016"/>

*** Right insula cortex surface area: 0.878(0.252)<ref name="Lee2016"/>

*** Right middle temporal gyrus surface area: 0.610(0.244)<ref name="Lee2016"/>

*** Right temporal pole surface area: 0.524(0.249)<ref name="Lee2016"/>

*** Right transverse temporal cortex thickness: 0.536(0.254)<ref name="Lee2016"/>

** Shape of neuroanatomical structures:

*** Accumbens Area: 0.230(0.134)<ref name="Ge2015"/>

*** Amygdala: 0.036(0.138)<ref name="Ge2015"/>

*** Caudate: 0.497(0.187)<ref name="Ge2015"/>

*** Cerebellum: 0.456(0.190)<ref name="Ge2015"/>

*** Corpus Callosum: 0.243(0.132)<ref name="Ge2015"/>

*** Hippocampus: 0.339(0.168)<ref name="Ge2015"/>

*** Lateral Ventricle: 0.207(0.152)<ref name="Ge2015"/>

*** 3rd Ventricle: 0.454(0.156)<ref name="Ge2015"/>

*** 4th Ventricle: 0.014(0.206)<ref name="Ge2015"/>

*** Pallidum: 0.074(0.116)<ref name="Ge2015"/>

*** Putamen: 0.365(0.146)<ref name="Ge2015"/>

*** Thalamus Proper: 0.132(0.143)<ref name="Ge2015"/>

* [[Intelligence]]: 0.40(0.11)/0.51(0.11),<ref>[http://www.nature.com/mp/journal/v16/n10/full/mp201185a.html "Genome-wide association studies establish that human intelligence is highly heritable and polygenic"], Davies et al 2011</ref> 0.47(?),<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3498585/ "Most Reported Genetic Associations with General Intelligence Are Probably False Positives"], Chabris et al 2012</ref> 0.24(0.20),<ref name="Deary2012">[https://www.researchgate.net/profile/David_Dave_Liewald/publication/221760226_Genetic_contributions_to_stability_and_change_in_intelligence_from_childhood_to_old_age/links/02e7e52ca9a723a8fa000000.pdf "Genetic contributions to stability and change in intelligence from childhood to old age"], Deary et al 2012</ref> 0.29(0.12)/0.26(0.11)/0.20(0.11)/0.35(0.12),<ref name="Plomin2013">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3652710/ "Common DNA Markers Can Account for More Than Half of the Genetic Influence on Cognitive Abilities"], Plomin et al 2013</ref> 0.47(0.18)/0.26(0.17)/0.23(0.13)/0.15(0.14)<ref>[http://www.sciencedirect.com/science/article/pii/S0160289613001049 "Intelligence indexes generalist genes for cognitive abilities"], Trzaskowski et al 2013</ref><ref>[https://link.springer.com/article/10.1007/s10519-013-9594-x/fulltext.html "DNA Evidence for Strong Genome-Wide Pleiotropy of Cognitive and Learning Abilities"], Trzaskowski et al 2013b<!-- no specific estimate given; may be redundant with one of Trzaskowski's many other papers --></ref> 0.29(0.05),<ref name="Marioni2014">[http://www.sciencedirect.com/science/article/pii/S0160289614000178 "Molecular genetic contributions to socioeconomic status and intelligence"], Marioni et al 2014</ref> 0.35(0.11),<ref>[http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112390 "Results of a 'GWAS Plus': General Cognitive Ability Is Substantially Heritable and Massively Polygenic"], Kirkpatrick et al 2014</ref> 0.60(0.26),<ref>[https://www.researchgate.net/profile/Maciej_Trzaskowski/publication/235379639_DNA_evidence_for_strong_genetic_stability_and_increasing_heritability_of_intelligence_from_age_7_to_12/links/004635162d9aa8ba50000000.pdf "DNA evidence for strong genetic stability and increasing heritability of intelligence from age 7 to 12"], Trzaskowski et al 2014a</ref> 0.32(0.14)/0.28(0.17),<ref name="Trzaskowski2014b">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907681/ "Genetic influence on family socioeconomic status and children's intelligence"], Trzaskowski et al 2014b</ref> 0.40(0.21)/0.46(0.06),<ref>[https://www.researchgate.net/profile/Beben_Benyamin/publication/235379638_Childhood_intelligence_is_heritable_highly_polygenic_and_associated_with_FNBP1L/links/5458a9090cf2bccc491183f0.pdf "Childhood intelligence is heritable, highly polygenic and associated with _FNBP1L_"], Benyamin et al 2014</ref> 0.56(0.25)/0.52(0.25),<ref name="Toro2014"/> 0.29%(0.05)/0.28(0.07),<ref>[http://www.nature.com/mp/journal/v20/n2/full/mp2014188a.html "Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (''n''=53949)"], Davies et al 2015</ref> 0.174(0.017),<ref>[http://www.nature.com/mp/journal/vaop/ncurrent/full/mp2015108a.html "A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence"], Spain et al 2015</ref> 0.00(?)/0.00(?),<ref name="Levine2015"/> 0.31(0.018),<ref name="Davies2016">[http://www.nature.com/mp/journal/vaop/ncurrent/full/mp201645a.html "Genome-wide association study of cognitive functions and educational attainment in UK Biobank (''n''=112151)"], Davies et al 2016</ref> 0.360(0.108),<ref name="Robinson2015">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4294962/ "The genetic architecture of pediatric cognitive abilities in the Philadelphia Neurodevelopmental Cohort"], Robinson et al 2015</ref> 0.23(0.02)<ref name="Hill2017"/>

** extremely high intelligence: 0.33(0.02)<ref name="Zabaneh2017">Zabaneh et al 2017, [https://www.nature.com/mp/journal/vaop/ncurrent/full/mp2017121a.html "A genome-wide association study for extremely high intelligence"]</ref>

*** extremely high intelligence variants in the major histocompatibility complex (MHC)/[[Human leukocyte antigen|HLA]] immune system gene complex: 0.0028(0.0018)<ref>Zabaneh et al 2017, [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5259706/ "Fine mapping genetic associations between the HLA region and extremely high intelligence"]</ref>

** rare/family variants: 0.31(0.03)<ref name="Hill2017"/>

** [[reaction time]]: 0.11(0.06)<ref name="Davies2016"/>

** memory: 0.05(0.06),<ref name="Davies2016"/> 0.00(?)/0.00(?)<ref name="Levine2015"/>

** [[working memory]]: 0.17(?)/0.07(?),<ref name="Levine2015"/> 0.108(0.096)<ref name="Robinson2015"/>

** Facial Memory: 0.064(0.093)<ref name="Robinson2015"/>

** Spatial Memory: 0.028(0.090)<ref name="Robinson2015"/>

** Verbal Memory: 0.244(0.097)<ref name="Robinson2015"/>

** Digit Symbol Test: 0.214(0.021)<ref name="Hill2017"/>

*** rare/family variants: 0.147 (0.028)<ref name="Hill2017"/>

** Logical memory: 0.119 (0.02)<ref name="Hill2017"/>

*** rare/family variants:0.203 (0.028)<ref name="Hill2017"/>

** Abstraction and Mental Flexibility: 0.064(0.096)<ref name="Robinson2015"/>

** Attention: 0.148(0.097)<ref name="Robinson2015"/>

** Language Reasoning: 0.302(0.098)<ref name="Robinson2015"/>

** vocabulary: 0.256(0.02)<ref name="Hill2017"/>

*** rare/family variants: 0.301(0.028)<ref name="Hill2017"/>

** TOWRE word reading fluency: 0.74 (0.04)/0.68 (0.04)<ref name="Harlaar2014">[http://onlinelibrary.wiley.com/doi/10.1111/cdev.12207/full "Word Reading Fluency: Role of Genome-Wide Single-Nucleotide Polymorphisms in Developmental Stability and Correlations With Print Exposure"], Harlaar et al 2014</ref>

