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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable

Nicholas B Larson1, Shannon McDonnell1, Lisa Cannon Albright2

  • 1Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America.

Genetic Epidemiology
|June 18, 2016
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Summary

This study introduces a computationally efficient probit regression method to analyze rare variants (RVs) for complex disease risk. The approach effectively identifies causal RVs within large datasets, improving upon existing Bayesian methods for genetic association studies.

Keywords:
MCMCNext-generation sequencingburden testingprostate cancer

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Area of Science:

  • Genetics
  • Statistical genetics
  • Computational biology

Background:

  • Rare variants (RVs) are significant contributors to complex disease risk.
  • Traditional single-marker methods are underpowered for analyzing RVs in sequencing studies.
  • Existing multimarker and Bayesian approaches face computational limitations with large numbers of RVs.

Purpose of the Study:

  • To develop a computationally efficient method for analyzing large numbers of rare variants (RVs).
  • To identify specific RVs driving associations with complex diseases.
  • To overcome computational limitations of existing Bayesian variable selection methods.

Main Methods:

  • Proposed a novel probit regression formulation for analyzing rare variants.
  • Developed a method capable of simultaneously analyzing hundreds to thousands of RVs.
  • Evaluated the approach using simulated data and real-world prostate cancer sequencing data.

Main Results:

  • The probit regression approach demonstrated computational efficiency in analyzing high-dimensional RV data.
  • The method successfully detected causal variation in simulated datasets.
  • Applied to prostate cancer data, the approach facilitated pathway-level RV analysis.

Conclusions:

  • The proposed probit regression method offers a computationally efficient alternative for rare variant association studies.
  • This approach enables the simultaneous analysis of a large number of RVs, improving power and interpretability.
  • The method has potential for broader application in complex disease genetics and personalized medicine.