Genome-wide complex trait analysis

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 SNPs with 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.[1] The GCTA framework extends to bivariate genetic correlations between traits;[2] 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.[3] GCTA is incapable of generating reliable or stable estimates of heritability when used on current SNP data.[4]

GCTA heritability estimates are useful because they can lower bound[5] the genetic contributions to traits such as intelligence without relying on the assumptions used in twin studies and other family studies and pedigree analyses, thereby corroborating[6][7][8] them, and enabling the design of well-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. 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 ANOVA/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, "A Marker-based Method for Inferences About Quantitative Inheritance in Natural Populations"[9]) to combine estimated genetic relatedness with phenotypic measurements to estimate variance components such as heritability or genetic correlations.[10] and subsequently applied to plants/animals[11][12][13][14][15][16][17]

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[18]/2007,[19] 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.[20][21] (Large GWASes on height have since confirmed the estimate.[22]) The GCTA algorithm was then described and a software implementation published in 2011.[23] 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

Robust heritability

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.[24] 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.[25] (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 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.

In addition to being more robust to violations of the twin study assumptions, SNP data can be easier to collect since it does not require rare twins and thus also heritability for rare traits can be estimated (with due correction for ascertainment bias).

GWAS power

GCTA estimates can be used to resolve the missing heritability problem and 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 polygenic variants and thus that much larger GWASes will be required to accurately estimate each SNP's effects and directly account for a fraction of the GCTA heritability.

Disadvantages

  1. 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.
  2. 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.[26] In contrast, twin studies can offer precise estimates with a fraction of the sample size.
  3. Computational inefficiency: The original GCTA implementation scales poorly with increasing data size ( ), 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,[27] 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 moment matching[28]
  4. 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, meta-analysis must be done.
    In contrast, there are alternative techniques which operate on summaries reported by GWASes without requiring the raw data[29] e.g. "LD score regression"[30] 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 LD Hub which provides a public web interface to >=177 traits with LD score regression.[31] Another method using summary data is HESS.[32]
  5. Confidence intervals may be incorrect, or outside the 0-1 range of heritability, and highly imprecise due to asymptotics[33]
  6. 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[34] as they represent mutation load being purged by negative selection. As with measurement error, this will bias GCTA estimates towards underestimating heritability.

Interpretation

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.[35] This is incorrect, as GCTA estimates are lower bounds.

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:

  1. 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;[36] 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 haplotypes[37] and imputation (SNPs can proxy for unobserved genetic variants which they tend to be inherited with); e.g. Yang et al. 2015[38] 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;[39] for 4 traits in the UK Biobank, imputing raised the SNP heritability estimates.[40] Additional genetic variants include de novo mutations/mutation load & structural variations such as copy-number variations.
  2. 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.
  3. 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,[41] such as intelligence. For example, an intelligence GCTA estimate of 0.31, based on an intelligence measurement with test-retest reliability , would after correction ( ), 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.

Implementations

GCTA
Original author(s) Jian Yang
Initial release 30 August 2010
Stable release
1.25.2 / 22 December 2015
Written in C++
Operating system Linux (Mac/Windows support dropped at v1.02)
Available in English
Type genetics
License GPL v3
Website cnsgenomics.com/software/gcta/; forums: gcta.freeforums.net
As of 22 May 2016

The original "GCTA" software package is the most widely used; its primary functionality covers the GREML estimation of SNP heritability, but includes other functionality:

  • Estimate the genetic relationship from genome-wide SNPs;
  • Estimate the inbreeding coefficient from genome-wide SNPs;
  • Estimate the variance explained by all the autosomal SNPs;
  • Partition the genetic variance onto individual chromosomes;
  • Estimate the genetic variance associated with the X-chromosome;
  • Test the effect of dosage compensation on genetic variance on the X-chromosome;
  • Predict the genome-wide additive genetic effects for individual subjects and for individual SNPs;
  • Estimate the LD structure encompassing a list of target SNPs;
  • Simulate GWAS data based upon the observed genotype data;
  • Convert Illumina raw genotype data into PLINK format;
  • Conditional & joint analysis of GWAS summary statistics without individual level genotype data
  • Estimating the genetic correlation between two traits (diseases) using SNP data
  • Mixed linear model association analysis
    — GCTA, cnsgenomics.com/software/gcta/

Other implementations and variant algorithms include:

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[56][57]). 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: " (standard error)"):

