14 research outputs found

    Phenome-wide heritability analysis of the UK Biobank

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    <div><p>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.</p></div

    SNP heritability estimates and their standard errors of the traits in the UK Biobank that show significantly different SNP heritability in females and males after multiple testing correction.

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    <p>The heritability estimates of rheumatoid arthritis, endocrine/diabetes and wheat products intake reported here are based on genome-wide SNPs and will be markedly reduced when the major histocompatibility complex (MHC) region (chr6:25-35Mb) is excluded from analysis, and thus need to be interpreted with caution.</p

    The number of traits in each of the 11 phenotypic domains that make up the 551 traits analyzed in the UK Biobank: 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).

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    <p>The number of traits in each of the 11 phenotypic domains that make up the 551 traits analyzed in the UK Biobank: 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).</p

    The SNP heritability estimates (h2), standard error (SE) estimates, sample sizes (N), and prevalence (prev) in the sample (for binary traits) for the top heritable traits in each phenotypic domain.

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    <p>All heritability estimates adjusted for genotyping array, UK Biobank assessment center, age at recruitment, sex (except male-specific traits indicated by *) and top 10 principal components of the genotype data as covariates. <sup>♯</sup>: additionally adjusted for weight; <sup>§</sup>: additionally adjusted for negative experience. See <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006711#pgen.1006711.s002" target="_blank">S1 Table</a> and <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006711#pgen.1006711.s003" target="_blank">S2 Table</a> for the way we binarized categorical variables and other relevant information.</p

    A head-to-head comparison of SNP heritability estimates (h2) for the self-reported illness codes and ICD-10 codes that represent the same or closely matched diseases.

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    <p>A head-to-head comparison of SNP heritability estimates (h2) for the self-reported illness codes and ICD-10 codes that represent the same or closely matched diseases.</p

    Supplementary Material

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    This is the Supplementary Material associated with the paper "Joint analysis of cortical area and thickness as a replacement for the analysis of the volume of the cerebral cortex", published in Cerebral Cortex. A pre-print is on biorXiv: http://dx.doi.org/10.1101/074666. To open, download and untar the file, and then open the "index.html" in any web browser

    Correlation results between cortical thickness of eight neocortical ROIs and total hippocampal volume in only AD individuals (n = 163).

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    <p>Spearman's rank correlation coefficients listed with p-values in parentheses. All analyses included age, gender, education, and APOE-ε4 carrier status as co-variates. HV = Hippocampal volume, CMF = Caudal portion of middle frontal gyrus thickness, ERC = Entorhinal cortex thickness, IPL = Inferior parietal lobule thickness, ITG = Inferior temporal gyrus thickness, ISC = Isthmus portion of cingulate cortex thickness, LOC = Lateral portion of occipital cortex thickness, MOF = Medial portion of orbital frontal cortex thickness, TPC = Temporal pole thickness, NS = Not significant.</p
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