Selection Signature Analysis of Whole-Genome Sequences to Identify Genome Differences Between Selected and Unselected Holstein Cattle

Abstract

A unique line of Holstein cattle has been maintained without selection in Minnesota since 1964. After many generations, unselected cattle produce less milk, but have better reproductive performance and health traits when compared with contemporary cows. Comparisons between this line of unselected Holstein and those under selection provide useful insights that connect selection and complex traits in cattle. Utilizing these unique resources and sequence data, we sought to identify genome changes due to selection. We sequenced 30 unselected and 54 selected Holstein cattle and compared their sequence variants to identify selection signatures. After many years, the two populations showed completely different patterns in their genome-level population structures and linkage disequilibrium. By integrating signals from five different detection methods, we detected consensus selection signatures from at least four methods covering 14,533 SNPs and 155 protein-coding genes. An integrated analysis of selection signatures with gene annotation, pathways, and the cattle QTL database demonstrated that the genomic regions under selection are related to milk productivity, health, and reproductive efficiency. The polygenic nature of these complex traits is evident from hundreds of selection signatures and candidate genes, suggesting that long-term artificial selection has acted on the whole genome rather than a few major genes. In summary, our study identified candidate selection signatures underlying phenotypic differences between unselected and selected Holstein cows and revealed insights into the genetic basis of complex traits in cattle.

Keywords: Holstein cattle; genome sequence; selection; selection signature.

Figure 1

Overview of study design and analysis.

Figure 2

Population LD and structure of two groups of Holstein cattle. (A) Genome-wide LD decay estimated from each population, with x indicating the distance between SNPs and the y-axis representing the squared correlation (r²) between pairs of SNPs. (B) Principal component analysis of two cattle groups, with x and y axes representing principal components 1 and 2, respectively.

Figure 3

Manhattan plots of genome-wide selection signatures by five different methods. (A) Genome-wide distribution of Fst windows. (B) Genome-wide distribution of -ln(Pi_ratio) windows. (C) Genome-wide distribution of XP-CLR windows. (D) Genome-wide distribution of iHS values. (E) Genome-wide distribution of XP-EHH values. Black dashed lines represent the threshold of the top 5% values, and windows above the black lines were considered candidate regions of selection signatures.

Figure 4

Identification of consensus selection signatures from four or more methods. (A) Venn diagram of candidate regions shared by five methods. (B) Number of candidate regions shared by four or five methods.

Figure 5

Gene and functional enrichment analysis of candidate selection signatures. (A) GO term enrichment for genes within candidate selection signatures. (B) Functional region proportion in overlapped genes. (C) KEGG term enrichment for genes within candidate regions.

Figure 6

Proportional distribution of QTLs annotated from selection signatures across trait categories. (A) Percentage of QTLs for different trait categories. (B) Enrichment analysis results of QTLs in milk production traits. (C) Enrichment analysis results of QTLs in reproduction traits.

Figure 7

Top 20 enriched traits for QTLs near selection signature SNPs. Richness factors were obtained by calculating the ratio of the number of QTLs annotated in the candidate regions and the total number of QTLs.