On the analysis of copy-number variations in genome-wide association studies: a translation of the family-based association test

2008 ◽  
Vol 32 (3) ◽  
pp. 273-284 ◽  
Author(s):  
Iuliana Ionita-Laza ◽  
George H. Perry ◽  
Benjamin A. Raby ◽  
Barbara Klanderman ◽  
Charles Lee ◽  
...  
Keyword(s):  
Copy Number ◽  
Association Studies ◽  
Copy Number Variations ◽  
Association Test ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
The Family ◽  
Family Based

scholarly journals Dissecting Molecular Genetic Mechanisms of 1q21.1 CNV in Neuropsychiatric Disorders

2021 ◽  
Vol 22 (11) ◽  
pp. 5811
Author(s):  
Joy Yoon ◽  
Yingwei Mao
Keyword(s):  
Copy Number ◽  
Association Studies ◽  
Molecular Genetic ◽  
Neuropsychiatric Disorders ◽  
Autism Spectrum ◽  
Copy Number Variations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genetic Mechanisms

Pathogenic copy number variations (CNVs) contribute to the etiology of neurodevelopmental/neuropsychiatric disorders (NDs). Increased CNV burden has been found to be critically involved in NDs compared with controls in clinical studies. The 1q21.1 CNVs, rare and large chromosomal microduplications and microdeletions, are detected in many patients with NDs. Phenotypes of duplication and deletion appear at the two ends of the spectrum. Microdeletions are predominant in individuals with schizophrenia (SCZ) and microcephaly, whereas microduplications are predominant in individuals with autism spectrum disorder (ASD) and macrocephaly. However, its complexity hinders the discovery of molecular pathways and phenotypic networks. In this review, we summarize the recent genome-wide association studies (GWASs) that have identified candidate genes positively correlated with 1q21.1 CNVs, which are likely to contribute to abnormal phenotypes in carriers. We discuss the clinical data implicated in the 1q21.1 genetic structure that is strongly associated with neurodevelopmental dysfunctions like cognitive impairment and reduced synaptic plasticity. We further present variations reported in the phenotypic severity, genomic penetrance and inheritance.

scholarly journals Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use

Genetics ◽  
2019 ◽  
Vol 213 (4) ◽  
pp. 1225-1236 ◽  
Author(s):  
Weimiao Wu ◽  
Zhong Wang ◽  
Ke Xu ◽  
Xinyu Zhang ◽  
Amei Amei ◽  
...  
Keyword(s):  
Association Analysis ◽  
Binary Data ◽  
Association Studies ◽  
Association Test ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Cocaine Use ◽  
Genome Wide ◽  
A Genome ◽  
Time Varying Covariates

Longitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for phenotype distribution due to ascertainment. Here, we propose L-BRAT (Longitudinal Binary-trait Retrospective Association Test), a retrospective, generalized estimating equation-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.

scholarly journals Statistical challenges for genome-wide association studies of suicidality using family data

2010 ◽  
Vol 25 (5) ◽  
pp. 307-309 ◽  
Author(s):  
J. Lasky-Su ◽  
C. Lange
Keyword(s):  
Association Studies ◽  
Statistical Tests ◽  
Genetic Association Studies ◽  
Genetic Effect ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Etiology ◽  
Genome Wide ◽  
Family Based ◽  
Study Designs

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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