Abstract
BackgroundGenome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. In current analyses, genotype calling and imputation of missing genotypes are usually considered as two separated tasks. The genotypes of SNPs are first determined one at a time from allele signal intensities. Then the missing genotypes, i.e., no-calls caused by not perfectly separated signal clouds, are imputed based on the linkage disequilibrium (LD) between multiple SNPs. Although many statistical methods have been developed to improve either genotype calling or imputation of missing genotypes, treating the two steps independently can lead to loss of genetic information.ResultsWe propose a novel genotype calling framework. In this framework, we consider the signal intensities and underlying LD structure of SNPs simultaneously by estimating both cluster parameters and haplotype frequencies. As a result, our new method outperforms some existing algorithms in terms of both call rates and genotyping accuracy. Our studies also suggest that jointly analyzing multiple SNPs in LD provides more accurate estimation of haplotypes than haplotype reconstruction methods that only use called genotypes.ConclusionOur study demonstrates that jointly analyzing signal intensities and LD structure of multiple SNPs is a better way to determine genotypes and estimate LD parameters.
Highlights
Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits
Our study demonstrates that jointly analyzing signal intensities and linkage disequilibrium (LD) structure of multiple SNPs is a better way to determine genotypes and estimate LD parameters
Our study indicates that different thresholds δ led to very similar conclusions, only those of δ = 0.95 are
Summary
Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. Recent advances in genotyping technologies have greatly improved genotype call rates and accuracy. This enhances our understanding of genetic variations that are responsible for complex human traits. Studies with SNPs show great promise to identify genetic determinants of complex disorders. Despite these achievements, the large number of SNPs in today's genome-wide studies poses a number of serious challenges. Excluding subjects that have any missing genotypes is impractical in many situations, as most subjects usually have one or more missing genotypes in large genotyping efforts
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