** Verbal fluency: 0.189(0.021)<ref name="Hill2017"/>

*** rare/family variants: 0.271(0.029)<ref name="Hill2017"/>

** Wide Range Achievement Test (Reading): 0.433(0.098)<ref name="Robinson2015"/>

** ART written/printed material exposure: 0.39(0.02)<ref name="Harlaar2014"/>

** Nonverbal Reasoning: 0.406(0.096)<ref name="Robinson2015"/>

** Spatial Reasoning: 0.357(0.101)<ref name="Robinson2015"/>

** Age Differentiation: 0.039(0.098)<ref name="Robinson2015"/>

** Emotion Differentiation: 0.000(0.092)<ref name="Robinson2015"/>

** Emotion Identification: 0.357(0.093)<ref name="Robinson2015"/>

** Trailing Making test/visual-numeric reasoning<ref name="Hagenaars2016"/>

* Trail Making test: 0.079(0.024)/0.224(0.026)/0.176(0.025)<ref name="Hagenaars2016">Hagenaars et al 2017, [http://biorxiv.org/content/early/2017/01/25/103119 "Genetic contributions to trail making test performance in UK Biobank"]</ref>

* Number sense: 0.00(0.29)<ref>[http://www.lauragermine.org/articles/intelligence2014.pdf "Why do we differ in number sense? Evidence from a genetically sensitive investigation"], Tosto et al 2013</ref>

* Economic preferences

** risk aversion: 0.137(0.152)<ref name="Benjamin2012">[http://www.nyu.edu/projects/dawes/The%20genetic%20architecture%20of%20economic%20and%20political%20preferences.pdf "The genetic architecture of economic and political preferences"], Benjamin 2012</ref>

** patience: 0.085(0.148) <ref name="Benjamin2012"/>

** trust: 0.242(0.146)<ref name="Benjamin2012"/>

** fair-mindedness: 0.00(0.15)<ref name="Benjamin2012"/>

* Political preferences

** immigration/crime: 0.203(0.147)<ref name="Benjamin2012"/>

** economic policy: 0.344(0.150)<ref name="Benjamin2012"/>

** environmentalism: 0.00(0.148)<ref name="Benjamin2012"/>

** feminism/equality: 0.00(0.147)<ref name="Benjamin2012"/>

** foreign policy: 0.354(0.149)<ref name="Benjamin2012"/>

* Happiness (self-rated): 0.05–0.10(0.05–0.10)<ref>[http://eprints.lse.ac.uk/51004/1/__lse.ac.uk_storage_LIBRARY_Secondary_libfile_shared_repository_Content_De%20Neve,%20JE_Molecular%20genetics_De%20Neve_Molecular%20genetics_2014.pdf "Molecular genetics and subjective well-being"], Rietveld et al 2013</ref>

* positive affect: 0.08(0.02)<ref name="Weiss2016"/>

* life satisfaction: 0.13(0.02)<ref name="Weiss2016"/>

* brain region activity response to faces<ref>[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004523 "Global Genetic Variations Predict Brain Response to Faces"], Dickie et al 2014</ref><!-- too many, over too many conditions, to bother listing -->

* [[Big Five personality traits]]

** [[Neuroticism]] (autosomal): 0.06(0.03),<ref name="Vinkhuyzen2012">[http://www.nature.com/tp/journal/v2/n4/full/tp201227a.html "Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion"], Vinkhuyzen et al 2012</ref> 0.147(0.07)/0.157(0.16),<ref>[http://www.cs.princeton.edu/~bee/pubs/DeMoor-JAMA-2015.pdf "Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder"], De Moor et al 2015</ref> 0.15(0.08),<ref name="Power2015">[http://www.philosonic.com/michaelpluess_construction/Files/PowerPluess_2015_Heritability%20estimates%20of%20the%20Big%20Five%20personality%20traits%20based%20on%20common%20genetic%20variants.pdf "Heritability estimates of the Big Five personality traits based on common genetic variants"], Power & Pluess 2015</ref> 0.156(0.0074),<ref>[http://www.gwern.net/docs/genetics/2016-smith.pdf "Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci"], Smith et al 2016</ref> 0.15(0.02),<ref name="Pierson2014"/> 0.15(0.02),<ref name="Weiss2016">Weiss et al 2016, [https://www.cambridge.org/core/journals/twin-research-and-human-genetics/article/personality-polygenes-positive-affect-and-life-satisfaction/4DB2BE673BF122FB9A0AF2147EED80C0/core-reader "Personality Polygenes, Positive Affect, and Life Satisfaction"]</ref> 0.108 (0.02),<ref name="Hill2017"/> 0.146(0.007)<ref name="Hill2017b">Hill et al 2017, [http://biorxiv.org/content/early/2017/06/06/146787 "Genetic contribution to two factors of neuroticism is associated with affluence, better health, and longer life"]</ref>, 0.1437(0.0042)<ref name="Luciano2018">Luciano et al 2018, [https://www.biorxiv.org/content/early/2018/08/27/401166 "The influence of X chromosome variants on trait neuroticism"]</ref>

*** X-chromosome only: 0.0034(0.00007)<ref name="Luciano2018"/>

*** rare/family variants: 0.192 (0.025)<ref name="Hill2017"/>

*** Neuroticism worry-vulnerability facet: 0.097(0.007)<ref name="Hill2017b"/>

*** Neuroticism anxiety-tension facet: 0.078(0.007)<ref name="Hill2017b"/>

** [[Extraversion]]: 0.12(0.03)<ref name="Vinkhuyzen2012"/> 0.00(0.15)/0.05(0.072),<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4751159/ "Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium"], van den Berg et al 2015</ref> 0.08(0.08),<ref name="Power2015"/> 0.130 (0.017)<ref name="Hill2017"/>

** [[Openness]]: 0.21(0.08)<ref name="Power2015"/>

** [[Conscientiousness]]: 0.01 (0.08),<ref name="Power2015"/> 0.16(0.02)<ref name="Pierson2014"/>

** [[Agreeableness]]: 0.001(0.08)<ref name="Power2015"/>

* Social Anxiety score: European-Americans: 0.12(0.033);<ref name="Stein2017"/> African-Americans: 0.12(0.134);<ref name="Stein2017"/> Hispanic: 0.21(0.102)<ref name="Stein2017">[https://www.dropbox.com/s/h1pojwuayp8t6y9/2017-stein.pdf "Genetic risk variants for social anxiety"], Stein et al 2017</ref>

* [[Biological basis of personality#Cloninger model of personality|Cloninger's personality dimensions]]:

** Harm Avoidance: 0.066(0.037)<ref name="Verweij2012">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3518920/ "Maintenance of genetic variation in human personality: Testing evolutionary models by estimating heritability due to common causal variants and investigating the effect of distant inbreeding"], Verweij et al 2012</ref>

