Whole-genome resequencing analyses of five pig breeds, including Korean wild and native, and three European origin breeds

Choi, Jung‐Woo; Chung, Won‐Hyong; Lee, Kyung-Tai; Cho, Eun Seok; Lee, Si-Woo; Choi, Bong-Hwan; Lee, Sang‐Heon; Lim, Won-Jun; Lim, Dajeong; Lee, Yun-Gyeong; Hong, Joon-Ki; Kim, Doo-Wan; Jeon, Hyeon-Jeong; Kim, Jiwoong; Kim, Namshin; Kim, Tae-Hun

doi:10.1093/dnares/dsv011

Cited by 53 publications

(49 citation statements)

References 40 publications

(40 reference statements)

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“…To test whether the recovered genes missing in the reference genome were under selection, we retrieved ∼365.55 Gb orphan reads against the reference genome from 117 publicly available pig genomes (Ai et al 2015;Choi et al 2015;Moon et al 2015) and aligned them to the intact scaffolds harboring missing genes across the 10 breed assemblies (∼636.38 Mb per assembly) (Supplemental Fig. S41).…”

Section: Detecting Coding Snps In Missing Genes Under Selectionmentioning

confidence: 99%

“…S42). The remaining 70 individuals (including 10 Korean wild boars and 60 European/North American domestic pigs) with intermediate coverage (15.87× of the reference genome, 6.99× of missing gene embedded scaffolds by 2.60 Gb orphan reads per individual) (Choi et al 2015;Moon et al 2015) were used to investigate the patterns of selected loci (Supplemental Fig. S41).…”

Section: Detecting Coding Snps In Missing Genes Under Selectionmentioning

confidence: 99%

“…There are over 730 distinct pig breeds worldwide, of which two thirds reside in Europe and China (Chen et al 2007), whose diverse phenotypes are shaped by the combined effects of local adaptation and artificial selection (Ai et al 2015). Efforts have been made to characterize the genetic variation that underlies this phenotypic diversity using resequencing data and the genome of the European domestic Duroc pig as a reference Rubin et al 2012;Choi et al 2015;Moon et al 2015). Nonetheless, resequencing is limiting in terms of capturing genetic variation and assessing gaps and misassigned regions of the reference genome (Weisenfeld et al 2014).…”

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confidence: 99%

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Comprehensive variation discovery and recovery of missing sequence in the pig genome using multiple de novo assemblies

et al. 2016

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Uncovering genetic variation through resequencing is limited by the fact that only sequences with similarity to the reference genome are examined. Reference genomes are often incomplete and cannot represent the full range of genetic diversity as a result of geographical divergence and independent demographic events. To more comprehensively characterize genetic variation of pigs (Sus scrofa), we generated de novo assemblies of nine geographically and phenotypically representative pigs from Eurasia. By comparing them to the reference pig assembly, we uncovered a substantial number of novel SNPs and structural variants, as well as 137.02-Mb sequences harboring 1737 protein-coding genes that were absent in the reference assembly, revealing variants left by selection. Our results illustrate the power of whole-genome de novo sequencing relative to resequencing and provide valuable genetic resources that enable effective use of pigs in both agricultural production and biomedical research.

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Section: Detecting Coding Snps In Missing Genes Under Selectionmentioning

confidence: 99%

Section: Detecting Coding Snps In Missing Genes Under Selectionmentioning

confidence: 99%

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confidence: 99%

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Comprehensive variation discovery and recovery of missing sequence in the pig genome using multiple de novo assemblies

et al. 2016

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“…We downloaded 107 public pig sequences from the short-read archive, http://www.ncbi.nlm.nih.gov/sra (Groenen et al 2012;Molnár et al 2014;Ai et al 2015;Bianco et al 2015a;Choi et al 2015), pertaining to 32 Large White (LW), 19 Landrace (LR), 14 Pietrain (PI), 27 Duroc (DU), and 15 Meishan (MS) pigs. Alignment was carried out with BWA (Li and Durbin 2009), PCR duplicates were removed with samtools, and bam files were then realigned around indels with GATK IndelRealigner tool (McKenna et al 2010).…”

