scholarly journals Comparison of Statistical Tests for Association between Rare Variants and Binary Traits

PLoS ONE ◽  
2012 ◽  
Vol 7 (8) ◽  
pp. e42530 ◽  
Author(s):  
Silviu-Alin Bacanu ◽  
Matthew R. Nelson ◽  
John C. Whittaker
Keyword(s):  
Rare Variants ◽  
Statistical Tests ◽  
Binary Traits ◽  
Tests For Association

scholarly journals Assessing Gene-Environment Interactions for Common and Rare Variants with Binary Traits Using Gene-Trait Similarity Regression

Genetics ◽  
2015 ◽  
Vol 199 (3) ◽  
pp. 695-710 ◽  
Author(s):  
Guolin Zhao ◽  
Rachel Marceau ◽  
Daowen Zhang ◽  
Jung-Ying Tzeng
Keyword(s):  
Rare Variants ◽  
Binary Traits ◽  
Gene Environment ◽  
Trait Similarity

scholarly journals Inherited rare, deleterious variants in ATM increase lung adenocarcinoma risk

2020 ◽  
Author(s):  
Myvizhi Esai Selvan ◽  
Marjorie G. Zauderer ◽  
Charles M. Rudin ◽  
Siân Jones ◽  
Semanti Mukherjee ◽  
...  
Keyword(s):  
Rare Variants ◽  
Statistical Tests ◽  
Treatment Options ◽  
Ct Scanning ◽  
Cancer Syndrome ◽  
Whole Exome ◽  
Gene Level ◽  
Prevalent Subtype ◽  
Penalized Logistic Regression ◽  
Control Association

ABSTRACTIntroductionLung cancer is the leading cause of cancer deaths in the world, and adenocarcinoma (LUAD) is its most prevalent subtype. Symptoms often appear in advanced disease when treatment options are limited. Identifying genetic risk factors will enable better identification of high-risk individuals.MethodsTo identify LUAD risk genes, we performed a case-control association study for gene-level burden of rare, deleterious variants (RDVs) in germline whole-exome sequencing (WES) data of 1,083 LUAD patients and 7,650 controls, split into discovery and validation cohorts. Of these, we performed WES on 97 patients and acquired the rest from multiple public databases. We annotated all rare variants for pathogenicity conservatively, using ACMG guidelines and ClinVar curation, and investigated gene-level RDV burden using penalized logistic regression. All statistical tests were two-sided.ResultsWe discovered and replicated the finding that the burden of germline ATM RDVs was significantly higher in LUAD patients versus controls (ORcombined=4.6; p=1.7e-04; 95% CI=2.2–9.5; 1.21% of cases; 0.24% of controls). Germline ATM RDVs were also enriched in an independent clinical cohort of 1,594 patients from the MSK-IMPACT study (0.63%). Additionally, we observed that an Ashkenazi Jewish (AJ) founder ATM variant, rs56009889, was statistically significantly more frequent in AJ cases versus AJ controls in our cohort (ORcombined, AJ=2.7, p=6.9e-03, 95% CI=1.3–5.3).ConclusionsOur results indicate that ATM is a moderate-penetrance LUAD risk gene, and that LUAD may be part of the ATM-related cancer syndrome spectrum. Individuals with ATM RDVs are at elevated LUAD risk and can benefit from increased surveillance (particularly CT scanning), early detection and chemoprevention programs, improving prognosis.

scholarly journals An evaluation of approaches for rare variant association analyses of binary traits in related samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ming-Huei Chen ◽  
Achilleas Pitsillides ◽  
Qiong Yang
Keyword(s):  
Logistic Regression ◽  
Rare Variants ◽  
Association Studies ◽  
Family Relationship ◽  
Genetic Association Studies ◽  
Error Rates ◽  
Ratio Test ◽  
Type I ◽  
Association Analyses ◽  
Binary Traits