** Novelty Seeking: 0.099(0.036)<ref name="Verweij2012"/>

** Reward Dependence: 0.042(0.036)<ref name="Verweij2012"/>

** Persistence: 0.081(0.037)<ref name="Verweij2012"/>

* optimism: 0.10(0.02)<ref name="Pierson2014"/>

* Psychology [[endophenotype]]s:<ref name="Iacono2014">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4231488/ "Knowns and unknowns for psychophysiological endophenotypes: Integration and response to commentaries"], Iacono et al 2014</ref>

** Total power: ~0.08(?)<ref name="Iacono2014"/>

** Theta power: ~0.04(?)<ref name="Iacono2014"/>

** Delta power: ~0.15(?)<ref name="Iacono2014"/>

** Beta power: ~0.19(?)<ref name="Iacono2014"/>

** CZ alpha power: ~0.21(?)<ref name="Iacono2014"/>

** O1O2 alpha power: ~0.45(?)<ref name="Iacono2014"/>

** Alpha frequency: ~0.49(?)<ref name="Iacono2014"/>

** SCL: ~0.23(?)<ref name="Iacono2014"/>

** SCR amplitude: ~0.25(?)<ref name="Iacono2014"/>

** SCR frequency: ~0.33(?)<ref name="Iacono2014"/>

** EDA factor: ~0.35(?)<ref name="Iacono2014"/>

** P3 amplitude: ~0.29(?)<ref name="Iacono2014"/>

** Antisaccade: ~0.47(?)<ref name="Iacono2014"/>

** Overall startle: ~0.49(?)<ref name="Iacono2014"/>

==== Psychiatric ====

* Antisocial Process Screening Devise (APSD; Psychopathic Symptoms); composite:0.00(0.12)/0.15(0.16)<ref name="Trzaskowski2013c"/>

** Callous-Unemotional: 0.02(0.12)/0.00(0.16),<ref name="Trzaskowski2013c"/> 0.07(0.12)<ref>[https://drrulab.files.wordpress.com/2016/01/genetics-of-callous-unemotional-behavior-in-children.pdf "Genetics of Callous-Unemotional Behavior in Children"], Viding et al 2013</ref>

** Impulsivity: 0.00(0.12)/0.24(0.16)<ref name="Trzaskowski2013c"/>

** Narcissism total: 0.00(0.12)/0.50(0.16)<ref name="Trzaskowski2013c"/>

* psychopathology in children: 0.38(0.16)<ref>[https://www.dropbox.com/s/oyp1a6thqsq09y9/2016-neumann.pdf "Single Nucleotide Polymorphism Heritability of a General Psychopathology Factor in Children"], Neumann et al 2016</ref>

* childhood trauma (sexual abuse, physical abuse, emotional abuse, emotional neglect, and physical neglect): 5-___domain continuous: 0.00(0.07),<ref name="Peyrot2017">Peyrot et al 2017, [https://www.dropbox.com/s/1srt9j8as2621h1/2017-peyrot.pdf?dl=0 "Does childhood trauma moderate polygenic risk for depression? A meta-analysis of 5,765 subjects from the Psychiatric Genomics Consortium"]</ref> 2-___domain dichotomous: 0.09(0.08)<ref name="Peyrot2017"/>

* anxiety: 0.16(0.11)<ref name="Trzaskowski2013">[http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0058676 "First genome-wide association study on anxiety-related behaviours in childhood"], Trzaskowski et al 2013</ref>

* epilepsy: 0.26(0.05)/0.27(0.06)<ref>[http://brain.oxfordjournals.org/content/137/10/2680.full "Describing the genetic architecture of epilepsy through heritability analysis"], Speed et al 2014</ref>

* [[Major depressive disorder|Depression]]: 0.21(0.021),<ref name="Lee2013"/> 0.32(0.09)/0.32(0.086),<ref name="Lubke2012">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3404250/ "Estimating the genetic variance of major depressive disorder due to all single nucleotide polymorphisms"], Lubke et al 2012</ref> 0.19(0.10),<ref name="Boardman2015">[https://www.researchgate.net/profile/Benjamin_Domingue/publication/264795159_What_can_genes_tell_us_about_the_relationship_between_education_and_health/links/554766090cf2f5349a86df6c.pdf "What can genes tell us about the relationship between education and health?"], Boardman et al 2015</ref> 0.15(0.02),<ref name="Pierson2014"/> 0.20(0.04),<ref name="Hall2017">Hall et al 2017, [http://biorxiv.org/content/early/2017/04/24/130229?rss=1 "Genome-Wide Meta-Analyses Of Stratified Depression In Generation Scotland And UK Biobank"]</ref> 0.14(0.03),<ref name="Peyrot2017"/> 0.31(0.13)<ref name="Milaneschi2015">Milaneschi et al 2015, [https://www.researchgate.net/profile/Abdel_Abdellaoui/publication/279515037_Polygenic_dissection_of_major_depression_clinical_heterogeneity/links/5594284108ae793d13797c34.pdf "Polygenic dissection of major depression clinical heterogeneity"]</ref>

** Age at onset: 0.17(0.10),<ref name="Ferentinos2015">[http://www.tara.tcd.ie/handle/2262/73542 "Familiality and SNP heritability of age at onset and episodicity in major depressive disorder"], Ferentinos et al 2015</ref>

** Episodicity: 0.09(0.14)<ref name="Ferentinos2015"/>

*** Moods and Feelings Questionnaire (MFQ; Depressive Symptoms): 0.00(0.1)/0.00(0.12)<ref name="Trzaskowski2013c"/>

** recurrent major depressive disorder: 0.20(0.03)<ref name="Hall2017"/>

** by sex:

*** male: 0.18(0.06)<ref name="Hall2017"/>

*** female: 0.22(0.06)<ref name="Hall2017"/>

** MDD decreased-appetite subtype: 0.38(0.17)<ref name="Milaneschi2015"/>

** MDD increased-appetite subtype: 0.43(0.20)<ref name="Milaneschi2015"/>

* patient response to antidepressive treatment: all response: 0.42(0.18), SSRI response: 0.428(0.23)<ref>Tansey et al 2013, [https://www.dropbox.com/s/er4748rth65uioh/2013-tansey.pdf "Contribution of Common Genetic Variants to Antidepressant Response"]</ref>

* [[Schizophrenia]]: 0.23 (0.008),<ref name="Lee2013"/> 0.23(0.01),<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3327879/ "Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs"], Lee et al 2012</ref> 0.32(0.03),<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3827979/ "Genome-wide association analysis identifies 13 new risk loci for schizophrenia"], Ripke et al 2013</ref> 0.39(0.12),<ref>[https://genetics.emory.edu/documents/Warren%20Publications/Goes%202015.pdf "Genome-Wide Association Study of Schizophrenia in Ashkenazi Jews"], Goes et al 2015</ref> 0.24(0.09)/0.28(0.03)/0.27(0.02),<ref>[http://www.sciencedirect.com/science/article/pii/S000292971300325X "Additive Genetic Variation in Schizophrenia Risk Is Shared by Populations of African and European Descent"], Candia et al 2013</ref> 0.274(0.007),<ref name="Loh2015">[http://biorxiv.org/content/biorxiv/early/2015/06/05/016527.full.pdf "Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis"], Loh et al 2015; see also [http://biorxiv.org/content/early/2015/06/05/016527 "Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis"], Loh et al 2015</ref> 0.20(0.025)<ref name="Gusev2014"/>

