Simultaneous selection of multiple important single nucleotide polymorphisms in familial genome wide association studies data

We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as ‘single...

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Vydané v:Scientific reports Ročník 13; číslo 1; s. 8476 - 13
Hlavní autori: Majumdar, Subhabrata, Basu, Saonli, McGue, Matt, Chatterjee, Snigdhansu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 25.05.2023
Nature Publishing Group
Nature Portfolio
Predmet:
631/208/205/2138
639/705/531
Alcohol
Alcohol Drinking
Association analysis
Computer applications
Datasets
Feature selection
Generation X
Genetic analysis
Genetic diversity
Genetic variance
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Humanities and Social Sciences
Humans
Hypotheses
Hypothesis testing
Information processing
Methods
Minnesota
Models, Genetic
multidisciplinary
Phenotype
Polymorphism, Single Nucleotide
Sample size
Science
Science (multidisciplinary)
Single-nucleotide polymorphism
Twins
Minnesota
ISSN:2045-2322, 2045-2322
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Shrnutí:We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as ‘single SNP association analysis’. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach—based on the e-values framework—for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-35379-y