AbstractRecognizing that family data provide unique advantage of identifying rare risk variants in genetic association studies, many cohorts with related samples have gone through whole genome sequencing in large initiatives such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Analyzing rare variants poses challenges for binary traits in that some genotype categories may have few or no observed events, causing bias and inflation in commonly used methods. Several methods have recently been proposed to better handle rare variants while accounting for family relationship, but their performances have not been thoroughly evaluated together. Here we compare several existing approaches including SAIGE but not limited to related samples using simulations based on the Framingham Heart Study samples and genotype data from Illumina HumanExome BeadChip where rare variants are the majority. We found that logistic regression with likelihood ratio test applied to related samples was the only approach that did not have inflated type I error rates in both single variant test (SVT) and gene-based tests, followed by Firth logistic regression that had inflation in its direction insensitive gene-based test at prevalence 0.01 only, applied to either related or unrelated samples, though theoretically logistic regression and Firth logistic regression do not account for relatedness in samples. SAIGE had inflation in SVT at prevalence 0.1 or lower and the inflation was eliminated with a minor allele count filter of 5. As for power, there was no approach that outperformed others consistently among all single variant tests and gene-based tests.

scholarly journals FastGWA-GLMM: a generalized linear mixed model association tool for biobank-scale data

2021 ◽  
Author(s):  
Jian Yang ◽  
Longda Jiang ◽  
Zhili Zheng
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Rare Variants ◽  
Sparse Matrix ◽  
Extreme Case ◽  
Generalized Linear Mixed Model ◽  
Binary Complex ◽  
Binary Traits ◽  
The Uk

Abstract Compared to linear mixed model-based genome-wide association (GWA) methods, generalized linear mixed model (GLMM)-based methods have better statistical properties when applied to binary traits but are computationally much slower. Here, leveraging efficient sparse matrix-based algorithms, we developed a GLMM-based GWA tool (called fastGWA-GLMM) that is orders of magnitude faster than the state-of-the-art tool (e.g., ~37 times faster when n=400,000) with more scalable memory usage. We show by simulation that the fastGWA-GLMM test-statistics of both common and rare variants are well-calibrated under the null, even for traits with an extreme case-control ratio (e.g., 0.1%). We applied fastGWA-GLMM to the UK Biobank data of 456,348 individuals, 11,842,647 variants and 2,989 binary traits (full summary statistics available at http://fastgwa.info/ukbimpbin) and identified 259 rare variants associated with 75 traits, demonstrating the use of imputed genotype data in a large cohort to discover rare variants for binary complex traits.

scholarly journals Adaptive tests for association analysis of rare variants

2011 ◽  
Vol 35 (5) ◽  
pp. 381-388 ◽  
Author(s):  
Wei Pan ◽  
Xiaotong Shen
Keyword(s):  
Association Analysis ◽  
Rare Variants ◽  
Adaptive Tests ◽  
Tests For Association

Comparação de testes coproparasitológicos em criadouro comercial de psitacídeos exóticos

2020 ◽  
Vol 3 (5) ◽  
pp. 100-107
Author(s):  
Marina Camargo de Sousa ◽  
◽  
Julia Ronzani Vial ◽  
Rodrigo Hidalgo Friciello Teixeira ◽  
Andrea Cristina Higa Nakaghi ◽  
...  
Keyword(s):  
Statistical Tests ◽  
Companion Animals ◽  
Clinical Importance ◽  
Breeding Site ◽  
Chi Square ◽  
Fecal Samples ◽  
Breeding Sites ◽  
Specificity And Sensitivity ◽  
Nematode Eggs ◽  
Commercial Breeding

Birds of the psittaciform order, composed by the Psittacidae and Loridae family have several characteristics making them more frequently kept as companion animals, promoting the increase of breeding sites in Brazil. The present study aimed to analyze the specificity and sensitivity of three different coproparasitological tests, Willis, Hoffman and Direto de feces, through statistical tests: Chi-Square and Kappa. 70 fecal samples of exotic parrots were collected from a commercial breeding site and these were submitted to the three tests, totaling 210 coproparasitological exams. Among the tests performed, 29,5% were positive for nematode eggs, cestodes and oocysts. Coproparasitological exams are inexpensive, have clinical importance, indicating the population of endoparasites and therapeutic treatments.

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