The Experts below are selected from a list of 3807 Experts worldwide ranked by ideXlab platform

Jiangwei Xia - One of the best experts on this subject based on the ideXlab platform.

  • multimarker and rare variants genomewide association studies for bone weight in Simmental Cattle
    Journal of Animal Breeding and Genetics, 2018
    Co-Authors: Jian Miao, Xiongjun Wang, J. Bao, S. Jin, Tianpeng Chang, Jiangwei Xia, Liu Yang, Bo Zhu
    Abstract:

    Bone weight, defined as the total weight of the bones in all the forequarter and hindquarter joints, can reflect somebody conformation traits and skeletal diseases. To gain a better understanding of the genetic determinants of bone weight, we used a composite strategy including multimarker and rare-marker association to perform genomewide association studies (GWAS) for that character in Simmental Cattle. Our strategy consisted of three models: (i) A traditional linear mixed model (LMM) was applied (Q+K-LMM); (ii) single nucleotide polymorphisms (SNPs) with p-values less than .05 from the LMM were selected to undergo the least absolute shrinkage and selector operator (Lasso) in the second stage (LMM-Lasso); (iii) genes containing two or more rare SNPs were examined by performing the sequence kernel association test (gene-based SKAT). A total of 1,225 Cattle were genotyped with an Illumina BovineHD BeadChip containing 770,000 SNPs. After the quality-control procedures, 1,217 individuals with 608,696 common SNPs and 105,787 rare SNPs (with 0.001 < minor allele frequency [MAF] <0.05) remained in the sample for analysis. A traditional LMM successfully mapped three genes associated with bone weight, while LMM-Lasso identified nine genes, which included all genes found by traditional LMM. Only a single gene, EPHB3, surpassed the significance threshold after Bonferroni correction in gene-based SKAT. In conclusion, based on functional annotation and results from previous endeavours, we believe that LCORL, RIMS2, LAP3, PRKAR2B, CHSY1, MAP2K6 and EPHB3 are candidate genes for bone weight. In general, such a comprehensive strategy for GWAS may be useful for researchers seeking to probe the full genetic architecture underlying economic traits in livestock.

  • Multimarker and rare variants genomewide association studies for bone weight in Simmental Cattle.
    Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie, 2018
    Co-Authors: Jian Miao, Bo Zhu, Xiongjun Wang, J. Bao, S. Jin, Tianpeng Chang, Jiangwei Xia, Liu Yang, Liangzhi Zhang
    Abstract:

    Bone weight, defined as the total weight of the bones in all the forequarter and hindquarter joints, can reflect somebody conformation traits and skeletal diseases. To gain a better understanding of the genetic determinants of bone weight, we used a composite strategy including multimarker and rare-marker association to perform genomewide association studies (GWAS) for that character in Simmental Cattle. Our strategy consisted of three models: (i) A traditional linear mixed model (LMM) was applied (Q+K-LMM); (ii) single nucleotide polymorphisms (SNPs) with p-values less than .05 from the LMM were selected to undergo the least absolute shrinkage and selector operator (Lasso) in the second stage (LMM-Lasso); (iii) genes containing two or more rare SNPs were examined by performing the sequence kernel association test (gene-based SKAT). A total of 1,225 Cattle were genotyped with an Illumina BovineHD BeadChip containing 770,000 SNPs. After the quality-control procedures, 1,217 individuals with 608,696 common SNPs and 105,787 rare SNPs (with 0.001 < minor allele frequency [MAF]

  • searching for new loci and candidate genes for economically important traits through gene based association analysis of Simmental Cattle
    Scientific Reports, 2017
    Co-Authors: Jiangwei Xia, Xue Gao, Lupei Zhang, Huizhong Fan, Bo Zhu, Yan Chen, Tianpeng Chang, Wengang Zhang, Yuxin Song, Huijiang Gao
    Abstract:

    Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental Cattle. Additionally, 21836 Cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.

