149 Integration of Selection Signatures Analyses and Weighted Single-step GWAS to Prioritize Candidate Genes for Body Conformation Traits in Pigs

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 76-77
Author(s):  
Seyed Milad Vahedi ◽  
Siavash Salek Ardestani ◽  
Duy Ngoc Do ◽  
Karim Karimi ◽  
Younes Miar
Keyword(s):  
Candidate Genes ◽  
Complex Traits ◽  
Body Height ◽  
Single Step ◽  
Fixation Index ◽  
Nucleotide Polymorphisms ◽  
Selection Signatures ◽  
Body Conformation ◽  
Yorkshire Pigs ◽  
Estimated Breeding Values

Abstract Body conformation traits such as body height (BH) and body length (BL) have been included in the swine industry’s selection criteria. The objective of this study was to identify the quantitative trait loci (QTLs) and candidate genes for pig conformation traits using an integration of selection signatures analyses and weighted single-step GWAS (WssGWAS). Body measurement records of 5,593 Yorkshire pigs of which 598 animals were genotyped with Illumina 50K panel were used. Estimated breeding values (EBVs) for BH and BL were computed using univariate animal models. Genotyped animals were grouped into top 5% and bottom 5% based on their EBVs, and selection signatures analyses were performed using fixation index (Fst), FLK, hapFLK, and Rsb statistics, which were then combined as a Mahalanobis distance (Md) framework. The WssGWAS was conducted to detect the single nucleotide polymorphisms (SNPs) associated with the studied traits. The top 1% SNPs (n=530) from Md distribution that overlapped with the top 1% SNPs from WssGWAS (n = 530) were used to detect the candidate genes. A total of 31 and six overlapped SNPs were found to be associated with BH and BL, respectively. Several candidate genes were identified for BH (PARVA, DCDC1, SYT1, CASTOR2, RGSL1, RGS8, RBMS3, TGFBR2, and HS6ST1) and BL (SNTB1, AK7, PAPOLA, KSR1, CHODL, and BMP2), explaining 2.58% and 0.42% of the trait’s genetic variation, respectively. Our results indicated that integrating data from the signatures of selection tests with WssGWAS could help elucidate genomic regions underlying complex traits.

scholarly journals Identifying Selection Signatures for Backfat Thickness in Yorkshire Pigs Highlights New Regions Affecting Fat Metabolism

Genes ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 254 ◽  
Author(s):  
Haoran Ma ◽  
Saixian Zhang ◽  
Kaili Zhang ◽  
Huiwen Zhan ◽  
Xia Peng ◽  
...  
Keyword(s):  
Fat Metabolism ◽  
Backfat Thickness ◽  
Fixation Index ◽  
Nucleotide Polymorphisms ◽  
Selection Signatures ◽  
Extended Haplotype ◽  
Single Nucleotide ◽  
Fundamental Information ◽  
Yorkshire Pigs

Identifying the genetic basis of improvement in pigs contributes to our understanding of the role of artificial selection in shaping the genome. Here we employed the Cross Population Extended Haplotype Homozogysity (XPEHH) and the Wright’s fixation index (FST) methods to detect trait-specific selection signatures by making phenotypic gradient differential population pairs, and then attempted to map functional genes of six backfat thickness traits in Yorkshire pigs. The results indicate that a total of 283 and 466 single nucleotide polymorphisms (SNPs) were identified as trait-specific selection signatures using FST and XPEHH, respectively. Functional annotation suggested that the genes overlapping with the trait-specific selection signatures such as OSBPL8, ASAH2, SMCO2, GBE1, and ABL1 are responsible for the phenotypes including fat metabolism, lean body mass and fat deposition, and transport in mouse. Overall, the study developed the methods of gene mapping on the basis of identification of selection signatures. The candidate genes putatively associated with backfat thickness traits can provide important references and fundamental information for future pig-breeding programs.

scholarly journals Indirect predictions with a large number of genotyped animals using the algorithm for proven and young

