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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

Haplotype-based methods for detecting uncommon causal variants with common SNPs.

Wan-Yu Lin¹, Nengjun Yi, Degui Zhi

¹Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

Genetic Epidemiology

|June 19, 2012

Summary

Detecting uncommon causal variants is challenging. A new wei-SIMc-matching test improves power by weighting haplotype similarities, offering a computationally feasible and reliable method for genetic studies.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

Genetics
Statistical Genetics
Genomic Association Studies

Background:

Detecting uncommon causal variants (minor allele frequency < 5%) is difficult using standard single-nucleotide polymorphism (SNP) arrays optimized for common variants (MAF > 5%).
Haplotypes offer insights into linkage disequilibrium (LD) structure and can identify uncommon variants missed by common variant tagging.

Purpose of the Study:

To develop and evaluate a novel statistical test, wei-SIMc-matching, to enhance the power of detecting uncommon causal variants.
To compare the performance of wei-SIMc-matching against existing haplotype-based tests under diverse genetic scenarios.

Main Methods:

Proposed the wei-SIMc-matching test, which inversely weights haplotype similarities by the standard deviation of haplotype counts.
Conducted systematic simulations across various linkage disequilibrium (LD) patterns to assess test power.
Compared wei-SIMc-matching with established haplotype-based tests, including similarity-based, global score, and maximum score statistic tests.

Main Results:

The wei-SIMc-matching test and a global score test demonstrated the highest power among all evaluated methods.
wei-SIMc-matching provided reliable asymptotic P-values across simulation conditions.
The global score test required permutation-based P-values for accuracy, particularly with low haplotype frequencies or skewed traits.

Conclusions:

The wei-SIMc-matching test is a powerful and computationally feasible method for detecting uncommon causal variants.
It effectively utilizes surrounding common single-nucleotide polymorphisms (SNPs) for identifying rare variants.
Recommended for genetic association studies aiming to detect uncommon causal variants due to its performance and reliability.