Human

Anthropometric

  • Height: 0.544(0.101),[20] 0.498(0.04),[22] 0.56(0.023),[38] 0.448(0.029),[58] 0.42(0.052),[59] 0.69(0.14),[60] 0.48(0.17)[61] 0.37(0.14):[62] 0.32(0.06),[63] 0.35(0.12),[64] 0.44(0.09),[65] 0.40(0.09)/0.33(0.09),[66] 0.62(0.061),[67] 0.687(0.016),[68] 0.56(0.23),[69] 0.51(0.01),[70] 0.47(0.15)/0.69(0.08)[71]
  • weight: 0.48(0.14),[62] 0.41(0.12),[64] 0.25(0.09),[66] 0.26(0.061),[67] 0.394(0.174),[72] 0.224(0.091)[72]
  • Body mass index (BMI): 0.42(0.17)[60] 0.14(0.05),[73] 0.50(0.05)[74] 0.31(0.07),[63] 0.43(0.10),[75] 0.21(0.061),[67] 0.424(0.018),[68] 0.27(0.025),[38] 0.165(0.029),[58] 0.24(0.01),[70] 0.26 (0.08)[71] 0.298(0.034)[76]
    • in children: 0.37(0.15)[77]
  • grip strength: 0.239(0.027)[78]
  • gestational (pregnancy) weight gain: maternal genome, 0.239(0.055);[79] fetal genome, 0.121(0.053)[79]
  • birthweight: maternal genome, 0.13(0.06);[79] fetal genome, 0.18(0.06)
  • waist-to-hip ratio (WHR): 0.13(0.05)[73] 0.188(0.037)[68]
  • waist circumference: 0.16(0.061)[67]
  • Breast size: 0.31(0.16)/0.47(0.25)[80]
  • Health (self-rated): 0.177(0.089),[75] 0.13(0.006)[81]
  • Hair color:
    • Blond: 0.165(0.081)[82]
    • Brown: 0.095(0.079)[82]
    • Red: 0.246(0.087)[82]
    • Black: 0.00(0.083)[82]
    • Light versus dark: 0.140(0.080)[82]
  • unibrow: 0.28(0.02)[70]
  • Male pattern hair loss (balding): autosomal SNPs, 0.473(0.013), X chromosome, 0.046(0.03), 0.519 total,[83] 0.94[84]
  • melanin index: 0.191(0.263)[72]
  • facial features:
    • Nares width: 0.504(0.187),[72] 0.226(0.094)[72]
    • Alar base width: 0.481(0.188),[72] 0.212(0.093)[72]
    • Nasal height: 0.441(0.186),[72] 0.03(0.076)[72]
    • Nasal ridge length: 0.524(0.188),[72] 0.059(0.078)[72]
    • Nasal tip protrusion: 0.401(0.191),[72] 0.177(0.088)[72]
    • External surface area: 0.449(0.187),[72] 0.121(0.086)[72]
    • Nostril area: 0.657(0.187),[72] 0.059(0.088)[72]
    • nasal root shape, mouth width: 0.669(0.138)[85]
    • facial width: 0.521(0.138)[85]
    • Allometry variation in shape due to size: 0.643(0.132)[85]
    • Centroid Size (facial size): 0.277(0.134)[85]
    • nasion to midendocanthion: 0.260(0.134)[85]
    • nasal width: 0.623(0.131)[85]
    • width of the nose, mandible height: 0.604(0.131)[85]
    • overall facial height, lower facial height: 0.579(0.139)[85]
    • outer canthal width: 0.421(0.141)[85]
    • nasal bridge length: 0.456(0.142)[85]
    • palpebral fissure length (average): 0.208(0.140)[85]
    • upper facial depth (average): 0.419(0.136)[85]
    • nose shape, height of the mouth: 0.211(0.138)[85]
    • upper facial height: 0.443(0.140)[85]
    • lower facial depth (average): 0.487(0.140)[85]
    • philtrum length: 0.486(0.130)[85]
    • midfacial depth (average): 0.469(0.139)[85]
    • upper and middle facial width: 0.308(0.139)[85]
    • upper facial height, midfacial width: 0.477(0.140)[85]
    • cheek protrusion: 0.074(0.137)[85]
    • nasal height: 0.244(0.137)[85]
    • midface protrusion, upper facial height: 0.431(0.125)[85]
    • midfacial landmark network around the nose and mouth: 0.433(0.138)[85]
    • morphological facial height: 0.159(0.137)[85]
    • inner canthal width: 0.392(0.142)[85]
    • nasal ala length (average): 0.311(0.140)[85]
    • lower facial height: 0.239(0.139)[85]
    • mouth width: 0.378(0.137)[85]
    • subnasal width: 0.373(0.134)[85]
    • cutaneous lower lip height: 0.177(0.134)[85]
    • nasal protrusion: 0.242(0.139)[85]
    • philtrum width: 0.337(0.126)[85]
    • lower vermilion height: 0.324(0.139)[85]
    • upper lip height: 0.314(0.131)[85]
    • chin height, nasion protrusion: 0.291(0.140)[85]
    • lower lip height: 0.283(0.134)[85]
    • nasal width, maxillary prognathism: 0.169(0.131)[85]
  • skin nevus (mole/lesion) density count: 0.58(0.025)[86]
  • age at menarche: 0.451(0.022)[68]
  • age at first birth: 0.15(0.04),[87] 0.19(0.039)[88]
  • age at menopause: 0.409(0.048)[68]
  • sex ratio of offspring: 0.026(0.017)[68]
  • number of offspring: 0.073(0.068)/0.102(0.028),[68] 0.10(0.05),[87] 0.22(0.026),[88] 0.21(0.05),[89] 0.20(0.10),[89] 0.19(0.09)[89]
  • left handedness: 0.004(0.145)[68]
  • Eye color: 0.59(0.01)[70]
  • Eye dimensions (axial length & corneal curvature): 0.46(0.16)/0.42(0.16)[61]
  • Cilantro tasting: 0.087[90]
  • cry cutting onions: 0.12(0.02)[70]
  • sweet vs salty: 0.35(0.03)[70]-

Social/behavioral

  • Education: 0.224(0.042),[91] 0.21(0.06),[92] 0.158(0.061),[93] 0.21(0.05),[94] 0.17(0.07),[63] 0.33 (0.10),[75] 0.23(0.09),[66] 0.156(0.021)[39]
    • rare/family variants: 0.281(0.03)[39]
    • test scores: 0.31(0.12)[95]
    • reading scores: 0.27(0.128)[96]
    • mathematics scores: 0.52 (0.163)[96]
  • Socioeconomic status (SES): 0.18(0.05),[94] 0.18(0.12)/0.19(0.12),[95] 0.18(0.12)/0.19(0.12)[97]
    • social deprivation: 0.21(0.005)[98]
    • household income: 0.11(0.007)[98]
  • Exercise:
    • Moderate to Vigorous Activity: 0.17(0.09)[99]
    • Sedentary Time: 0.25(0.09)[99]
    • Total Physical Activity: 0.21(0.10)[99]
  • ability to delay gratification/delay discounting (Monetary Choice Questionnaire): 0.122(0.017)[100]
  • Tiredness: 0.084(0.006)[101]
  • Insomnia: 0.08(0.02)[70]
  • Chronotype/morningness: 0.25(0.03),[70] 0.194(?),[102] 0.377(?)[102]
  • Adult antisocial behavior: 0.55(0.41)[103]
  • trust
    • trust in people: 0.07(0.17)[104]
    • trust in friends: 0.06(0.24)[104]
  • loneliness: 0.27(0.12)[105]
  • family relationship satisfaction: 0.053(0.014)[106]
  • friendship satisfaction: 0.056(0.014)[106]
  • Non-substance related Behavioral Disinhibition: 0.28(0.102),[107] 0.19(0.16)[108]
  • Stressful life events: 0.3(0.15)[109]
  • carsickness: 0.2(0.01)[70]