2020 ◽  
Vol 98 (6) ◽  
Author(s):  
Andre L S Garcia ◽  
Yutaka Masuda ◽  
Shogo Tsuruta ◽  
Stephen Miller ◽  
Ignacy Misztal ◽  
...  
Keyword(s):  
Random Sample ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Nucleotide Polymorphisms ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Large Numbers ◽  
Minimum Number ◽  
Best Linear Unbiased ◽  
Estimated Breeding Values

Abstract Reliable single-nucleotide polymorphisms (SNP) effects from genomic best linear unbiased prediction BLUP (GBLUP) and single-step GBLUP (ssGBLUP) are needed to calculate indirect predictions (IP) for young genotyped animals and animals not included in official evaluations. Obtaining reliable SNP effects and IP requires a minimum number of animals and when a large number of genotyped animals are available, the algorithm for proven and young (APY) may be needed. Thus, the objectives of this study were to evaluate IP with an increasingly larger number of genotyped animals and to determine the minimum number of animals needed to compute reliable SNP effects and IP. Genotypes and phenotypes for birth weight, weaning weight, and postweaning gain were provided by the American Angus Association. The number of animals with phenotypes was more than 3.8 million. Genotyped animals were assigned to three cumulative year-classes: born until 2013 (N = 114,937), born until 2014 (N = 183,847), and born until 2015 (N = 280,506). A three-trait model was fitted using the APY algorithm with 19,021 core animals under two scenarios: 1) core 2013 (random sample of animals born until 2013) used for all year-classes and 2) core 2014 (random sample of animals born until 2014) used for year-class 2014 and core 2015 (random sample of animals born until 2015) used for year-class 2015. GBLUP used phenotypes from genotyped animals only, whereas ssGBLUP used all available phenotypes. SNP effects were predicted using genomic estimated breeding values (GEBV) from either all genotyped animals or only core animals. The correlations between GEBV from GBLUP and IP obtained using SNP effects from core 2013 were ≥0.99 for animals born in 2013 but as low as 0.07 for animals born in 2014 and 2015. Conversely, the correlations between GEBV from ssGBLUP and IP were ≥0.99 for animals born in all years. IP predictive abilities computed with GEBV from ssGBLUP and SNP predictions based on only core animals were as high as those based on all genotyped animals. The correlations between GEBV and IP from ssGBLUP were ≥0.76, ≥0.90, and ≥0.98 when SNP effects were computed using 2k, 5k, and 15k core animals. Suitable IP based on GEBV from GBLUP can be obtained when SNP predictions are based on an appropriate number of core animals, but a considerable decline in IP accuracy can occur in subsequent years. Conversely, IP from ssGBLUP based on large numbers of phenotypes from non-genotyped animals have persistent accuracy over time.

scholarly journals Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242200
Author(s):  
Natalia Anatolievna Zinovieva ◽  
Arsen Vladimirovich Dotsev ◽  
Alexander Alexandrovich Sermyagin ◽  
Tatiana Evgenievna Deniskova ◽  
Alexandra Sergeevna Abdelmanova ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Candidate Genes ◽  
High Density ◽  
Fixation Index ◽  
Nucleotide Polymorphism ◽  
Selection Signatures ◽  
Single Nucleotide ◽  
Cattle Breeds ◽  
Native Cattle ◽  
Genomic Regions

