Phenome-wide heritability analysis of the UK Biobank
The heritability of a trait refers to the proportion of phenotypic variance that is attributable to genetic variation among individuals ., Heritability is commonly measured as either the contribution of total genetic variation ( broad-sense heritability , H2 ) , or the fraction due to additive genetic variation ( narrow-sense heritability , h2 ) 1 ., A large body of evidence from twin studies has documented that essentially all human complex traits are heritable ., For example , a recent meta-analysis of virtually all twin studies published between 1958 and 2012 , encompassing 17 , 804 traits , reported that the overall narrow-sense heritability estimate across all human traits was 49% , although estimates varied widely across phenotypic domains 2 ., Over the past decade , the availability of genome-wide genotyping has enabled the direct estimation of additive heritability attributable to common genetic variation ( “SNP heritability” or hSNP2 ) 3–5 ., These estimates do not capture non-additive genetic effects such as dominance or epistasis , and provide a lower bound for narrow-sense heritability because they also do not capture contributions ( e . g . , from rare variants ) that are not assayed by most genotyping microarrays and are not well tagged by genotyped variants ., Nevertheless , estimates of SNP heritability can provide important information about the genetic basis of complex traits such as the proportion of phenotypic variation that could be explained by common-variant genome-wide association studies ( GWAS ) ., However , heritability is not a fixed property of a phenotype but depends on the population in which it is estimated ., As a ratio of variances , it can vary with population-specific differences in both genetic background and environmental variation 1 ., For example , twin data have documented variations in the heritability of childhood IQ by socioeconomic status ( SES ) 6 , highlighting that different environment may have different relative contributions to the variance of a phenotype ., In addition , heritability estimates for a range of complex phenotypes have been shown to vary according to the sex and age distributions of the sampled populations 2 ., Identifying variables that may affect the heritability of complex traits has implications for the design of GWAS , highlighting subgroups and environmental conditions in which common-variant contributions may be diminished or magnified ., To date , however , studies of complex trait heritability and the effect of modifying variables have produced mixed results likely due to sample size limitations and population-specific differences in genetic and environmental variance that may be operating in different cohorts ., The UK Biobank ( http://www . ukbiobank . ac . uk ) provides a unique opportunity to estimate the heritability of traits across a broad phenotypic spectrum in a single population sample ., The UK Biobank is a large prospective population-based cohort study that enrolled 500 , 000 participants aged 40–69 years between 2006 and 2010 7 ., The study has collected a wealth of phenotypic data from questionnaires , physical and biological measurements , and electronic health records as well as genome-wide genotype data ., However , this rich data source also presents analytic challenges ., For example , with the large sample size , existing heritability estimation methods such as genome-wide complex trait analysis ( GCTA ) 3–5 and LD ( linkage disequilibrium ) score regression 8 become computationally expensive and memory intensive , and thus can be difficult to apply ., Here we implemented a computationally and memory efficient approach to estimate the heritability for 551 complex traits derived from the interim data release ( 152 , 736 subjects ) of the UK Biobank , comprising both quantitative phenotypes and disease categories ., We then examined how heritability estimates are modified by three major demographic variables: age , sex and socioeconomic status ( SES ) ., Our results underscore the importance of considering population characteristics in estimating and interpreting heritability , and may inform efforts to apply genetic risk prediction models for a broad range of human phenotypes ., We report the heritability for 551 traits that were made available to us through the interim data release of the UK Biobank ( downloaded on Mar 3 , 2016 ) and had sufficient sample sizes to achieve accurate heritability estimates ( standard error of the heritability estimate smaller than 0 . 