Whole genome sequences of 297 Duolang sheep for litter size

Abstract

Litter size is a critical economic trait in the sheep industry. Like many other breeds, Duolang sheep typically produce one lamb per ewe per lambing. To date, genetic studies of this trait in that breed have largely relied on candidate gene approaches. To expand the genomic resources for this breed, we sequenced 297 genomes, generating approximately 10.52 trillion bases with an average coverage of 13.35X. High-quality alignments with a mapping rate exceeding 99% enabled the identification of 43,968,128 SNPs and 6,504,047 InDels. This dataset provides a valuable resource for identifying genetic variants associated with litter size through genome-wide association studies (GWAS) and lays the foundation for future genetic improvement efforts through genomic selection in Duolang sheep. Beyond trait mapping, the dataset also supports broader applications, including analyses of genetic diversity, phylogenetic relationships, population history, adaptive introgression, and breed-specific characteristics. Additionally, the moderate-coverage WGS data are suitable for structural variant (SV) detection and downstream analyses such as association mapping and the identification of SVs underlying phenotypic traits.

Fig. 1

Overview of the experimental and analytical workflow. This figure illustrates the full pipeline used in the study, including sample collection, whole-genome sequencing, read trimming, variant calling, and filtering steps. Sequencing reads were processed following standard GATK best practices for variant discovery, with the exception of applying more stringent filtering parameters in the VariantFiltration module to reduce false positives. This workflow ensures high-quality variant calls and provides a reproducible framework for downstream genomic analyses.

Fig. 2

Quality assessment of sequencing reads across 302 Duolang sheep. This figure presents density plots summarizing key quality metrics of the sequencing data. (a) Total number of bases generated per individual; (b) Total number of reads per individual; (c) GC content distribution across individuals; (d) Proportion of reads with a mean base quality score >20; and (e) Proportion of reads with a mean base quality score >30. These results demonstrate overall high sequencing quality and consistency across samples, supporting the reliability of downstream variant detection and analysis.

Fig. 3

Alignment quality metrics across 297 Duolang ewes. This figure shows density plots summarizing key alignment statistics following read mapping to the reference genome. (a) Mapping ratio, indicating the proportion of reads successfully aligned to the genome; (b) Mean sequencing depth per individual, reflecting the average number of reads covering each genomic position; and (c) Average mapping quality score, representing the confidence in alignment accuracy. The consistently high mapping metrics across samples demonstrate reliable data quality suitable for downstream variant analysis.

Fig. 4

Functional annotation and chromosomal distribution of detected variants. (a) Functional classification of SNPs and InDels based on gene structure, showing the proportion of variants located in intergenic, intronic, exonic, and other genomic regions. (b) Density plot illustrating the genomic distribution of variants across autosomes and the X chromosome. The variants are generally evenly distributed, with minor deviations observed on specific chromosomes, consistent with known genomic features in sheep.