Psychological

  • Overall brain size: 0.845(0.457)/0.00(0.476)/0.00(0.483)/0.574(0.468),[110] 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)[69]
  • Volume of neuroanatomical structures: 100 brain volumes & latent factors thereof, median 0.348[111]
    • Global:
      • Intracranial volume: 0.880(0.238)[113]
      • Overall mean cortical thickness: 0.796(0.244)[113]
    • Frontal:
      • Left precentral gyrus thickness: 0.718(0.249)[113]
      • Left rostral anterior cingulate cortex thickness: 0.737(0.243)[113]
      • Left superior frontal gyrus thickness: 0.597(0.246)[113]
      • Right lateral orbital frontal cortex thickness: 0.483(0.240)[113]
      • Right pars opercularis surface area: 0.545(0.252)[113]
      • Right paracentral lobule thickness: 0.494(0.252)[113]
      • Right precentral gyrus thickness: 0.731(0.244)[113]
    • Occipital:
      • Left cuneus cortex thickness: 0.550(0.244)[113]
      • Left lateral occipital cortex thickness: 0.498(0.248)[113]
      • Right cuneus cortex thickness: 0.723(0.251)[113]
    • Parietal:
      • Left inferior parietal cortex thickness: 0.566(0.248)[113]
      • Left postcentral gyrus thickness: 0.501(0.249)[113]
      • Left posterior-cingulate cortex thickness: 0.601(0.246)[113]
      • Left precuneus cortex surface area: 0.555(0.262)[113]
      • Left precuneus cortex thickness: 0.896(0.245)[113]
      • Left superior parietal gyrus surface area: 0.558(0.251)[113]
      • Left superior parietal gyrus thickness: 0.903(0.241)[113]
      • Right postcentral gyrus thickness: 0.760(0.246)[113]
      • Right precuneus cortex surface area: 0.547(0.246)[113]
      • Right precuneus cortex thickness: 0.965(0.243)[113]
      • Right superior parietal gyrus thickness: 0.941(0.239)[113]
      • Right supramarginal gyrus thickness: 0.769(0.240)[113]
    • Temporal:
      • Left banks superior temporal sulcus thickness: 0.680(0.242)[113]
      • Left entorhinal cortex thickness: 0.587(0.249)[113]
      • Left fusiform gyrus surface area: 0.566(0.259)[113]
      • Left insula cortex surface area: 0.561(0.251)[113]
      • Left superior temporal gyrus surface area: 0.658(0.244)[113]
      • Left transverse temporal cortex thickness: 0.555(0.245)[113]
      • Right entorhinal cortex surface area: 0.651(0.251)[113]
      • Right insula cortex surface area: 0.878(0.252)[113]
      • Right middle temporal gyrus surface area: 0.610(0.244)[113]
      • Right temporal pole surface area: 0.524(0.249)[113]
      • Right transverse temporal cortex thickness: 0.536(0.254)[113]
    • Shape of neuroanatomical structures:
      • Accumbens Area: 0.230(0.134)[112]
      • Amygdala: 0.036(0.138)[112]
      • Caudate: 0.497(0.187)[112]
      • Cerebellum: 0.456(0.190)[112]
      • Corpus Callosum: 0.243(0.132)[112]
      • Hippocampus: 0.339(0.168)[112]
      • Lateral Ventricle: 0.207(0.152)[112]
      • 3rd Ventricle: 0.454(0.156)[112]
      • 4th Ventricle: 0.014(0.206)[112]
      • Pallidum: 0.074(0.116)[112]
      • Putamen: 0.365(0.146)[112]
      • Thalamus Proper: 0.132(0.143)[112]
  • Intelligence: 0.40(0.11)/0.51(0.11),[114] 0.47(?),[115] 0.24(0.20),[3] 0.29(0.12)/0.26(0.11)/0.20(0.11)/0.35(0.12),[64] 0.47(0.18)/0.26(0.17)/0.23(0.13)/0.15(0.14)[116][117] 0.29(0.05),[94] 0.35(0.11),[118] 0.60(0.26),[119] 0.32(0.14)/0.28(0.17),[97] 0.40(0.21)/0.46(0.06),[120] 0.56(0.25)/0.52(0.25),[69] 0.29%(0.05)/0.28(0.07),[121] 0.174(0.017),[122] 0.00(?)/0.00(?),[123] 0.31(0.018),[92] 0.360(0.108),[124] 0.23(0.02)[39]
    • extremely high intelligence: 0.33(0.02)[125]
      • extremely high intelligence variants in the major histocompatibility complex (MHC)/HLA immune system gene complex: 0.0028(0.0018)[126]
    • rare/family variants: 0.31(0.03)[39]
    • reaction time: 0.11(0.06)[92]
    • memory: 0.05(0.06),[92] 0.00(?)/0.00(?)[123]
    • working memory: 0.17(?)/0.07(?),[123] 0.108(0.096)[124]
    • Facial Memory: 0.064(0.093)[124]
    • Spatial Memory: 0.028(0.090)[124]
    • Verbal Memory: 0.244(0.097)[124]
    • Digit Symbol Test: 0.214(0.021)[39]
      • rare/family variants: 0.147 (0.028)[39]
    • Logical memory: 0.119 (0.02)[39]
      • rare/family variants:0.203 (0.028)[39]
    • Abstraction and Mental Flexibility: 0.064(0.096)[124]
    • Attention: 0.148(0.097)[124]
    • Language Reasoning: 0.302(0.098)[124]
    • vocabulary: 0.256(0.02)[39]
      • rare/family variants: 0.301(0.028)[39]
    • TOWRE word reading fluency: 0.74 (0.04)/0.68 (0.04)[127]
    • Verbal fluency: 0.189(0.021)[39]
      • rare/family variants: 0.271(0.029)[39]
    • Wide Range Achievement Test (Reading): 0.433(0.098)[124]
    • ART written/printed material exposure: 0.39(0.02)[127]
    • Nonverbal Reasoning: 0.406(0.096)[124]
    • Spatial Reasoning: 0.357(0.101)[124]
    • Age Differentiation: 0.039(0.098)[124]
    • Emotion Differentiation: 0.000(0.092)[124]
    • Emotion Identification: 0.357(0.093)[124]
    • Trailing Making test/visual-numeric reasoning[128]
  • Trail Making test: 0.079(0.024)/0.224(0.026)/0.176(0.025)[128]
  • Number sense: 0.00(0.29)[129]
  • Economic preferences
    • risk aversion: 0.137(0.152)[93]
    • patience: 0.085(0.148) [93]
    • trust: 0.242(0.146)[93]
    • fair-mindedness: 0.00(0.15)[93]
  • Political preferences
    • immigration/crime: 0.203(0.147)[93]
    • economic policy: 0.344(0.150)[93]
    • environmentalism: 0.00(0.148)[93]
    • feminism/equality: 0.00(0.147)[93]
    • foreign policy: 0.354(0.149)[93]
  • Happiness (self-rated): 0.05–0.10(0.05–0.10)[130]
  • positive affect: 0.08(0.02)[131]
  • life satisfaction: 0.13(0.02)[131]
  • brain region activity response to faces[132]
  • Big Five personality traits
  • Social Anxiety score: European-Americans: 0.12(0.033);[140] African-Americans: 0.12(0.134);[140] Hispanic: 0.21(0.102)[140]
  • Cloninger's personality dimensions:
    • Harm Avoidance: 0.066(0.037)[56]
    • Novelty Seeking: 0.099(0.036)[56]
    • Reward Dependence: 0.042(0.036)[56]
    • Persistence: 0.081(0.037)[56]
  • optimism: 0.10(0.02)[70]
  • Psychology endophenotypes:[141]
    • Total power: ~0.08(?)[141]
    • Theta power: ~0.04(?)[141]
    • Delta power: ~0.15(?)[141]
    • Beta power: ~0.19(?)[141]
    • CZ alpha power: ~0.21(?)[141]
    • O1O2 alpha power: ~0.45(?)[141]
    • Alpha frequency: ~0.49(?)[141]
    • SCL: ~0.23(?)[141]
    • SCR amplitude: ~0.25(?)[141]
    • SCR frequency: ~0.33(?)[141]
    • EDA factor: ~0.35(?)[141]
    • P3 amplitude: ~0.29(?)[141]
    • Antisaccade: ~0.47(?)[141]
    • Overall startle: ~0.49(?)[141]