1; 15 disease codes excluded ) using a computationally and memory efficient heritability estimation method ( see Methods and S1 Text ) ., The 551 traits can be classified into 11 general phenotypic domains as defined by the UK Biobank to group individual data fields into broadly related sets: cognitive function ( 5 traits ) , early life factors ( 7 traits ) , health and medical history ( 60 traits ) , hospital in-patient main diagnosis ICD-10 codes ( 194 traits ) , life style and environment ( 88 traits ) , physical measures ( 50 traits ) , psychosocial factors ( 40 traits ) , self-reported cancer codes ( 9 traits ) , self-reported non-cancer illness codes ( 79 traits ) , sex-specific factors ( 14 traits ) , and sociodemographics ( 5 traits ) ., ICD-10 ( the International Classification of Diseases , version-10 ) is a medical classification list published by the World Health Organization ( WHO ) , which contains thousands of diagnostic codes ., Fig 1 shows the percentage of each domain that makes up the 551 traits we analyzed ., Using the top-level categories and chapters of the self-reported disease and ICD-10 coding tree , we can further break down self-reported non-cancer illness codes and ICD-10 codes into different functional domains ( S1 Fig ) ., We note that since we only analyzed disease codes that had prevalence greater than 1% in the sample , distribution of the disease traits across functional domains was skewed ., For example , we investigated a large number of gastrointestinal and musculoskeletal traits , while diseases that have low prevalence in the sampled population such as psychiatric disorders were not well represented ., Table 1 lists the top heritable traits in each domain ( the most heritable trait and traits with heritability estimates greater than 0 . 30 ) ., S1 Table and S2 Table show the heritability estimates , standard error estimates , sample sizes , covariates adjusted , prevalence in the sample ( for binary traits ) and other relevant information for all the traits we analyzed ., Common genetic variants appear to have an influence on most traits we investigated , although heritability estimates showed heterogeneity within and across trait domains ., Complex traits that exhibited high SNP heritability ( larger than 0 . 40 ) included human height ( 0 . 685+/-0 . 004 ) , skin color ( very fair/fair vs . other , 0 . 556+/-0 . 008 ) , ease of skin tanning ( very/moderately tanned vs . mildly/occasionally/never tanned , 0 . 454+/-0 . 006 ) , comparative height at age 10 ( taller than average , 0 . 439+/-0 . 007; shorter than average , 0 . 405+/-0 . 008 ) , rheumatoid arthritis ( 0 . 821+/-0 . 046 ) , hypothyroidism/myxedema ( 0 . 814+/-0 . 017 ) , malignant neoplasm of prostate ( 0 . 426+/-0 . 093 ) , and diabetes diagnosed by doctor ( 0 . 414+/-0 . 016 ) , among others ., On the other end of the spectrum , traits such as duration of walks/moderate activity/vigorous activity , frequency of stair climbing , ever had stillbirth , spontaneous miscarriage or termination , painful gums , stomach disorder , fracture , injuries to the head/knee/leg , and pain in joint had zero or close to zero heritability estimates , indicating that their phenotypic variation is largely determined by environmental factors , or there is widespread heterogeneity or substantial measurement error in these phenotypes ., SNP heritability estimates for several phenotypes , including diseases with known immune-mediated pathogenesis ( rheumatoid arthritis , psoriasis , diabetes , hypothyroidism ) , were markedly reduced when the major histocompatibility complex ( MHC ) region was excluded from analysis ( S4 Table ) , and thus need to be interpreted with caution ( see Discussion ) ., A substantial fraction of the phenotypes we examined were based on self-report illness codes or diagnostic ( ICD-10 ) codes , which may be noisy and have low specificity ., However , the SNP heritability estimates for 14 pairs of self-reported illness and ICD-10 codes that represent the same or closely matched diseases were largely consistent and had a Pearson correlation of 0 . 78 ( Table 2 ) , indicating that both phenotypic approaches captured useful and comparable variations in these phenotypes ., Heritability analysis stratified by sex identified a number of traits whose heritability showed significant difference in males and females after multiple testing correction ( Fig 2 ) ., For example , the analyses of diastolic and systolic blood pressure , and self-reported hypertension and high blood pressure provided consistent evidence that the heritability of blood pressure related traits and diseases is significantly higher in females than in males ., A majority of physical measures showed decreasing heritability with age ( S3 Table ) ., More specifically , 33 out of 50 physical measures had a significant decreasing trend in heritability estimates after accounting for multiple testing correction ( mean slope of the 33 traits -0 . 0035 , i . e . , heritability estimates decrease by 3 . 