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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Related Experiment Video

Updated: Apr 11, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Estimating genetic load from 5000 Chinese exomes.

Xiaoyue Du1, Xiaoxi Zhang2, Jiucun Wang3

  • 1State Key Laboratory of Genetics and Development of Complex Phenotypes, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai 200438, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao
|September 1, 2025
PubMed
Summary

Genetic load in Chinese populations reveals population-specific disease risks and adaptive genetic variations. Understanding these patterns is crucial for precision medicine tailored to diverse ethnic groups.

Keywords:
ChineseEthnic stratificationGenetic loadRare variantsWhole exome sequencing

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Area of Science:

  • Genomics
  • Population Genetics
  • Human Adaptation

Background:

  • Genome sequencing advances allow genetic load estimation via deleterious mutation profiling.
  • Chinese populations are underrepresented in genetic load studies.
  • Understanding genetic variation is key for personalized medicine.

Purpose of the Study:

  • To analyze genetic load and mutation profiles in diverse Chinese populations.
  • To identify population-specific genetic variants and adaptive signatures.
  • To highlight the importance of population-specific data for precision medicine.

Main Methods:

  • Whole-exome sequencing data from 5002 individuals across Han subgroups and ethnic minorities.
  • Systematic curation of ClinVar pathogenic/likely pathogenic variants.
  • Gene-based rare-variant collapsing analyses.

Main Results:

  • Most pathogenic variants (93.4%) are ultra-rare, with exceptions like GJB2 and HBB variants showing regional prevalence.
  • LDLR variants are common in autosomal dominant mutation carriers across Han subgroups.
  • Adaptive signatures indicate regional gene-environment interactions (e.g., MTHFR, ALDH2, ABCC11).
  • Elevated retinitis pigmentosa risk identified in South Han (S-Han) populations.

Conclusions:

  • Genetic load in Chinese populations is shaped by demographic history, population structure, and adaptation.
  • Population-specific genetic profiles are essential for effective precision medicine.
  • This study provides a foundation for understanding genetic diversity and health in China.