Psychiatric

  • Antisocial Process Screening Devise (APSD; Psychopathic Symptoms); composite:0.00(0.12)/0.15(0.16)[142]
    • Callous-Unemotional: 0.02(0.12)/0.00(0.16),[142] 0.07(0.12)[143]
    • Impulsivity: 0.00(0.12)/0.24(0.16)[142]
    • Narcissism total: 0.00(0.12)/0.50(0.16)[142]
  • psychopathology in children: 0.38(0.16)[144]
  • childhood trauma (sexual abuse, physical abuse, emotional abuse, emotional neglect, and physical neglect): 5-domain continuous: 0.00(0.07),[145] 2-domain dichotomous: 0.09(0.08)[145]
  • anxiety: 0.16(0.11)[62]
  • epilepsy: 0.26(0.05)/0.27(0.06)[146]
  • Depression: 0.21(0.021),[147] 0.32(0.09)/0.32(0.086),[59] 0.19(0.10),[75] 0.15(0.02),[70] 0.20(0.04),[148] 0.14(0.03),[145] 0.31(0.13)[149]
    • Age at onset: 0.17(0.10),[150]
    • Episodicity: 0.09(0.14)[150]
      • Moods and Feelings Questionnaire (MFQ; Depressive Symptoms): 0.00(0.1)/0.00(0.12)[142]
    • recurrent major depressive disorder: 0.20(0.03)[148]
    • by sex:
    • MDD decreased-appetite subtype: 0.38(0.17)[149]
    • MDD increased-appetite subtype: 0.43(0.20)[149]
  • patient response to antidepressive treatment: all response: 0.42(0.18), SSRI response: 0.428(0.23)[151]
  • Schizophrenia: 0.23 (0.008),[147] 0.23(0.01),[152] 0.32(0.03),[153] 0.39(0.12),[154] 0.24(0.09)/0.28(0.03)/0.27(0.02),[155] 0.274(0.007),[51] 0.20(0.025)[156]
  • Bipolar disorder:[157] 0.25(0.012),[147] 0.37(0.04)[158] 0.59(0.06),[65] 0.26(0.032),[156] 0.26(0.032)[159]
  • postpartum depression: 0.22(0.12)[160]
  • Borderline Personality: 0.23(0.09)[161]
  • Tourette syndrome: 0.58(0.09)[162]
  • Obsessive compulsive disorder: 0.37(0.07)[162]
  • Empathy Quotient: 0.11(0.014)[163]
  • Systemizing Quotient-Revised: 0.12(0.012)[163]
  • Social and Communication Disorders Checklist (SCDC): 0.24(0.07)[164]
  • Autism spectrum disorders: 0.17(0.025),[147] 0.396(0.082)/0.498(0.118),[165] 0.655(0.139),[165] 0.494(0.096),[166] 0.24(0.07)[167]
    • Childhood Asperger Syndrome Test (CAST; Autistic-Like Symptoms); composite: 0.09(0.12)/0.00(0.16)[142]
      • Communication: 0.00(0.12)/0.00(0.15)[142]
      • Nonsocial: 0.00(0.12)/0.00(0.16)[142]
      • Social: 0.06(0.12)/0.00(0.16)[142]
    • male/female differences in autism etiology[168]
    • autism symptoms (SCDC):
  • ADHD: 0.28(0.023),[147] 0.40(0.14),[170] 0.42(0.13),[171] 0.5902(0.279)[172]
    • hyperactivity-impulsivity: 0.5383(0.262)[172]
    • inattention: 0.4365(0.301)[172]
  • ADHD symptoms (SDQ-ADHD):
  • DSM-IV–based ADHD scale from the Conners' Parent Rating Scale–Revised (CPRS-R); Conners composite: 0.00(0.12)[142]
    • Hyperactivity-impulsivity: 0.06(0.12)[142]
    • Inattention: 0.00(0.12)[142]
  • 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)[170]
  • childhood aggression: 0.10(0.06)/0.54(0.19)/0.46(0.35)/0.08(0.06)[173]
  • Preschool internalizing problems: 0.26(0.07)/0.18(0.30)/0.13(0.33)[174]
  • Strengths and Difficulties Questionnaire (SDQ; Behavior Problems); composite: 0.00(0.1)/0.00(0.12)/0.11(0.15)[142]
    • Anxiety: 0.02(0.12)/0.00(0.12)/0.11(0.15)[142]
    • Conduct: 0.00(0.12)/0.00(0.12)/0.26(0.15)[142]
    • Hyperactivity: 0.00(0.12)/0.00(0.12)/0.05(0.15)[142]
    • Peer problems: 0.00(0.1)/0.16(0.12)/0.00(0.15),[142] 0.04(0.05)/0.06(0.05)/0.11(0.06)/0.02(0.05)[175]
  • Psychotism:
    • Paranoia 0.14(0.13)[176]
    • Hallucinations: 0.00(0.12)[176]
    • Cognitive Disorganization: 0.19(0.13)[176]
    • Grandiosity: 0.17(0.13)[176]
    • Anhedonia: 0.20(0.12)[176]
    • Negative Symptoms: 0.00(0.12)[176]
  • Parkinson's Disease: 0.22(0.02),[177] 0.27(0.05),[178] 0.28(0.05)[179]
    • Early onset: 0.15(0.14)[178]
    • Late onset: 0.31(0.07)[178]
  • dementia with Lewy bodies: 0.31(0.03)[179]
  • Alzheimer's disease: 0.60(0.05)[179]
  • PTSD: 0.12(0.05)[180]
    • female PTSD: 0.21(0.09)[180]
    • male PTSD: 0.08(0.10)[180]

