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

Lin, Danyu

doi:10.1002/gepi.22183

Cited by 21 publications

(19 citation statements)

References 28 publications

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“…We plan next to consider multiple testing in the context of rare variant analysis ( Pulit et al . 2017 ; Lin 2019 ) which necessarily will increase the number of hypothesis tests for which we need to correct and introduce the consideration of gene- and more generally set-based association tests.…”

Section: Discussionmentioning

confidence: 99%

Revisiting the genome-wide significance threshold for common variant GWAS

Chen

Boehnke

Wen

et al. 2021

G3 Genes|Genomes|Genetics

107

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Over the last decade, GWAS meta-analyses have used a strict P-value threshold of 5 × 10−8 to classify associations as significant. Here, we use our current understanding of frequently studied traits including lipid levels, height, and BMI to revisit this genome-wide significance threshold. We compare the performance of studies using the P = 5 × 10−8 threshold in terms of true and false positive rate to other multiple testing strategies: (1) less stringent P-value thresholds, (2) controlling the FDR with the Benjamini–Hochberg and Benjamini–Yekutieli procedure, and (3) controlling the Bayesian FDR with posterior probabilities. We applied these procedures to re-analyze results from the Global Lipids and GIANT GWAS meta-analysis consortia and supported them with extensive simulation that mimics the empirical data. We observe in simulated studies with sample sizes ∼20,000 and >120,000 that relaxing the P-value threshold to 5 × 10−7 increased discovery at the cost of 18% and 8% of additional loci being false positive results, respectively. FDR and Bayesian FDR are well controlled for both sample sizes with a few exceptions that disappear under a less stringent definition of true positives and the two approaches yield similar results. Our work quantifies the value of using a relaxed P-value threshold in large studies to increase their true positive discovery but also show the excess false positive rates due to such actions in modest-sized studies. These results may guide investigators considering different thresholds in replication studies and downstream work such as gene-set enrichment or pathway analysis. Finally, we demonstrate the viability of FDR-controlling procedures in GWAS.

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Section: Discussionmentioning

confidence: 99%

Revisiting the genome-wide significance threshold for common variant GWAS

Chen

Boehnke

Wen

et al. 2021

G3 Genes|Genomes|Genetics

107

View full text Add to dashboard Cite

show abstract

“…We did not observe evidence of significant genome-wide inflation (Table S4). We adopted a significance threshold of 5 x 10 -9 /31 traits, or p<1.61 x 10 -10 , based on reasonable estimates of the number of independent tests for testing all common and low frequency variants genome-wide [47]. Our initial analyses identified several putative novel signals at the HBB locus; however, these results were difficult to interpret as known sickle cell trait variant rs334, which is known to have impacts on numerous traits including kidney function [36] and HbA1c [48] was excluded from the TOPMed freeze 8 reference panel.…”

Section: Methodsmentioning

confidence: 99%

Analyses of Biomarker Traits in Diverse UK Biobank Participants Identify Associations Missed by European-centric Analysis Strategies

Sun

Graff

Rowland

et al. 2020

Preprint

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Despite the dramatic underrepresentation of non-European populations in human genetics studies, researchers continue to exclude participants of non-European ancestry, even when these data are available. This practice perpetuates existing research disparities and can lead to important and large effect size associations being missed. Here, we conducted genome-wide association studies (GWAS) of 31 serum and urine biomarker quantitative traits in African (n=9354), East Asian (n=2559) and South Asian (n=9823) UK Biobank participants ancestry. We adjusted for all known GWAS catalog variants for each trait, as well as novel signals identified in European ancestry UK Biobank participants alone. We identify 12 novel signals in African ancestry and 3 novel signals in South Asian participants (p<1.61 × 10−10). Many of these signals are highly plausible and rare in Europeans (1% or lower minor allele frequency), including cis pQTLs for the genes encoding serum biomarkers like gamma-glutamyl transferase and apolipoprotein A, PIEZ01 and G6PD variants with impacts on HbA1c through likely erythocytic mechanisms, and a coding variant in GPLD1, a gene which cleaves GPI-anchors, associated with normally GPI-anchored protein alkaline phosphatase in serum. This work illustrates the importance of using the genetic data we already have in diverse populations, with many novel discoveries possible in even modest sample sizes.

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“…We tested up to 102,674,666 SNVs and 7,722,116 indels ( Supplemental Table 2 ). Genome-wide significance was determined at the P < 5E-9 level 28 . For each locus, we defined the top variant as the most significant variant within a 2Mb window.…”

Section: Methodsmentioning

confidence: 99%

“…2). Genome-wide significance was determined at the P < 5E-9 level 28 . For each locus, we defined the top variant as the most significant variant within a 2Mb window, and novel loci were defined as those that were located over 1Mb apart from previously reported locus.…”

Section: Single Variant Association Analysismentioning

confidence: 99%

Whole genome sequencing association analysis of quantitative red blood cell phenotypes: the NHLBI TOPMed program

Hu¹,

Stilp²,

McHugh³

et al. 2020

Preprint

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Whole genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these newly discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3bp indel p.Lys2169del (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis [OMIM 194380], associated with higher MCHC. In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically-diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

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

Cited by 21 publications

References 28 publications

Revisiting the genome-wide significance threshold for common variant GWAS

Revisiting the genome-wide significance threshold for common variant GWAS

Analyses of Biomarker Traits in Diverse UK Biobank Participants Identify Associations Missed by European-centric Analysis Strategies

Whole genome sequencing association analysis of quantitative red blood cell phenotypes: the NHLBI TOPMed program

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