Comparative assessment of methods for estimating individual genome-wide homozygosity-by-descent from human genomic data.

Abstract : BACKGROUND: Genome-wide homozygosity estimation from genomic data is becoming an increasingly interesting research topic. The aim of this study was to compare different methods for estimating individual homozygosity-by-descent based on the information from human genome-wide scans rather than genealogies. We considered the four most commonly used methods and investigated their applicability to single-nucleotide polymorphism (SNP) data in both a simulation study and by using the human genotyped data. A total of 986 inhabitants from the isolated Island of Vis, Croatia (where inbreeding is present, but no pedigree-based inbreeding was observed at the level of F > 0.0625) were included in this study. All individuals were genotyped with the Illumina HumanHap300 array with 317,503 SNP markers. RESULTS: Simulation data suggested that multi-point FEstim is the method most strongly correlated to true homozygosity-by-descent. Correlation coefficients between the homozygosity-by-descent estimates were high but only for inbred individuals, with nearly absolute correlation between single-point measures. CONCLUSIONS: Deciding who is really inbred is a methodological challenge where multi-point approaches can be very helpful once the set of SNP markers is filtered to remove linkage disequilibrium. The use of several different methodological approaches and hence different homozygosity measures can help to distinguish between homozygosity-by-state and homozygosity-by-descent in studies investigating the effects of genomic autozygosity on human health.
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BMC Genomics, BioMed Central, 2010, 11 (1), pp.139. 〈10.1186/1471-2164-11-139〉
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Dernière modification le : jeudi 11 janvier 2018 - 06:20:15

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Ozren Polašek, Caroline Hayward, Céline Bellenguez, Veronique Vitart, Ivana Kolčić, et al.. Comparative assessment of methods for estimating individual genome-wide homozygosity-by-descent from human genomic data.. BMC Genomics, BioMed Central, 2010, 11 (1), pp.139. 〈10.1186/1471-2164-11-139〉. 〈inserm-00663772〉

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