MOTIVATION: Copy number variations (CNVs) are increasingly recognized as an substantial source of individual genetic variation, and hence there is a growing interest in investigating the evolutionary history of CNVs as well as their impact on complex disease susceptibility. CNV/SNP haplotypes are critical for this research, but although many methods have been proposed for inferring integer copy number, few have been designed for inferring CNV haplotypic phase and none of these are applicable at genome-wide scale. Here, we present a method for inferring missing CNV genotypes, predicting CNV allelic configuration and for inferring CNV haplotypic phase from SNP/CNV genotype data. Our method, implemented in the software polyHap v2.0, is based on a hidden Markov model, which models the joint haplotype structure between CNVs and SNPs. Thus, haplotypic phase of CNVs and SNPs are inferred simultaneously. A sampling algorithm is employed to obtain a measure of confidence/credibility of each estimate. RESULTS: We generated diploid phase-known CNV-SNP genotype datasets by pairing male X chromosome CNV-SNP haplotypes. We show that polyHap provides accurate estimates of missing CNV genotypes, allelic configuration and CNV haplotypic phase on these datasets. We applied our method to a non-simulated dataset-a region on Chromosome 2 encompassing a short deletion. The results confirm that polyHap's accuracy extends to real-life datasets. AVAILABILITY: Our method is implemented in version 2.0 of the polyHap software package and can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin.
MOTIVATION: Copy number variations (CNVs) are increasingly recognized as an substantial source of individual genetic variation, and hence there is a growing interest in investigating the evolutionary history of CNVs as well as their impact on complex disease susceptibility. CNV/SNP haplotypes are critical for this research, but although many methods have been proposed for inferring integer copy number, few have been designed for inferring CNV haplotypic phase and none of these are applicable at genome-wide scale. Here, we present a method for inferring missing CNV genotypes, predicting CNV allelic configuration and for inferring CNV haplotypic phase from SNP/CNV genotype data. Our method, implemented in the software polyHap v2.0, is based on a hidden Markov model, which models the joint haplotype structure between CNVs and SNPs. Thus, haplotypic phase of CNVs and SNPs are inferred simultaneously. A sampling algorithm is employed to obtain a measure of confidence/credibility of each estimate. RESULTS: We generated diploid phase-known CNV-SNP genotype datasets by pairing male X chromosome CNV-SNP haplotypes. We show that polyHap provides accurate estimates of missing CNV genotypes, allelic configuration and CNV haplotypic phase on these datasets. We applied our method to a non-simulated dataset-a region on Chromosome 2 encompassing a short deletion. The results confirm that polyHap's accuracy extends to real-life datasets. AVAILABILITY: Our method is implemented in version 2.0 of the polyHap software package and can be downloaded from http://www.imperial.ac.uk/medicine/people/l.coin.
Authors: Donald F Conrad; Dalila Pinto; Richard Redon; Lars Feuk; Omer Gokcumen; Yujun Zhang; Jan Aerts; T Daniel Andrews; Chris Barnes; Peter Campbell; Tomas Fitzgerald; Min Hu; Chun Hwa Ihm; Kati Kristiansson; Daniel G Macarthur; Jeffrey R Macdonald; Ifejinelo Onyiah; Andy Wing Chun Pang; Sam Robson; Kathy Stirrups; Armand Valsesia; Klaudia Walter; John Wei; Chris Tyler-Smith; Nigel P Carter; Charles Lee; Stephen W Scherer; Matthew E Hurles Journal: Nature Date: 2009-10-07 Impact factor: 49.962
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Authors: Robert B Scharpf; Terri H Beaty; Holger Schwender; Samuel G Younkin; Alan F Scott; Ingo Ruczinski Journal: BMC Bioinformatics Date: 2012-12-12 Impact factor: 3.169
Authors: Zsófia Bánlaki; Julianna Anna Szabó; Ágnes Szilágyi; Attila Patócs; Zoltán Prohászka; George Füst; Márton Doleschall Journal: Genome Biol Evol Date: 2013 Impact factor: 3.416