Bivariate genome-wide association study suggests that the DARC gene influences lean body mass and age at menarche

Hai, Rong; Zhang, Lei; Pei, Yu‐Fang; Zhao, Lan‐Juan; Ran, Shu; Han, Yingying; Zhu, Xinyao; Shen, Hui; Tian, Qing; Deng, Hong‐Wen

doi:10.1007/s11427-012-4327-6

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…The SNP (Hapmap40339-BTA-117016; Table 4) on BTA3 which was associated with both capacity and body depth is 7 kb away from the gene, Duffy blood group, chemokine receptor (DARC) . Hai et al [40] performed a bivariate GWAS in human to identify the SNPs associated with lean body mass and age at menarche and reported that DARC may play an important role in regulating the metabolisms of both these traits. The SNP (BTA-110160-no-rs; Table 4) on BTA8 associated with chest width is 121 kb away from the growth arrest specific 1 (GAS1) gene.…”

Section: Discussionmentioning

confidence: 99%

Genome wide association studies for body conformation traits in the Chinese Holstein cattle population

Fang

Liu³

et al. 2013

BMC Genomics

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BackgroundGenome-wide association study (GWAS) is a powerful tool for revealing the genetic basis of quantitative traits. However, studies using GWAS for conformation traits of cattle is comparatively less. This study aims to use GWAS to find the candidates genes for body conformation traits.ResultsThe Illumina BovineSNP50 BeadChip was used to identify single nucleotide polymorphisms (SNPs) that are associated with body conformation traits. A least absolute shrinkage and selection operator (LASSO) was applied to detect multiple SNPs simultaneously for 29 body conformation traits with 1,314 Chinese Holstein cattle and 52,166 SNPs. Totally, 59 genome-wide significant SNPs associated with 26 conformation traits were detected by genome-wide association analysis; five SNPs were within previously reported QTL regions (Animal Quantitative Trait Loci (QTL) database) and 11 were very close to the reported SNPs. Twenty-two SNPs were located within annotated gene regions, while the remainder were 0.6–826 kb away from known genes. Some of the genes had clear biological functions related to conformation traits. By combining information about the previously reported QTL regions and the biological functions of the genes, we identified DARC, GAS1, MTPN, HTR2A, ZNF521, PDIA6, and TMEM130 as the most promising candidate genes for capacity and body depth, chest width, foot angle, angularity, rear leg side view, teat length, and animal size traits, respectively. We also found four SNPs that affected four pairs of traits, and the genetic correlation between each pair of traits ranged from 0.35 to 0.86, suggesting that these SNPs may have a pleiotropic effect on each pair of traits.ConclusionsA total of 59 significant SNPs associated with 26 conformation traits were identified in the Chinese Holstein population. Six promising candidate genes were suggested, and four SNPs showed genetic correlation for four pairs of traits.

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Section: Discussionmentioning

confidence: 99%

Genome wide association studies for body conformation traits in the Chinese Holstein cattle population

Fang

Liu³

et al. 2013

BMC Genomics

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“…Removal of all samples with a missing phenotype will reduce sample size, thus attenuating the power of any statistical inference. For example, a range of real studies removed between 3%-31% 12-17 of samples. Other studies completely removed phenotypes with high levels of missing data, and imputed remaining missing data with off-the-shelf methods from mainstream statistics 18-20 .…”

Section: Introductionmentioning

confidence: 99%

A multiple-phenotype imputation method for genetic studies

et al. 2016

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Genetic association studies have yielded a wealth of biologic discoveries. However, these have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of these datasets. Joint genotype-phenotype analyses of complex, high-dimensional datasets represent an important way to move beyond simple GWAS with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. In this paper we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real datasets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.

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“…Mouse human homology and measurement of body composition. Investigators who have conducted human genome-wide association studies of lean body mass, measured using methods similar to those used here in mice, report no associations to the homologous Burly1 region [89][90][91][92][93][94]. However, this may be due to low power of human studies to detect genes with smaller effect sizes because, relative to studies of body mass index, far fewer human subjects have been measured for lean body weight.…”

Section: Challenges In Assessing Body Composition Loci On Mouse Chrommentioning

confidence: 81%

Burly1is a mouse QTL for lean body mass that maps to a 0.8-Mb region on chromosome 2

Lin

Fesi

Marquis

et al. 2017

Preprint

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Our goal was to fine map a mouse QTL for lean body mass (Burly1) using information from several populations including newly created congenic mice derived from the B6 (host) and 129 (donor) strains. The results from each mapping population were concordant and showed that Burly1 is likely a single QTL in a 0.8-Mb region at 151.9-152.7 Mb (rs33197365 to rs3700604) on mouse chromosome 2. Results from mice of all the mapping populations we studied including intercrossed, backcrossed, consomic, and congenic strains indicate that lean body mass was increased by the B6-derived allele relative to the 129-derived allele. We determined that the congenic region harboring Burly1 contains 26 protein-coding genes, 11 noncoding RNA elements (e.g., lncRNA), and 4 pseudogenes, with 1949 predicted functional variants. The effect of the Burly1 locus on lean body weight was apparent at all ages measured and did not affect food intake or locomotor activity. However, congenic mice with the B6-allele produced more heat per kilogram of lean body weight than did controls, pointing to a genotype effect on lean mass metabolism. These results show the value of integrating information from several mapping populations to refine the map location of body composition QTLs. peer-reviewed)

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Bivariate genome-wide association study suggests that the DARC gene influences lean body mass and age at menarche

Cited by 11 publications

References 37 publications

Genome wide association studies for body conformation traits in the Chinese Holstein cattle population

Genome wide association studies for body conformation traits in the Chinese Holstein cattle population

A multiple-phenotype imputation method for genetic studies

Burly1is a mouse QTL for lean body mass that maps to a 0.8-Mb region on chromosome 2

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