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. 2022 Apr 29;12(9):1155.
doi: 10.3390/ani12091155.

Weighted Single-Step Genome-Wide Association Study Uncovers Known and Novel Candidate Genomic Regions for Milk Production Traits and Somatic Cell Score in Valle del Belice Dairy Sheep

Hossein Mohammadi  1 , Amir Hossein Khaltabadi Farahani  1 , Mohammad Hossein Moradi  1 , Salvatore Mastrangelo  2 , Rosalia Di Gerlando  2 , Maria Teresa Sardina  2 , Maria Luisa Scatassa  3 , Baldassare Portolano  2 , Marco Tolone  2
Affiliations

Weighted Single-Step Genome-Wide Association Study Uncovers Known and Novel Candidate Genomic Regions for Milk Production Traits and Somatic Cell Score in Valle del Belice Dairy Sheep

Hossein Mohammadi et al. Animals (Basel). .

Abstract

The objective of this study was to uncover genomic regions explaining a substantial proportion of the genetic variance in milk production traits and somatic cell score in a Valle del Belice dairy sheep. Weighted single-step genome-wide association studies (WssGWAS) were conducted for milk yield (MY), fat yield (FY), fat percentage (FAT%), protein yield (PY), protein percentage (PROT%), and somatic cell score (SCS). In addition, our aim was also to identify candidate genes within genomic regions that explained the highest proportions of genetic variance. Overall, the full pedigree consists of 5534 animals, of which 1813 ewes had milk data (15,008 records), and 481 ewes were genotyped with a 50 K single nucleotide polymorphism (SNP) array. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. The results showed that top ranked genomic windows (1 Mb windows) explained 3.49, 4.04, 5.37, 4.09, 3.80, and 5.24% of the genetic variances for MY, FY, FAT%, PY, PROT%, and total SCS, respectively. Among the candidate genes found, some known associations were confirmed, while several novel candidate genes were also revealed, including PPARGC1A, LYPLA1, LEP, and MYH9 for MY; CACNA1C, PTPN1, ROBO2, CHRM3, and ERCC6 for FY and FAT%; PCSK5 and ANGPT1 for PY and PROT%; and IL26, IFNG, PEX26, NEGR1, LAP3, and MED28 for SCS. These findings increase our understanding of the genetic architecture of six examined traits and provide guidance for subsequent genetic improvement through genome selection.

Keywords: candidate genes; dairy sheep; milk fat; milk protein; somatic cell scores; window regions.

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Conflict of interest statement

The authors declare that they do not have any conflict of interest.

Figures

Figure 1
Figure 1
Manhattan plots of the additive genetic variance (%) explained by 1.0 Mb window of adjacent SNPs for milk production traits and SCS: (A) milk yield, (B) fat yield, (C) protein yield, (D) fat percentage, (E) protein percentage, and (F) SCS. Each dot represents a window.
Figure 1
Figure 1
Manhattan plots of the additive genetic variance (%) explained by 1.0 Mb window of adjacent SNPs for milk production traits and SCS: (A) milk yield, (B) fat yield, (C) protein yield, (D) fat percentage, (E) protein percentage, and (F) SCS. Each dot represents a window.
Figure 1
Figure 1
Manhattan plots of the additive genetic variance (%) explained by 1.0 Mb window of adjacent SNPs for milk production traits and SCS: (A) milk yield, (B) fat yield, (C) protein yield, (D) fat percentage, (E) protein percentage, and (F) SCS. Each dot represents a window.
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