5 percent per decade ) ., The age-varying SNP heritability estimates and their standard errors for 12 traits that showed both significant slopes and significantly different heritability estimates between the first ( 40–49 years ) and last age range ( 64–73 years ) are shown in Fig 3A ., S2 Fig shows the mean and standard deviation of the 12 traits in each age range ., When we stratified heritability by the Townsend deprivation index , a proxy for SES , education ( has college or university degree or not ) was the only trait on which SES had a significant moderating effect after accounting for multiple testing correction ., Fig 3B shows that the heritability of education increases with increasing SES ., Estimating the heritability of complex , polygenic traits is an important component of defining the genetic basis of human phenotypes ., In addition , heritability estimates provide a theoretical upper bound for the utility of genetic risk prediction models 9 ., We calculated the common-variant heritability of 551 phenotypes derived from the interim data release of the UK Biobank , and confirmed that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population ., Two aspects of our work are particularly notable ., First , we developed a computationally and memory efficient method that enabled us to calculate the most extensive population-based survey of SNP heritability to date ., Second , we found that the heritability for a number of phenotypes is moderated by major demographic variables , demonstrating the dependence of heritability on population characteristics ., We discuss each of these advances and the limitations of the biobank data and our analyses below ., Classical methods to estimate SNP heritability , such as GCTA ( also known as the GREML method ) , rely on the restricted maximum likelihood ( ReML ) algorithm 3–5 , which can give unbiased heritability estimates in quantitative trait analysis and non-ascertained case-control studies , and is statistically efficient when the trait is Gaussian distributed 10 ., However , ReML is an iterative optimization algorithm , which is computationally and memory intensive , and thus can be difficult to apply when analyzing data sets with hundreds of thousands of subjects ., An alternative and widely used SNP heritability estimation method is LD score regression , which is based on GWAS summary statistics and an external reference panel for the LD structure 8 ., The approach can thus be easily applied to complex traits on which large-scale GWAS results are available , and allows meta-analysis of heritability estimates from different studies ., Recently , LD score regression has been extended to partition heritability by functional annotation 11 , and to estimate the genetic correlation between two traits 12 , 13 ., However , when applying LD score regression to novel phenotypes in a large cohort , conducting GWAS is often time-consuming ., In the present study , we implemented a computationally and memory efficient moment-matching method for heritability estimation , which is closely related to the Haseman-Elston regression 14–16 and phenotype-correlation genetic-correlation ( PCGC ) regression 10 , and produces unbiased SNP heritability estimates for both continuous and binary traits ., The moment-matching method is theoretically less statistically efficient than the ReML algorithm ( i . e . , produces larger standard error on the point estimate ) when analyzing quantitative traits , but the power loss is expected to be small 17 and is less of an issue given large sample sizes , such as in the UK Biobank ., The moment-matching method is also mathematically equivalent to LD score regression if the following conditions are satisfied: ( 1 ) the out-of-sample LD scores estimated from the reference panel and the in-sample LD scores estimated from individual-level genotype data are identical; ( 2 ) the intercept in the LD score regression model is constrained to 1 ( i . e . , assuming that there is no confound and population stratification in the data ) ; and ( 3 ) a particular weight is used in the LD score regression ( more specifically , the reciprocal of the LD score , which is close to the default setting in the LD score regression software ) 18 ., Here , since we have constrained our analysis to a white British ( Caucasian ) sample and have accounted for potential population stratification by including top PCs of the genotype data as covariates , the two methods should produce similar estimates ., See Box 1 for an empirical comparison between the moment-matching method , LD score regression and GCTA ., Using the moment-matching method , we found that a large number of traits we examined display significant heritability ., For traits whose heritability has been intensively studied , our estimates are generally in line with prior studies ., For example , twin and pedigree studies have estimated the heritability of human height and body mass index ( BMI ) to be approximately 80% and 40–60% see e . g . , 19–21 , respectively , although recent studies have shown that heritability may be overestimated in family studies due to , for instance , improper modeling of common environment , assortative mating in humans , genetic interactions , and suboptimal statistical methods 10 , 22–25 ., Using genome-wide SNP data from unrelated individuals , it has been shown that common SNPs explain a large proportion of the height and BMI variation in the population , although SNP heritability estimates are lower than twin estimates 4 , 5 , 26 ., Specifically , the first GCTA analysis estimated the SNP heritability of human height to be 0 . 