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Biomedical subjects

Xuemin Wang

Publications and source records attributed to Xuemin Wang.

2 recordsLinked to original sources

Estimation Model of Pig Weight Based on Body Measurements and Analysis of Its Genetic Basis.

Body weight and body measurements are key indicators of growth and economic efficiency in pigs, but conventional weighing is labor-intensive and stressful, increasing disease risk and necessitating non-contact estimation. We measured five dimensions (body length, chest circumference, abdominal circumference, body width, and body height) in 811 Suzi black pigs and constructed six multiple linear regression models using different combinations. All models had R2&#x2009;>&#x2009;0.91, with adjusted R2 also exceeding 0.91, and the model combining length, chest, and abdominal circumference gave the lowest RMSE, balancing accuracy and practicality. Separately, we performed GWAS on 165 genotyped individuals (100&#x2009;K SNP chip and GBS) for age (as a growth rate proxy), body weight, and the five measurements. No SNP reached genome-wide significance (p&#x2009;<&#x2009;1.86&#x2009;&#xd7;&#x2009;10-6), but three suggestive loci (p&#x2009;<&#x2009;1.39&#x2009;&#xd7;&#x2009;10-5) were detected: SNP 4_12&#x2009;319&#x2009;200 for age (35.55% variance), a pleiotropic SNP 1_60&#x2009;912&#x2009;826 associated with length, chest, and abdominal circumference (42.10%, 53.06%, and 45.97% variance), and SNP 1_60&#x2009;638&#x2009;159 for abdominal circumference. Positional mapping identified EPHA7 as the nearest candidate gene. Enrichment analyses revealed focal adhesion, receptor tyrosine kinase, IgSF-CAM, integrin, and PI3K-Akt pathways, with EPHA7 and FYN as key regulators. Notably, the three traits in the best model mapped to the same pleiotropic locus, suggesting a shared genetic basis. This study provides a practical estimation tool and suggestive markers, supporting non-contact weighing systems and molecular breeding.

Animals

A Swedish genome-wide haplotype association analysis identifies novel candidate loci associated with endometrial cancer risk.

Genome-wide association studies [GWAS] have identified a limited number of endometrial cancer risk loci by analyzing single nucleotide polymorphisms [SNPs]. We hypothesized that analyzing haplotypes rather than SNPs could provide novel and more detailed information on genetic cancer susceptibility loci. To examine the association of a SNP or haplotype with endometrial cancer risk we performed a two-stage haplotype GWAS. The discovery GWAS included a sub-cohort of 1,116 Swedish endometrial cancer cases and 5,021 controls from previously published GWAS data. A sliding window analysis was employed with window sizes of 1-25 SNPs using a logistic regression model. The Swedish haplotype analysis identified 15 novel candidate risk loci (2q31.1, 4p16.1, 4p15.31,&#xa0;6q13, 7p21.1, 9p13.3, 10q26.3, 11q21, 12q13.11, 13q12.11, 15q13.3, 16q24.3, 19q13.32, 20p12.3 and 22q13.2) with OR ranging from 1.6 to 3.3 and p-values from 4.25&#x2009;&#xd7;&#x2009;10-8 to 9.86&#x2009;&#xd7;&#x2009;10-15. A second replication haplotype analysis of the Swedish novel loci was performed using two cohorts from Belgium and Germany. In spite of small sample sizes in the replication cohorts, there was still support for most loci with positive ORs. In addition, the findings in the two European cohorts motivates further studies to search for founder haplotypes. These novel findings suggested that endometrial cancer loci, identified through haplotype analysis, conferred a higher risk compared to previous single-variant GWAS.

Humans