Abstract
Background: In genetic association studies with quantitative trait loci (QTL), the association between a candidate genetic marker and the trait of interest is commonly examined by the omnibus F test or by the t-test corresponding to a given genetic model or mode of inheritance. It is known that the t-test with a correct model specification is more powerful than the F test. However, since the underlying genetic model is rarely known in practice, the use of a model-specific t-test may incur substantial power loss. Robustefficient tests, such as the Maximin Efficiency Robust Test (MERT) and MAX3 have been proposed in the literature.
Methods: In this paper, we propose a novel two-step robust-efficient approach, namely, the genetic model selection (GMS) method for quantitative trait analysis. GMS selects a genetic model by testing Hardy-Weinberg disequilibrium (HWD) with extremal samples of the population in the first step and then applies the corresponding genetic modelspecific t-test in the second step.
Results: Simulations show that GMS is not only more efficient than MERT and MAX3, but also has comparable power to the optimal t-test when the genetic model is known.
Conclusion: Application to the data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort demonstrates that the proposed approach can identify meaningful biological SNPs on chromosome 19.
Keywords: Genetic association studies, quantitative trait loci, extreme samples, genetic model selection, hardy-weinberg disequilibrium, maximin efficiency robust test.
Graphical Abstract
Call for Papers in Thematic Issues
Advanced AI Techniques in Big Genomic Data Analysis
The thematic issue on "Advanced AI Techniques in Big Genomic Data Analysis" aims to explore the cutting-edge methodologies and applications of artificial intelligence (AI) in the realm of genomic research, where vast amounts of data pose both challenges and opportunities. This issue will cover a broad spectrum of AI-driven strategies, ...read more
Applications of Single-cell Sequencing Technology in Reproductive Medicine
Single cell sequencing (SCS) technology utilizes individual cells' genetic material to sequence their genome, transcriptome, and epigenetics at the molecular level. It offers insights into cell heterogeneity and enables the study of limited biological materials. Since its recognition as a valuable technique in 2011, single cell sequencing has yielded numerous ...read more
Current Genomics in Cardiovascular Research
Cardiovascular diseases are the main cause of death in the world, in recent years we have had important advances in the interaction between cardiovascular disease and genomics. In this Research Topic, we intend for researchers to present their results with a focus on basic, translational and clinical investigations associated with ...read more
Deep learning in Single Cell Analysis
The field of biology is undergoing a revolution in our ability to study individual cells at the molecular level, and to integrate data from multiple sources and modalities. This has been made possible by advances in technologies for single-cell sequencing, multi-omics profiling, spatial transcriptomics, and high-throughput imaging, as well as ...read more
Related Journals
Related Books
Fundamentals of Cellular and Molecular Biology
Industrial Applications of Soil Microbes
Genome Editing in Bacteria (Part 2)
Aromatherapy: The Science of Essential Oils
In Vitro Propagation and Secondary Metabolite Production from Medicinal Plants: Current Trends (Part 2)
Micropropagation of Medicinal Plants
Software and Programming Tools in Pharmaceutical Research
Biotechnology and Drug Development for Targeting Human Diseases
In Vitro Propagation and Secondary Metabolite Production from Medicinal Plants: Current Trends (Part 1)
Molecular and Physiological Insights into Plant Stress Tolerance and Applications in Agriculture- Part 2
17