Literature DB >> 35201341

gJLS2: an R package for generalized joint location and scale analysis in X-inclusive genome-wide association studies.

Wei Q Deng1,2, Lei Sun3,4.   

Abstract

A joint analysis of location and scale can be a powerful tool in genome-wide association studies to uncover previously overlooked markers that influence a quantitative trait through both mean and variance, as well as to prioritize candidates for gene-environment interactions. This approach has recently been generalized to handle related samples, dosage data, and the analytically challenging X-chromosome. We disseminate the latest advances in methodology through a user-friendly R software package with added functionalities to support genome-wide analysis on individual-level or summary-level data. The implemented R package can be called from PLINK or directly in a scripting environment, to enable a streamlined genome-wide analysis for biobank-scale data. Application results on individual-level and summary-level data highlight the advantage of the joint test to discover more genome-wide signals as compared to a location or scale test alone. We hope the availability of gJLS2 software package will encourage more scale and/or joint analyses in large-scale datasets, and promote the standardized reporting of their P-values to be shared with the scientific community.
© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.

Entities:  

Keywords:  PLINK; R; X-chromosome association; gene–environment interactions; joint location and scale; variance heterogeneity

Mesh:

Year:  2022        PMID: 35201341      PMCID: PMC8982384          DOI: 10.1093/g3journal/jkac049

Source DB:  PubMed          Journal:  G3 (Bethesda)        ISSN: 2160-1836            Impact factor:   3.154


  15 in total

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Journal:  G3 (Bethesda)       Date:  2019-05-07       Impact factor: 3.154

8.  Quantifying the contribution of dominance deviation effects to complex trait variation in biobank-scale data.

Authors:  Ali Pazokitoroudi; Alec M Chiu; Kathryn S Burch; Bogdan Pasaniuc; Sriram Sankararaman
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Authors:  Wei Zhou; Jonas B Nielsen; Lars G Fritsche; Rounak Dey; Maiken E Gabrielsen; Brooke N Wolford; Jonathon LeFaive; Peter VandeHaar; Sarah A Gagliano; Aliya Gifford; Lisa A Bastarache; Wei-Qi Wei; Joshua C Denny; Maoxuan Lin; Kristian Hveem; Hyun Min Kang; Goncalo R Abecasis; Cristen J Willer; Seunggeun Lee
Journal:  Nat Genet       Date:  2018-08-13       Impact factor: 38.330

10.  The X factor: A robust and powerful approach to X-chromosome-inclusive whole-genome association studies.

Authors:  Bo Chen; Radu V Craiu; Lisa J Strug; Lei Sun
Journal:  Genet Epidemiol       Date:  2021-07-05       Impact factor: 2.344

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