Biology subjects

Miga, K.

Publications and source records attributed to Miga, K..

2 recordsLinked to original sources

bioRxiv2018

Diverse haplotypes span human centromeres and include archaic lineages within and out of Africa

Despite critical roles in chromosome segregation and disease, the repetitive structure and vast size of centromeres and their surrounding heterochromatic regions impede studies of genomic variation. We report here large-scale haplotypes (cenhaps) in humans that span the centromere-proximal regions of all metacentric chromosomes, including the arrays of highly repeated -satellites on which centromeres form. Cenhaps reveal surprisingly deep diversity, including entire introgressed Neanderthal centromeres and equally ancient lineages among Africans. These centromere-spanning haplotypes contain variants, including large differences in -satellite DNA content, which may influence the fidelity and bias of chromosome transmission. The discovery of cenhaps creates new opportunities to investigate their contribution to phenotypic variation, especially in meiosis and mitosis, as well as to more incisively model the unexpectedly rich evolution of these challenging genomic regions.\n\nOne Sentence SummaryGenomic polymorphism across centromeric regions of humans is organized into large-scale haplotypes with great diversity, including entire Neanderthal centromeres.

genomics↗

bioRxiv2017

Genome Graphs

There is increasing recognition that a single, monoploid reference genome is a poor universal reference structure for human genetics, because it represents only a tiny fraction of human variation. Adding this missing variation results in a structure that can be described as a mathematical graph: a genome graph. We demonstrate that, in comparison to the existing reference genome (GRCh38), genome graphs can substantially improve the fractions of reads that map uniquely and perfectly. Furthermore, we show that this fundamental simplification of read mapping transforms the variant calling problem from one in which many non-reference variants must be discovered de-novo to one in which the vast majority of variants are simply re-identified within the graph. Using standard benchmarks as well as a novel reference-free evaluation, we show that a simplistic variant calling procedure on a genome graph can already call variants at least as well as, and in many cases better than, a state-of-the-art method on the linear human reference genome. We anticipate that graph-based references will supplant linear references in humans and in other applications where cohorts of sequenced individuals are available.

bioinformatics↗