On Quality Control Measures in Genome-Wide Association Studies: A Test to Assess the Genotyping Quality of Individual Probands in Family-Based Association Studies and an Application to the HapMap Data

AbstractThe etiology of suicide is complex in nature with both environmental and genetic causes that are extremely diverse. This extensive heterogeneity weakens the relationship between genotype and phenotype and as a result, we face many challenges when studying the genetic etiology of suicide. We are now in the midst of a genetics revolution, where genotyping costs are decreasing and genotyping speed is increasing at a fast rate, allowing genetic association studies to genotype thousands to millions of SNPs that cover the entire human genome. As such, genome-wide association studies (GWAS) are now the norm. In this article we address several statistical challenges that occur when studying the genetic etiology of suicidality in the age of the genetics revolution. These challenges include: (1) the large number of statistical tests; (2) complex phenotypes that are difficult to quantify; and (3) modest genetic effect sizes. We address these statistical issues in the context of family-based study designs. Specifically, we discuss several statistical extensions of family-based association tests (FBATs) that work to alleviate these challenges. As our intention is to describe how statistical methodology may work to identify disease variants for suicidality, we avoid the mathematical details of the methodologies presented.

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Common statistical issues in genome-wide association studies: a review on power, data quality control, genotype calling and population structure

Current Opinion in Lipidology ◽

10.1097/mol.0b013e3282f5dd77 ◽

2008 ◽

Vol 19 (2) ◽

pp. 133-143 ◽

Cited By ~ 57

Author(s):

Yik Y Teo

Keyword(s):

Quality Control ◽

Population Structure ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Data Quality Control ◽

Genotype Calling ◽

Genome Wide ◽

Statistical Issues ◽

Control Genotype

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On the Analysis of Genome-Wide Association Studies in Family-Based Designs: A Universal, Robust Analysis Approach and an Application to Four Genome-Wide Association Studies

PLoS Genetics ◽

10.1371/journal.pgen.1000741 ◽

2009 ◽

Vol 5 (11) ◽

pp. e1000741 ◽

Cited By ~ 34

Author(s):

Sungho Won ◽

Jemma B. Wilk ◽

Rasika A. Mathias ◽

Christopher J. O'Donnell ◽

Edwin K. Silverman ◽

...

Keyword(s):

Association Studies ◽

Genome Wide Association ◽

Analysis Approach ◽

Genome Wide Association Studies ◽

Robust Analysis ◽

Genome Wide ◽

Family Based

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Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

10.21203/rs.2.12350/v1 ◽

2019 ◽

Author(s):

Amber C. A. Hendriks ◽

Frans A.G. Reubsaet ◽

A.M.D. (Mirjam) Kooistra ◽

John W. A. Rossen ◽

Bas E. Dutilh ◽

...

Keyword(s):

Escherichia Coli ◽

Disease Severity ◽

Genetic Variants ◽

Association Studies ◽

Control Measures ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Severity Scores ◽

Shigella Spp

Abstract Background We investigated the association of symptoms and disease severity of shigellosis patients with genetic determinants of infecting Shigella and entero-invasive Escherichia coli (EIEC), because determinants that predict disease outcome per individual patient could be used to prioritize control measures. For this purpose, genome wide association studies (GWAS) were performed using presence or absence of single genes, combinations of genes, and k-mers. All genetic variants were derived from draft genome sequences of isolates from a multicenter cross-sectional study conducted in the Netherlands during 2016 and 2017. Clinical data of patients consisting of binary/dichotomous representation of symptoms and their calculated severity scores were also available from this study. To verify the suitability of the used methods, the genetic differences between the genera Shigella and Escherichia were used as control. Results The obtained isolates were representative for a population structure as encountered in a Western European country. No association was found between single genes or combinations of genes and separate symptoms or disease severity scores. One potentially associated intergenic region was found using a k-mer approach, however, this turned out to be a false positive. Our benchmark characteristic, genus, resulted in eight associated genes and >3,000,000 k-mers, indicating adequate performance of the used algorithms. Conclusions To conclude, using several microbial GWAS methods, genetic variants in Shigella spp. and EIEC that can predict specific symptoms or a more severe course of disease were not identified, suggesting that disease severity of shigellosis is dependent on other factors than the genetic variation of the infecting bacteria. Specific genes or gene fragments of isolates from patients are unsuitable to predict outcomes and cannot be used for development, prioritization and optimization of guidelines for control measures of shigellosis or infections with EIEC.

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