Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits

Gilly, Arthur; Süveges, Dániel; Kuchenbaecker, Karoline; Pollard, Martin; Southam, Lorraine; Hatzikotoulas, Konstantinos; Farmaki, Aliki‐Eleni; Bjørnland, Thea; Waples, Ryan K.; Appel, Emil V. R.; Casalone, Elisabetta; Melloni, Giorgio; Kilian, Britt; Rayner, Nigel W.; Ντάλλα, Ιωάννα; Kundu, Kousik; Walter, Klaudia; Danesh, John; Butterworth, Adam S.; Barroso, Inês; Tsafantakis, Emmanouil; Dedoussis, George; Moltke, Ida; Zeggini, Eleftheria

doi:10.1038/s41467-018-07070-8

Cited by 32 publications

(27 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we describe WGS from the first 53,831 TOPMed samples selected from data sets that are now available via dbGaP controlled-access (Supplementary Tables 1 and 2); additional data will be made available as quality control (QC), variant calling and dbGaP curation are completed. Our work identifies and characterizes the rare variants that comprise the majority of human genomic variation 7, [12][13][14] and extends previous efforts that relied on genotyping arrays [15][16][17] , low-coverage WGS 7,8 , exome sequencing 2,12,18 , or analyses of smaller sample collections [19][20][21][22] . Since rare variants represent more recent and potentially more deleterious changes that can impact protein function, gene expression, or other biologically important elements, their discovery and study are crucial for understanding the genetics and biology of human health and disease 13,23,24 .…”

Section: Summary Paragraphmentioning

confidence: 75%

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Taliun¹,

Harris²,

Kessler³

et al. 2019

Preprint

494

665

View full text Add to dashboard Cite

Summary paragraphThe Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency <1% and 46% are singletons. These rare variants provide insights into mutational processes and recent human evolutionary history. The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and extends the reach of nearly all genome-wide association studies to include variants down to ~0.01% in frequency.

show abstract

Section: Summary Paragraphmentioning

confidence: 75%

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Taliun¹,

Harris²,

Kessler³

et al. 2019

Preprint

494

665

View full text Add to dashboard Cite

show abstract

“…Variant calling was performed after filtering out contamination, spatial artifacts and duplicates, according to the Genome Analysis Toolkit (GATK) v. 3.5-0-g36282e4 Best Practices. The sequencing and variant calling is described in detail in a previous publication 26 .…”

Section: Sequencing and Variant Callingmentioning

confidence: 99%

“…Sequencing data quality control has been described before 26 . Briefly, Variant-level QC was performed using the Variant Quality Score Recalibration tool (VQSR) from the Genome Analysis Toolkit (GATK) v. 3.5-0-g36282e4.…”

Section: Quality Controlmentioning

confidence: 99%

“…We use the linear mixed model extension of SKAT-O implemented in MONSTER 30 , using the MUMMY wrapper 26 (https://github.com/hmgu-itg/burden_testing). Following our previously-reported analysis strategy 26 , we test for rare variant burden association on a gene-by-gene basis: firstly, restricting burdens to coding variants with Ensembl most severe consequence stronger than missense; secondly, including all coding variants weighted by CADD 31 ; thirdly, including exon and regulatory variants using the phred-scaled Eigen score 32 ; and, finally, regulatory variants only weighted by Eigen. Regulatory regions are linked to a gene if they overlap an Ensembl-documented eQTL for that gene in any tissue.…”

Section: Rare Variant Associationmentioning

confidence: 99%

See 1 more Smart Citation

Whole genome sequencing analysis of the cardiometabolic proteome

Gilly

Park

Png

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets and biomarkers. Here, we comprehensively assess the genetic architecture of 257 circulating protein biomarkers of cardiometabolic relevance through high-depth (22.5x) whole-genome sequencing (WGS) in 1,328 individuals. We discover 131 independent sequence variant associations (P<7.45×10−11) across the allele frequency spectrum, all of which replicate in an independent cohort (n=1,605, 18.4x WGS). We identify for the first time replicating evidence for rare-variant cis-acting protein quantitative trait loci for five genes, involving both coding and non-coding variation. We construct and validate polygenic scores that explain up to 45% of protein level variation. We find causal links between protein levels and disease risk, identifying high-value biomarkers and drug development targets.

show abstract

“…We then compared 176 independent suggestively associated signals at p<5x10 -7 (Supplementary Table 1). These 177 signals were then cross-referenced with a larger (n=1,457) study based on 22x WGS on the 178 same traits in the same cohort (Gilly, et al, 2018). We only considered signals to be true if 179 they displayed evidence for association with at most a two order of magnitude attenuation 180 compared to our suggestive significance threshold (P<5x10 -5 ).…”

Section: Comparison Of Association Summary Statistics With Imputed Gwmentioning

confidence: 99%

Very low depth whole genome sequencing in complex trait association studies

Gilly

Kuchenbaecker

Southam

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

33 Background 34Very low depth sequencing is a cost-effective approach to capture low-frequency and rare 35 variation in complex trait association studies. Here, we perform cohort-wide whole genome 36 sequencing (WGS) at 1x depth coupled to genome-wide association analysis in 2,347 37 individuals from two isolated populations. 38 Results 39We establish a robust pipeline for calling 1x WGS data, achieving an average minor allele 40 concordance of 97% when compared to genotyping chip data. 9.5% of variants called using 41 1x WGS are variants with a high predicted quality not captured by genome-wide association 42 study (GWAS) data in the same individuals imputed to a dense haplotype reference panel. 43Of the 54 association signals arising from genome-wide association analysis of 1x WGS 44 variants with 25 haematological traits (at p<5x10 -7 ), only 57% are recapitulated by the 45 imputed GWAS results in the same samples. Differences in strength of evidence for 46 association are smaller for common than for low-frequency and rare variant signals. We 47 further exemplify power gains by establishing robust evidence for a novel association 48 between rs6489858, an intronic variant in RPH3A and increased lymphocyte count 49 (beta=0.13, SE=0.11, p=8x10 -12 ), which replicates in an independent dataset comprising 50 173,480 samples. 51 Conclusions 52We show that 1x WGS is an efficient alternative to imputed GWAS chip designs for 53 empowering next-generation association studies in complex traits. We demonstrate that 54 population-scale 1x WGS allows the interrogation of a large number of low-frequency and 55All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/169789 doi: bioRxiv preprint first posted online alternative, cost-efficient approach to capture low-frequency variation in large studies. 75

show abstract

Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits

Cited by 32 publications

References 45 publications

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Whole genome sequencing analysis of the cardiometabolic proteome

Very low depth whole genome sequencing in complex trait association studies

Contact Info

Product

Resources

About