Nature CommunicationsGeneticsPeer-reviewed journal article
Acquisition of ampliconic sequences marks a selfish mouse t-haplotype
Callie M. SwanepoeliD, Gaojianyong WangiD, Lucy ZhangiD, Björn BrändliD, Hermann BaueriD, Pavel A. TsaytleriD, Frank Ulrich MülleriD, Bernhard G. HerrmanniD, Jacob L. MuelleriD
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Abstract
Mendelian genetics posits equal transmission of alleles, but selfish alleles can bias the transmission of large genomic regions or entire chromosomes1-4. One long-standing question is how transmission bias evolves to encompass large genomic regions. Mus musculus (house mouse) t-haplotypes exhibit up to 99% transmission bias from heterozygous males5-14 and harbor selfish alleles9-14 genetically linked to large inversions spanning the proximal half of chromosome 1715-20. Here, by generating a high-quality, single-haplotype assembly of a t- haplotype, we reveal the evolution of eight large amplicons with known11,13,21 and candidate selfish alleles as a distinct genetic feature. Three amplicons are conserved in closely related Mus species, and two have known selfish alleles in the oldest inversion, implicating amplicons and an inversion drove the origins of a selfish chromosome 17 [~]3MYA. The remaining t- haplotype amplicons harbor gene families expressed predominantly in haploid spermatids, newly acquired retrogenes, and the most differentially expressed genes in wild-type/t-haplotype spermatids. Targeted deletion of a [~]1.8Mb amplicon with candidate selfish alleles on the t- haplotype reduces selfish transmission in heterozygous males by 3%. Notably, the evolution of selfish allele-containing amplicons and inversions on the t-haplotype parallels mammalian sex chromosome evolution as signatures of selfish transmission. We propose amplicon acquisition and large inversions initiate evolutionary arms races between selfish haplotypes and serve as genome-wide signatures of selfish transmission.
- organism: mouse
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: differential expression
- approach: statistical model
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