Partitioning heritability by functional category using GWAS summary statistics

Hk, Finucane; Bulik-Sullivan, Brendan; Gusev, Alexander; Trynka, Gosia; Reshef, Yakir; Loh, Po−Ru; Anttilla, V; Zang, Chongzhi; Farh, Kyle Kai How; Ripke, Stephan; Day, Felix R.; Purcell, Shaun; Stahl, Eli A.; Lindström, Sara; Perry, J R; Okada, Yukinori; Raychaudhuri, Soumya; Daly, Mark J.; Patterson, Nick; Bm, Neale; Al, Price

doi:10.1101/014241

Cited by 59 publications

(112 citation statements)

References 46 publications

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“…We assessed functional enrichment of all ANM-SNP associations in regions containing active histone marks across 10 physiological cell-type groups using stratified LD score regression 6 (see Methods and Supplementary Table 5). Only the ' kidney related cell types' group showed significant enrichment (P=0.003), which could reflect the mesonephric embryonic origin of ovarian parenchymal cells 7 .…”

Section: Gwas Hapmap 2 Meta-analysismentioning

confidence: 99%

“…An estimate of the total variance explained by highlighted ANM SNPs was calculated using REML (restricted maximum likelihood) implemented in GCTA 42 We used stratified LD score regression to quantify evidence of functional enrichment specific to groups of cell types 6 . We used the same baseline model as in Finucane et al 6 which comprises 53 overlapping categories including basic annotations such as coding, UTR, promoter, and intron, as well as several histone marks, DNase I Hypersensitivity Site (DHS) regions, chromHMM predictions 47 , regions that are conserved in mammals 48 , super enhancers 49 , and FANTOM5 enhancers 50 .…”

Section: Estimating Variance Explained By Snp Setsmentioning

confidence: 99%

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Large-Scale Genomic Analyses Link Reproductive Aging to Hypothalamic Signaling, Breast Cancer Susceptibility, and BRCA1-Mediated DNA Repair

Day

Ruth

Thompson

et al. 2015

Obstetrical &Amp; Gynecological Survey

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Menopause timing has a substantial impact on infertility and risk of disease, including breast cancer, but the underlying mechanisms are poorly understood. We report a dual strategy in ~7 0,000 women to identify common and low-frequency protein-coding variation associated with age at natural menopause (ANM). We identified 44 regions with common variants, including two harbouring additional rare missense alleles of large effect. We found enrichment of signals in/near genes involved in delayed puberty, highlighting the first molecular links between the onset and end of reproductive lifespan. Pathway analyses revealed a major association with DNA damageresponse (DDR) genes, including the first common coding variant in BRCA1 associated with any complex trait. Mendelian randomisation analyses supported a causal effect of later ANM on breast cancer risk (~6% risk increase per-year, P=3×10 −14 ), likely mediated by prolonged sex hormone exposure, rather than DDR mechanisms.

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Section: Gwas Hapmap 2 Meta-analysismentioning

confidence: 99%

Section: Estimating Variance Explained By Snp Setsmentioning

confidence: 99%

Large-Scale Genomic Analyses Link Reproductive Aging to Hypothalamic Signaling, Breast Cancer Susceptibility, and BRCA1-Mediated DNA Repair

Day

Ruth

Thompson

et al. 2015

Obstetrical &Amp; Gynecological Survey

Self Cite

101

214

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“…The advent of high throughput micro-array genotyping and now next generation sequencing technologies has meant that genome-wide data can be leveraged to ask fundamental questions concerning the underlying genetic architecture of common complex traits and diseases including the degree to which genetic variation affecting complex phenotypes is tagged by SNPs on genome-wide arrays (Yang et al, 2010;Yang et al, 2011;Lee et al, 2011), the degree to which this variation represents different functional categories and/or biological pathways (Gusev et al 2014 ;Finucane et al, 2015), and the extent to which genetic aetiologies are shared across different phenotypes (Lee et al 2012;Bulik-Sullivan et al, 2015b). To date most of these types of analyses have been performed using genetic restricted maximum likelihood analysis (GREML) as implemented in software packages such as GCTA and LDAK (Yang et al, 2010;Yang et al, 2011;Lee et al, 2011;Speed et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In this way genome-wide inflation of test statistics due to genuine polygenicity can be distinguished from biases such as population stratification and cryptic relatedness. The basic method is very flexible and can be adapted to estimate SNP heritability, calculate a more accurate and efficient genome-wide inflation correction factor than genomic control (Bulik-Sullivan et al, 2015a), partition the SNP heritability by functional category (Finucane et al, 2015), and estimate the genetic correlation between different complex traits and diseases (BulikSullivan et al, 2015b), all using GWAS summary-level results data (Table 1).…”

