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Related Experiment Video

Updated: May 29, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Combining markers into haplotypes can improve population structure inference.

Lucie M Gattepaille¹, Mattias Jakobsson

¹Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, SE-752 36, Uppsala, Sweden.

|August 27, 2011

Summary

Harnessing genetic markers like single-nucleotide polymorphisms (SNPs) through haplotypes significantly improves population assignment accuracy. This method, especially for linked markers, reduces incorrect ancestry assignments by up to 97%.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Population Genetics
Genomics
Bioinformatics

Background:

High-throughput technologies generate dense genetic marker data (e.g., single-nucleotide polymorphisms or SNPs) for numerous individuals.
These datasets often contain markers in linkage disequilibrium (LD), posing challenges for population structure inference.

Purpose of the Study:

To investigate the utility of haplotypes for population structure inference using SNP data.
To introduce and evaluate a statistic, Gain of Informativeness for Assignment (GIA), for quantifying the benefit of using haplotypes.

Main Methods:

Developed the Gain of Informativeness for Assignment (GIA) statistic based on information theory.
Analyzed a two-locus, two-allele model to assess GIA for markers in linkage equilibrium and linkage disequilibrium.
Applied GIA as a criterion for haplotype construction and tested on simulated and empirical population genetic data.

Main Results:

Combining unlinked markers into haplotypes generally does not improve, and can decrease, assignment accuracy (nonpositive GIA).
For markers in linkage disequilibrium (LD), haplotypes often yield positive GIA, significantly enhancing population assignment.
Simulated data showed incorrect assignments reduced by 26%–97%; empirical French and German data saw a 73% reduction.

Conclusions:

Haplotype-based assignment, guided by GIA, offers substantial improvements over single-locus analysis for population structure inference.
This approach is particularly valuable for large-scale population genomic studies with markers in LD.
The GIA statistic provides a robust metric for optimizing haplotype construction in population assignment tasks.