Optimal sequencing depth design for whole genome re-sequencing in pigs

• Published in: BMC bioinformatics Vol. 20; no. 1; pp. 556 - 12
• Main Authors: Jiang, Yifan, Jiang, Yao, Wang, Sheng, Zhang, Qin, Ding, Xiangdong
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.11.2019; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Animals; Base Sequence; Budgets; DNA sequencing; Genetics, Population; Genome coverage; Genomes; Genomics; High-Throughput Nucleotide Sequencing - methods; Pig; Polymorphism, Single Nucleotide - genetics; Principal Component Analysis; Reproducibility of Results; Sequencing depth; Swine; Swine - genetics; Whole Genome Sequencing - methods; Whole-genome sequencing; Iran; Whole-genome sequencing; Sequencing depth; Genome coverage; Pig
• DOI: 10.1186/s12859-019-3164-z
• ISSN: 1471-2105

As whole-genome sequencing is becoming a routine technique, it is important to identify a cost-effective depth of sequencing for such studies. However, the relationship between sequencing depth and biological results from the aspects of whole-genome coverage, variant discovery power and the quality of variants is unclear, especially in pigs. We sequenced the genomes of three Yorkshire boars at an approximately 20X depth on the Illumina HiSeq X Ten platform and downloaded whole-genome sequencing data for three Duroc and three Landrace pigs with an approximately 20X depth for each individual. Then, we downsampled the deep genome data by extracting twelve different proportions of 0.05, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 and 0.9 paired reads from the original bam files to mimic the sequence data of the same individuals at sequencing depths of 1.09X, 2.18X, 3.26X, 4.35X, 6.53X, 8.70X, 10.88X, 13.05X, 15.22X, 17.40X, 19.57X and 21.75X to evaluate the influence of genome coverage, the variant discovery rate and genotyping accuracy as a function of sequencing depth. In addition, SNP chip data for Yorkshire pigs were used as a validation for the comparison of single-sample calling and multisample calling algorithms. Our results indicated that 10X is an ideal practical depth for achieving plateau coverage and discovering accurate variants, which achieved greater than 99% genome coverage. The number of false-positive variants was increased dramatically at a depth of less than 4X, which covered 95% of the whole genome. In addition, the comparison of multi- and single-sample calling showed that multisample calling was more sensitive than single-sample calling, especially at lower depths. The number of variants discovered under multisample calling was 13-fold and 2-fold higher than that under single-sample calling at 1X and 22X, respectively. A large difference was observed when the depth was less than 4.38X. However, more false-positive variants were detected under multisample calling. Our research will inform important study design decisions regarding whole-genome sequencing depth. Our results will be helpful for choosing the appropriate depth to achieve the same power for studies performed under limited budgets.