Native cattle breeds can carry specific signatures of selection reflecting their adaptation to the local environmental conditions and response to the breeding strategy used. In this study, we comprehensively analysed high-density single nucleotide polymorphism (SNP) genotypes to characterise the population structure and detect the selection signatures in Russian native Yaroslavl and Kholmogor dairy cattle breeds, which have been little influenced by introgression with transboundary breeds. Fifty-six samples of pedigree-recorded purebred animals, originating from different breeding farms and representing different sire lines, of the two studied breeds were genotyped using a genome-wide bovine genotyping array (Bovine HD BeadChip). Three statistical analyses—calculation of fixation index (FST) for each SNP for the comparison of the pairs of breeds, hapFLK analysis, and estimation of the runs of homozygosity (ROH) islands shared in more than 50% of animals—were combined for detecting the selection signatures in the genome of the studied cattle breeds. We confirmed nine and six known regions under putative selection in the genomes of Yaroslavl and Kholmogor cattle, respectively; the flanking positions of most of these regions were elucidated. Only two of the selected regions (localised on BTA 14 at 24.4–25.1 Mbp and on BTA 16 at 42.5–43.5 Mb) overlapped in Yaroslavl, Kholmogor and Holstein breeds. In addition, we detected three novel selection sweeps in the genome of Yaroslavl (BTA 4 at 4.74–5.36 Mbp, BTA 15 at 17.80–18.77 Mbp, and BTA 17 at 45.59–45.61 Mbp) and Kholmogor breeds (BTA 12 at 82.40–81.69 Mbp, BTA 15 at 16.04–16.62 Mbp, and BTA 18 at 0.19–1.46 Mbp) by using at least two of the above-mentioned methods. We expanded the list of candidate genes associated with the selected genomic regions and performed their functional annotation. We discussed the possible involvement of the identified candidate genes in artificial selection in connection with the origin and development of the breeds. Our findings on the Yaroslavl and Kholmogor breeds obtained using high-density SNP genotyping and three different statistical methods allowed the detection of novel putative genomic regions and candidate genes that might be under selection. These results might be useful for the sustainable development and conservation of these two oldest Russian native cattle breeds.

scholarly journals Can Cross-Country Genomic Predictions Be a Reasonable Strategy to Support Germplasm Exchange? – A Case Study With Hydrogen Cyanide in Cassava

2021 ◽  
Vol 12 ◽  
Author(s):  
Lívia Gomes Torres ◽  
Eder Jorge de Oliveira ◽  
Alex C. Ogbonna ◽  
Guillaume J. Bauchet ◽  
Lukas A. Mueller ◽  
...  
Keyword(s):  
Hydrogen Cyanide ◽  
Fixation Index ◽  
Phenotypic Characterization ◽  
International Institute ◽  
Nucleotide Polymorphisms ◽  
Phenotypic Data ◽  
Germplasm Exchange ◽  
Cross Country ◽  
Estimated Breeding Values ◽  
New Research

Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava’s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm’s of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ∼14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs’ estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers’ effects that were trained with data from other research institutes/country’s germplasm to estimate their clones’ GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8–0.85) compared to the prediction accuracy of either germplasm’s sources individually (0.51–0.67). CC GP proved to have potential use under the present study’s scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa’s germplasm, whereas for IITA’s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.

scholarly journals Whole genome variants across 57 pig breeds enable comprehensive identification of genetic signatures that underlie breed features

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jingya Xu ◽  
Yuhua Fu ◽  
Yan Hu ◽  
Lilin Yin ◽  
Zhenshuang Tang ◽  
...  
Keyword(s):  
Body Weight ◽  
Candidate Genes ◽  
Complex Traits ◽  
Coat Color ◽  
Body Height ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Pig Breeds ◽  
Genome Variants

Abstract Background A large number of pig breeds are distributed around the world, their features and characteristics vary among breeds, and they are valuable resources. Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved pig breeds. Results In this study, we performed GWAS using a standard mixed linear model with three types of genome variants (SNP, InDel, and CNV) that were identified from public, whole-genome, sequencing data sets. We used 469 pigs of 57 breeds, and we identified and analyzed approximately 19 million SNPs, 1.8 million InDels, and 18,016 CNVs. We defined six biological phenotypes by the characteristics of breed features to identify the associated genome variants and candidate genes, which included coat color, ear shape, gradient zone, body weight, body length, and body height. A total of 37 candidate genes was identified, which included 27 that were reported previously (e.g., PLAG1 for body weight), but the other 10 were newly detected candidate genes (e.g., ADAMTS9 for coat color). Conclusion Our study indicated that using GWAS across a modest number of breeds with high density genome variants provided efficient mapping of complex traits.

