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A pure likelihood approach to the analysis of genetic association data: an alternative to Bayesian and frequentist analysis.

ABSTRACT: Investigators performing genetic association studies grapple with how to measure strength of association evidence, choose sample size, and adjust for multiple testing. We apply the evidential paradigm (EP) to genetic association studies, highlighting its strengths. The EP uses likelihood ratios (LRs), as opposed to P-values or Bayes' factors, to measure strength of association evidence. We derive EP methodology to estimate sample size, adjust for multiple testing, and provide informative graphics for drawing inferences, as illustrated with a Rolandic Epilepsy (RE) fine-mapping study. We focus on controlling the probability of observing weak evidence for or against association (W) rather than type I errors (M). For example, for LR> or =32 representing strong evidence, at one locus with n=200 cases, n=200 controls, W=0.134, whereas M=0.005. For n=300 cases and controls, W=0.039 and M=0.004. These calculations are based on detecting an OR=1.5. Despite the common misconception, one is not tied to this planning value for analysis; rather one calculates the likelihood at all possible values to assess evidence for association. We provide methodology to adjust for multiple tests across m loci, which adjusts M and W for m. We do so for (a) single-stage designs, (b) two-stage designs, and (c) simultaneously controlling family-wise error rate (FWER) and W. Method (c) chooses larger sample sizes than (a) or (b), whereas (b) has smaller bounds on the FWER than (a). The EP, using our innovative graphical display, identifies important SNPs in elongator protein complex 4 (ELP4) associated with RE that may not have been identified using standard approaches.

SUBMITTER: Strug LJ

PROVIDER: S-EPMC2911506 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionThe SARS-CoV-2 virus can currently pose a serious health threat and can lead to severe COVID-19 outcomes, especially for populations suffering from comorbidities. Currently, the data available on the risk for severe COVID-19 outcomes due to an HIV infection with or without comorbidities paint a heterogenous picture. In this meta-analysis, we summarized the likelihood for severe COVID-19 outcomes among people living with HIV (PLHIV) with or without comorbidities.MethodsFollowing PRISMA guidelines, we utilized PubMed, Web of Science and medRxiv to search for studies describing COVID-19 outcomes in PLHIV with or without comorbidities up to 25 June 2021. Consequently, we conducted two meta-analyses, based on a classic frequentist and Bayesian perspective of higher quality studies.Results and discussionWe identified 2580 studies (search period: January 2020-25 June 2021, data extraction period: 1 January 2021-25 June 2021) and included nine in the meta-analysis. Based on the frequentist meta-analytical model, PLHIV with diabetes had a seven times higher risk of severe COVID-19 outcomes (odd ratio, OR = 6.69, 95% CI: 3.03-19.30), PLHIV with hypertension a four times higher risk (OR = 4.14, 95% CI: 2.12-8.17), PLHIV with cardiovascular disease an odds ratio of 4.75 (95% CI: 1.89-11.94), PLHIV with respiratory disease an odds ratio of 3.67 (95% CI: 1.79-7.54) and PLHIV with chronic kidney disease an OR of 9.02 (95% CI: 2.53-32.14) compared to PLHIV without comorbidities. Both meta-analytic models converged, thereby providing robust summative evidence. The Bayesian meta-analysis produced similar effects overall, with the exclusion of PLHIV with respiratory diseases who showed a non-significant higher risk to develop severe COVID-19 outcomes compared to PLHIV without comorbidities.ConclusionsOur meta-analyses show that people with HIV, PLHIV with coexisting diabetes, hypertension, cardiovascular disease, respiratory disease and chronic kidney disease are at a higher likelihood of developing severe COVID-19 outcomes. Bayesian analysis helped to estimate small sample biases and provided predictive likelihoods. Clinical practice should take these risks due to comorbidities into account and not only focus on the HIV status alone, vaccination priorities should be adjusted accordingly.

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A pure likelihood approach to the analysis of genetic association data: an alternative to Bayesian and frequentist analysis.

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