* [[Bipolar disorder]]:<ref>[http://biorxiv.org/content/early/2016/03/22/044412 "Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder"], Hou et al 2016</ref> 0.25(0.012),<ref name="Lee2013"/> 0.37(0.04)<ref name="Lee2011"/> 0.59(0.06),<ref name="Speed2012">[http://www.sciencedirect.com/science/article/pii/S0002929712005332 "Improved heritability estimation from genome-wide SNPs"], Speed et al 2012</ref> 0.26(0.032),<ref name="Gusev2014"/> 0.26(0.032)<ref name="Gusev2013">[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003993 "Quantifying missing heritability at known GWAS loci"], Gusev et al 2013</ref>

* [[postpartum depression]]: 0.22(0.12)<ref name="Byrne2014">Byrne et al 2014, [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341990/ "Applying polygenic risk scores to postpartum depression"]</ref>

* [[Borderline Personality]]: 0.23(0.09)<ref name="Lubke2014">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3872258/ "Genome-wide analyses of borderline personality features"], Lubke et al 2014</ref>

* [[Tourette syndrome]]: 0.58(0.09)<ref name="Davis2013">[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003864 "Partitioning the heritability of Tourette syndrome and obsessive compulsive disorder reveals differences in genetic architecture"], Davis 2013</ref>

* [[Obsessive compulsive disorder]]: 0.37(0.07)<ref name="Davis2013"/>

* Empathy Quotient: 0.11(0.014)<ref name="Warrier2016">[http://biorxiv.org/content/early/2016/04/29/050682 "Genome-wide analyses of empathy and systemizing: heritability and correlates with sex, education, and psychiatric risk"], Warrier et al 2016</ref>

* Systemizing Quotient-Revised: 0.12(0.012)<ref name="Warrier2016"/>

* Social and Communication Disorders Checklist (SCDC): 0.24(0.07)<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4986048/ "Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population"], Robinson et al 2015</ref>

* [[Autism spectrum disorders]]: 0.17(0.025),<ref name="Lee2013"/> 0.396(0.082)/0.498(0.118),<ref name="Klei2012">[http://molecularautism.biomedcentral.com/articles/10.1186/2040-2392-3-9 "Common genetic variants, acting additively, are a major source of risk for autism"], Klei et al 2012</ref> 0.655(0.139),<ref name="Klei2012"/> 0.494(0.096),<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137411/ "Most genetic risk for autism resides with common variation"], Gaugler et al 2014</ref> 0.24(0.07)<ref>[http://pubman.mpdl.mpg.de/pubman/item/escidoc:2196732:6/component/escidoc:2240876/art_10.1186_2040-2392-5-18.pdf "Variability in the common genetic architecture of social-communication spectrum phenotypes during childhood and adolescence"], St Pourcain et al 2014</ref>

** Childhood Asperger Syndrome Test (CAST; Autistic-Like Symptoms); composite: 0.09(0.12)/0.00(0.16)<ref name="Trzaskowski2013c"/>

*** Communication: 0.00(0.12)/0.00(0.15)<ref name="Trzaskowski2013c"/>

*** Nonsocial: 0.00(0.12)/0.00(0.16)<ref name="Trzaskowski2013c"/>

*** Social: 0.06(0.12)/0.00(0.16)<ref name="Trzaskowski2013c"/>

** male/female differences in autism etiology<ref>Mitra et al 2016, [http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006425 "Pleiotropic Mechanisms Indicated for Sex Differences in Autism"]</ref>

** autism symptoms (SCDC):

*** age 8: 0.24(0.07)<ref name="Stergiakouli2017"/>

*** age 11: 0.16(0.07)<ref name="Stergiakouli2017"/>

*** age 14: 0.08(0.07)<ref name="Stergiakouli2017"/>

*** age 17: 0.45(0.09)<ref name="Stergiakouli2017"/>

* [[Attention-deficit/hyperactivity disorder|ADHD]]: 0.28(0.023),<ref name="Lee2013">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3800159/ "Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs"], Lee et al 2013</ref> 0.40(0.14),<ref name="Pappa2015"/> 0.42(0.13),<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4321789/ "Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants"], Yang et al 2013</ref> 0.5902(0.279)<ref name="Bidwell2017">Bidwell et al 2017, [https://www.dropbox.com/s/3b14bdryixht79n/2017-bidwell.pdf "Genetic influences on ADHD symptom dimensions: Examination of a priori candidates, gene-based tests, genome-wide variation, and SNP heritability"]</ref>

** hyperactivity-impulsivity: 0.5383(0.262)<ref name="Bidwell2017"/>

** inattention: 0.4365(0.301)<ref name="Bidwell2017"/>

* ADHD symptoms (SDQ-ADHD):

** age 12: 0.19(0.07)<ref name="Stergiakouli2017">Stergiakouli et al 2017, [https://molecularautism.biomedcentral.com/articles/10.1186/s13229-017-0131-2 "Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development"]</ref>

** age 13: 0.18(0.07)<ref name="Stergiakouli2017"/>

* DSM-IV–based ADHD scale from the Conners' Parent Rating Scale–Revised (CPRS-R); Conners composite: 0.00(0.12)<ref name="Trzaskowski2013c">[http://www.sciencedirect.com/science/article/pii/S0890856713005182 "No genetic influence for childhood behavior problems from DNA analysis"], Trzaskowski et al 2013</ref>

** Hyperactivity-impulsivity: 0.06(0.12)<ref name="Trzaskowski2013c"/>

** Inattention: 0.00(0.12)<ref name="Trzaskowski2013c"/>

* Child behavioral problems (ADHD, externalizing problems, total problems): 0.40(0.14)/0.37(0.14)/0.45(0.14)/0.20(0.14)/0.12(0.10)/0.12(0.10)/0.18(0.10)/0.16(0.11)/0.71(0.22)/0.44(0.22)/0.11(0.16)<ref name="Pappa2015">[https://www.researchgate.net/profile/Irene_Pappa/publication/278969768_Single_Nucleotide_Polymorphism_Heritability_of_Behavior_Problems_in_Childhood_Genome-Wide_Complex_Trait_Analysis/links/55ed557508ae3e121847fffd.pdf "Single nucleotide polymorphism heritability of behavior problems in childhood: genome-wide complex trait analysis"], Pappa et al 2015</ref>

* childhood aggression: 0.10(0.06)/0.54(0.19)/0.46(0.35)/0.08(0.06)<ref>[http://pubman.mpdl.mpg.de/pubman/item/escidoc:2176524/component/escidoc:2332385/Pappa_etal_AMJMedGenB_2015.pdf "A genome-wide approach to children's aggressive behavior: The EAGLE consortium"], Pappa et al 2015b</ref>

* Preschool internalizing problems: 0.26(0.07)/0.18(0.30)/0.13(0.33)<ref>[http://www.tweelingenregister.org/nederlands/verslaggeving/NTR-publicaties_2014/Benke_JAACAP_2014.pdf "A genome-wide association meta-analysis of preschool internalizing problems"], Benke et al 2014</ref>

