Computationally efficient familywise error rate control in genome‐wide association studies using score tests for generalized linear models

Halle, Kari Krizak; Bakke, Øyvind; Djurovic, Srdjan; Bye, Anja; Ryeng, Einar; Wisløff, Ulrik; Andreassen, Ole A.; Langaas, Mette

doi:10.1111/sjos.12451

Cited by 3 publications

(5 citation statements)

References 55 publications

(111 reference statements)

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“…The proportion of phenotypic variance explained by significant markers was estimated by dividing the additive genetic variance explained by the marker by the total phenotypic variance ( V p ) for the associated trait:

h_{SNP}^{2} = \frac{2 p q (a^{2})}{V_{p}}

, where p and q are allele frequencies, and a is the marker effect size (Falconer & Mackay, ). For repeatabel analyses, multiple testing correction was carried out using the FWER approximation method (Halle et al., ). Prior to calculation of significance thresholds, one of each pair of SNPs with correlation r > 0.999 was removed.…”

Section: Methodsmentioning

confidence: 99%

Inferences of genetic architecture of bill morphology in house sparrow using a high‐density SNP array point to a polygenic basis

et al. 2018

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Understanding the genetic architecture of quantitative traits can provide insights into the mechanisms driving phenotypic evolution. Bill morphology is an ecologically important and phenotypically variable trait, which is highly heritable and closely linked to individual fitness. Thus, bill morphology traits are suitable candidates for gene mapping analyses. Previous studies have revealed several genes that may influence bill morphology, but the similarity of gene and allele effects between species and populations is unknown. Here, we develop a custom 200K SNP array and use it to examine the genetic basis of bill morphology in 1857 house sparrow individuals from a large-scale, island metapopulation off the coast of Northern Norway. We found high genomic heritabilities for bill depth and length, which were comparable with previous pedigree estimates. Candidate gene and genomewide association analyses yielded six significant loci, four of which have previously been associated with craniofacial development. Three of these loci are involved in bone morphogenic protein (BMP) signalling, suggesting a role for BMP genes in regulating bill morphology. However, these loci individually explain a small amount of variance. In combination with results from genome partitioning analyses, this indicates that bill morphology is a polygenic trait. Any studies of eco-evolutionary processes in bill morphology are therefore dependent on methods that can accommodate polygenic inheritance of the phenotype and molecular-scale evolution of genetic architecture.

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h_{SNP}^{2} = \frac{2 p q (a^{2})}{V_{p}}

Section: Methodsmentioning

confidence: 99%

Inferences of genetic architecture of bill morphology in house sparrow using a high‐density SNP array point to a polygenic basis

et al. 2018

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“…In the context of high‐dimensional studies, it is well‐known that FWER‐controlling procedures are conservative and typically lead to substantially less statistical power than procedures that control the proportion of false discoveries 2,28 . A computationally efficient alternative to the MMM approach, based on score test statistics, has been proposed recently 16,29,30 . The latter methodology needs only one fit of a GLM model.…”

Section: Introductionmentioning

confidence: 99%

“…Large-scale multiple testing under general and strong dependency remains challenging and an active research topic in modern statistics. 7,8,12,13 Among other approaches like, for instance, considering block dependencies in genetics, [14][15][16] several multi-factor models have been proposed to model dependencies among test statistics. [5][6][7][8]13 In particular, a general framework to approximate the false discovery proportion (FDP) has been introduced.…”

Section: Introductionmentioning

confidence: 99%

“…2,28 A computationally efficient alternative to the MMM approach, based on score test statistics, has been proposed recently. 16,29,30 The latter methodology needs only one fit of a GLM model. However, this methodology requires a data regime in which p ≪ n, where n denotes the sample size, in order to approximate the (limiting) covariance matrix with sufficient accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Multiple multi‐sample testing under arbitrary covariance dependency

Vladimir

Dickhaus

2023

Statistics in Medicine

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Modern high‐throughput biomedical devices routinely produce data on a large scale, and the analysis of high‐dimensional datasets has become commonplace in biomedical studies. However, given thousands or tens of thousands of measured variables in these datasets, extracting meaningful features poses a challenge. In this article, we propose a procedure to evaluate the strength of the associations between a nominal (categorical) response variable and multiple features simultaneously. Specifically, we propose a framework of large‐scale multiple testing under arbitrary correlation dependency among test statistics. First, marginal multinomial regressions are performed for each feature individually. Second, we use an approach of multiple marginal models for each baseline‐category pair to establish asymptotic joint normality of the stacked vector of the marginal multinomial regression coefficients. Third, we estimate the (limiting) covariance matrix between the estimated coefficients from all marginal models. Finally, our approach approximates the realized false discovery proportion of a thresholding procedure for the marginal p‐values for each baseline‐category logit pair. The proposed approach offers a sensible trade‐off between the expected numbers of true and false findings. Furthermore, we demonstrate a practical application of the method on hyperspectral imaging data. This dataset is obtained by a matrix‐assisted laser desorption/ionization (MALDI) instrument. MALDI demonstrates tremendous potential for clinical diagnosis, particularly for cancer research. In our application, the nominal response categories represent cancer (sub‐)types.

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“…Conducting numerous tests poses challenges in meeting the required significance threshold. In high-dimensional GWAS with hundreds of thousands of SNPs, each test is conducted at some nominal significance level, potentially leading to a high number of false positives (FPs) [5][6][7]. This limitation arises from the difficulty of detecting SNPs with minor effects genuinely associated with the disease.…”

Section: Introductionmentioning

confidence: 99%

Statistical Study Design for Analyzing Multiple Gene Loci Correlation in DNA Sequences

Kamoljitprapa,

Baksh,

De Gaetano

et al. 2023

Mathematics

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This study presents a novel statistical and computational approach using nonparametric regression, which capitalizes on correlation structure to deal with the high-dimensional data often found in pharmacogenomics, for instance, in Crohn’s inflammatory bowel disease. The empirical correlation between the test statistics, investigated via simulation, can be used as an estimate of noise. The theoretical distribution of −log10(p-value) is used to support the estimation of that optimal bandwidth for the model, which adequately controls type I error rates while maintaining reasonable power. Two proposed approaches, involving normal and Laplace-LD kernels, were evaluated by conducting a case-control study using real data from a genome-wide association study on Crohn’s disease. The study successfully identified single nucleotide polymorphisms on the NOD2 gene associated with the disease. The proposed method reduces the computational burden by approximately 33% with reasonable power, allowing for a more efficient and accurate analysis of genetic variants influencing drug responses. The study contributes to the advancement of statistical methodology for analyzing complex genetic data and is of practical advantage for the development of personalized medicine.

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Computationally efficient familywise error rate control in genome‐wide association studies using score tests for generalized linear models

Abstract: and FWER-adjusted p-values can be calculated as an alternative to using a local significance level.

Cited by 3 publications

References 55 publications

Inferences of genetic architecture of bill morphology in house sparrow using a high‐density SNP array point to a polygenic basis

Inferences of genetic architecture of bill morphology in house sparrow using a high‐density SNP array point to a polygenic basis

Multiple multi‐sample testing under arbitrary covariance dependency

Statistical Study Design for Analyzing Multiple Gene Loci Correlation in DNA Sequences

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