An Accurate Method for Inferring Relatedness in Large Datasets of Unphased Genotypes via an Embedded Likelihood-Ratio Test
Studies that map disease genes rely on accurate annotations that indicate whether individuals in the studied cohorts are related to each other or not. For example, in genome-wide association studies, the cohort members are assumed to be unrelated to one another. Investigators can correct for individuals in a cohort with previously-unknown shared familial descent by detecting genomic segments that are shared between them, which are considered to be identical by descent (IBD). Alternatively, elevated frequencies of IBD segments near a particular locus among affected individuals can be indicative of a disease-associated gene. As genotyping studies grow to use increasingly large sample sizes and meta-analyses begin to include many data sets, accurate and efficient detection of hidden relatedness becomes a challenge. To enable disease-mapping studies of increasingly large cohorts, a fast and accurate method to detect IBD segments is required.
We present PARENTE, a novel method for detecting related pairs of individuals and shared haplotypic segments within these pairs. PARENTE is a computationally-efficient method based on an embedded likelihood ratio test. As demonstrated by the results of our simulations, our method exhibits better accuracy than the current state of the art, and can be used for the analysis of large genotyped cohorts. PARENTE’s higher accuracy becomes even more significant in more challenging scenarios, such as detecting shorter IBD segments or when an extremely low false-positive rate is required. PARENTE is publicly and freely available at http://parente.stanford.edu/.
KeywordsPopulation genetics IBD relatedness
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- 4.Bercovici, S., Meek, C., Wexler, Y., Geiger, D.: Estimating genome-wide ibd sharing from snp data via an efficient hidden markov model of ld with application to gene mapping. Bioinformatics 26(12), i175–i182 (2010)Google Scholar
- 9.Carey, V.J.: Mathematical and statistical methods for genetic analysis (2nd ed.). kenneth lange. Journal of the American Statistical Association 100, 712 (2005)Google Scholar
- 10.Conrad, D.F., Keebler, J.E.M., DePristo, M.A., Lindsay, S.J., Zhang, Y., Casals, F., Idaghdour, Y., Hartl, C.L., Torroja, C., Garimella, K.V., Zilversmit, M., Cartwright, R., Rouleau, G.A., Daly, M., Stone, E.A., Hurles, M.E., Awadalla, P., for the 1000 Genomes Project: Variation in genome-wide mutation rates within and between human families. Nature Genetics (2011)Google Scholar
- 12.Ghahramani, Z., Jordan, M.I., Smyth, P.: Factorial hidden markov models. In: Machine Learning. MIT Press (1997)Google Scholar
- 15.Henn, B.M., Hon, L., Macpherson, J.M., Eriksson, N., Saxonov, S., Pe’er, I., Mountain, J.L.: Cryptic distant relatives are common in both isolated and cosmopolitan genetic samples. PLoS ONE 7(4), e34267 (2012)Google Scholar
- 17.Kyriazopoulou-Panagiotopoulou, S., Kashef Haghighi, D., Aerni, S.J., Sundquist, A., Bercovici, S., Batzoglou, S.: Reconstruction of genealogical relationships with applications to phase iii of hapmap. Bioinformatics 27(13), i333–i341 (2011)Google Scholar
- 19.Li, M.-H., Strandén, I., Tiirikka, T., Sevón-Aimonen, M.-L., Kantanen, J.: A comparison of approaches to estimate the inbreeding coefficient and pairwise relatedness using genomic and pedigree data in a sheep population. PLoS ONE 6(11), e26256 (2011)Google Scholar
- 21.1000 Genomes Project. A map of human genome variation from population-scale sequencing. Nature 467(7319),1061–1073 (2010)Google Scholar
- 23.Nalls, M.A., Simon-Sanchez, J., Gibbs, J.R., Paisan-Ruiz, C., Bras, J.T., Tanaka, T., Matarin, M., Scholz, S., Weitz, C., Harris, T.B., Ferrucci, L., Hardy, J., Singleton, A.B.: Measures of autozygosity in decline: Globalization, urbanization, and its implications for medical genetics. PLoS Genet 5(3), e1000415 (2009)Google Scholar
- 24.Ott, J.: Analysis of Human Genetic Linkage. The Johns Hopkins series in contemporary medicine and public health. Johns Hopkins University Press (1999)Google Scholar
- 25.Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., Sham, P.C.: PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81(3), 559–575 (2007)CrossRefGoogle Scholar
- 26.Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 257–286 (1989)Google Scholar
- 27.Ralph, P., Coop, G.: The geography of recent genetic ancestry across Europe (July 2012)Google Scholar
- 28.WTCCC. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447(7145), 661–678 (2007)Google Scholar