* Strengths and Difficulties Questionnaire (SDQ; Behavior Problems); composite: 0.00(0.1)/0.00(0.12)/0.11(0.15)<ref name="Trzaskowski2013c"/>

** Anxiety: 0.02(0.12)/0.00(0.12)/0.11(0.15)<ref name="Trzaskowski2013c"/>

** Conduct: 0.00(0.12)/0.00(0.12)/0.26(0.15)<ref name="Trzaskowski2013c"/>

** Hyperactivity: 0.00(0.12)/0.00(0.12)/0.05(0.15)<ref name="Trzaskowski2013c"/>

** Peer problems: 0.00(0.1)/0.16(0.12)/0.00(0.15),<ref name="Trzaskowski2013c"/> 0.04(0.05)/0.06(0.05)/0.11(0.06)/0.02(0.05)<ref>[https://link.springer.com/article/10.1007/s00439-014-1514-5/fulltext.html "Heritability and genome-wide analyses of problematic peer relationships during childhood and adolescence"], St Pourcain et al 2015 <!-- backup link for supplemental: https://www.dropbox.com/s/l4pakhesfoofali/2015-pourcain-supplementary.docx --></ref>

* Psychotism:

** Paranoia 0.14(0.13)<ref name="Sieradzka2015">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4561057/ "Heritability of Individual Psychotic Experiences Captured by Common Genetic Variants in a Community Sample of Adolescents"], Sieradzka 2015</ref>

** Hallucinations: 0.00(0.12)<ref name="Sieradzka2015"/>

** Cognitive Disorganization: 0.19(0.13)<ref name="Sieradzka2015"/>

** Grandiosity: 0.17(0.13)<ref name="Sieradzka2015"/>

** Anhedonia: 0.20(0.12)<ref name="Sieradzka2015"/>

** Negative Symptoms: 0.00(0.12)<ref name="Sieradzka2015"/>

* [[Parkinson's Disease]]: 0.22(0.02),<ref>[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1002141 "Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease"], Do et al 2011</ref> 0.27(0.05),<ref name="Keller2012">[http://hmg.oxfordjournals.org/content/21/22/4996.full "Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease"], Keller et al 2012</ref> 0.28(0.05)<ref name="Guerreiro2016"/>

** Early onset: 0.15(0.14)<ref name="Keller2012"/>

** Late onset: 0.31(0.07)<ref name="Keller2012"/>

* [[dementia with Lewy bodies]]: 0.31(0.03)<ref name="Guerreiro2016">Guerreiro et al 2016, [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4759606/ "Genome-wide analysis of genetic correlation in dementia with Lewy bodies, Parkinson's and Alzheimer's diseases"]</ref>

* [[Alzheimer's disease]]: 0.60(0.05)<ref name="Guerreiro2016"/>

* PTSD: 0.12(0.05)<ref name="Duncan2017">Duncan et al 2017, [http://www.nature.com/mp/journal/vaop/ncurrent/full/mp201777a.html "Largest GWAS of PTSD (''N''=20070) yields genetic overlap with schizophrenia and sex differences in heritability"]</ref>

** female PTSD: 0.21(0.09)<ref name="Duncan2017"/>

** male PTSD: 0.08(0.10)<ref name="Duncan2017"/>

==== Drug use ====

* [[Caffeine]] use: 0.07(?)<ref>[http://www.nature.com/mp/journal/v20/n5/extref/mp2014107x1.pdf "Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption"], The Coffee and Caffeine Genetics Consortium et al 2014</ref>

* [[Marijuana]] ever: 0.06(0.102),<ref>Verweij et al 2013, [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3548058/ "The genetic aetiology of cannabis use initiation: a meta-analysis of genome-wide association studies and a SNP-based heritability estimation"]</ref> 0.25(0.088)<ref>[https://link.springer.com/article/10.1007/s10519-015-9723-9 "Heritability, SNP- and Gene-Based Analyses of Cannabis Use Initiation and Age at Onset"], Minca et al 2015</ref>

** marijuana use disorder: 0.09(0.03)<ref>Demontis et al 2017, [https://www.biorxiv.org/content/early/2017/12/21/237321 "Genome-wide association study implicates CHRNA2 in cannabis use disorder"] ([https://www.biorxiv.org/content/biorxiv/suppl/2017/12/21/237321.DC1/237321-1.pdf supplement table 6])</ref>

* [[Smoking]] ever: 0.19(0.087)<ref name="Lubke2012"/>

* Smoking, current: 0.24(0.096),<ref name="Lubke2012"/> 0.19(0.102),<ref name="McGue2013"/> 0.18(0.16),<ref name="Vrieze2013">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3579160/ "Three mutually informative ways to understand the genetic relationships among behavioral disinhibition, alcohol use, drug use, nicotine use/dependence, and their co-occurrence: Twin biometry, GCTA, and genome-wide scoring"], Vrieze et al 2013</ref> 0.19(0.04)<ref name="Pierson2014"/>

* alcohol

** alcohol consumption: 0.14(0.071),<ref name="McGue2013">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886341/ "A genome-wide association study of behavioral disinhibition"], McGue et al 2013</ref> 0.16(0.16)<ref name="Vrieze2013"/> 0.19 (0.11),<ref name="Webb2017">[http://journal.frontiersin.org/article/10.3389/fgene.2017.00030/abstract "Molecular genetic influences on normative and problematic alcohol use in a population-based sample of college students"], Webb et al 2017</ref> 0.13(0.01)<ref name="Clarke2017">Clarke et al 2017, [http://biorxiv.org/content/early/2017/03/14/116707 "Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112,117)"]</ref>

*** male: 0.16(0.01)<ref name="Clarke2017"/>

*** female: 0.13(0.01)<ref name="Clarke2017"/>

** alcohol dependence: 0.08(0.107),<ref name="McGue2013"/> 0.12(0.16),<ref name="Vrieze2013"/> 0.235(0.03)<ref name="Zaitlen2013"/> 0.02(0.10)<ref name="Webb2017"/>

*** alcohol abuse (Alcohol Use Disorders Identification Test/AUDIT): 0.1205(0.0191)<ref name="Sanchez-Roige2017">Sanchez-Roige et al 2017, [http://biorxiv.org/content/early/2017/06/15/147397 "Genome-wide association study of Alcohol Use Disorder Identification Test (AUDIT) scores in 20,328 research participants of European ancestry"]</ref>

** alcohol dependence diagnosis: 0.30(0.136)<ref name="Palmer2015b">Palmer et al 2015, [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4644467/ "Shared Additive Genetic Influences on DSM-IV Criteria for Alcohol Dependence in Subjects of European Ancestry"]</ref>

*** alcohol tolerance: 0.242(0.129)<ref name="Palmer2015b"/>

*** alcohol withdrawal: 0.281(0.174)<ref name="Palmer2015b"/>

*** using alcohol longer than intended: 0.324(0.158)<ref name="Palmer2015b"/>

*** Unsuccessful attempts to cut down alcohol consumption: 0.197(0.146)<ref name="Palmer2015b"/>

*** Great time spent using/recovering from alcohol: 0.072(0.104)<ref name="Palmer2015b"/>

*** Social/Occupation activities foregone due to alcohol: 0.199(0.091)<ref name="Palmer2015b"/>

*** Continued use of alcohol despite problems: 0.237(0.109)<ref name="Palmer2015b"/>

** maximum drinks: 0.01(0.12)<ref name="Webb2017"/>

* Illicit Drugs: 0.37(0.102),<ref name="McGue2013"/> 0.22(0.16),<ref name="Vrieze2013"/>

