Dataset Information

Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization -GALLOP algorithm.

ABSTRACT: Genome-wide association studies (GWAS) with longitudinal phenotypes provide opportunities to identify genetic variations associated with changes in human traits over time. Mixed models are used to correct for the correlated nature of longitudinal data. GWA studies are notorious for their computational challenges, which are considerable when mixed models for thousands of individuals are fitted to millions of SNPs. We present a new algorithm that speeds up a genome-wide analysis of longitudinal data by several orders of magnitude. It solves the equivalent penalized least squares problem efficiently, computing variances in an initial step. Factorizations and transformations are used to avoid inversion of large matrices. Because the system of equations is bordered, we can re-use components, which can be precomputed for the mixed model without a SNP. Two SNP effects (main and its interaction with time) are obtained. Our method completes the analysis a thousand times faster than the R package lme4, providing an almost identical solution for the coefficients and p-values. We provide an R implementation of our algorithm.

SUBMITTER: Sikorska K

PROVIDER: S-EPMC5931565 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization -GALLOP algorithm.

Sikorska Karolina K Lesaffre Emmanuel E Groenen Patrick J F PJF Rivadeneira Fernando F Eilers Paul H C PHC

Scientific reports 20180501 1

Genome-wide association studies (GWAS) with longitudinal phenotypes provide opportunities to identify genetic variations associated with changes in human traits over time. Mixed models are used to correct for the correlated nature of longitudinal data. GWA studies are notorious for their computational challenges, which are considerable when mixed models for thousands of individuals are fitted to millions of SNPs. We present a new algorithm that speeds up a genome-wide analysis of longitudinal da ...[more]

PMID: 29717146

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Project description:BackgroundPatients with osteoarthritis (OA) are exposed to an increased risk of adverse outcomes of COVID-19, and they tend to experience disruption in access to healthcare services and exercise facilities. However, a deep understanding of this comorbidity phenomenon and the underlying genetic architecture of the two diseases is still unclear. In this study, we aimed to untangle the relationship between OA and COVID-19 outcomes by conducting a large-scale genome-wide cross-trait analysis.MethodsGenetic correlation and causal relationships between OA and COVID-19 outcomes (critical COVID-19, COVID-19 hospitalization, and COVID-19 infection) were estimated by linkage disequilibrium score regression and Mendelian Randomization approaches. We further applied Multi-Trait Analysis of GWAS and colocalization analysis to identify putative functional genes associated with both OA and COVID-19 outcomes.ResultsSignificant positive genetic correlations between OA susceptibility and both critical COVID-19 (rg=0.266, P=0.0097) and COVID-19 hospitalization (rg=0.361, P=0.0006) were detected. However, there was no evidence to support causal genetic relationships between OA and critical COVID-19 (OR=1.17[1.00-1.36], P=0.049) or OA and COVID-19 hospitalization OR=1.08[0.97-1.20], P=0.143). These results were robustly consistent after the removal of obesity-related single nucleotide polymorphisms (SNPs). Moreover, we identified a strong association signal located near the FYCO1 gene (lead SNPs: rs71325101 for critical COVID-19, Pmeta=1.02×10-34; rs13079478 for COVID-19 hospitalization, Pmeta=1.09×10-25).ConclusionOur findings further confirmed the comorbidity of OA and COVID-19 severity, but indicate a non-causal impact of OA on COVID-19 outcomes. The study offers an instructive perspective that OA patients did not generate negative COVID-19 outcomes during the pandemic in a causal way. Further clinical guidance can be formulated to enhance the quality of self-management in vulnerable OA patients.

Dataset Information

Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization -GALLOP algorithm.

Publications

Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization -GALLOP algorithm.

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