scholarly journals Genome-Wide Assessment of Runs of Homozygosity in Chinese Wagyu Beef Cattle

Animals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1425 ◽  
Author(s):  
Guoyao Zhao ◽  
Tianliu Zhang ◽  
Yuqiang Liu ◽  
Zezhao Wang ◽  
Lei Xu ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Candidate Genes ◽  
Complex Traits ◽  
Average Length ◽  
Body Height ◽  
Reproductive Traits ◽  
Valuable Insight ◽  
Runs Of Homozygosity ◽  
Genome Wide ◽  
Candidate Regions

Runs of homozygosity (ROH) are continuous homozygous regions that generally exist in the DNA sequence of diploid organisms. Identifications of ROH leading to reduction in performance can provide valuable insight into the genetic architecture of complex traits. Here, we evaluated genome-wide patterns of homozygosity and their association with important traits in Chinese Wagyu beef cattle. We identified a total of 29,271 ROH segments from 462 animals. Within each animal, an average number of ROH was 63.36 while an average length was 62.19 Mb. To evaluate the enrichment of ROH across genomes, we initially identified 280 ROH regions by merging ROH events across all individuals. Of these, nine regions containing 154 candidate genes, were significantly associated with six traits (body height, chest circumference, fat coverage, backfat thickness, ribeye area, and carcass length; p < 0.01). Moreover, we found 26 consensus ROH regions with frequencies exceeding 10%, and several regions overlapped with QTLs, which are associated with body weight, calving ease, and stillbirth. Among them, we observed 41 candidate genes, including BCKDHB, MAB21L1, SLC2A13, FGFR3, FGFRL1, CPLX1, CTNNA1, CORT, CTNNBIP1, and NMNAT1, which have been previously reported to be related to body conformation, meat quality, susceptibility, and reproductive traits. In summary, we assessed genome-wide autozygosity patterns and inbreeding levels in Chinese Wagyu beef cattle. Our study identified many candidate regions and genes overlapped with ROH for several important traits, which could be unitized to assist the design of a selection mating strategy in beef cattle.

scholarly journals Genome-wide comparative analyses reveal selection signatures underlying adaptation and production in Tibetan and Poll Dorset sheep

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yingyue Zhang ◽  
Xianglan Xue ◽  
Yue Liu ◽  
Adam Abied ◽  
Yangyang Ding ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Coat Color ◽  
Genomic Diversity ◽  
Mean Corpuscular Hemoglobin ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Selection Signatures ◽  
Corpuscular Hemoglobin ◽  
Genome Wide ◽  
Tibetan Sheep

AbstractThe identification of genome-wide selection signatures can provide insights on the mechanisms of natural and/or artificial selection and uncover genes related to biological functions and/or phenotypes. Tibetan sheep are an important livestock in Tibet, providing meat and wool for Tibetans who are renown for breeding livestock that adapt well to high altitudes. Using whole-genome sequences with an effective sequencing depth of 5×, we investigated the genomic diversity and structure and, identified selection signatures of White Tibetan, Oula and Poll Dorset sheep. We obtained 30,163,679 Single Nucleotide Polymorphisms (SNPs) and 5,388,372 indels benchmarked against the ovine Oar_v4.0 genome assembly. Next, using FST, ZHp and XP-EHH approaches, we identified selection signatures spanning a set of candidate genes, including HIF1A, CAPN3, PRKAA1, RXFP2, TRHR and HOXA10 that are associated with pathways and GO categories putatively related to hypoxia responses, meat traits and disease resistance. Candidate genes and GO terms associated with coat color were also identified. Finally, quantification of blood physiological parameters, revealed higher levels of mean corpuscular hemoglobin measurement and mean corpuscular hemoglobin concentration in Tibetan sheep compared with Poll Dorset, suggesting a greater oxygen-carrying capacity in the Tibetan sheep and thus better adaptation to high-altitude hypoxia. In conclusion, this study provides a greater understanding of genome diversity and variations associated with adaptive and production traits in sheep.