45 using relatively sparse genotyping data ( approximately 300 , 000 SNPs ) and showed that the estimate could be higher if imperfect LD between SNPs and causal variants are corrected 4 ., A more recent study leveraging whole-genome sequencing data and imputed genetic variants concluded that narrow-sense heritability is likely to be 60–70% for height and 30–40% for BMI 27 ., Here , we estimated the SNP heritability of human height and BMI to be 0 . 685+/-0 . 004 and 0 . 274+/-0 . 004 , respectively , which are comparable to the expected range ., The SNP heritability estimates of other complex traits of interest , such as age at menarche in girls ( 0 . 239+/-0 . 007 ) , diastolic ( 0 . 184+/-0 . 004 ) and systolic ( 0 . 156+/-0 . 004 ) blood pressures , education ( has colleague or university degree or not , 0 . 294+/-0 . 007 ) , neuroticism ( 0 . 130+/-0 . 005 ) , smoking ( ever smoked or not , 0 . 174+/-0 . 006 ) , asthma ( 0 . 340+/-0 . 010 ) and hypertension ( 0 . 263+/-0 . 007 ) were also more modest and lower than twin estimates , as expected 2 ., Heritability is , by definition , a ratio of variances , reflecting the proportion of phenotypic variance attributable to individual differences in genotypes ., Because the genetic architecture and non-genetic influences on a trait may differ depending on the population sampled , heritability itself may vary ., Examples of this have been reported in the twin literature ., In one well-known study , Turkheimer and colleagues 6 reported that the heritability of IQ is moderated by SES in a sample of 320 7-year-old twin pairs of mixed ancestry ., In that study , the heritability of IQ was essentially 0 at the lowest end of SES but substantial at the highest end ., Subsequent studies of the moderating effects of SES on the heritability of cognitive ability and development using twin designs have produced mixed results 28–33 ., In our analysis , using SNP data , we observed no moderating effect of SES ( as measured by the Townsend deprivation index ) on the heritability of cognitive traits ( including fluid intelligence ) , possibly due to the age range of participants in the UK Biobank ( middle and old age ) in contrast to many previous studies targeting childhood or early adulthood , and the cross-national differences in gene-by-SES interaction on intelligence as shown by a recent meta-analysis 34 ., In addition , the brief cognitive tests available in the UK Biobank may have had limited sensitivity for capturing individual differences in IQ ( see discussion below ) ., On the other hand , the heritability of education showed significant interactions with SES , with increasing heritability at higher SES levels ., Prior evidence has suggested that education has substantial genetic correlation with IQ and may be a suitable proxy phenotype for genetic analyses of cognitive performance 35; thus our results may indirectly support earlier studies of the SES moderation of IQ heritability ., With two exceptions , significant sex differences we observed indicated greater heritability for women compared to men ., Our results are consistent with findings from some twin studies but not others ., For example , we found that women exhibited significantly greater heritability for measured waist circumference and blood pressure ., Twin studies have also reported greater female heritability for waist circumference 36 but no substantial sex difference in heritability of blood pressure 2 , 37 ., A substantial difference between the heritability of rheumatoid arthritis ( RA ) in males compared to females was observed , although the MHC region has a large impact on the SNP heritability estimates of autoimmune diseases , and thus this finding needs to be interpreted with caution ( see discussion below ) ., While RA is known to be more common in women , a twin analysis found no sex difference in heritability among Finnish and UK twin pairs , though power was limited in that analysis 38 ., Intriguingly , greater heritability was observed among men for the personality trait of miserableness , a component of neuroticism , suggesting that environmental factors may be more influential for this trait among women or that measurement error differs by sex ., We examined age effects on heritability for a subset of variables and found that a number of physical measures indexing body size , adiposity , height , as well as systolic blood pressure and lung function , showed declining heritability with age ., Age-related declines in heritability may reflect the cumulative effect of environmental perturbations over the lifespan ., Prior twin studies of age effects on the heritability of anthropometric traits in adults have had inconsistent results 39–41 ., Haworth