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Related Experiment Video

Updated: May 27, 2026

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

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

Published on: August 20, 2019

Incorporating linkage information into a common disease/rare variant framework.

Anthony L Hinrichs¹, Brian K Suarez

¹Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA. tony@fire.wustl.edu

Genetic Epidemiology

|December 1, 2011

Summary

Linkage analysis effectively identifies rare variants with large effects in pedigrees. Utilizing pedigree data enhances sequencing efficiency and aids in finding genetic variants associated with common diseases.

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

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

Published on: August 21, 2016

Area of Science:

Genetics
Genomic Analysis
Statistical Genetics

Background:

Advances in sequencing technology enable the study of the common disease/rare variant hypothesis.
The Genetic Analysis Workshop 17 (GAW17) dataset includes sequence data from unrelated individuals and large pedigrees with simulated phenotypes.

Purpose of the Study:

To investigate methods for using pedigree and linkage information to identify causal genes and rare variants.
To assess the efficiency of sequencing selected individuals based on linkage data.
To evaluate the utility of pedigree information in association testing for common diseases.

Main Methods:

Utilized identity-by-descent (IBD) information for linkage analysis to detect rare variants.
Employed heterogeneity linkage analysis and single-pedigree analysis for common disease risk loci.
Assessed the efficiency of targeted sequencing in individuals identified through linkage analysis.
Incorporated pedigree linkage information to weight case-control association tests.

Main Results:

Linkage analysis is highly effective for detecting rare variants with large effects segregating within pedigrees.
Targeted sequencing of a small genome fraction (2.5%) in selected individuals can identify a significant proportion (52%) of risk variants.
Pedigree-based association tests share challenges with unrelated individual tests, such as variant binning and counting.
Linkage information from pedigrees can improve the power of association tests.

Conclusions:

Pedigree data significantly enhances the efficiency and power of identifying rare variants associated with common disorders.
Linkage analysis remains a powerful tool for detecting rare, high-penetrance variants.
Strategic sequencing informed by linkage analysis offers a more efficient approach than whole-exome sequencing in large cohorts.