* DSM-IV drug dependence diagnoses (DD): 0.36(0.13)<ref name="Palmer2015">[http://www.downstate.edu/hbnl/documents/2015-Palmer-Examiningtheroleofcommongeneticvariantsonalcoholtobaccocannabisandillicitdrugdep.pdf "Examining the role of common genetic variants on alcohol, tobacco, cannabis and illicit drug dependence: Genetics of vulnerability to drug dependence"], Palmer et al 2015</ref>

** factor score based on problem use (PU; i.e. 1+ [[DSM-IV]] symptoms): 0.25(0.13)<ref name="Palmer2015"/>

** drug dependence vulnerability (DV; a ratio of DSM-IV symptoms to the number of substances used): 0.33(0.13)<ref name="Palmer2015"/>

==== Disease ====

* allergic diseases: 0.075(0.007)<ref name="Zhu2017"/>

* [[Allergic rhinitis]]: 0.074(0.015)<ref name="Loh2015"/>

* [[Amyotrophic lateral sclerosis]]: 0.085(0.005)<ref>[http://www.research.ed.ac.uk/portal/files/26903768/MinE_GWAS_manuscript_NG.pdf "Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis"], van Rheenen et al 2016</ref>

* [[Asthma]]: 0.264(0.067),<ref name="Zaitlen2013"/> 0.152(0.018),<ref name="Loh2015"/> 0.072(0.007),<ref name="Zhu2017">Zhu et al 2017, [http://biorxiv.org/content/early/2017/05/26/133322 "Shared Genetic Architecture Of Asthma With Allergic Diseases: A Genome-wide Cross Trait Analysis Of 112,000 Individuals From UK Biobank"]</ref> 0.38(0.015)<ref name="Ek2017">Ek et al 2017, ["Genome-wide association analysis identifies 26 novel loci for asthma, hay fever and eczema"]</ref>

** Airway hyperresponsiveness (AHR): 0.45(0.29)<ref name="McGeachie2016">McGeachie et al 2016, [http://onlinelibrary.wiley.com/doi/10.1002/iid3.133/full "Whole genome prediction and heritability of childhood asthma phenotypes"]</ref>

** Serum total IgE (IGE): 0.53(0.27)<ref name="McGeachie2016"/>

** Eosinophil count (EOS): 0.29(0.32)<ref name="McGeachie2016"/>

** Pre-bronchodilator FEV1: 0.81(0.22)<ref name="McGeachie2016"/>

** Post-bronchodilator FEV: 0.83(0.22)<ref name="McGeachie2016"/>

** Bronchodilator response (BDR): 0.67(0.24)<ref name="McGeachie2016"/>

** Steroid responsiveness endophenotype (SRE): 0.00(0.42)<ref name="McGeachie2016"/>

** Normal lung growth only: 0.47(0.27)<ref name="McGeachie2016"/>

** Normal lung growth with early decline: 0.55(0.23)<ref name="McGeachie2016"/>

** Reduced lung growth only: 0.49(0.26)<ref name="McGeachie2016"/>

** Reduced lung growth with early decline: 0.17(0.27)<ref name="McGeachie2016"/>

** Early decline with normal or reduced lung growth: 0.22(0.28)<ref name="McGeachie2016"/>

** Reduced lung growth with or without early decline: 0.95(0.19)<ref name="McGeachie2016"/>

* [[hay fever]]: 0.53(0.05)<ref name="Pierson2014"/>

** hay fever/eczema: 0.30(0.020)<ref name="Ek2017"/>

* [[Multiple sclerosis]]: 0.19(0.009),<ref name="Gusev2013"/> 0.3(0.02),<ref>[http://www.nature.com/articles/srep00770 "Estimating the proportion of variation in susceptibility to multiple sclerosis captured by common SNPs"], Watson et al 2012</ref> 0.19(0.009)<ref name="Gusev2014"/>

* autoimmune Systemic RA+SLE+SSc+AS (rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, ankylosing spondylitis): 0.2(0.048)<ref name="Zaitlen2013"/>

* T-cell mediated autoimmune disease: 0.192(0.033)<ref name="Zaitlen2013"/>

* [[Crohn's disease]]: 0.18 0.024),<ref name="Gusev2013"/> 0.61(0.08),<ref name="Lee2011">[http://www.sciencedirect.com/science/article/pii/S0002929711000206 "Estimating missing heritability for disease from genome-wide association studies"], Lee et al 2011</ref> 0.54(0.06),<ref name="Speed2012"/> 0.18(0.024),<ref name="Gusev2014"/> 0.46(0.020)<ref name="Chen2014">Chen et al 2014, [http://hmg.oxfordjournals.org/content/23/17/4710.full "Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data"]</ref>

* [[Ulcerative colitis]]: 0.17(0.017),<ref name="Gusev2013"/> 0.17(0.017)<ref name="Gusev2014"/>

* [[psoriasis]]: 0.349(0.06)<ref>Yin et al 2014, [https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-15-87 "Common variants explain a large fraction of the variability in the liability to psoriasis in a Han Chinese population"]</ref>

* [[celiac disease]]: 0.33(0.042)<ref name="Stahl2012"/>

* [[Macular degeneration]]: 0.242(0.029),<ref name="Loh2015"/> 0.36(0.016)<ref name="Chen2014"/>

* [[Arthritis]]: 0.11(0.031),<ref name="Gusev2013"/> 0.57(0.06),<ref name="Speed2012"/> 0.098(0.014),<ref name="Loh2015"/> 0.11(0.031),<ref name="Gusev2014"/> 0.126(0.026)/0.261(0.061),<ref name="Zaitlen2013"/> 0.32(0.037)<ref name="Stahl2012">Stahl et al 2012, [http://coruscant.itmat.upenn.edu/pubs/stahlea_natgenet_polygene.pdf "Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis"]</ref>

* [[Osteoporosis]]: 0.195(0.024)<ref name="Loh2015"/>

* [[Ankylosing spondylitis]]: 0.18(0.028)<ref name="Gusev2014">[http://www.sciencedirect.com/science/article/pii/S0002929714004261 "Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases"], Gusev et al 2014a; see also [http://biorxiv.org/content/early/2014/04/20/004309 "Regulatory variants explain much more heritability than coding variants across 11 common diseases"], Gusev et al 2014b</ref>

* [[breast cancer]] (BC): 0.117(0.051),<ref name="Zaitlen2013"/> 0.57(0.11)/0.32(0.17)<ref name="Li2013"/>

* [[prostate cancer]] (PC): 0.204(0.056),<ref name="Zaitlen2013"/> 0.30(0.06)<ref>[http://biorxiv.org/content/early/2015/07/31/023440.1 "The contribution of rare variation to prostate cancer heritability"], Mancuso et al 2015</ref>

