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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.While some alleles of a given gene might be observed commonly, other variants...

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Related Experiment Video

Updated: Jun 8, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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

Published on: August 21, 2016

Identifying candidate causal variants via trans-population fine-mapping.

Yik-Ying Teo1, Rick T H Ong, Xueling Sim

  • 1Department of Statistics and Applied Probability, National University of Singapore, Singapore. statyy@nus.edu.sg

Genetic Epidemiology
|September 15, 2010
PubMed
Summary

Trans-population fine-mapping leverages genetic variation across diverse populations to pinpoint causal variants. Integrating statistical evidence by summing test statistics is superior to standard meta-analysis for prioritizing variants.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Related Experiment Videos

Last Updated: Jun 8, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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

Published on: August 21, 2016

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Population Genetics
  • Genomic Fine-Mapping

Background:

  • Genome-wide association studies (GWAS) identify numerous loci linked to disease susceptibility.
  • Causal variants underlying GWAS signals are often indirectly associated with phenotypes.
  • High linkage disequilibrium (LD) complicates fine-mapping by confounding causal variants with correlated markers.

Purpose of the Study:

  • To explore integrating cross-population data for narrowing candidate causal variant regions.
  • To compare the efficacy of trans-population fine-mapping with LD variation between populations.
  • To evaluate strategies for pooling multi-population data to prioritize causal variants.

Main Methods:

  • Utilized trans-population fine-mapping approaches.
  • Analyzed patterns of linkage disequilibrium (LD) variation across populations.
  • Compared data pooling strategies, including summing test statistics versus meta-analysis.

Main Results:

  • Trans-population analysis effectively reduces the number of candidate causal variants.
  • Benefits are most pronounced in regions with significant inter-population LD variation.
  • Directly summing test statistics outperforms standard meta-analytic procedures for variant prioritization.

Conclusions:

  • Cross-population genetic data integration is beneficial for fine-mapping studies.
  • Leveraging LD differences between populations enhances causal variant identification.
  • Summing test statistics is a more effective method for prioritizing candidate causal variants.