Drug use

  • Caffeine use: 0.07(?)[181]
  • Marijuana ever: 0.06(0.102),[182] 0.25(0.088)[183]
    • marijuana use disorder: 0.09(0.03)[184]
  • Smoking ever: 0.19(0.087)[59]
  • Smoking, current: 0.24(0.096),[59] 0.19(0.102),[107] 0.18(0.16),[108] 0.19(0.04)[70]
  • alcohol
    • alcohol consumption: 0.14(0.071),[107] 0.16(0.16)[108] 0.19 (0.11),[185] 0.13(0.01)[186]
    • alcohol dependence: 0.08(0.107),[107] 0.12(0.16),[108] 0.235(0.03)[68] 0.02(0.10)[185]
      • alcohol abuse (Alcohol Use Disorders Identification Test/AUDIT): 0.1205(0.0191)[187]
    • alcohol dependence diagnosis: 0.30(0.136)[188]
      • alcohol tolerance: 0.242(0.129)[188]
      • alcohol withdrawal: 0.281(0.174)[188]
      • using alcohol longer than intended: 0.324(0.158)[188]
      • Unsuccessful attempts to cut down alcohol consumption: 0.197(0.146)[188]
      • Great time spent using/recovering from alcohol: 0.072(0.104)[188]
      • Social/Occupation activities foregone due to alcohol: 0.199(0.091)[188]
      • Continued use of alcohol despite problems: 0.237(0.109)[188]
    • maximum drinks: 0.01(0.12)[185]
  • Illicit Drugs: 0.37(0.102),[107] 0.22(0.16),[108]
  • DSM-IV drug dependence diagnoses (DD): 0.36(0.13)[189]
    • factor score based on problem use (PU; i.e. 1+ DSM-IV symptoms): 0.25(0.13)[189]
    • drug dependence vulnerability (DV; a ratio of DSM-IV symptoms to the number of substances used): 0.33(0.13)[189]

Disease

Biological

Neanderthal admixture

Neanderthal admixture as a risk factor for:[211]

