Whole-genome sequence data uncover loss of genetic diversity due to selection

Abstract

Background: Whole-genome sequence (WGS) data give access to more complete structural genetic information of individuals, including rare variants, not fully covered by single nucleotide polymorphism chips. We used WGS to investigate the amount of genetic diversity remaining after selection using optimal contribution (OC), considering different methods to estimate the relationships used in OC. OC was applied to minimise average relatedness of the selection candidates and thus miminise the loss of genetic diversity in a conservation strategy, e.g. for establishment of gene bank collections. Furthermore, OC was used to maximise average genetic merit of the selection candidates at a given level of relatedness, similar to a genetic improvement strategy. In this study, we used data from 277 bulls from the 1000 bull genomes project. We measured genetic diversity as the number of variants still segregating after selection using WGS data, and compared strategies that targeted conservation of rare (minor allele frequency <5 %) versus common variants.

Results: When OC without restriction on the number of selected individuals was applied, loss of variants was minimal and most individuals were selected, which is often unfeasible in practice. When 20 individuals were selected, the number of segregating rare variants was reduced by 29 % for the conservation strategy, and by 34 % for the genetic improvement strategy. The overall number of segregating variants was reduced by 30 % when OC was restricted to selecting five individuals, for both conservation and genetic improvement strategies. For common variants, this loss was about 15 %, while it was much higher, 72 %, for rare variants. Fewer rare variants were conserved with the genetic improvement strategy compared to the conservation strategy.

Conclusions: The use of WGS for genetic diversity quantification revealed that selection results in considerable losses of genetic diversity for rare variants. Using WGS instead of SNP chip data to estimate relationships slightly reduced the loss of rare variants, while using 50 K SNP chip data was sufficient to conserve common variants. The loss of rare variants could be mitigated by a few percent (up to 8 %) depending on which method is chosen to estimate relationships from WGS data.

Fig. 1

Segregating variants after selection for conservation (cons). Relationships are computed based on pedigree, 50 K SNP chip (SNP) or whole-genome sequence (WGS), using either the method described by Yang et al. [24] or the similarity-based method. The first histogram is for all variants (MAF ≥ 1 %), the second is for common variants (MAF ≥ 5 %) and the last is for rare variants (MAF between 1 and 5 %). In each histogram, the first block corresponds to the case without constraint on the number of selected individuals, the second, third and fourth histograms correspond to the cases that constrain the number of selected individuals to 20, 10 and 5, respectively

Fig. 2

Segregating variants after selection for genetic improvement and conservation (impcons). Relationships are computed based on pedigree, 50 K SNP chip (SNP) or whole-genome sequence (WGS), using either the method described by Yang et al. [24] or the similarity-based method. The first histogram is for all variants (MAF ≥ 1 %), the second for common variants (MAF ≥ 5 %) and the last for rare variants (MAF between 1 and 5 %). In each histogram, the first block corresponds to the case without constraint on the number of selected individuals, the second, third and fourth histograms correspond to the cases that constrain the number of selected individuals to 20, 10 and 5, respectively

Fig. 3

Average genetic merit after selection for conservation (cons), and genetic improvement and conservation (impcons) strategies. The dark blue symbol and line represent the full pedigree, the beige represent the scenario for which Yang’s estimated relationships from SNP chip data are used, the orange represent the scenario for which similarity-based estimated relationships from SNP chip are used, the dark beige represent the scenario Yang’s estimated relationships from WGS data are used, finally, the brown represent the scenario for which similarity-based estimated relationships from WGS data are used. The first plot represents the evolution of average genetic merit when the constraint on the number of selected individuals goes from none to 20, 10 and 5 in the strategy cons, with minimised $\mathrm{\Delta }F$, the second plot represents the evolution of average genetic merit when the constraint on the number of selected individuals goes from none to 20, 10 and 5 in the strategy impcons, with $\mathrm{\Delta }F$ fixed to 0.01

Fig. 4

Evolution of the average relationship of the selected group for conservation (cons) strategy. Each plot represents the evolution of average relationship in the group of selected individuals in the cons strategy. The plots on the first row correspond to the use of Yang’s estimated relationships from SNP and WGS data, respectively and the plots on the second row to the use of similarity-based estimated relationships from SNP and WGS data, respectively