Abstract
Selection on complex traits can rapidly drive evolution, especially in stressful environments. This polygenic selection does not leave intense sweep signatures on the genome, rather many loci experience small allele frequency shifts, resulting in large cumulative phenotypic changes. Directional selection and local adaptation are changing populations; but, identifying loci underlying polygenic or environmental selection has been difficult. We use genomic data on tens of thousands of cattle from three populations, distributed over time and landscapes, in linear mixed models with novel dependent variables to map signatures of selection on complex traits and local adaptation. We identify 207 genomic loci associated with an animal’s birth date, representing ongoing selection for monogenic and polygenic traits. Additionally, hundreds of additional loci are associated with continuous and discrete environments, providing evidence for historical local adaptation. These candidate loci highlight the nervous system’s possible role in local adaptation. While advanced technologies have increased the rate of directional selection in cattle, it has likely been at the expense of historically generated local adaptation, which is especially problematic in changing climates. When applied to large, diverse cattle datasets, these selection mapping methods provide an insight into how selection on complex traits continually shapes the genome. Further, understanding the genomic loci implicated in adaptation may help us breed more adapted and efficient cattle, and begin to understand the basis for mammalian adaptation, especially in changing climates. These selection mapping approaches help clarify selective forces and loci in evolutionary, model, and agricultural contexts.
Highlights
As climate changes, organisms either migrate, rapidly adapt, or perish
Across all 36 scenarios of random selection, with drift acting as the only force by which alleles could change in frequency, we detected an average of 0.15 Generation Proxy Selection Mapping (GPSM) false positive single nucleotide polymorphism (SNP) (q-value < 0.1) per replicate
The average number of false positive SNPs detected per replicate ranged from 0.0 to 0.45 across all scenarios, accounting for, at most, 1 false positive GPSM SNP per 100,000 tests
Summary
The genes and alleles that underlie adaptation have been difficult to identify, except for a handful of large-effect variants that underwent selective sweeps [1]. Many selection mapping methods rely on allele frequency differences between diverged or artificially defined populations (e.g. FST, FLK, XP-CLR) [4,5,6], making the detection of selection within a largely panmictic population difficult. Others rely on detecting the disruption of normal LD patterns (iHS, EHH, ROH, etc.) [7,8,9] In cattle, these methods have successfully identified genomic regions under selection that control Mendelian and simple traits like coat color, the absence of horns, or large-effect genes involved in domestication [10,11,12,13,14,15]. Millions of North American Bos taurus beef cattle have been exposed to strong artificial and environmental selection for more than 50 years (~10 generations) [17], making them a powerful model for studying the impacts selection has on genomes over short time periods and across diverse environments
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