Animal/plant


See also

References

  1. ↑ Figure 3 of Yang et al 2010, or Figure 3 of Ritland & Ritland 1996
  2. ↑ Lee et al 2012, "Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood"
  3. 1 2 "Genetic contributions to stability and change in intelligence from childhood to old age", Deary et al 2012
  4. ↑ Krishna Kumar, Siddharth; Feldman, Marcus W.; Rehkopf, David H.; Tuljapurkar, Shripad (2016-01-05). "Limitations of GCTA as a solution to the missing heritability problem". Proceedings of the National Academy of Sciences of the United States of America. 113 (1): E61–70. doi:10.1073/pnas.1520109113. ISSN 1091-6490. PMC 4711841. PMID 26699465.
  5. ↑ "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
  6. ↑ Eric Turkheimer ("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 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."
  7. ↑ "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." --"Genetics and intelligence differences: five special findings", Plomin & Deary 2015
  8. ↑ "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."
  9. ↑ see also Ritland 1996b, "Estimators for pairwise relatedness and individual inbreeding coefficients"; Ritland & Ritland 1996, "Inferences about quantitative inheritance based on natural population structure in the yellow monkeyflower, Mimulus guttatus"; Lynch & Ritland 1999, "Estimation of Pairwise Relatedness With Molecular Markers"; Ritland 2000, "Marker-inferred relatedness as a tool for detecting heritability in nature"; Thomas 2005, "The estimation of genetic relationships using molecular markers and their efficiency in estimating heritability in natural populations"
  10. ↑ pg800-803, ch27 "REML Estimation of Genetic Variances", Genetics and Analysis of Quantitative Traits, Lynch & Walsh 1998; ISBN 0878934812
  11. ↑ Mousseau et al 1998, "A novel method for estimating heritability using molecular markers"
  12. ↑ Thomas et al 2002, "The use of marker-based relationship information to estimate the heritability of body weight in a natural population: a cautionary tale"
  13. ↑ Wilson et al 2003, "Marker-assisted estimation of quantitative genetic parameters in rainbow trout Oncorhynchus mykiss"
  14. ↑ Klaper et al 2001, "Heritability of Phenolics in Quercus laevis Inferred Using Molecular Markers"
  15. ↑ van Kleunen & Ritland 2004, "Predicting evolution of floral traits associated with mating system in a natural plant population"
  16. ↑ van Kleunen & Ritland 2005, "Estimating Heritabilities and Genetic Correlations with Marker-Based Methods: An Experimental Test in Mimulus guttatus"
  17. ↑ Shikano 2005, "Marker-based estimation of heritability for body color variation in Japanese flounder Paralichthys olivaceus"
  18. ↑ Visscher et al 2006, "Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings"
  19. ↑ Visscher et al 2007, "Genome partitioning of genetic variation for height from 11,214 sibling pairs"
  20. 1 2 "Common SNPs explain a large proportion of heritability for human height", Yang et al 2010
  21. ↑ "A Commentary on ‘Common SNPs Explain a Large Proportion of the Heritability for Human Height’ by Yang et al. (2010)", Visscher et al 2010
  22. 1 2 "Defining the role of common variation in the genomic and biological architecture of adult human height", Wood et al 2014
  23. ↑ "GCTA: A Tool for Genome-wide Complex Trait Analysis", Yang et al 2011
  24. ↑ "Meta-analysis of the heritability of human traits based on fifty years of twin studies", Polderman et al 2015
  25. ↑ Barnes, J. C.; Wright, John Paul; Boutwell, Brian B.; Schwartz, Joseph A.; Connolly, Eric J.; Nedelec, Joseph L.; Beaver, Kevin M. (2014-11-01). "Demonstrating the Validity of Twin Research in Criminology" (PDF). Criminology. 52 (4): 588–626. doi:10.1111/1745-9125.12049. ISSN 1745-9125.
  26. ↑ "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, Behavioral Genetics, Plomin et al 2012
  27. ↑ "Meta-analysis of GREML results from multiple cohorts", Yang 2015
  28. ↑ "Phenome-wide Heritability Analysis of the UK Biobank", Ge et al 2016
  29. ↑ Pasaniuc & Price 2016, "Dissecting the genetics of complex traits using summary association statistics"
  30. ↑ "LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies", Bulik-Sullivan et al 2015
  31. ↑ "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
  32. ↑ "Contrasting the genetic architecture of 30 complex traits from summary association data", Shi et al 2016
  33. ↑ "Fast and Accurate Construction of Confidence Intervals for Heritability", Schweiger et al 2016
  34. ↑ "Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection", Gazal et al 2017
  35. ↑ "Still Chasing Ghosts: A New Genetic Methodology Will Not Find the 'Missing Heritability'", Charney 2013
  36. ↑ "Deep Sequencing of 10,000 Human Genomes", Telenti 2015
  37. ↑ "Haplotypes of common SNPs can explain missing heritability of complex diseases", Bhatia et al 2015
  38. 1 2 3 "Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index", Yang et al 2015
  39. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Hill et al 2017, "Genomic analysis of family data reveals additional genetic effects on intelligence and personality"
  40. ↑ Evans et al 2017, "Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits"
  41. ↑ Methods of Meta-Analysis: Correcting Error and Bias in Research Findings, Hunter & Schmidt 2004
  42. ↑ "Fast linear mixed models for genome-wide association studies", Lippert 2011
  43. ↑ "Improved linear mixed models for genome-wide association studies", Listgarten et al 2012
  44. ↑ "Advantages and pitfalls in the application of mixed-model association methods", Yang et al 2014
  45. ↑ "A lasso multi-marker mixed model for association mapping with population structure correction", Rakitsch et al 2012
  46. ↑ "Genome-wide efficient mixed-model analysis for association studies", Zhou & Stephens 2012
  47. ↑ "Variance component model to account for sample structure in genome-wide association studies", Kang et al 2012
  48. ↑ "Advanced Complex Trait Analysis", Gray et al 2012
  49. ↑ "Regional Heritability Advanced Complex Trait Analysis for GPU and Traditional Parallel Architecture", Cebamanos et al 2012
  50. ↑ "Efficient Bayesian mixed model analysis increases association power in large cohorts", Loh et al 2012
  51. 1 2 3 4 5 6 7 8 9 10 11 "Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis", Loh et al 2015; see also "Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis", Loh et al 2015
  52. ↑ "Mixed Models for Meta-Analysis and Sequencing", Bulik-Sullivan 2015
  53. ↑ "Massively expedited genome-wide heritability analysis (MEGHA)", Ge et al 2015
  54. ↑ Speed et al 2016, "Re-evaluation of SNP heritability in complex human traits"
  55. ↑ Evans et al 2017, "Narrow-sense heritability estimation of complex traits using identity-by-descent information."
  56. 1 2 3 4 5 "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
  57. ↑ "The Evolutionary Genetics of Personality", Penke et al 2007; "The Evolutionary Genetics of Personality Revisited", Penke & Jokela 2016
  58. 1 2 3 4 "Genome partitioning of genetic variation for complex traits using common SNPs", Yang et al 2011
  59. 1 2 3 4 5 "Estimating the genetic variance of major depressive disorder due to all single nucleotide polymorphisms", Lubke et al 2012
  60. 1 2 "Inference of the Genetic Architecture Underlying BMI and Height with the Use of 20,240 Sibling Pairs", Hemani et al 2013
  61. 1 2 "Coordinated Genetic Scaling of the Human Eye: Shared Determination of Axial Eye Length and Corneal Curvature", Guggenheim et al 2013
  62. 1 2 3 "First genome-wide association study on anxiety-related behaviours in childhood", Trzaskowski et al 2013
  63. 1 2 3 "Testing the key assumption of heritability estimates based on genome-wide genetic relatedness", Conley et al 2014
  64. 1 2 3 "Common DNA Markers Can Account for More Than Half of the Genetic Influence on Cognitive Abilities", Plomin et al 2013
  65. 1 2 3 4 5 6 7 8 "Improved heritability estimation from genome-wide SNPs", Speed et al 2012
  66. 1 2 3 "Genome-Wide Estimates of Heritability for Social Demographic Outcomes", Domingue et al 2016
  67. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 "Dominant Genetic Variation and Missing Heritability for Human Complex Traits: Insights from Twin versus Genome-wide Common SNP Models", Chen et al 2015
  68. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 "Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative and Dichotomous Traits", Zaitlen et al 2013
  69. 1 2 3 "Genomic architecture of human neuroanatomical diversity", Toro et al 2014 (supplement)
  70. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Figure 4, "Like Mother, Like Daughter: Analysis of Parent-Child Phenotypic Correlations for Hundreds of Phenotypic Traits", Pierson et al 2014