scholarly journals Genome-Wide Detection of Genetic Loci and Candidate Genes for Body Conformation Traits in Duroc × Landrace × Yorkshire Crossbred Pigs

2021 ◽  
Vol 12 ◽  
Author(s):  
Hui Zhang ◽  
Zhanwei Zhuang ◽  
Ming Yang ◽  
Rongrong Ding ◽  
Jianping Quan ◽  
...  
Keyword(s):  
Body Weight ◽  
Candidate Genes ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Body Height ◽  
Gene Set Enrichment Analysis ◽  
Phenotypic Data ◽  
Body Conformation ◽  
Genome Wide ◽  
A Genome

The Duroc × (Landrace × Yorkshire) hybrid pigs (DLY) are the most popular commercial pigs, providing consumers with the largest source of pork. In order to gain more insights into the genetic architecture of economically important traits in pigs, we performed a genome-wide association study (GWAS) using the GeneSeek Porcine 50 K SNP Chip to map the genetic markers and genes associated with body conformation traits (BCT) in 311 DLY pigs. The quantitative traits analyzed included body weight (BW), carcass length (CL), body length (BL), body height (BH), and body mass index (BMI). BMI was defined as BMICL, BMIBL, and BMIBH, respectively, based on CL, BL, and BH phenotypic data. We identified 82 SNPs for the seven traits by GEMMA-based and FarmCPU-based GWASs. Both methods detected two quantitative trait loci (QTL) on SSC8 and SSC17 for body conformation traits. Several candidate genes (such as TNFAIP3, KDM4C, HSPG2, BMP2, PLCB4, and GRM5) were found to be associated with body weight and body conformation traits in pigs. Notably, the BMP2 gene had pleiotropic effects on CL, BL, BH, BMICL, and BMIBL and is proposed as a strong candidate gene for body size due to its involvement in growth and bone development. Furthermore, gene set enrichment analysis indicated that most of the pathway terms are associated with regulation of cell growth, negative regulation of cell population proliferation, and chondrocyte differentiation. We anticipate that these results further advance our understanding of the genetic architecture of body conformation traits in the popular commercial DLY pigs and provide new insights into the genetic architecture of BMI in pigs.

scholarly journals Trait-specific Selection Signature Detection Reveals Novel Loci of Meat Quality in Large White Pigs

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu Shen ◽  
Haiyan Wang ◽  
Jiahao Xie ◽  
Zixuan Wang ◽  
Yunlong Ma
Keyword(s):  
Candidate Genes ◽  
Meat Quality ◽  
Genetic Improvement ◽  
Genetic Basis ◽  
Muscle Development ◽  
Fixation Index ◽  
Quality Traits ◽  
Selection Signatures ◽  
Meat Quality Traits ◽  
Improvement Programs

In past decades, meat quality traits have been shaped by human-driven selection in the process of genetic improvement programs. Exploring the potential genetic basis of artificial selection and mapping functional candidate genes for economic traits are of great significance in genetic improvement of pigs. In this study, we focus on investigating the genetic basis of five meat quality traits, including intramuscular fat content (IMF), drip loss, water binding capacity, pH at 45 min (pH45min), and ultimate pH (pH24h). Through making phenotypic gradient differential population pairs, Wright’s fixation index (FST) and the cross-population extended haplotype homozogysity (XPEHH) were applied to detect selection signatures for these five traits. Finally, a total of 427 and 307 trait-specific selection signatures were revealed by FST and XPEHH, respectively. Further bioinformatics analysis indicates that some genes, such as USF1, NDUFS2, PIGM, IGSF8, CASQ1, and ACBD6, overlapping with the trait-specific selection signatures are responsible for the phenotypes including fat metabolism and muscle development. Among them, a series of promising trait-specific selection signatures that were detected in the high IMF subpopulation are located in the region of 93544042-95179724bp on SSC4, and the genes harboring in this region are all related to lipids and muscle development. Overall, these candidate genes of meat quality traits identified in this analysis may provide some fundamental information for further exploring the genetic basis of this complex trait.

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