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Karyotyping

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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes.

Iman Hamid¹, Katharine L Korunes¹, Daniel R Schrider²

¹Department of Evolutionary Anthropology, Duke University, Durham, NC.

Molecular Biology and Evolution

|March 22, 2023

Summary

This study introduces a novel deep learning method to pinpoint adaptive genes in admixed populations. The approach accurately identifies selection signals, improving upon existing methods for genetic ancestry analysis.

Keywords:

admixture convolutional neural network introgression object detection selection

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Published on: May 8, 2020

Area of Science:

Population genetics
Genomics
Bioinformatics

Background:

Gene flow during population admixture can introduce adaptive alleles.
Genetic ancestry patterns are used to detect positive selection after admixture.
Current methods for detecting selection based on local ancestry have limitations, including potential false positives/negatives and broad genomic inferences.

Purpose of the Study:

To develop a new computational method for identifying local ancestry outliers indicative of positive selection.
To overcome limitations of existing methods by incorporating genomic context and avoiding user-defined statistics.
To provide a more precise and biologically interpretable way to detect selection in admixed populations.

Main Methods:

Developed a deep learning object detection model applied to local ancestry-painted genome images.
Utilized simulated data to test the robustness of the method under various demographic scenarios.
Applied the method to human genotype data from Cabo Verde.

Main Results:

The deep learning method demonstrated robustness to demographic misspecifications in simulations.
The approach successfully localized a known adaptive locus in Cabo Verdean human data to a narrow genomic region.
Compared to existing ancestry-based methods, this new technique provided more precise localization of selection signals.

Conclusions:

The developed deep learning method offers a more accurate and refined approach to detecting positive selection in admixed populations.
This method improves upon traditional techniques by leveraging genomic context and reducing the inference of wide genomic regions under selection.
The findings have implications for understanding adaptation and genetic variation in admixed populations, enhancing biological interpretation.