  • pathway based genome wide association studies for two meat production traits in Simmental Cattle
    Scientific Reports, 2016
    Co-Authors: Huizhong Fan, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Xiaojing Zhou, Wengang Zhang, Yuxin Song, Fei Liu, Huijiang Gao
    Abstract:

    Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental Cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of Cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.

  • a genome wide scan for copy number variations using high density single nucleotide polymorphism array in Simmental Cattle
    Animal Genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.

Huijiang Gao - One of the best experts on this subject based on the ideXlab platform.

  • Selection and effectiveness of informative SNPs for paternity in Chinese Simmental Cattle based on a high-density SNP array.
    Gene, 2018
    Co-Authors: Tianliu Zhang, Huijiang Gao, Lupei Zhang, Guo Liping, Shi Mingyan, Yan Chen, Xue Gao
    Abstract:

    Incorrect paternity assignment in Cattle can significantly influence the accuracy of genetic evaluation. Recent advances in high-throughput technology have facilitated the identification of single nucleotide polymorphism (SNP) markers and their applications for filiation and individual identification. We genotyped 1074 bulls from a reference population of Chinese Simmental Cattle for genomic selection using a BovineSNP770K BeadChip. Among them, a total of 136 bulls were randomly selected to design a suitable low-density SNP panel for paternity testing in Simmental Cattle. Our results showed that 50 SNPs were determined to be the most informative markers in parental testing, with an accuracy of 99.89% for CPE (cumulative probability of exclusion) in the unknown female parent case. The 50 highly informative SNP markers were distributed across 25 chromosomes, and the mean intermarker distance per chromosome was 26.72 Mb. The average minor allele frequency (MAF), expected heterozygosity (HE), and polymorphic information content (PIC) values were 0.3748, 0.4998, and 0.4818, respectively. Finally, the 50 identified SNPs were used to estimate paternity for the remaining 938 of 1074 bulls from 23 farms. Our results revealed that 76.75% of the 938 bulls were assigned parentage to the pedigree sires with 95% confidence, and the rate of pedigree record mistakes ranged from 9.52%-39.29% in different herds. Our study is the first attempt to provide valuable insights into the extraction of informative markers through the application of high-density SNP chips for paternity testing in Chinese Simmental Cattle.

  • searching for new loci and candidate genes for economically important traits through gene based association analysis of Simmental Cattle
    Scientific Reports, 2017
    Co-Authors: Jiangwei Xia, Xue Gao, Lupei Zhang, Huizhong Fan, Bo Zhu, Yan Chen, Tianpeng Chang, Wengang Zhang, Yuxin Song, Huijiang Gao
    Abstract:

    Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental Cattle. Additionally, 21836 Cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.

  • pathway based genome wide association studies for two meat production traits in Simmental Cattle
    Scientific Reports, 2016
    Co-Authors: Huizhong Fan, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Xiaojing Zhou, Wengang Zhang, Yuxin Song, Fei Liu, Huijiang Gao
    Abstract:

    Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental Cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of Cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.

  • a genome wide scan for copy number variations using high density single nucleotide polymorphism array in Simmental Cattle
    Animal Genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.

  • A genome‐wide scan for copy number variations using high‐density single nucleotide polymorphism array in Simmental Cattle
    Animal genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.

