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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

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Published on: June 15, 2011

Controlling false discoveries in genome scans for selection.

Olivier François¹, Helena Martins¹, Kevin Caye¹

¹Centre National de la Recherche Scientifique, Université Grenoble-Alpes, TIMC-IMAG UMR 5525, Grenoble, 38042, France.

Molecular Ecology

|December 17, 2015

Summary

Population differentiation (PD) and ecological association (EA) tests help identify local adaptation in genomic data. This study unifies these methods, offering guidelines to control false discoveries and improve local adaptation inference.

Keywords:

control of false discovery rates genome scans for selection

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Area of Science:

Population genomics
Evolutionary biology
Statistical genetics

Background:

Population differentiation (PD) and ecological association (EA) tests are key for detecting local adaptation using genomic data.
These methods identify loci under natural selection but are susceptible to false positives from demographic history and genetic background.
Existing research focuses on improving test corrections for confounding factors.

Purpose of the Study:

To provide a unified framework for PD and EA statistical tests.
To address open questions on controlling false discoveries and implementing test corrections.
To guide the combination of multiple genome scan methods for robust inference.

Main Methods:

Clarifying relationships between allele frequency differentiation and EA methods.
Applying techniques from genomewide association studies (GWAS), such as inflation factors and linear mixed models.
Developing a unified statistical framework for genome scan methods.

Main Results:

Demonstrated how GWAS techniques enhance genome scan methods.
Provided guidelines for best practices in population and landscape genomic statistical testing.
Showcased how combining well-calibrated tests boosts power to detect local adaptation.

Conclusions:

A unified framework improves understanding and application of PD and EA tests.
Proper statistical corrections and test combinations are crucial for reliable local adaptation inference.
This work enhances the ability to detect local adaptation patterns in large population genomic datasets.