and colleagues showed that the heritability of BMI increases over childhood 42 ., A recent meta-analysis of 32 twin studies documented a non-monotonic relationship between BMI heritability and age ( from childhood to late adulthood ) , with a peak around age 20 and decline thereafter 43 ., An age-related decline in indices of body size may reflect a decreasing contribution of genetically-regulated growth processes over the lifespan ., However , we were unable to assess the entire trajectory of heritability due to the age range ( 40–73 years ) of the UK Biobank participants ., Some but not all studies have also suggested varying or declining heritability with age for blood pressure , lung function and age at first birth 39 , 44–50 ., Our results should be interpreted in light of the limitations associated with the biobank data ., First , the UK Biobank is restricted to middle and old age groups , which may be subject to sample selection bias ., For example , older and physically/cognitively impaired subjects may be underrepresented in the study , which may have an impact on the heritability estimates stratified by age ., Mortality selection can also alter the results of genetic analyses as shown by recent analyses 51 ., In addition , the UK Biobank participants comprised a relatively high proportion of well-educated , skilled professionals 52 , potentially leading to the underrepresentation or restricted range of certain traits such as smoking relative to other cohorts ., Therefore , our heritability estimates may be specific to this UK population and may not generalize to other settings or ancestry groups ., Second , although the UK Biobank has collected a wealth of phenotypes , measurements associated with a particular phenotypic domain may not be comprehensive ., For example , only five cognitive tests were included in the UK Biobank ., The reasoning task ( fluid intelligence test ) was brief and had a narrow range; the reaction time was averaged from a small number of trials; and the visual memory test ( pairs-matching test ) had a significant floor effect ( a large number of participants made zero or very few mistakes , and thus the scores do not fully reflect individual differences ) ., In addition , all cognitive tests had relatively low reliability across repeat measurements 53 ., These noisy measurements may thus downwardly bias heritability estimates of cognition ., The Townsend deprivation index , which we used to stratify phenotypes , was calculated based on the national census output area of each participant in which their postcode was located at the time of recruitment , and thus can only serve as a proxy for SES ., Third , the phenotypes were limited to those for which we had sufficient data to estimate heritability with adequate precision ., Therefore , diseases with low prevalence in the sampled population were not well represented in our analysis ., We expect to analyze traits with lower prevalence ( e . g . , 0 . 5% ) when the genetic data for all UK Biobank participants becomes available ., We also assumed in our analysis that the population prevalence of a binary trait is identical to the observed sample prevalence , but diseases such as schizophrenia and stroke are naturally under-ascertained and thus their sample prevalence is often lower than population prevalence ., In addition , we note that since we used medical history to define cases and controls , the prevalence of many diseases we investigated reflected lifetime prevalence , which may be different from cross-sectional prevalence used in other studies ., We also binarized categorical ( multinomial or ordinal ) variables to facilitate analysis , but this might not optimally represent variation in these variables with respect to heritability ., Fourth , a substantial fraction of the phenotypes we examined were based on self-report or diagnostic ( ICD-10 ) codes , which may or may not validly capture the phenotypes they represent ., For example , a recent UK Biobank study shows that 51% of the participants who reported RA were not on RA-relevant medication , a proxy measure of valid diagnosis 54 ., However , our head-to-head comparison of the heritability estimates between self-reported illness and ICD-10 codes showed largely consistent results , indicating that both phenotypic approaches at least captured comparable variations in these phenotypes ., Prior research evaluating phenotypes derived from electronic health records ( EHR ) indicate that greater phenotypic validity can be achieved when diagnostic codes are supplemented with text mining methods 55–58 ., The specificity of the disease codes might also be improved by leveraging the medication records in the UK Biobank ., Methodologically , our SNP heritability estimation approach , despite its superior computational and memory performance compared to existing methods , also has several limitations ., First , heritability estimation always relies on a number of assumptions on the genetic