Lupei Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Selection and effectiveness of informative SNPs for paternity in Chinese Simmental Cattle based on a high-density SNP array.
    Gene, 2018
    Co-Authors: Tianliu Zhang, Huijiang Gao, Lupei Zhang, Guo Liping, Shi Mingyan, Yan Chen, Xue Gao
    Abstract:

    Incorrect paternity assignment in Cattle can significantly influence the accuracy of genetic evaluation. Recent advances in high-throughput technology have facilitated the identification of single nucleotide polymorphism (SNP) markers and their applications for filiation and individual identification. We genotyped 1074 bulls from a reference population of Chinese Simmental Cattle for genomic selection using a BovineSNP770K BeadChip. Among them, a total of 136 bulls were randomly selected to design a suitable low-density SNP panel for paternity testing in Simmental Cattle. Our results showed that 50 SNPs were determined to be the most informative markers in parental testing, with an accuracy of 99.89% for CPE (cumulative probability of exclusion) in the unknown female parent case. The 50 highly informative SNP markers were distributed across 25 chromosomes, and the mean intermarker distance per chromosome was 26.72 Mb. The average minor allele frequency (MAF), expected heterozygosity (HE), and polymorphic information content (PIC) values were 0.3748, 0.4998, and 0.4818, respectively. Finally, the 50 identified SNPs were used to estimate paternity for the remaining 938 of 1074 bulls from 23 farms. Our results revealed that 76.75% of the 938 bulls were assigned parentage to the pedigree sires with 95% confidence, and the rate of pedigree record mistakes ranged from 9.52%-39.29% in different herds. Our study is the first attempt to provide valuable insights into the extraction of informative markers through the application of high-density SNP chips for paternity testing in Chinese Simmental Cattle.

  • searching for new loci and candidate genes for economically important traits through gene based association analysis of Simmental Cattle
    Scientific Reports, 2017
    Co-Authors: Jiangwei Xia, Xue Gao, Lupei Zhang, Huizhong Fan, Bo Zhu, Yan Chen, Tianpeng Chang, Wengang Zhang, Yuxin Song, Huijiang Gao
    Abstract:

    Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental Cattle. Additionally, 21836 Cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.

  • pathway based genome wide association studies for two meat production traits in Simmental Cattle
    Scientific Reports, 2016
    Co-Authors: Huizhong Fan, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Xiaojing Zhou, Wengang Zhang, Yuxin Song, Fei Liu, Huijiang Gao
    Abstract:

    Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental Cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of Cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.

  • a genome wide scan for copy number variations using high density single nucleotide polymorphism array in Simmental Cattle
    Animal Genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.

  • A genome‐wide scan for copy number variations using high‐density single nucleotide polymorphism array in Simmental Cattle
    Animal genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.

Huizhong Fan - One of the best experts on this subject based on the ideXlab platform.

  • searching for new loci and candidate genes for economically important traits through gene based association analysis of Simmental Cattle
    Scientific Reports, 2017
    Co-Authors: Jiangwei Xia, Xue Gao, Lupei Zhang, Huizhong Fan, Bo Zhu, Yan Chen, Tianpeng Chang, Wengang Zhang, Yuxin Song, Huijiang Gao
    Abstract:

    Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental Cattle. Additionally, 21836 Cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.

  • pathway based genome wide association studies for two meat production traits in Simmental Cattle
    Scientific Reports, 2016
    Co-Authors: Huizhong Fan, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Xiaojing Zhou, Wengang Zhang, Yuxin Song, Fei Liu, Huijiang Gao
    Abstract:

    Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental Cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of Cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.

  • a genome wide scan for copy number variations using high density single nucleotide polymorphism array in Simmental Cattle
    Animal Genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.

  • A genome‐wide scan for copy number variations using high‐density single nucleotide polymorphism array in Simmental Cattle
    Animal genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.

  • Genome-wide detection of selective signatures in Simmental Cattle.
    Journal of applied genetics, 2014
    Co-Authors: Huizhong Fan, Lupei Zhang, Xue Gao, Jing-jing Zhang, Huijiang Gao
    Abstract:

    Artificial selection has greatly improved the beef production performance and changed its genetic basis. High-density SNP markers provide a way to track these changes and use selective signatures to search for the genes associated with artificial selection. In this study, we performed extended haplotype homozygosity (EHH) tests based on Illumina BovineSNP50 (54 K) Chip data from 942 Simmental Cattle to identify significant core regions containing selective signatures, then verified the biological significance of these identified regions based on some commonly used bioinformatics analyses. A total of 224 regions over the whole genome in Simmental Cattle showing the highest significance and containing some important functional genes, such as GHSR, TG and CANCNA2D1 were chosen. We also observed some significant terms in the enrichment analyses of second GO terms and KEGG pathways, indicating that these genes are associated with economically relevant Cattle traits. This is the first detection of selection signature in Simmental Cattle. Our findings significantly expand the selection signature map of the Cattle genome, and identify functional candidate genes under positive selection for future genetic research.