Section: Sequence Data Analysismentioning

confidence: 99%

Evaluating Sequence-Based Genomic Prediction with an Efficient New Simulator

et al. 2017

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The vast amount of sequence data generated to analyze complex traits is posing new challenges in terms of the analysis and interpretation of the results. Although simulation is a fundamental tool to investigate the reliability of genomic analyses and to optimize experimental design, existing software cannot realistically simulate complete genomes. To remedy this, we have developed a new strategy (Sequence-Based Virtual Breeding, SBVB) that uses real sequence data and simulates new offspring genomes and phenotypes in a very efficient and flexible manner. Using this tool, we studied the efficiency of full sequence in genomic prediction compared to SNP arrays. We used real porcine sequences from three breeds as founder genomes of a 2500-animal pedigree and two genetic architectures: "neutral" and "selective." In the neutral architecture, frequencies and allele effects were sampled independently whereas, in the selective case, SNPs were sites putatively under selection after domestication and a negative correlation between effect and frequency was induced. We compared the effectiveness of different genotyping strategies for genomic selection, including the use of full sequence commercial arrays or randomly chosen SNP sets in both outbred and crossbred experimental designs. We found that accuracy increases using sequence instead of commercial chips but modestly, perhaps by # 4%. This result was robust to extreme genetic architectures. We conclude that full sequence is unlikely to offset commercial arrays for predicting genetic value when the number of loci is relatively large and the prior given to each SNP is uniform. Using sequence to improve selection thus requires optimized prior information and, likely, increased population sizes. The code and manual for SBVB are available at https://github.com/mperezenciso/sbvb0. KEYWORDS complex trait; genomic selection; sequence; forward simulation; pig; GenPred; shared data resource A SCERTAINING the genetic basis of complex traits has been a goal of geneticists for decades; however, this endeavor is proving to be more difficult to attain than anticipated, even with current massive data sets. Nevertheless, molecular information can still be used for genetic prediction. Genomic selection (GS) relies on linkage disequilibrium (LD) between markers and the causal mutations, without the need to identify them (Meuwissen et al. 2001). So far, GS and genome-wide association studies (GWAS) have been mainly performed with manufactured genotyping array SNPs, but the current genomics status quo is being challenged by the dramatic improvement in sequencing technologies. Arraybased experimental designs are now being superseded by analyses of sequence data at population scale. Similarly, for agriculture, the drop in sequencing costs makes it conceivable that GS programs can routinely employ genome sequencing instead of genotyping arrays in the near future.Sequence data contains all the information needed (i.e., the causal variants) to make the most accurate prediction of genetic m...

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“…We collected SNPs from 287 pigs, and 6 related species, published in several studies (8, 13, 14, 20, 21) or generated by our laboratory. These samples originated mainly from Eurasia and represent both domestication locations of the pig (22).…”

Section: Introductionmentioning

confidence: 99%

PigVar: a database of pig variations and positive selection signatures

Zhou

Otecko

et al. 2017

Database

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Pigs are excellent large-animal models for medical research and a promising organ donor source for transplant patients. Next-generation sequencing technology has yielded a dramatic increase in the volume of genomic data for pigs. However, the limited amount of variation data provided by dbSNP, and non-congruent criteria used for calling variation, present considerable hindrances to the utility of this data. We used a uniform pipeline, based on GATK, to identify non-redundant, high-quality, whole-genome SNPs from 280 pigs and 6 outgroup species. A total of 64.6 million SNPs were identified in 280 pigs and 36.8 million in the outgroups. We then used LUMPY to identify a total of 7 236 813 structural variations (SVs) in 211 pigs. Positively selected loci were identified through five statistical tests of different evolutionary attributes of the SNPs. Combining the non-redundant variations and the evolutionary selective scores, we built the first pig-specific variation database, PigVar (http://www.ibiomedical.net/pigvar/), which is a web-based open-access resource. PigVar collects parameters of the variations including summary lists of the locations of the variations within protein-coding and long intergenic non-coding RNA (lincRNA) genes, whether the SNPs are synonymous or non-synonymous, their ancestral and derived states, geographic sampling locations, as well as breed information. The PigVar database will be kept operational and updated to facilitate medical research using the pig as model and agricultural research including pig breeding. Database URL: http://www.ibiomedical.net/pigvar/

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Whole-genome resequencing analyses of five pig breeds, including Korean wild and native, and three European origin breeds

Cited by 53 publications

References 40 publications

Comprehensive variation discovery and recovery of missing sequence in the pig genome using multiple de novo assemblies

Comprehensive variation discovery and recovery of missing sequence in the pig genome using multiple de novo assemblies

Evaluating Sequence-Based Genomic Prediction with an Efficient New Simulator

PigVar: a database of pig variations and positive selection signatures

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