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Related Experiment Video

Updated: Feb 9, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

Novel Methods for Family-Based Genetic Studies.

¹Division of Pulmonary Medicine, Allergy and Immunology; Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, PA, USA. qi.yan@chp.edu.

Methods in Molecular Biology (Clifton, N.J.)

|June 8, 2018

Summary

This summary is machine-generated.

This study introduces advanced statistical methods for family-based rare variant analysis. These methods improve the detection of genetic markers associated with diseases, particularly rare ones.

Keywords:

Gene-based analysis Mixed model Rare genetic markers Sequence kernel association test Statistical analysis

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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Published on: December 22, 2017

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) identify disease susceptibility genes using microarray and sequencing technologies.
Traditional statistical methods struggle to detect rare genetic markers, which are often analyzed at the set level.
The sequence kernel association test (SKAT) is a common method for rare genetic marker analysis.

Purpose of the Study:

To present three statistical approaches for family-based rare variant analysis.
To extend rare variant analysis to accommodate family structures.
To address the analysis of continuous, binary, and multiple correlated traits.

Main Methods:

Application of the sequence kernel association test (SKAT) framework.
Development of specific statistical models for different trait types (continuous, binary, multiple correlated).
Utilizing family-based study designs for enhanced power in rare variant detection.

Main Results:

Demonstration of effective statistical approaches for family-based rare variant analysis.
Successful extension of SKAT to family data across various trait types.
Improved ability to identify disease-associated rare genetic markers in families.

Conclusions:

The presented statistical methods offer powerful tools for family-based rare variant association studies.
These approaches enhance the identification of genetic contributions to diseases, especially those linked to rare variants.
The methods are applicable to diverse genetic and phenotypic data structures in family studies.