Detection of single nucleotide polymorphisms associated with litter size in goats using genotyping-by-sequencing and association analysis.

Abstract

Objective: Improving fertility is a key goal in goat production. This study aimed to detect single nucleotide polymorphisms (SNPs) associated with female goat reproductive performance for use in selection processes.

Methods: Nine reproductive traits were evaluated, including litter size and age at the first, second, and third parities, as well as intervals between parities, in 31 female goats (2 purebred and 29 crossbred goats in various combinations of seven breeds). DNA was extracted from blood, and SNP data were obtained using the genotyping by sequencing (GBS) method. After filtering for allele depth and missing genotype data, the retained SNPs were subjected to population structure analysis and association analysis with the nine traits. For the association analysis, SNPs with false discovery rates ≤ 0.05 were considered significant. PCR allele competitive extension (PACE) genotyping assay was applied to develop genetic markers.

Results: An average of 304,852 SNPs were initially detected in the 31 female goats. After filtering, 21,665 SNPs were retained. The first two principal components obtained from individual genotypes classified the 31 goats into three clusters. In the association analysis, six SNPs on four chromosomes were significantly associated with the litter size at first parity. The most significant SNP was detected on chromosome 4, and three genes-IKAROS family zinc finger 1 (IKZF1), fidgetin-like 1 (FIGNL1), and dopa decarboxylase (DDC)-were found within 100 kb downstream and upstream of the SNP. The PACE genotyping assay confirmed genotypes at this SNP with a 96% concordance rate.

Conclusion: SNPs significantly associated with litter size at first parity, candidate genes, and the PACE genotyping methods applied in this study can be used for selecting female goats in future genetic improvement programs. However, further study on the frequency of genetic mutation with a larger sample size and functional studies of the candidate genes are required.