Integrating SNP data to reveal the adaptive selection features of goat populations in extreme environments

Abstract

The frequent occurrence of extreme climate events globally has elevated the requirements for environmental adaptability in livestock and poultry. Some goat populations have shown strong adaptability in specific extreme environments, and their genomes often leave genetic traces of adaptive evolution. This study integrated global goat single nucleotide polymorphism (SNP) chip data and raster data of 11 environmental variables. We retained 162 native goat populations and analyzed the environmental data of their regions. We detected 23 candidate genes related to environmental adaptation using selection signal analysis and genome-environment association analysis. After that, we screened out goat populations in extreme environments based on environmental data. Then, we used three selection signal analysis methods (F_ST, XPEHH and θπ methods) to detect the genomes of these goat populations. In four different extreme environments (high elevation, hot, cold, and arid), 91, 43, 21, and 115 candidate genes were identified, respectively. Combined with studies related to environmental adaptation, we found that genes such as GULP1, GPC5, GPC6, and PDE4D may play important roles in the adaptation of goats to extreme environments. This study provides new insights into the adaptive mechanism of goats in extreme environments and provides an important theoretical basis for goat breed improvement and stress resistance breeding. At the same time, these findings also provide a reference for the study of the adaptability of other livestock in extreme environments.

Keywords: Extreme environment; Genetic-environmental association; Goat; SNP; Selection signal.

Fig. 1

Geographic distribution of 162 goat populations worldwide. The color of the dots indicates the PCA clustering grouping (CL1 and CL2) of the environmental variables to which the population belongs

Fig. 2

Geographic distribution of annual average values for 9 environmental variables (A) Cloud Cover (cld), percentage (%); (B) Diurnal Temperature Range (dtr), degrees Celsius (°C); (C) Elevation (elev), meters (m); (D) Frost Day Frequency (frs), days per month; (E) Potential Evapotranspiration (pet), millimeters per day (mm/day); (F) Precipitation (pre), millimeters per month (mm/month); (G) Average Temperature (tavg), degrees Celsius (°C); (H) Vapor Pressure (vap), hectopascals (hPa); (I) Wet Day Frequency (wet), days per month

Fig. 3

PCA analysis based on environmental variables and population genetic structure analysis (A) PCA distribution of 162 goat populations based on environmental variables. The color of the points represents the grouping of populations (CL1 and CL2), and the shape indicates the geographic region of the populations, including Africa (AF), Asia (AS), Europe (EU), Latin America (LA), North America (NAm), and Oceania (OC); (B) PCA distribution of 4096 individuals from 162 goat populations based on SNP data; (C) NJ tree of 4096 individuals from 162 goat populations. The color of the outer circle indicates the geographical region of the individual; the color of the inner branch indicates the group to which the individual belongs (CL1 and CL2); (D) Admixture analysis of 162 goat populations (K = 5)

Fig. 4

Detection of candidate genomic regions associated with environmental adaptation (A) Genome-wide distribution of F_ST values between CL1 and CL2; (B) Genome-wide distribution of z-scores of SNP loci in the LFMM test. The dashed line indicates the top 1% threshold level; (C) Overlapped genes identified from the F_ST, LFMM and Samβada approaches in CL1 and CL2; the number in parenthesis shows the number of overlapping genes expected by chance; (D) Functional enrichment analysis of 198 genes identified using at least two methods

Fig. 5

Detection of genomic regions associated with high-elevation adaptation. (A) Genome-wide distribution of F_ST values in goats from high elevations (G-high) and goats from low elevations (G-low). The dashed line in the figure represents the top 1% threshold level. The figure shows the genes covered within the 50 kb interval upstream and downstream of the first three significant sites; (B) The θπ value of SNPs between G-high and G-low populations in gene regions with strong selective sweep signals

Fig. 6

Selection sweep analysis of goat populations in extreme temperature areas (A) F_ST values of SNPs in gene regions with strong selective scan signals in the G-hot goat genome; (B) Genome-wide distribution of XPEHH scores in G-hot and G-cold goat populations. Positive values indicate the selection signal in the G-hot population, negative values represent the selection signal in the G-cold population, and the dashed line represents the top 0.5% threshold level. The genes covered within the 50 kb interval upstream and downstream of the first three significant sites are shown on both sides of the figure; (C) F_ST values of SNPs in gene regions with strong selective scan signals in the G-cold goat genome

Fig. 7

Detection of genomic regions associated with arid adaptation (A) Global classification of arid and humid regions based on the AI_UNEP. AI_UNEP ranges from 0-0.05 for hyper-arid, 0.05–0.20 for arid, 0.20–0.50 for semi-arid, 0.50–0.65 for dry sub-humid, and above 0.65 for humid; (B) Genome-wide distribution of F_ST values in G-arid and G-humid goat populations. The dashed line in the figure indicates the top 1% threshold level; (C) The θπ value of SNPs between G-arid and G-humid populations in gene regions with strong selective scan signals