Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies

AbstractGenome-wide association studies have been successful in uncovering novel genetic variants that are associated with disease status or cross-sectional phenotypic traits. Researchers are beginning to investigate how genes play a role in the development of a trait over time. Linear mixed effects models (LMM) are commonly used to model longitudinal data; however, it is unclear if the failure to meet the models distributional assumptions will affect the conclusions when conducting a genome-wide association study. In an extensive simulation study, we compare coverage probabilities, bias, type 1 error rates and statistical power when the error of the LMM is either heteroscedastic or has a non-Gaussian distribution. We conclude that the model is robust to misspecification if the same function of age is included in the fixed and random effects. However, type 1 error of the genetic effect over time is inflated, regardless of the model misspecification, if the polynomial function for age in the fixed and random effects differs. In situations where the model will not converge with a high order polynomial function in the random effects, a reduced function can be used but a robust standard error needs to be calculated to avoid inflation of the type 1 error. As an illustration, a LMM was applied to longitudinal body mass index (BMI) data over childhood in the ALSPAC cohort; the results emphasised the need for the robust standard error to ensure correct inference of associations of longitudinal BMI with chromosome 16 single nucleotide polymorphisms.

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Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

10.1101/2020.06.24.20139576 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jack W. O’Sullivan ◽

John P. A. Ioannidis

Keyword(s):

Effect Size ◽

Association Studies ◽

Genome Wide Association ◽

P Value ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Single Nucleotide ◽

Genome Wide ◽

The Uk ◽

Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

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Meta-genome-wide association studies identify a locus on chromosome 1 and multiple variants in the MHC region for serum C-peptide in type 1 diabetes

Diabetologia ◽

10.1007/s00125-018-4555-9 ◽

2018 ◽

Vol 61 (5) ◽

pp. 1098-1111 ◽

Cited By ~ 14

Author(s):

Delnaz Roshandel ◽

◽

Rose Gubitosi-Klug ◽

Shelley B. Bull ◽

Angelo J. Canty ◽

...

Keyword(s):

Type 1 Diabetes ◽

Association Studies ◽

Genome Wide Association ◽

Chromosome 1 ◽

Genome Wide Association Studies ◽

C Peptide ◽

Genome Wide ◽

Multiple Variants

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Genome-Wide Association Study Identifies Loci Associated with Sensitive Skin

Cosmetics ◽

10.3390/cosmetics7020049 ◽

2020 ◽

Vol 7 (2) ◽

pp. 49

Author(s):

Miranda A. Farage ◽

Yunxuan Jiang ◽

Jay P. Tiesman ◽

Pierre Fontanillas ◽

Rosemarie Osborne

Keyword(s):

Genome Wide Association Study ◽

Association Studies ◽

Genome Wide Association ◽

European Ancestry ◽

P Value ◽

Genome Wide Association Studies ◽

Online Questionnaire ◽

Sensitive Skin ◽

Genome Wide ◽

Skin Conditions

Individuals suffering from sensitive skin often have other skin conditions and/or diseases, such as fair skin, freckles, rosacea, or atopic dermatitis. Genome-wide association studies (GWAS) have been performed for some of these conditions, but not for sensitive skin. In this study, a total of 23,426 unrelated participants of European ancestry from the 23andMe database were evaluated for self-declared sensitive skin, other skin conditions, and diseases using an online questionnaire format. Responders were separated into two groups: those who declared they had sensitive skin (n = 8971) and those who declared their skin was not sensitive (controls, n = 14,455). A GWAS of sensitive skin individuals identified three genome-wide significance loci (p-value < 5 × 10−8) and seven suggestive loci (p-value < 1 × 10−6). Of the three most significant loci, all have been associated with pigmentation and two have been associated with acne.

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Integrative Pathway-Based Approach for Genome-Wide Association Studies: Identification of New Pathways for Rheumatoid Arthritis and Type 1 Diabetes

PLoS ONE ◽

10.1371/journal.pone.0078577 ◽

2013 ◽

Vol 8 (10) ◽

pp. e78577 ◽

Cited By ~ 7

Author(s):

Finja Büchel ◽

Florian Mittag ◽

Clemens Wrzodek ◽

Andreas Zell ◽

Thomas Gasser ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Type 1 Diabetes ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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A meta-analysis of the genome-wide association studies on two genetically correlated phenotypes (self-reported headache and self-reported migraine) identifies four new risk loci for headaches (N=397,385)

10.1101/2021.09.15.21263668 ◽

2021 ◽

Author(s):

Weihua Meng ◽

Parminder Reel ◽

Charvi Nangia ◽

Aravind Rajendrakumar ◽

Harry Hebert ◽

...

Keyword(s):

Association Studies ◽

Meta Analysis ◽

The Self ◽

Genome Wide Association ◽

P Value ◽

Clinical Settings ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Genetic Mechanisms ◽

The Uk

Headache is one of the commonest complaints that doctors need to address in clinical settings. The genetic mechanisms of different types of headache are not well understood. In this study, we performed a meta-analysis of genome-wide association studies (GWAS) on the self-reported headache phenotype from the UK Biobank cohort and the self-reported migraine phenotype from the 23andMe resource using the metaUSAT for genetically correlated phenotypes (N=397,385). We identified 38 loci for headaches, of which 34 loci have been reported before and 4 loci were newly identified. The LRP1-STAT6-SDR9C7 region in chromosome 12 was the most significantly associated locus with a leading P value of 1.24 x 10-62 of rs11172113. The ONECUT2 gene locus in chromosome 18 was the strongest signal among the 4 new loci with a P value of 1.29 x 10-9 of rs673939. Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more new variants for headaches. This study has paved way for a large GWAS meta-analysis study involving cohorts of different, though genetically correlated headache phenotypes.

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