  71. 1 2 "Application of linear mixed models to study genetic stability of height and body mass index across countries and time", Trzaskowski et al 2016
  72. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Zaidi et al 2017, "Investigating the case of human nose shape and climate adaptation"
  73. 1 2 3 4 5 6 7 "Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits", Vattikuti et al 2012
  74. ↑ "Using Genome Wide Estimates of Heritability to Examine the Relevance of Gene-Environment Interplay", Domingue & Boardman 2013
  75. 1 2 3 4 "What can genes tell us about the relationship between education and health?", Boardman et al 2015
  76. ↑ Akiyama et al 2017, "Genome-wide association study identifies 112 new loci for body mass index in the Japanese population"
  77. ↑ "Finding the missing heritability in pediatric obesity: the contribution of genome-wide complex trait analysis", Llewellyn et al 2013
  78. ↑ Willems et al 2017, "Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness"
  79. 1 2 3 Warrington et al 2017, "Maternal and fetal genetic contribution to gestational weight gain"
  80. 1 2 "Large-scale genotyping identifies a new locus at 22q13.2 associated with female breast size", Li et al 2013
  81. ↑ "Molecular genetic contributions to self-rated health", Harris et al 2016
  82. 1 2 3 4 5 "Heritability and Genome-Wide Association Studies for Hair Color in a Dutch Twin Family Based Sample", Lin et al 2015
  83. ↑ "Genetic Prediction of Male Pattern Baldness", Hagenaars et al 2016
  84. ↑ Pirastu et al 2017, "GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk"
  85. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Cole et al 2017, "Human Facial Shape and Size Heritability and Genetic Correlations"
  86. ↑ Duffy et al 2017, "Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways"
  87. 1 2 "Human Fertility, Molecular Genetics, and Natural Selection in Modern Societies", Tropf et al 2015
  88. 1 2 "Mega-analysis of 31,396 individuals from 6 countries uncovers strong gene-environment interaction for human fertility", Tropf et al 2016
  89. 1 2 3 "Assortative mating and differential fertility by phenotype and genotype across the 20th century", Conley et al 2016 (supplement)
  90. ↑ "A Genetic Variant Near Olfactory Receptor Genes Associates With Cilantro Preference", Wu et al 2012
  91. ↑ "GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment", Rietveld et al 2013
  92. 1 2 3 4 "Genome-wide association study of cognitive functions and educational attainment in UK Biobank (n=112151)", Davies et al 2016
  93. 1 2 3 4 5 6 7 8 9 10 "The genetic architecture of economic and political preferences", Benjamin 2012
  94. 1 2 3 "Molecular genetic contributions to socioeconomic status and intelligence", Marioni et al 2014
  95. 1 2 "Genetic link between family socioeconomic status and children's educational achievement estimated from genome-wide SNPs", Krapohl & Plomin 2016
  96. 1 2 Davis et al 2014, "The correlation between reading and mathematics ability at age twelve has a substantial genetic component"
  97. 1 2 "Genetic influence on family socioeconomic status and children's intelligence", Trzaskowski et al 2014b
  98. 1 2 "Molecular genetic contributions to social deprivation and household income in UK Biobank (n=112,151)", Hill et al 2016
  99. 1 2 3 "Assessing causality in the association between child adiposity and physical activity levels: A Mendelian randomization analysis", Richmond et al 2014
  100. ↑ Sanchez-Roige et al 2017, "Genetics of the Research Domain Criteria (RDoC): genome-wide association study of delay discounting"
  101. ↑ "Genetic contributions to self-reported tiredness", Deary et al 2016
  102. 1 2 "Genome-wide association analysis identifies novel loci for chronotype in 100,420 individuals from the UKBiobank", Lane et al 2016 (supplement)
  103. ↑ "Unraveling the genetic etiology of adult antisocial behavior: A genome-wide association study", Tielbeek et al 2012
  104. 1 2 Wootton et al 2016, "Exploring the Genetic Etiology of Trust in Adolescents: Combined Twin and DNA Analyses"
  105. ↑ Gao et al 2016, "Genome-Wide Association Study of Loneliness Demonstrates a Role for Common Variation"
  106. 1 2 Warrier et al 2017, "Genome-wide association study of social relationship satisfaction: significant loci and correlations with psychiatric conditions"
  107. 1 2 3 4 5 "A genome-wide association study of behavioral disinhibition", McGue et al 2013
  108. 1 2 3 4 5 "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
  109. ↑ "Estimating the heritability of reporting stressful life events captured by common genetic variants", Power et al 2013
  110. ↑ "Mapping the Genetic Variation of Regional Brain Volumes as Explained by All Common SNPs from the ADNI Study", Bryant et al 2013
  111. ↑ Zhao et al 2018, "Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies"
  112. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 "Heritability of Neuroanatomical Shape", Ge et al 2015
  113. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 "Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia", Lee et al 2016
  114. ↑ "Genome-wide association studies establish that human intelligence is highly heritable and polygenic", Davies et al 2011
  115. ↑ "Most Reported Genetic Associations with General Intelligence Are Probably False Positives", Chabris et al 2012
  116. ↑ "Intelligence indexes generalist genes for cognitive abilities", Trzaskowski et al 2013
  117. ↑ "DNA Evidence for Strong Genome-Wide Pleiotropy of Cognitive and Learning Abilities", Trzaskowski et al 2013b
  118. ↑ "Results of a 'GWAS Plus': General Cognitive Ability Is Substantially Heritable and Massively Polygenic", Kirkpatrick et al 2014
  119. ↑ "DNA evidence for strong genetic stability and increasing heritability of intelligence from age 7 to 12", Trzaskowski et al 2014a
  120. ↑ "Childhood intelligence is heritable, highly polygenic and associated with _FNBP1L_", Benyamin et al 2014
  121. ↑ "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
  122. ↑ "A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence", Spain et al 2015
  123. 1 2 3 4 5 6 7 8 "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
  124. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 "The genetic architecture of pediatric cognitive abilities in the Philadelphia Neurodevelopmental Cohort", Robinson et al 2015
  125. ↑ Zabaneh et al 2017, "A genome-wide association study for extremely high intelligence"
  126. ↑ Zabaneh et al 2017, "Fine mapping genetic associations between the HLA region and extremely high intelligence"
  127. 1 2 "Word Reading Fluency: Role of Genome-Wide Single-Nucleotide Polymorphisms in Developmental Stability and Correlations With Print Exposure", Harlaar et al 2014
  128. 1 2 Hagenaars et al 2017, "Genetic contributions to trail making test performance in UK Biobank"
  129. ↑ "Why do we differ in number sense? Evidence from a genetically sensitive investigation", Tosto et al 2013
  130. ↑ "Molecular genetics and subjective well-being", Rietveld et al 2013
  131. 1 2 3 Weiss et al 2016, "Personality Polygenes, Positive Affect, and Life Satisfaction"
  132. ↑ "Global Genetic Variations Predict Brain Response to Faces", Dickie et al 2014
  133. 1 2 "Common SNPs explain some of the variation in the personality dimensions of neuroticism and extraversion", Vinkhuyzen et al 2012
  134. ↑ "Meta-analysis of genome-wide association studies for neuroticism, and the polygenic association with major depressive disorder", De Moor et al 2015
  135. 1 2 3 4 5 "Heritability estimates of the Big Five personality traits based on common genetic variants", Power & Pluess 2015
  136. ↑ "Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci", Smith et al 2016
  137. 1 2 3 Hill et al 2017, "Genetic contribution to two factors of neuroticism is associated with affluence, better health, and longer life"
  138. 1 2 Luciano et al 2018, "The influence of X chromosome variants on trait neuroticism"
  139. ↑ "Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium", van den Berg et al 2015
  140. 1 2 3 "Genetic risk variants for social anxiety", Stein et al 2017
  141. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 "Knowns and unknowns for psychophysiological endophenotypes: Integration and response to commentaries", Iacono et al 2014
  142. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 "No genetic influence for childhood behavior problems from DNA analysis", Trzaskowski et al 2013
  143. ↑ "Genetics of Callous-Unemotional Behavior in Children", Viding et al 2013
  144. ↑ "Single Nucleotide Polymorphism Heritability of a General Psychopathology Factor in Children", Neumann et al 2016
  145. 1 2 3 Peyrot et al 2017, "Does childhood trauma moderate polygenic risk for depression? A meta-analysis of 5,765 subjects from the Psychiatric Genomics Consortium"
  146. ↑ "Describing the genetic architecture of epilepsy through heritability analysis", Speed et al 2014