Section: Introductionmentioning

confidence: 99%

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

Zheng

Erzurumluoglu

Elsworth

et al. 2016

Preprint

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Motivation: LD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously. Results: In this manuscript, we describe LD Hub -a centralized database of summary-level GWAS results for 173 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by Bioinformatics Advance Access published September 22, 20162 uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies. Availability and implementation:The web interface and instructions for using LD Hub are available at

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“…However, some studies estimate the SNP-heritability of Autism Spectrum Disorder to be between 50% -60% [40,41], while other studies give estimates ranging from 17% -24% [10,42]. [45].…”

Section: Examples Of Applications To Psychiatric Disordersmentioning

confidence: 99%

The genetic architecture of psychiatric disorders

Maier¹

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The genetic nature of psychiatric disorders was observed by clinicians long before DNA had been identified as the molecule of inheritance. The greatest identified risk factor of many psychiatric disorders still is a positive family history. Until recently this knowledge has not contributed substantially to treatment efforts or to a better understanding of the disease processes because we lacked the necessary genetic data. Advances in genotyping technologies have brought an end to this data shortage which is leading to a better understanding of the genetic architecture of psychiatric disorders. Two patterns started to emerge which were uncommon in earlier studied Mendelian disorders. (i) most of the genetic part of disease risk is conferred by a large number of genetic loci of small effect, and (ii) genetic loci often influence a large number of traits at the same time. While this is true of many traits ("complex" traits), these two phenomena (polygenicity and pleiotropy) are particularly pronounced in psychiatric disorders. This has wide-reaching consequences for the analysis and interpretation of genetic data and provides challenges as well as opportunities. This thesis focuses on two areas in particular: genetic heterogeneity and genetic risk prediction.Genetic heterogeneity in a phenotypically homogenous group describes a situation where different and distinct genetic risk profiles are causing similar symptoms in different people.This can be easily identified under a Mendelian inheritance pattern, but proves to be challenging under polygenicity. The presence of genetic heterogeneity can limit the accuracy of genetic risk prediction.The aim of genetic risk prediction is to use the information that has been gathered on the effects of genetic loci to estimate the genetic liability of an individual to develop a disease.Here, pleiotropy offers an opportunity to increase the accuracy of genetic prediction by leveraging information from multiple diseases at the same time.The aim in this thesis is to describe several projects which center around the two concepts of genetic risk prediction based on multiple traits and of genetic heterogeneity. Chapter 1 sets the scene with an overview of recently developed polygenic methods. Chapter 2 deals with the effects of genetic heterogeneity on heritability estimates and demonstrates how genetic heterogeneity might contribute to the phenomenon of missing heritability, which is 3 the discrepancy between twin study heritability estimates and the variance explained by the sum of individual genetic loci. Chapter 3 addresses the question of whether genotype clustering can detect groups with different genetic risk profiles. Chapter 4 describes the implementation of a multivariate extension to the univariate Best Linear Unbiased Prediction (BLUP) method and its application to five psychiatric traits. Chapters 4 and 5 investigate whether this multivariate BLUP model can be approximated when only summary statistics, not individual level genotype data, are available for the predi...

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Partitioning heritability by functional category using GWAS summary statistics

Cited by 59 publications

References 46 publications

Large-Scale Genomic Analyses Link Reproductive Aging to Hypothalamic Signaling, Breast Cancer Susceptibility, and BRCA1-Mediated DNA Repair

Large-Scale Genomic Analyses Link Reproductive Aging to Hypothalamic Signaling, Breast Cancer Susceptibility, and BRCA1-Mediated DNA Repair

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

The genetic architecture of psychiatric disorders

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