MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes

Genome‐wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies....

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Vydáno v:Genetic epidemiology Ročník 34; číslo 8; s. 816 - 834
Hlavní autoři: Li, Yun, Willer, Cristen J., Ding, Jun, Scheet, Paul, Abecasis, Gonçalo R.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.12.2010
Témata:
Alleles
Base Sequence
Chromosomes
Genetic Markers
Genome, Human
Genome-Wide Association Study - methods
Genotype
Haplotypes
haplotyping
Humans
imputation
Markov Chains
Polymorphism, Single Nucleotide - genetics
Sensitivity and Specificity
sequencing
Software
ISSN:0741-0395, 1098-2272, 1098-2272
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Shrnutí:Genome‐wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies. We have previously implemented a computationally efficient Markov Chain framework for genotype imputation and haplotyping in the freely available MaCH software package. The approach describes sampled chromosomes as mosaics of each other and uses available genotype and shotgun sequence data to estimate unobserved genotypes and haplotypes, together with useful measures of the quality of these estimates. Our approach is already widely used to facilitate comparison of results across studies as well as meta‐analyses of GWAS. Here, we use simulations and experimental genotypes to evaluate its accuracy and utility, considering choices of genotyping panels, reference panel configurations, and designs where genotyping is replaced with shotgun sequencing. Importantly, we show that genotype imputation not only facilitates cross study analyses but also increases power of genetic association studies. We show that genotype imputation of common variants using HapMap haplotypes as a reference is very accurate using either genome‐wide SNP data or smaller amounts of data typical in fine‐mapping studies. Furthermore, we show the approach is applicable in a variety of populations. Finally, we illustrate how association analyses of unobserved variants will benefit from ongoing advances such as larger HapMap reference panels and whole genome shotgun sequencing technologies. Genet. Epidemiol. 34: 816‐834, 2010. © 2010 Wiley‐Liss, Inc.
Bibliografie:istex:94A034A6D348DBD324C942B0F94B75249CF6F289
ArticleID:GEPI20533
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ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0741-0395
1098-2272
1098-2272
DOI:10.1002/gepi.20533