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Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...

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

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Generalized Structured Component Analysis in candidate gene association studies: applications and limitations.

Paul A Thompson¹, Dorothy V M Bishop¹, Else Eising²

¹Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK.

Wellcome Open Research

|February 2, 2021

Summary

Generalized Structured Component Analysis (GSCA) can replicate genetic findings but may lose power with too many SNPs. It is best for validating candidate SNPs, not for initial discovery in genetic association studies.

Keywords:

GSCA Structural equation modelling developmental language disorder genetics power analysis simulation

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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Published on: July 27, 2021

Area of Science:

Genetics
Statistical genetics
Computational biology

Background:

Generalized Structured Component Analysis (GSCA) offers a component-based approach to structural equation modeling.
GSCA contrasts with traditional univariate genetic association analyses that test individual single nucleotide polymorphisms (SNPs).

Purpose of the Study:

To evaluate GSCA's ability to replicate findings in developmental language disorders.
To assess GSCA's validity under restrictive conditions (small sample sizes, numerous SNPs).
To compare GSCA performance against univariate association analysis.

Main Methods:

Replication of two previous genetic association findings using GSCA.
Simulation study to test GSCA under varying sample sizes and SNP numbers.
Comparative analysis of GSCA and PLINK v1.9 univariate association tests.

Main Results:

GSCA demonstrated the ability to replicate previous genetic findings.
Simulation results indicated that statistical power is influenced by the ratio of effective SNPs to total SNPs within a gene.
Including a large number of SNPs in GSCA models can dilute true genetic effects.

Conclusions:

GSCA is a valuable tool for replicating genetic associations with pre-identified candidate SNPs.
GSCA is not recommended for exploratory genetic analyses due to potential dilution of true effects.
The method's power is sensitive to the proportion of SNPs with effects within a gene.