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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...
Heritability01:06

Heritability

Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic" a trait is,...
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...
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...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

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Related Experiment Video

Updated: May 18, 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

Estimating genetic effects and quantifying missing heritability explained by identified rare-variant associations.

Dajiang J Liu1, Suzanne M Leal

  • 1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.

American Journal of Human Genetics
|October 2, 2012
PubMed
Summary

New algorithms reduce bias in rare-variant (RV) association studies, providing unbiased estimates of genetic effects. This improves understanding of RV contributions to complex traits and heritability, overcoming limitations of current aggregate analysis methods.

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Related Experiment Videos

Last Updated: May 18, 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

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Next-generation sequencing enables rare-variant (RV) association studies for complex traits.
  • Single-variant analysis is underpowered; aggregate RV tests are commonly used.
  • Aggregate RV tests yield biased estimates due to the "winner's curse" and heterogeneity.

Purpose of the Study:

  • To develop methods for obtaining unbiased genetic effect estimates in aggregate RV association studies.
  • To address the "winner's curse" bias inherent in current RV analysis.
  • To accurately quantify the contribution of RVs to complex trait heritability.

Main Methods:

  • Developed bootstrap-sample-split algorithms to mitigate the "winner's curse" bias.
  • Utilized theoretical analysis and simulations to validate the methods.
  • Evaluated the underestimation of genetic variance in aggregate RV analyses.

Main Results:

  • The proposed algorithms successfully reduce bias in aggregate RV association studies.
  • Demonstrated that genetic variance is often substantially underestimated in aggregate RV analyses.
  • Unbiased estimates are crucial for understanding population-specific RV heritability.

Conclusions:

  • The developed methods provide unbiased genetic estimates for aggregate RV analyses.
  • Underestimation of genetic variance is a significant issue in current aggregate RV studies.
  • Accurate RV effect estimation is vital for understanding complex trait etiology and heritability.