Xue Gao - One of the best experts on this subject based on the ideXlab platform.

  • Selection and effectiveness of informative SNPs for paternity in Chinese Simmental Cattle based on a high-density SNP array.
    Gene, 2018
    Co-Authors: Tianliu Zhang, Huijiang Gao, Lupei Zhang, Guo Liping, Shi Mingyan, Yan Chen, Xue Gao
    Abstract:

    Incorrect paternity assignment in Cattle can significantly influence the accuracy of genetic evaluation. Recent advances in high-throughput technology have facilitated the identification of single nucleotide polymorphism (SNP) markers and their applications for filiation and individual identification. We genotyped 1074 bulls from a reference population of Chinese Simmental Cattle for genomic selection using a BovineSNP770K BeadChip. Among them, a total of 136 bulls were randomly selected to design a suitable low-density SNP panel for paternity testing in Simmental Cattle. Our results showed that 50 SNPs were determined to be the most informative markers in parental testing, with an accuracy of 99.89% for CPE (cumulative probability of exclusion) in the unknown female parent case. The 50 highly informative SNP markers were distributed across 25 chromosomes, and the mean intermarker distance per chromosome was 26.72 Mb. The average minor allele frequency (MAF), expected heterozygosity (HE), and polymorphic information content (PIC) values were 0.3748, 0.4998, and 0.4818, respectively. Finally, the 50 identified SNPs were used to estimate paternity for the remaining 938 of 1074 bulls from 23 farms. Our results revealed that 76.75% of the 938 bulls were assigned parentage to the pedigree sires with 95% confidence, and the rate of pedigree record mistakes ranged from 9.52%-39.29% in different herds. Our study is the first attempt to provide valuable insights into the extraction of informative markers through the application of high-density SNP chips for paternity testing in Chinese Simmental Cattle.

  • searching for new loci and candidate genes for economically important traits through gene based association analysis of Simmental Cattle
    Scientific Reports, 2017
    Co-Authors: Jiangwei Xia, Xue Gao, Lupei Zhang, Huizhong Fan, Bo Zhu, Yan Chen, Tianpeng Chang, Wengang Zhang, Yuxin Song, Huijiang Gao
    Abstract:

    Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental Cattle. Additionally, 21836 Cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.

  • pathway based genome wide association studies for two meat production traits in Simmental Cattle
    Scientific Reports, 2016
    Co-Authors: Huizhong Fan, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Xiaojing Zhou, Wengang Zhang, Yuxin Song, Fei Liu, Huijiang Gao
    Abstract:

    Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental Cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of Cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.

  • a genome wide scan for copy number variations using high density single nucleotide polymorphism array in Simmental Cattle
    Animal Genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.

  • A genome‐wide scan for copy number variations using high‐density single nucleotide polymorphism array in Simmental Cattle
    Animal genetics, 2015
    Co-Authors: Huizhong Fan, Huijiang Gao, Xue Gao, Lupei Zhang, Yan Chen, Jiangwei Xia, Shengyun Jing, Hongyan Ren
    Abstract:

    Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome-wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental Cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18kb to 1.76Mb, with an average length of 134.78kb and a median length of 61.95kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of Cattle CNVs derived from high-density SNP data, which expands the current CNV map of the Cattle genome and provides useful information for investigation of genomic structural variation in Cattle.