architecture ., For example , the moment-matching method we used here , as well as the established GCTA and LD score regression approaches , implicitly assumes that the causal SNPs are randomly spread over the genome , which is independent of the MAF spectrum and the LD structure , and the effect sizes of causal SNPs are Gaussian distributed and have a specific relationship to their MAFs ., Although it has been shown that SNP heritability estimates are reasonably robust to many of these modeling assumptions 59 , the estimates can be biased if , for instance , causal SNPs are rarer or more common than uniformly distributed on the MAF spectrum , or are enriched in high or low LD regions across the genome ., For example , the heritability estimates for some autoimmune diseases such as psoriasis and RA dropped dramatically when the MHC region ( chr6:25-35Mb ) was removed when constructing the genetic similarity matrix , indicating , as expected , that causal variants for these diseases are disproportionally enriched in the MHC region ., S4 Table lists all the traits whose heritability estimates decreased by 0 . 2 or more when the MHC region was taken out , and thus need to be interpreted with caution ., Methods to correct for MAF properties and region-specific LD heterogeneity of causal variants have been proposed 27 , 59 , 60 ., For example , we can stratify MAF and LD structure into different bins , compute a genetic similarity matrix within each bin , and fit a mixed effects model with multiple variance components 27 , 60 ., This approach can give heritability estimates that are more robust to properties of the underlying genetic architecture , but has the downside of increased computational burden and reduced statistical power ., A different direction to explore is to estimate SNP heritability using imputed data ( in contrast to the genotype data here ) , which might capture more genetic variation from rare variants , or common variants that are not well tagged by the genotyped SNPs , and thus lead to increased heritability estimates ., Second , heritability analysis models , including the one we employed in the present study , typically assume that genetic and environmental effects are independent , i . e . , no gene-by-environment ( GxE ) interaction exists ., This is certainly a simplification of the real world where GxE interactions are expected for many complex traits ., Recent computational studies have also shown that ignoring GxE interactions in heritability analysis can produce biased estimates 25 ., However , modeling GxE would require collecting relevant environmental variables for each phenotype and more sophisticated statistical modeling approaches , e . g . , incorporating multiple random effects in the heritability analysis model 5 , 61 ., Due to the limited measurements of environment collected by the UK Biobank , and the extensive analyses we have conducted across the phenotypic spectrum , explicitly modeling the environmental factors and GxE interactions is not feasible ., We therefore took an alternative approach to examine the moderating effects of three major demographic variables on heritability estimates by stratifying samples ., Of note , consistent heritability estimates across different levels of the stratifying variable do not completely eliminate the potential existence of GxE interactions ., Specifically , recent studies have identified genetic heterogeneity in human traits such as BMI and fertility 62 , 63 , indicating that the genetic architecture of a trait may be different across environments ( i . e . , the genetic correlation of a trait in different environments may be significantly smaller than, 1 ) even if the overall heritability estimates are similar ., Dissection of common and unique environmental influences and their interactive effects with genetics on different complex traits , and the shared and unique genetic effects across environments are important future directions to explore ., Lastly , as reviewed in 64 , a number of empirical genetic similarity measurements computable from genome-wide SNP data have been proposed , which , when utilized in heritability analysis , can give different estimates with different interpretations ., In addition , recent studies have argued that estimation error associated with genetic similarity measurements and the ill-posedness of the empirical genetic similarity matrix may produce unstable and unreliable SNP heritability estimates 65 ., However , this is an area under active investigation and debate 64–67 ., Here , as the first study to screen all UK Biobank variables and provide an overview of the distribution of SNP heritability across different trait domains , and to examine the effect of potential modifying variables on heritability estimates , we used a straightforward and classical modeling approach that is most widely used ., To obtain more insights into the genetic architecture and find the most appropriate and robust model for each individual