Estimating Genome‐Wide Significance for Whole‐Genome Sequencing Studies

Xu, Changjiang; Tachmazidou, Ioanna; Walter, Klaudia; Ciampi, Antonio; Zeggini, Eleftheria; Greenwood, Celia

doi:10.1002/gepi.21797

Cited by 78 publications

(71 citation statements)

References 15 publications

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“…The study design differences between 1KG and GoNL may have also impacted the number of variants available for simulation and testing, though both projects had sufficient depth of coverage to detect the vast majority of variants with frequency >0.5% (Abecasis et al., , Auton et al., ; Francioli et al., ). Despite these drawbacks, our estimate for genome‐wide significance in Europeans is consistent with previously published work that examined the joint multiple testing burden for single‐variant and set‐based (i.e., genic burden testing) approaches ( P ∼ 5.85–8.43 × 10 −9 ; Xu et al., ), where single variants likely contributed far more to the overall testing burden than did genic set tests. Further, our null simulations likely yield more precise estimates of genome‐wide significance than methods that prune SNPs on the basis of their LD (Fadista et al., ), as it is difficult to know which threshold will accurately identify all possible independent tests.…”

Section: Discussionsupporting

confidence: 88%

“…We also do not address genic burden testing approaches, designed to test a collection of rare variants in the same gene or window in association to disease (Lee et al., ). A genome‐wide significance estimate already exists for these tests ( P ∼ 2.68–7.32

\times

10 −8 ; Xu et al., ). Though burden tests may improve power through variant aggregation, it remains difficult to know which variants exactly should be included in such a test.…”

Section: Discussionmentioning

confidence: 99%

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Resetting the bar: Statistical significance in whole‐genome sequencing‐based association studies of global populations

Pulit

With

Bakker

2016

Genetic Epidemiology

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Genome-wide association studies (GWAS) of common disease have been hugely successful in implicating loci that modify disease risk. The bulk of these associations have proven robust and reproducible, in part due to community adoption of statistical criteria for claiming significant genotype-phenotype associations. As the cost of sequencing continues to drop, assembling large samples in global populations is becoming increasingly feasible. Sequencing studies interrogate not only common variants, as was true for genotyping-based GWAS, but variation across the full allele frequency spectrum, yielding many more (independent) statistical tests. We sought to empirically determine genome-wide significance thresholds for various analysis scenarios. Using whole-genome sequence data, we simulated sequencing-based disease studies of varying sample size and ancestry. We determined that future sequencing efforts in >2,000 samples of European, Asian, or admixed ancestry should set genome-wide significance at approximately P = 5 × 10 , and studies of African samples should apply a more stringent genome-wide significance threshold of P = 1 × 10 . Adoption of a revised multiple test correction will be crucial in avoiding irreproducible claims of association.

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Section: Discussionsupporting

confidence: 88%

\times

10 −8 ; Xu et al., ). Though burden tests may improve power through variant aggregation, it remains difficult to know which variants exactly should be included in such a test.…”

Section: Discussionmentioning

confidence: 99%

Resetting the bar: Statistical significance in whole‐genome sequencing‐based association studies of global populations

Pulit

With

Bakker

2016

Genetic Epidemiology

View full text Add to dashboard Cite

show abstract

“…By randomly generating case‐control phenotypes for the 1000 Genomes data, Kanai, Tanaka and Okada () also obtained genomewide significance thresholds that are more liberal than ours. On the other hand, by using the WGS data from chromosome 3 in the UK10K project and randomly assigning normally distributed phenotypes and then extrapolating from the length of chromosome 3 to the length of the whole genome, Xu et al () obtained genomewide significance thresholds that are slightly more stringent than ours.…”

Section: Discussionmentioning

confidence: 64%

A simple and accurate method to determine genomewide significance for association tests in sequencing studies

