Improved genome inference in the MHC using a population reference graph.
Although much is known about human genetic variation, such information is typically ignored in assembling new genomes. Instead, reads are mapped to a single reference, which can lead to poor characterization of regions of high sequence or structural diversity. We introduce a population reference graph, which combines multiple reference sequences and catalogs of variation. The genomes of new samples are reconstructed as paths through the graph using an efficient hidden Markov model, allowing for recombination between different haplotypes and additional variants. By applying the method to the 4.5-Mb extended MHC region on human chromosome 6, combining 8 assembled haplotypes, the sequences of known classical HLA alleles and 87,640 SNP variants from the 1000 Genomes Project, we demonstrate using simulations, SNP genotyping, and short-read and long-read data how the method improves the accuracy of genome inference and identified regions where the current set of reference sequences is substantially incomplete.
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
Alexander
A
Dilthey
Department of Quantitative Sciences, GlaxoSmithKline, Stevenage, UK.
Charles
C
Cox
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
Zamin
Z
Iqbal
Department of Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina, USA.
Matthew R
MR
Nelson
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
Gil
G
McVean
D000465:Algorithms; D003198:Computer Simulation; D015894:Genome, Human; D060005:Genotyping Techniques / Q000592:standards; D006239:Haplotypes; D000949:Histocompatibility Antigens Class II / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D012015:Reference Standards; D017422:Sequence Analysis, DNA
D000949:Histocompatibility Antigens Class II