trait , more systematic investigation is needed ., In sum , using a computationally and memory efficient approach , we provide estimates of the SNP heritability for 551 complex traits across the phenome captured in the population-based UK Biobank ., We further identify phenotypes for which the contribution of genetic variation is modified by demographic factors ., These results underscore the importance of considering population characteristics in interpreting heritability , highlight phenotypes and subgroups that may warrant priority for genetic association studies , and may inform efforts to apply genetic risk prediction models for a broad range of human phenotypes ., This study utilized deidentified data from the baseline assessment of the UK Biobank , a prospective cohort study of 500 , 000 individuals ( age 40–69 years ) recruited across Great Britain during 2006–2010 7 ., The protocol and consent were approved by the UK Biobank’s Research Ethics Committee ., The UK Biobank collected phenotypic data from a variety of sources including questionnaires regarding mental and physical health , food intake , family history and lifestyle , a baseline physical assessment , computerized cognitive testing , linkage with health records , and blood samples for biochemical and DNA analysis ., Details about the UK Biobank project are provided at http://www . ukbiobank . ac . uk ., Data for the current analyses were obtained under an approved data request ( Ref: 13905 ) ., The interim release of the genotype data for the UK Biobank ( downloaded on Mar 3 , 2016 ) comprises 152 , 736 samples ., Two closely related arrays from Affymetrix , the UK BiLEVE Axiom array and the UK Biobank Axiom array , were used to genotype approximately 800 , 000 markers with good genome-wide coverage ., Details of the design of the arrays and sample processing can be found at http://biobank . ctsu . ox . ac . uk/crystal/refer . cgi ?, id=146640 and http://biobank . ctsu . ox . ac . uk/crystal/refer . cgi ?, id=155583 ., Prior to the release of the genotype data , stringent quality control ( QC ) was performed at the Wellcome Trust Centre for Human Genetics , Oxford , UK ., Procedures were documented in detail at http://
Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation , and provides an upper bound for the utility of genetic risk prediction models ., Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants ( SNP heritability ) across a broad phenotypic spectrum ., Here , we present a computationally and memory efficient heritability estimation method that can handle large sample sizes , and report the SNP heritability for 551 complex traits derived from the interim data release ( 152 , 736 subjects ) of the large-scale , population-based UK Biobank , comprising both quantitative phenotypes and disease codes ., We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population , and identify phenotypes whose heritability is moderated by age ( e . g . , a majority of physical measures including height and body mass index ) , sex ( e . g . , blood pressure related traits ) and socioeconomic status ( education ) ., Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank , and underscores the importance of considering population characteristics in interpreting heritability .
Heritability of a trait refers to the proportion of phenotypic variation that is due to genetic variation among individuals ., It provides important information about the genetic basis of complex traits and indicates whether a phenotype is an appropriate target for more specific statistical and molecular genetic analyses ., Recent studies have leveraged the increasingly ubiquitous genome-wide data and documented the heritability attributable to common genetic variation captured by genotyping microarrays for a wide range of human traits ., However , heritability is not a fixed property of a phenotype and can vary with population-specific differences in the genetic background and environmental variation ., Here , using a computationally and memory efficient heritability estimation method , we report the heritability for a large number of traits derived from the large-scale , population-based UK Biobank , and , for the first time , demonstrate the moderating effect of three major demographic variables ( age , sex and socioeconomic status ) on heritability estimates derived from genome-wide common genetic variation ., Our study represents the first comprehensive heritability analysis across the phenotypic spectrum in the UK Biobank .
genome-wide association studies, dermatology, medicine and health sciences, population genetics, sociology, social sciences, neuroscience, learning and memory, skin neoplasms, cognition, genome analysis, memory, population biology, malignant skin neoplasms, genetic polymorphism, social stratification, phenotypes, heredity, genetics, biology and life sciences, genomics, evolutionary biology, cognitive science, computational biology, complex traits, human genetics