The evidential statistical paradigm in genetics

Self Cite

The likelihood function represents statistical evidence given data and a model. The evidential paradigm (EP), an alternative to Bayesian and Frequentist paradigms, provides considerable theory demonstrating evidence strength for different parameter values via the ratio of likelihoods at different parameter values; thus, enabling inference directly from the likelihood function. The likelihood function, however, can be difficult to compute; for example, in genetic association studies with a binary outcome in large pedigrees. Composite likelihood (CL) is an alternative when the real likelihood is intractable. We show CLs have the two large sample properties of the EP for reliable evidence interpretation: (1) CL supports the true value over a false value by an arbitrarily large factor; and (2) the probability of favouring a false value over the true value is small and bounded. Using simulation, and in a genetic association analysis of reading disability (RD) in large rolandic epilepsy pedigrees, we show that the CL approach yields valid statistical inference and identifies RD associated variants. When compared to analyses using generalized estimating equations, results show a similar prioritization of SNPs, although the CL approach provides additional complementary information, and more intuitive solutions to the multiple hypothesis testing problem.

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Composite Likelihood Inference In the Evidential Paradigmmentioning

confidence: 99%

“…Since CLs are usually misspecified likelihoods (i.e., the subset of data used in lower dimensional likelihood products are usually not independent), one needs to establish the properties of EP analysis that ensure reliable inference (Royall & Tsou, 2003;Strug, 2018). In addition to the consistency property, the probability of misleading evidence (the analog of the type I error probability) needs to be described by the bump function and bounded (Royall, 2000;Strug, 2018). It is possible that the lower dimensional likelihood objects are not correctly specified.…”

Section: Composite Likelihood Inference In the Evidential Paradigmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genetic association analysis with pedigrees: Direct inference using the composite likelihood ratio

Baskurt

Strug

2018

Self Cite

Presidential address: Six open questions to genetic epidemiologists

König

2019

Given the rapid pace with which genomics and other ‐omics disciplines are evolving, it is sometimes necessary to shift down a gear to consider more general scientific questions. In this line, in my presidential address I formulate six questions for genetic epidemiologists to ponder on. These cover the areas of reproducibility, statistical significance, chance findings, precision medicine and related fields such as bioinformatics and data science. Possible hints at responses are presented to foster our further discussion of these topics.

RoPE: A robust profile likelihood method for differential gene expression analysis

Zhong

Strug

2023

Self Cite

Variation in RNA‐Seq data creates modeling challenges for differential gene expression (DE) analysis. Statistical approaches address conventional small sample sizes and implement empirical Bayes or non‐parametric tests, but frequently produce different conclusions. Increasing sample sizes enable proposal of alternative DE paradigms. Here we develop RoPE, which uses a data‐driven adjustment for variation and a robust profile likelihood ratio DE test. Simulation studies show RoPE can have improved performance over existing tools as sample size increases and has the most reliable control of error rates. Application of RoPE demonstrates that an active Pseudomonas aeruginosa infection downregulates the SLC9A3 Cystic Fibrosis modifier gene.