* [[Hematoma]] volume: 0.60(0.70)<ref name="Devan2013"/>

* Intracerebral hemorrhage mortality: 0.40(0.70)<ref name="Devan2013"/>

* [[Intracerebral hemorrhage]] risk: 0.44(0.21)<ref name="Devan2013">[http://stroke.ahajournals.org/content/44/6/1578.full.pdf "Heritability Estimates Identify a Substantial Genetic Contribution to Risk and Outcome of Intracerebral Hemorrhage"], Devan et al 2013</ref>

* [[Dyslipidemia]]: 0.263(0.014)<ref name="Loh2015"/>

* [[HIV]] viral load: 0.084(0.04)<ref>[http://biorxiv.org/content/biorxiv/early/2015/10/14/029017.full.pdf "Estimating the respective contributions of human and viral genetic variation to HIV control"], Bartha et al 2015</ref>

* [[esophageal adenocarcinoma]]: 0.0(0.21)<ref name="Ek2013">Ek et al 2013, [http://jnci.oxfordjournals.org/content/105/22/1711.full "Germline genetic contributions to risk for esophageal adenocarcinoma, Barretts Esophagus, and gastroesophageal reflux"]</ref>

* [[Barrett's esophagus]]: 0.35(0.06)<ref name="Ek2013"/>

* [[Gastroesophageal reflux disease]]: 0.25(0.05)<ref name="Ek2013"/>

* [[developmental dysplasia of the hip]]: 0.55(0.06)<ref>Hatzikotoulas et al 2017, [https://www.dropbox.com/s/r3dq3uq61saztjn/2017-hatzikotoulas.pdf "National clinical audit data decodes the genetic architecture of developmental dysplasia of the hip"]</ref>

==== Heart-related ====

* [[Hypertension]]: 0.42(0.06),<ref name="Speed2012"/> 0.255(0.014),<ref name="Loh2015"/> 0.37(0.053),<ref name="Gusev2014"/> 0.60(0.089)<ref name="Gusev2013"/>

** in pregnancy: 0.083(0.043)<ref name="Zaitlen2013"/>

* fasting [[triglyceride]]s (TG): 0.16(0.05),<ref name="Vattikuti2012"/> 0.31(0.061)<ref name="Chen2015"/>

* Total cholesterol: 0.15(0.061)<ref name="Chen2015">[http://www.sciencedirect.com/science/article/pii/S0002929715004061 "Dominant Genetic Variation and Missing Heritability for Human Complex Traits: Insights from Twin versus Genome-wide Common SNP Models"], Chen et al 2015</ref>

* fasting [[high-density lipoprotein]] (HDL): 0.12(0.05)<ref name="Vattikuti2012"/><ref name="Morrison2013">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4030301/ "Whole-genome sequence–based analysis of high-density lipoprotein cholesterol"], Morrison et al 2013</ref> 0.24(0.061),<ref name="Chen2015"/> 0.45(0.017)<ref name="Zaitlen2013"/>

* low density lipoprotein cholesterol (LDL): 0.16(0.061),<ref name="Chen2015"/> 0.199(0.063)<ref name="Zaitlen2013">[http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1003520 "Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative and Dichotomous Traits"], Zaitlen et al 2013</ref>

* [[systolic blood pressure]] (SBP): 0.24(0.05)<ref name="Vattikuti2012"/>

* [[Cardiovascular disease]]: 0.092(0.015)<ref name="Loh2015"/>

* [[Coronary artery disease]]: 0.30(0.058),<ref name="Gusev2013"/> 0.39(0.06),<ref name="Speed2012"/> 0.31(0.057),<ref name="Gusev2014"/> 0.146(0.017),<ref name="Zaitlen2013"/> 0.41(0.067)<ref name="Stahl2012"/>

* [[Ischemic stroke]]:

** all: 0.379(0.052)<ref name="Bevan2012">Bevan et al 2012, [https://s3.amazonaws.com/academia.edu.documents/46072372/Genetic_Heritability_of_Ischemic_Stroke_20160530-6664-1umiyy2.pdf "Genetic heritability of ischemic stroke and the contribution of previously reported candidate gene and genome-wide associations"]</ref>

** large-vessel disease: 0.403(0.076)<ref name="Bevan2012"/>

** small-vessel disease: 0.161(0.077)<ref name="Bevan2012"/>

** Cardioembolic stroke: 0.326(0.074)<ref name="Bevan2012"/>

==== Diabetes-related ====

* [[Diabetes Type I]]: 0.13(0.030),<ref name="Gusev2013"/> 0.28 (0.04),<ref name="Lee2011"/> 0.73(0.06),<ref name="Speed2012"/> 0.13 (0.030)<ref name="Gusev2014"/>

* [[Diabetes type II]]: 0.35(0.06),<ref name="Speed2012"/> 0.297(0.022),<ref name="Loh2015"/> 0.37(0.065),<ref name="Gusev2014"/> 0.254(0.041),<ref name="Zaitlen2013"/> 0.36(0.066),<ref name="Gusev2013"/> 0.286(?),<ref>[http://biorxiv.org/content/early/2016/02/25/041335 "Type 2 Diabetes Risk Prediction Incorporating Family History Revealing a Substantial Fraction of Missing Heritability"], Gim et al 2016</ref> 0.51(0.065)<ref name="Stahl2012"/>

* [[Fasting glucose]]: 0.198(0.075),<ref name="Zheng2013">[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810463/ "Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits"], Zheng et al 2013 <!-- while focused on GxE, they necessarily estimate the main effects as well, check the supplements --></ref> 0.22(0.059),<ref name="Lubke2012"/> 0.10(0.05),<ref name="Vattikuti2012"/> 0.17(0.061)<ref name="Chen2015"/>

* [[HbA1c]]: 0.20(0.061)<ref name="Chen2015"/>

* fasting [[insulin]] (INS): 0.202(0.075),<ref name="Zheng2013"/> 0.09(0.05)<ref name="Vattikuti2012"/>

==== Biological ====

* X chromosome heritability in 20 UK Biobank traits: males, 0.0062(0.0034); females, 0.0030 (0.00020)<ref>Sidorenko et al 2018, [https://www.biorxiv.org/content/early/2018/10/03/433870 "The effect of X-linked dosage compensation on complex trait variation"]</ref>

* [[QT interval]] (QTi): 0.209(0.050)<ref name="Yang2011"/>

* [[von Willebrand factor]] (vWF): 0.252(0.051)<ref name="Yang2011"/>

* [[Hemoglobin]]: 0.21(0.061)<ref name="Chen2015"/>

* [[Cystatin]]: 0.27(0.061)<ref name="Chen2015"/>

* [[Creatinine]]: 0.18(0.061)<ref name="Chen2015"/>

* estimated [[Renal function|glomerular filtration rate]] (eGFR): 0.32(0.061)<ref name="Chen2015"/>

* [[Vitamin D]] blood levels: 0.23(0.147)<ref>[http://jn.nutrition.org/content/145/4/799.full.pdf "Genetic and Environmental Factors Are Associated with Serum 25-Hydroxyvitamin D Concentrations in Older African Americans"], Hansen et al 2015</ref>

* [[Epigenetic clock|Epigenetic age acceleration]]: 0.41(?)<ref name="Levine2015">[http://www.impactaging.com/papers/v7/n12/full/100864.html "Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer's disease related cognitive functioning "], Levine et al 2015</ref>

