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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.
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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Updated: Nov 9, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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A Fast Multi-Locus Ridge Regression Algorithm for High-Dimensional Genome-Wide Association Studies.

Jin Zhang1,2, Min Chen1, Yangjun Wen1

  • 1College of Science, Nanjing Agricultural University, Nanjing, China.

Frontiers in Genetics
|April 15, 2021
PubMed
Summary
This summary is machine-generated.

The new FastRR algorithm enhances genome-wide association studies by detecting minor genetic effects. This method improves quantitative trait nucleotide detection and effect estimation in large genomic datasets.

Keywords:
genome-wide association studyminor effectmixed linear modelmulti-locus algorithmpolygenic backgroundstatistical power

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Mixed linear models (MLM) are standard for genome-wide association studies (GWAS) but struggle to detect minor genetic effects.
  • Existing methods often overlook the combined impact of multiple genetic markers (polygenes) on traits, limiting discoveries in the big data era.

Purpose of the Study:

  • To develop a novel algorithm, Fast multi-locus ridge regression (FastRR), to address the limitations of current MLM approaches in GWAS.
  • To improve the detection of both major and minor quantitative trait nucleotides (QTNs) and enhance the accuracy of effect estimation.

Main Methods:

  • The FastRR algorithm employs a multi-step approach: covariance matrix whitening, selection of highly correlated single nucleotide polymorphisms (SNPs), and multi-locus deshrinking ridge regression.
  • It is designed to handle high-dimensional genomic datasets efficiently.

Main Results:

  • FastRR demonstrates superior power in detecting both large and small effect QTNs compared to existing methods.
  • The algorithm provides more accurate QTN effect estimations and stable results across diverse polygenic backgrounds.
  • FastRR offers a significant advantage in computational speed.

Conclusions:

  • The FastRR algorithm is a powerful and efficient alternative for multi-locus GWAS, particularly in large-scale genomic datasets.
  • It enhances the ability to identify polygenic effects, advancing our understanding of quantitative traits in genetics.