Open AccessRapid genotype imputation from sequence with reference panels
Author(s) -
R. W. Davies,
Marek Kučka,
Dingwen Su,
Sinan Shi,
Maeve Flanagan,
Christopher Cunniff,
Yingguang Frank Chan,
Simon Myers
Publication year - 2021
Publication title -
nature genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.861
H-Index - 573
eISSN - 1546-1718
pISSN - 1061-4036
DOI - 10.1038/s41588-021-00877-0
Subject(s) - imputation (statistics) , quilt , biology , reference genome , genotyping , genomics , computational biology , 1000 genomes project , genetics , genome , genotype , missing data , computer science , single nucleotide polymorphism , gene , machine learning , archaeology , history
Inexpensive genotyping methods are essential to modern genomics. Here we present QUILT, which performs diploid genotype imputation using low-coverage whole-genome sequence data. QUILT employs Gibbs sampling to partition reads into maternal and paternal sets, facilitating rapid haploid imputation using large reference panels. We show this partitioning to be accurate over many megabases, enabling highly accurate imputation close to theoretical limits and outperforming existing methods. Moreover, QUILT can impute accurately using diverse technologies, including long reads from Oxford Nanopore Technologies, and a new form of low-cost barcoded Illumina sequencing called haplotagging, with the latter showing improved accuracy at low coverages. Relative to DNA genotyping microarrays, QUILT offers improved accuracy at reduced cost, particularly for diverse populations that are traditionally underserved in modern genomic analyses, with accuracy nearly doubling at rare SNPs. Finally, QUILT can accurately impute (four-digit) human leukocyte antigen types, the first such method from low-coverage sequence data.