* [[Amyloid|Amyloid plaque]]: 0.03(?)<ref name="Levine2015"/>

* [[Senile plaques|Neuritic plaque]]: 0.05(?)<ref name="Levine2015"/>

* Diffuse plaque: 0.38(?)<ref name="Levine2015"/>

* [[Neurofibrillary tangle]]s (NFT): 0.00(?)<ref name="Levine2015"/>

* [[thyroid hormone]] levels:

** [[Thyroid-stimulating hormone|TSH]]: 0.24(0.255) <ref name="Taylor2015">[http://www.uk10k.org/assets/25743335.pdf "Whole-genome sequence-based analysis of thyroid function"], Taylor et al 2015</ref>

** [[FT4]]: 0.20(0.306)<ref name="Taylor2015"/>

* [[HOMA-IR]]: 0.209(0.075)<ref name="Zheng2013"/>

* [[HOMA-B]]: 0.187(0.077)<ref name="Zheng2013"/>

* [[Apolipoprotein A1]]: 0.17(0.061)<ref name="Chen2015"/>

* [[Apolipoprotein B]]: 0.14(0.071)<ref name="Chen2015"/>

* [[C-reactive protein]]: 0.37(0.061)<ref name="Chen2015"/>

* [[Immunoglobulin A]]: 0.24(0.061)<ref name="Chen2015"/>

* [[monocyte]] white blood cell count: 0.343(0.032)<ref name="Zaitlen2013"/>

* [[Genetic recombination|recombination]] rate: 0.099(0.023)<ref name="Zaitlen2013"/>

* [[telomere]] length: 0.31(0.14)<ref>[http://www.tandfonline.com./doi/abs/10.1080/19485565.2015.1120645 "Estimating Telomere Length Heritability in an Unrelated Sample of Adults: Is Heritability of Telomere Length Modified by Life Course Socioeconomic Status?"], Faul et al 2016</ref>

===== Neanderthal admixture =====

[[Neanderthal]] admixture as a risk factor for:<ref>{{br-separated entries |[http://news.harvard.edu/gazette/story/2014/01/neanderthals-dna-legacy-linked-to-modern-ailments/ "Neanderthals’ DNA legacy linked to modern ailments: Humans inherited variants affecting disease risk, infertility, skin and hair characteristics"], Stephanie Dutchen, 2014-01-29 |[https://www.dropbox.com/s/urpbjiinbp57q9p/2016-simonti.pdf "The phenotypic legacy of admixture between modern humans and Neandertals"], Corinne N. Simonti et al, 2016-02-11}}</ref>{{Unreliable source? |reason=Reports GCTA estimates on a weird scale without SEs. |date=September 2016}}

* [[Mood disorder]]s

* [[Major depressive disorder|Depression]]

* [[Actinic keratosis]]

* [[Seborrheic keratosis]]

* [[Obesity]]

* [[Overweight]]

* [[Acute upper respiratory infections]]

* [[Coronary atherosclerosis]]

* [[Hypercoagulability|Hypercoagulation]]

* [[Tobacco use]]<ref>{{br-separated entries |[http://mbe.oxfordjournals.org/content/33/10/2648 Divergent ah receptor ligand selectivity during hominin evolution], Troy D. Hubbard et al, 2016-08-02 |[https://www.theguardian.com/science/2016/aug/02/smoke-signals-dna-adaptation-helped-early-humans-deal-with-toxic-fumes Smoke signals: DNA adaptation helped early humans deal with toxic fumes], Naomi Stewart, 2016-08-02}}</ref>

* [[Type 2 diabetes]]

* [[Crohn's disease]]

* [[Lupus]]

* [[Biliary cirrhosis]]

* [[Infertility]]

=== Animal/plant ===

* [[Boar taint]]: 0.118(0.064)<ref>[http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-15-424 "Analysis of the genetics of boar taint reveals both single SNPs and regional effects"], Rowe et al 2014</ref>

* [[Merino sheep]] body size: ?<ref>[https://asas.org/docs/default-source/wcgalp-posters/599_paper_9386_manuscript_602_0.pdf "Genome-Wide Association Study on Body Weight Reveals Major Loci on OAR6 in Australian Merino Sheep"], Al-Mamun et al 2014</ref><!-- no total reported -->

* [[Anopheles arabiensis|Mosquito]] behavior:

** host preference (cattle vs human): 0.94(3.47)<ref name="Main2016">[http://biorxiv.org/content/early/2016/04/05/044701 "The genetic basis of host preference and indoor resting behavior in the major African malaria vector, ''Anopheles arabiensis''"], Main et al 2016</ref>

** resting behavior (indoors vs outdoors): 0.05(2.34)<ref name="Main2016"/>

* [[Cassava]] resistance to [[Cassava mosaic disease]]: 0.51<ref>[http://biorxiv.org/content/biorxiv/early/2015/11/11/031179.full.pdf "Genome-wide association and prediction reveals the genetic architecture of cassava mosaic disease resistance and prospects for rapid genetic improvement"], Wolfe et al 2015</ref>

<!-- todo: summarize tempest in a teapot == Unbiasedness == http://infoproc.blogspot.com/2016/01/gcta-missing-heritability-and-all-that.html http://infoproc.blogspot.com/2016/02/missing-heritability-and-gcta-update-on.html http://biorxiv.org/content/biorxiv/early/2016/09/09/074310.full.pdf

robust to heteroscedastic errors: Domingue et al 2016

see also Conley -->

== See also ==

Line 609 ⟶ 135:

* [http://espace.library.uq.edu.au/view/UQ:342517/UQ342517_OA.pdf "Research review: Polygenic methods and their application to psychiatric traits"], Wray et al. 2014

* [http://medicine.tums.ac.ir:803/Users/Javad_TavakoliBazzaz/Medical%20Genetics-2/Heritability%20in%20the%20genomics%20era.pdf "Heritability in the genomics era — concepts and misconceptions"] {{Webarchive|url=https://web.archive.org/web/20160522201032/http://medicine.tums.ac.ir:803/Users/Javad_TavakoliBazzaz/Medical%20Genetics-2/Heritability%20in%20the%20genomics%20era.pdf |date=2016-05-22 }}, Visscher et al. 2008

* [http://www.sciencedirect.com/science/article/pii/S2001037015000458 "Uncovering the Genetic Architectures of Quantitative Traits"], Lee et al. 2016

* [http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12129/pdf "Estimating heritability using genomic data"], Stanton-Geddes et al. 2013

Line 628 ⟶ 154:

* [https://www.youtube.com/watch?v=b32OwqBPHkI "Genomics, Big Data, Medicine, and Complex Traits"] (Peter Visscher talk)

* [https://www.dropbox.com/s/1otmbu840xejjv1/MCTFR_talk.pdf "The Genetic Architectures of Psychological Traits"], Lee 2014 slides

* [https://www.youtube.com/watch?v=VI-5HlYQpNE "Heritability-based models for prediction of complex traits"], [https://sites.google.com/site/baldingstatisticalgenetics/home David Balding] {{Webarchive|url=https://web.archive.org/web/20161008184835/https://sites.google.com/site/baldingstatisticalgenetics/home |date=2016-10-08 }} 2015

[[Category:Behavioural genetics]]

Line 635 ⟶ 161:

[[Category:Statistical genetics]]

[[Category:Twin studies]]

[[Category:Genetics studies]]

[[Category:Quantitative genetics]]

[[Category:Molecular genetics]]