High-density LD-based structural variations analysis in ten Native and Mestizo Mexican populations

Area of Science:

• Genomics
• Population Genetics
• Bioinformatics

Background:

• Structural Variations (SVs) are crucial for understanding genome diversity and evolution.
• Linkage Disequilibrium (LD) patterns, particularly between Single Nucleotide Polymorphisms (SNPs), can reveal population structure and demographic history.
• Mexican populations exhibit unique genetic diversity influenced by Native and Mestizo ancestries.

Purpose of the Study:

• To conduct a genome-wide characterization of Structural Variations (SVs) in 10 Native and Mestizo Mexican populations.
• To investigate the relationship between SVs, inferred from SNP-based Linkage Disequilibrium (LD), and population structure.
• To identify potential SVs associated with health conditions prevalent in the Mexican population.

Main Methods:

• Utilized SNP genotyping data from 383 individuals across 10 Mexican populations.
• Analyzed deviations in expected short-range Linkage Disequilibrium (LD) between Single Nucleotide Polymorphisms (SNPs) to identify SVs.
• Mapped identified SVs to genes to assess their potential functional and health-related implications.

Main Results:

• Identified 4,375 structural variations across the 10 populations, averaging 19,438 SNPs per population.
• Characterized mean SV sizes ranging from 2,845-8,646 kb, with an average of 50.14 SNPs per SV.
• Discovered 506 distinct SV regions, with 54 regions common to all 10 populations, and identified 8,443 genes affected by these variations, including FTO, ABCA1, and ELMO1 linked to obesity and diabetes.

Conclusions:

• SVs, detected via SNP-LD deviations, effectively capture population structure and demographic history.
• These identified SVs represent potential targets for understanding population-specific diseases in Mexican populations.
• The study provides a valuable genomic resource for future research into the genetic basis of health and disease in Mexico.