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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method.

Ming Li¹, Zihuai He², Xiaoran Tong³

¹Department of Epidemiology and Biostatistics, Indiana University at Bloomington, Indiana 47405.

|August 15, 2018

Summary

A new method, family-based genetic random field (FGRF), enhances the detection of gene-phenotype associations in family sequencing studies. FGRF improves statistical power, especially when genetic causes of complex diseases vary across families.

Keywords:

alcohol dependence family-based association study genetic heterogeneity population stratification rare variants

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Area of Science:

Genetics and Genomics
Statistical Genetics
Complex Disease Etiology

Background:

Complex diseases often have heterogeneous genetic causes, involving multiple rare variants across different genes.
Family-based studies are crucial for identifying rare variants and mitigating population stratification in genetic research.
Existing statistical methods for family-based sequencing data analysis are limited, particularly in handling genetic heterogeneity.

Purpose of the Study:

To introduce a novel statistical framework, family-based genetic random field (FGRF), for analyzing family-based sequencing data.
To develop a method that accounts for the heterogeneous genetic etiology of complex diseases.
To improve the detection of gene-phenotype associations in family studies, especially under genetic heterogeneity.

Main Methods:

Developed a random field framework named family-based genetic random field (FGRF).
FGRF can analyze within-family and between-family genetic information, separately or jointly.
Applied FGRF to sequencing data from the Minnesota Twin Family Study.

Main Results:

FGRF demonstrates comparable statistical power to existing methods when genetic heterogeneity is absent.
FGRF significantly improves statistical power in the presence of genetic heterogeneity across families.
The analysis identified several genes, including SAMD14, potentially associated with alcohol dependence.

Conclusions:

FGRF is a robust statistical method for detecting gene-phenotype associations in family-based sequencing studies.
The method effectively handles genetic heterogeneity, a common feature in complex disease etiology.
FGRF offers advantages similar to conventional family-based association tests, including robustness to population stratification.