Cretu Stancu, M. | bioRxiv Science

bioRxiv2018

No evidence that mate choice in humans is dependent on the MHC

A long-standing hypothesis in biology proposes that various species select mates with a major histocompatibility complex (MHC) composition divergent from their own, so as to improve immune response in offspring. However, human and animal studies investigating this mate selection hypothesis have returned inconsistent results. Here, we analyze 239 mate-pairs of Dutch ancestry, all with whole-genome sequence data collected by the Genome of the Netherlands project, to investigate whether mate selection in humans is MHC dependent. We find no evidence for MHC-mediated mate selection in this sample (with an average MHC genetic similarity in mate pairs (Qc) = 0.829; permutation-based p = 0.703). Limiting the analysis to only common variation or considering the extended MHC region does not change our findings (Qc = 0.671, p = 0.513; and Qc = 0.844, p = 0.696, respectively). We demonstrate that the MHC in mate-pairs is no more genetically dissimilar (on average) than a pair of two randomly selected individuals, and conclude that there is no evidence to suggest that mate choice is influenced by genetic variation in the MHC.\n\nAuthor summaryStudies within various animal species have shown that the genetic content of the major histocompatibility complex (MHC) can influence mate choice. Such mate selection would be advantageous, as mating between individuals with different alleles across MHC genes would produce offspring with a more diverse MHC and therefore possess improved immune response to various pathogens. Studies of the influence on the MHC in human mate selection have been far less conclusive. Two studies of MHC-dependent mate selection performed on SNP data collected as part of the HapMap Consortium returned conflicting results: the first study reported significantly different MHC variation between mate pairs, and the second report refuted this claim. Here, we analyze a dataset comprised of 239 whole-genome sequenced Dutch mate pairs, a sample set an order of magnitude larger than the HapMap data and containing denser characterization of genetic variation. We find no evidence that the MHC influences mate selection in our population, and we show that this finding is robust to potential confounding factors and the types and frequencies of genetic variants analysed.

genetics↗

bioRxiv2017

Mapping And Phasing Of Structural Variation In Patient Genomes Using Nanopore Sequencing

Structural genomic variants form a common type of genetic alteration underlying human genetic disease and phenotypic variation. Despite major improvements in genome sequencing technology and data analysis, the detection of structural variants still poses challenges, particularly when variants are of high complexity. Emerging long-read single-molecule sequencing technologies provide new opportunities for detection of structural variants. Here, we demonstrate sequencing of the genomes of two patients with congenital abnormalities using the ONT MinION at 11x and 16x mean coverage, respectively. We developed a bioinformatic pipeline - NanoSV - to efficiently map genomic structural variants (SVs) from the long-read data. We demonstrate that the nanopore data are superior to corresponding short-read data with regard to detection of de novo rearrangements originating from complex chromothripsis events in the patients. Additionally, genome-wide surveillance of SVs, revealed 3,253 (33%) novel variants that were missed in short-read data of the same sample, the majority of which are duplications < 200bp in size. Long sequencing reads enabled efficient phasing of genetic variations, allowing the construction of genome-wide maps of phased SVs and SNVs. We employed read-based phasing to show that all de novo chromothripsis breakpoints occurred on paternal chromosomes and we resolved the long-range structure of the chromothripsis. This work demonstrates the value of long-read sequencing for screening whole genomes of patients for complex structural variants.

genomics↗