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Block network mapping approach to quantitative trait locus analysis.

Zeina Z Shreif¹, Daniel M Gatti², Vipul Periwal³

¹Laboratory of Biological Modeling, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA.

BMC Bioinformatics

|December 24, 2016

Summary

This summary is machine-generated.

A new network-based method, Block Network Mapping, enhances the detection of quantitative trait loci by integrating genomic marker data. This approach improves analytical power and reduces false discoveries compared to traditional methods.

Keywords:

Bayes’ theorem Interval mapping QTL mapping

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Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Experimental biology generates vast genomic variation data.
Interpreting this data is crucial for developing therapies for complex diseases.
Network organization can mitigate noise in biological data.

Purpose of the Study:

To investigate if a network approach combining linear and correlative genomic marker information in a Bayesian framework improves quantitative trait loci detection power.

Main Methods:

Block Network Mapping integrates Similarity Network Fusion with Bayesian locus likelihood evaluation.
This method leverages correlations in sampled genotypes.

Main Results:

Block Network Mapping significantly improves the area under the receiver operating characteristic and power compared to interval mapping with expectation maximization.
The method exhibits a monotonically decreasing false discovery rate relative to effect size.

Conclusions:

Block Network Mapping offers a data-driven alternative for quantitative trait loci mapping.
It effectively utilizes genotype correlations and can complement existing methods like Interval Mapping.