  147. 1 2 3 4 5 "Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs", Lee et al 2013
  148. 1 2 3 4 Hall et al 2017, "Genome-Wide Meta-Analyses Of Stratified Depression In Generation Scotland And UK Biobank"
  149. 1 2 3 Milaneschi et al 2015, "Polygenic dissection of major depression clinical heterogeneity"
  150. 1 2 "Familiality and SNP heritability of age at onset and episodicity in major depressive disorder", Ferentinos et al 2015
  151. ↑ Tansey et al 2013, "Contribution of Common Genetic Variants to Antidepressant Response"
  152. ↑ "Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs", Lee et al 2012
  153. ↑ "Genome-wide association analysis identifies 13 new risk loci for schizophrenia", Ripke et al 2013
  154. ↑ "Genome-Wide Association Study of Schizophrenia in Ashkenazi Jews", Goes et al 2015
  155. ↑ "Additive Genetic Variation in Schizophrenia Risk Is Shared by Populations of African and European Descent", Candia et al 2013
  156. 1 2 3 4 5 6 7 8 9 10 11 "Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases", Gusev et al 2014a; see also "Regulatory variants explain much more heritability than coding variants across 11 common diseases", Gusev et al 2014b
  157. ↑ "Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder", Hou et al 2016
  158. 1 2 3 "Estimating missing heritability for disease from genome-wide association studies", Lee et al 2011
  159. 1 2 3 4 5 6 7 8 9 "Quantifying missing heritability at known GWAS loci", Gusev et al 2013
  160. ↑ Byrne et al 2014, "Applying polygenic risk scores to postpartum depression"
  161. ↑ "Genome-wide analyses of borderline personality features", Lubke et al 2014
  162. 1 2 "Partitioning the heritability of Tourette syndrome and obsessive compulsive disorder reveals differences in genetic architecture", Davis 2013
  163. 1 2 "Genome-wide analyses of empathy and systemizing: heritability and correlates with sex, education, and psychiatric risk", Warrier et al 2016
  164. ↑ "Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population", Robinson et al 2015
  165. 1 2 "Common genetic variants, acting additively, are a major source of risk for autism", Klei et al 2012
  166. ↑ "Most genetic risk for autism resides with common variation", Gaugler et al 2014
  167. ↑ "Variability in the common genetic architecture of social-communication spectrum phenotypes during childhood and adolescence", St Pourcain et al 2014
  168. ↑ Mitra et al 2016, "Pleiotropic Mechanisms Indicated for Sex Differences in Autism"
  169. 1 2 3 4 5 6 Stergiakouli et al 2017, "Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development"
  170. 1 2 "Single nucleotide polymorphism heritability of behavior problems in childhood: genome-wide complex trait analysis", Pappa et al 2015
  171. ↑ "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
  172. 1 2 3 Bidwell et al 2017, "Genetic influences on ADHD symptom dimensions: Examination of a priori candidates, gene-based tests, genome-wide variation, and SNP heritability"
  173. ↑ "A genome-wide approach to children's aggressive behavior: The EAGLE consortium", Pappa et al 2015b
  174. ↑ "A genome-wide association meta-analysis of preschool internalizing problems", Benke et al 2014
  175. ↑ "Heritability and genome-wide analyses of problematic peer relationships during childhood and adolescence", St Pourcain et al 2015
  176. 1 2 3 4 5 6 "Heritability of Individual Psychotic Experiences Captured by Common Genetic Variants in a Community Sample of Adolescents", Sieradzka 2015
  177. ↑ "Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease", Do et al 2011
  178. 1 2 3 "Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease", Keller et al 2012
  179. 1 2 3 Guerreiro et al 2016, "Genome-wide analysis of genetic correlation in dementia with Lewy bodies, Parkinson's and Alzheimer's diseases"
  180. 1 2 3 Duncan et al 2017, "Largest GWAS of PTSD (N=20070) yields genetic overlap with schizophrenia and sex differences in heritability"
  181. ↑ "Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption", The Coffee and Caffeine Genetics Consortium et al 2014
  182. ↑ Verweij et al 2013, "The genetic aetiology of cannabis use initiation: a meta-analysis of genome-wide association studies and a SNP-based heritability estimation"
  183. ↑ "Heritability, SNP- and Gene-Based Analyses of Cannabis Use Initiation and Age at Onset", Minca et al 2015
  184. ↑ Demontis et al 2017, "Genome-wide association study implicates CHRNA2 in cannabis use disorder" (supplement table 6)
  185. 1 2 3 "Molecular genetic influences on normative and problematic alcohol use in a population-based sample of college students", Webb et al 2017
  186. 1 2 3 Clarke et al 2017, "Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112,117)"
  187. ↑ Sanchez-Roige et al 2017, "Genome-wide association study of Alcohol Use Disorder Identification Test (AUDIT) scores in 20,328 research participants of European ancestry"
  188. 1 2 3 4 5 6 7 8 Palmer et al 2015, "Shared Additive Genetic Influences on DSM-IV Criteria for Alcohol Dependence in Subjects of European Ancestry"
  189. 1 2 3 "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
  190. 1 2 Zhu et al 2017, "Shared Genetic Architecture Of Asthma With Allergic Diseases: A Genome-wide Cross Trait Analysis Of 112,000 Individuals From UK Biobank"
  191. ↑ "Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis", van Rheenen et al 2016
  192. 1 2 Ek et al 2017, ["Genome-wide association analysis identifies 26 novel loci for asthma, hay fever and eczema"]
  193. 1 2 3 4 5 6 7 8 9 10 11 12 13 McGeachie et al 2016, "Whole genome prediction and heritability of childhood asthma phenotypes"
  194. ↑ "Estimating the proportion of variation in susceptibility to multiple sclerosis captured by common SNPs", Watson et al 2012
  195. 1 2 Chen et al 2014, "Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data"
  196. ↑ Yin et al 2014, "Common variants explain a large fraction of the variability in the liability to psoriasis in a Han Chinese population"
  197. 1 2 3 4 Stahl et al 2012, "Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis"
  198. ↑ "The contribution of rare variation to prostate cancer heritability", Mancuso et al 2015
  199. 1 2 3 "Heritability Estimates Identify a Substantial Genetic Contribution to Risk and Outcome of Intracerebral Hemorrhage", Devan et al 2013
  200. ↑ "Estimating the respective contributions of human and viral genetic variation to HIV control", Bartha et al 2015
  201. 1 2 3 Ek et al 2013, "Germline genetic contributions to risk for esophageal adenocarcinoma, Barretts Esophagus, and gastroesophageal reflux"
  202. ↑ Hatzikotoulas et al 2017, "National clinical audit data decodes the genetic architecture of developmental dysplasia of the hip"
  203. ↑ "Whole-genome sequence–based analysis of high-density lipoprotein cholesterol", Morrison et al 2013
  204. 1 2 3 4 Bevan et al 2012, "Genetic heritability of ischemic stroke and the contribution of previously reported candidate gene and genome-wide associations"
  205. ↑ "Type 2 Diabetes Risk Prediction Incorporating Family History Revealing a Substantial Fraction of Missing Heritability", Gim et al 2016
  206. 1 2 3 4 "Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits", Zheng et al 2013
  207. ↑ Sidorenko et al 2018, "The effect of X-linked dosage compensation on complex trait variation"
  208. ↑ "Genetic and Environmental Factors Are Associated with Serum 25-Hydroxyvitamin D Concentrations in Older African Americans", Hansen et al 2015
  209. 1 2 "Whole-genome sequence-based analysis of thyroid function", Taylor et al 2015
  210. ↑ "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
  211. ↑ "Neanderthals’ DNA legacy linked to modern ailments: Humans inherited variants affecting disease risk, infertility, skin and hair characteristics", Stephanie Dutchen, 2014-01-29
    "The phenotypic legacy of admixture between modern humans and Neandertals", Corinne N. Simonti et al, 2016-02-11
  212. ↑ Divergent ah receptor ligand selectivity during hominin evolution, Troy D. Hubbard et al, 2016-08-02
    Smoke signals: DNA adaptation helped early humans deal with toxic fumes, Naomi Stewart, 2016-08-02
  213. ↑ "Analysis of the genetics of boar taint reveals both single SNPs and regional effects", Rowe et al 2014
  214. ↑ "Genome-Wide Association Study on Body Weight Reveals Major Loci on OAR6 in Australian Merino Sheep", Al-Mamun et al 2014
  215. 1 2 "The genetic basis of host preference and indoor resting behavior in the major African malaria vector, Anopheles arabiensis", Main et al 2016
  216. ↑ "Genome-wide association and prediction reveals the genetic architecture of cassava mosaic disease resistance and prospects for rapid genetic improvement", Wolfe et al 2015

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