Improving the Accuracy and Efficiency of Identity-by-Descent Detection in Population Data

By Brian L. Browning, Sharon R. Browning. Genetics, 2013

630 citations recorded by the source corpus.

Segments of indentity-by-descent (IBD) detected from high-density genetic data are useful for many applications, including long-range phase determination, phasing family data, imputation, IBD mapping, and heritability analysis in founder populations. We present Refined IBD, a new method for IBD segment detection. Refined IBD achieves both computational efficiency and highly accurate IBD segment reporting by searching for IBD in two steps. The first step (identification) uses the GERMLINE algorithm to find shared haplotypes exceeding a length threshold. The second step (refinement) evaluates candidate segments with a probabilistic approach to assess the evidence for IBD. Like GERMLINE, Refined IBD allows for IBD reporting on a haplotype level, which facilitates determination of multi-individual IBD and allows for haplotype-based downstream analyses. To investigate the properties of Refined IBD, we simulate SNP data from a model with recent superexponential population growth that is designed to match United Kingdom data. The simulation results show that Refined IBD achieves a better power/accuracy profile than fastIBD or GERMLINE. We find that a single run of Refined IBD achieves greater power than 10 runs of fastIBD. We also apply Refined IBD to SNP data for samples from the United Kingdom and from Northern Finland and describe the IBD sharing in these data sets. Refined IBD is powerful, highly accurate, and easy to use and is implemented in Beagle version 4.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/5cc44b8340abe0796848ba7aaf15505beda3321e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

Citations in the corpus

Second-generation PLINK: rising to the challenge of larger and richer datasets (2014, methodology): “To support easier interoperation with newer software like BEAGLE 4 [2], IMPUTE2 [3], GATK [4], VCFtools [5], BCFtools [6], and GCTA [7], features such as the import/export of VCF and Oxford-format ﬁles and an eﬃcient cross-platform genomic relationship matrix (GRM) calculator have been introduced.”
Novel Methods to Optimize Genotypic Imputation for Low‐Coverage, Next‐Generation Sequence Data in Crop Plants (2014, methodology): “4 (Browning and Browning, 2013), which we found in preliminary tests to be the most comparable available algorithm: it can generate haplotypes, tolerate high levels of missing data, and impute entire chromosomes with tens of thousands of markers in one run.”