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PRIMAL: Fast and Accurate Pedigree-based Imputation from Sequence Data in a Founder Population

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  • Oren E Livne
  • Lide Han
  • Gorka Alkorta-Aranburu
  • William Wentworth-Sheilds
  • Mark Abney
  • Carole Ober
  • Dan L Nicolae

Abstract

Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL (PedigRee IMputation ALgorithm), a fast and accurate pedigree-based phasing and imputation algorithm for founder populations. PRIMAL incorporates both existing and original ideas, such as a novel indexing strategy of Identity-By-Descent (IBD) segments based on clique graphs. We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences. Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies. Using the IBD cliques we were also able to infer the parental origin of 83% of alleles, and genotypes of deceased recent ancestors for whom no genotype information was available. This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes, as well as parental origin effect of disease risk alleles in >1,000 individuals at minimal cost.Author Summary: The recent availability of whole genome and whole exome sequencing allows genetic studies of human diseases and traits at an unprecedented resolution, although their cost limits the size of the studied sample. To overcome this limitation and design cost-efficient studies, we developed a two step method: sequencing of relatively few members of a well-characterized founder population followed by pedigree-based whole genome imputation of many other individuals with genome-wide genotype data. We show that by sequencing only 98 Hutterites, we can impute 7 million variants in an additional 1,317 Hutterites with >99% accuracy and an average call rate of 87%. Furthermore, parental origin was assigned to 83% of the alleles. Such studies in the Hutterites and other founder populations should yield new insights into the genetic architecture of common diseases, gene expression traits, and clinically relevant biomarkers of disease, and ultimately provide outstanding opportunities for personalized medicine in these well-characterized populations.

Suggested Citation

  • Oren E Livne & Lide Han & Gorka Alkorta-Aranburu & William Wentworth-Sheilds & Mark Abney & Carole Ober & Dan L Nicolae, 2015. "PRIMAL: Fast and Accurate Pedigree-based Imputation from Sequence Data in a Founder Population," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-14, March.
  • Handle: RePEc:plo:pcbi00:1004139
    DOI: 10.1371/journal.pcbi.1004139
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    References listed on IDEAS

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    1. Bryan N Howie & Peter Donnelly & Jonathan Marchini, 2009. "A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 5(6), pages 1-15, June.
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    Cited by:

    1. Amy Ko & Rasmus Nielsen, 2017. "Composite likelihood method for inferring local pedigrees," PLOS Genetics, Public Library of Science, vol. 13(8), pages 1-21, August.
    2. Esther Ulitzsch & Qiwei He & Vincent Ulitzsch & Hendrik Molter & André Nichterlein & Rolf Niedermeier & Steffi Pohl, 2021. "Combining Clickstream Analyses and Graph-Modeled Data Clustering for Identifying Common Response Processes," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 190-214, March.
    3. Mark Reppell & John Novembre, 2018. "Using pseudoalignment and base quality to accurately quantify microbial community composition," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-23, April.

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