Lin

2019

Genetic Epidemiology

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Whole‐exome sequencing (WES) and whole‐genome sequencing (WGS) studies are underway to investigate the impact of genetic variants on complex diseases and traits. It is customary to perform single‐variant association tests for common variants and region‐based association tests for rare variants. The latter may target variants with similar or opposite effects, interrogate variants with different frequencies or different functional annotations, and examine a variety of regions. The large number of tests that are performed necessitates adjustment for multiple testing. The conventional Bonferroni correction is overly conservative as the test statistics are correlated. To address this challenge, we propose a simple and accurate method based on parametric bootstrap to assess genomewide significance. We show that the correlations of the test statistics are determined primarily by the genotypes, such that the same significance threshold can be used in different studies that share a common sequencing platform. We demonstrate the usefulness of the proposed method with WES data from the National Heart, Lung, and Blood Institute Exome Sequencing Project and WGS data from the 1000 Genomes Project. We recommend the p value of 5 × 1 0 − 9 as the genomewide significance threshold for testing all common and low‐frequency variants (MAFs ≥ 0.1%) in the human genome.

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“…We tested bi-allelic single-nucleotide variants (SNVs) with MAF ≥ 0.5% for association, declaring genome-wide statistical significance at P ≤ 1.2 × 10 −8 (accounting for all independent SNVs above this MAF threshold; Supplementary Methods) 18 . The sequence kernel association test (SKAT) was used to assess association of regions containing SNVs with MAF ≤ 5% and ≤ 1%.…”

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confidence: 99%

Whole‐genome sequencing identifies EN1 as a determinant of bone density and fracture

Zheng¹,

Forgetta²,

Hsu³

et al. 2015

Nature

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507

576

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SUMMARY The extent to which low-frequency (minor allele frequency [MAF] between 1–5%) and rare (MAF ≤ 1%) variants contribute to complex traits and disease in the general population is largely unknown. Bone mineral density (BMD) is highly heritable, is a major predictor of osteoporotic fractures and has been previously associated with common genetic variants1–8, and rare, population-specific, coding variants9. Here we identify novel non-coding genetic variants with large effects on BMD (ntotal = 53,236) and fracture (ntotal = 508,253) in individuals of European ancestry from the general population. Associations for BMD were derived from whole-genome sequencing (n=2,882 from UK10K), whole-exome sequencing (n= 3,549), deep imputation of genotyped samples using a combined UK10K/1000Genomes reference panel (n=26,534), and de-novo replication genotyping (n= 20,271). We identified a low-frequency non-coding variant near a novel locus, EN1, with an effect size 4-fold larger than the mean of previously reported common variants for lumbar spine BMD8 (rs11692564[T], MAF = 1.7%, replication effect size = +0.20 standard deviations [SD], Pmeta = 2×10−14), which was also associated with a decreased risk of fracture (OR = 0.85; P = 2×10−11; ncases = 98,742 and ncontrols = 409,511). Using an En1Cre/flox mouse model, we observed that conditional loss of En1 results in low bone mass, likely as a consequence of high bone turn-over. We also identified a novel low-frequency non-coding variant with large effects on BMD near WNT16 (rs148771817[T], MAF = 1.1%, replication effect size = +0.39 SD, Pmeta = 1×10−11). In general, there was an excess of association signals arising from deleterious coding and conserved non-coding variants. These findings provide evidence that low-frequency non-coding variants have large effects on BMD and fracture, thereby providing rationale for whole-genome sequencing and improved imputation reference panels to study the genetic architecture of complex traits and disease in the general population.

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Estimating Genome‐Wide Significance for Whole‐Genome Sequencing Studies

Cited by 78 publications

References 15 publications

Resetting the bar: Statistical significance in whole‐genome sequencing‐based association studies of global populations

Resetting the bar: Statistical significance in whole‐genome sequencing‐based association studies of global populations

A simple and accurate method to determine genomewide significance for association tests in sequencing studies

Whole‐genome sequencing identifies EN1 as a determinant of bone density and fracture

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