Whole population, genome-wide mapping of hidden relatedness.

By Alexander Gusev, J. Lowe, M. Stoffel, Mark J. Daly, David Altshuler, J. Breslow, J. Friedman, Itsik Pe'er. Genome Research, 2009

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We present GERMLINE, a robust algorithm for identifying segmental sharing indicative of recent common ancestry between pairs of individuals. Unlike methods with comparable objectives, GERMLINE scales linearly with the number of samples, enabling analysis of whole-genome data in large cohorts. Our approach is based on a dictionary of haplotypes that is used to efficiently discover short exact matches between individuals. We then expand these matches using dynamic programming to identify long, nearly identical segmental sharing that is indicative of relatedness. We use GERMLINE to comprehensively survey hidden relatedness both in the HapMap as well as in a densely typed island population of 3000 individuals. We verify that GERMLINE is in concordance with other methods when they can process the data, and also facilitates analysis of larger scale studies. We bolster these results by demonstrating novel applications of precise analysis of hidden relatedness for (1) identification and resolution of phasing errors and (2) exposing polymorphic deletions that are otherwise challenging to detect. This finding is supported by concordance of detected deletions with other evidence from independent databases and statistical analyses of fluorescence intensity not used by GERMLINE.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/075bf4762a97faafdabfd14f539c995dc3bc594f

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Citations in the corpus

BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data (2016, methodology): “While several methods exist to detect these RoH (Browning and Browning, 2010; Gusev et al ., 2009; Purcell et al ., 2007) they were primarily designed for genotyping data and do not exploit all the information available from population sequencing, which includes fuller information about allele…”
Fast and accurate long-range phasing in a UK Biobank cohort (2015, unknown): “First, identifying IBD is difficult both in terms of accuracy and computational cost; moreover, the most widely used IBD inference methods rely on first phasing the data [30–32].”
Novel Methods to Optimize Genotypic Imputation for Low‐Coverage, Next‐Generation Sequence Data in Crop Plants (2014, background): “1), but is less sensitive than other algorithms if the samples are highly heterozygous, since we make no efort to phase (Gusev et al., 2009; Browning and Browning, 2011, 2013).”
A fast, powerful method for detecting identity by descent. (2011, methodology): “Although the number of pairs of samples grows quadratically with sample size, one can efficiently identify the pairs of sampled haplotypes with identical HMM states in the leading window by using a dictionary data structure, as noted by Gusev et al.5 For each identified pair of haplotypes, H1 and H2”
Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data (2015, background): “Other methods exist that model the transition of IBD along the genome [23, 18, 11, 6, 28, 8, 23], and the most common approaches use population allele frequencies to determine the likelihood of observing a particular genotype given a certain level of IBD at multiple loci and assume linkage equilibri”
1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana (2016, background): “In addition to groups of nearly identical individuals, 47%of North American pairs exhibit extensive haplotype sharing (total identity-by-descent length over 85 Mb, as inferred using Beagle and GERMLINE, Figure S1) (Browning and Browning, 2009; Gusev et al., 2009), indicating recent mixing among a li”
Genome structural variation discovery and genotyping (2011, background): “and currently in the large-scale discovery of CNVs in a broad variety of population”
RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. (2013, methodology): “IBD analysis will also benefit from this work, given that phase accuracy significantly affects the power to detect IBD segments.(32) Other future work includes using RFMix in situations where no proxy reference panel for one or more of the ancestral populations exists.”
Genome-wide association study identifies susceptibility loci for IgA nephropathy (2011, methodology): “and pairwise inflammatory bowel disease status was determined using GERMLINE softwar”