Network Analysis of Linkage Disequilibrium Reveals Genome Architecture in Chum Salmon

Abstract Many studies exclude loci that exhibit linkage disequilibrium (LD); however, high LD can signal reduced recombination around genomic features such as chromosome inversions or sex-determining regions. Chromosome inversions and sex-determining regions are often involved in adaptation, allowin...

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Bibliographic Details
Published in:G3 : genes - genomes - genetics 2020-05, Vol.10 (5), p.1553-1561
Main Authors: McKinney, Garrett, McPhee, Megan V, Pascal, Carita, Seeb, James E, Seeb, Lisa W
Format: Article
Language:English
Subjects:
Animals
Chromosome Inversion
chum salmon
Female
Genomics
inversion
Investigations
Linkage Disequilibrium
Male
network analysis
Oncorhynchus keta
Polymorphism, Single Nucleotide
Sex Chromosomes
x-chromosome
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Summary:Abstract Many studies exclude loci that exhibit linkage disequilibrium (LD); however, high LD can signal reduced recombination around genomic features such as chromosome inversions or sex-determining regions. Chromosome inversions and sex-determining regions are often involved in adaptation, allowing for the inheritance of co-adapted gene complexes and for the resolution of sexually antagonistic selection through sex-specific partitioning of genetic variants. Genomic features such as these can escape detection when loci with LD are removed; in addition, failing to account for these features can introduce bias to analyses. We examined patterns of LD using network analysis to identify an overlapping chromosome inversion and sex-determining region in chum salmon. The signal of the inversion was strong enough to show up as false population substructure when the entire dataset was analyzed, while the effect of the sex-determining region on population structure was only obvious after restricting analysis to the sex chromosome. Understanding the extent and geographic distribution of inversions is now a critically important part of genetic analyses of natural populations. Our results highlight the importance of analyzing and understanding patterns of LD in genomic dataset and the perils of excluding or ignoring loci exhibiting LD. Blindly excluding loci in LD would have prevented detection of the sex-determining region and chromosome inversion while failing to understand the genomic features leading to high-LD could have resulted in false interpretations of population structure.
ISSN:2160-1836
2160-1836
DOI:10.1534/g3.119.400972