Innovative Association Methods in Genetics Studies

In this blog post, we delve into the innovative methodologies emerging in the field of genetic association studies, particularly focusing on the advancements made in 2020. These methodologies play a crucial role in deciphering the complex interactions between genotypes and phenotypes, which are central to understanding the genetic basis of diseases and traits.

Introduction
Genetic association studies aim to identify genetic variants that are associated with specific traits or diseases. The traditional approach involves scanning the genome for single nucleotide polymorphisms (SNPs) and testing their correlation with the traits of interest. However, this method faces challenges, including the need to correct for population structure and the high dimensionality of genetic data. In response, researchers have developed new association methods to overcome these obstacles, enhancing the power and accuracy of genetic studies.

New Association Methods in Genetics Studies
Causal Inference in Genetic Trio Studies
Bates, Sesia, Sabatti, and Candès (2020) introduced a method that leverages the natural randomness in meiosis as a randomized experiment. This approach enables the drawing of causal inferences from genetic data, including parents and offspring, by utilizing a conditional independence test that identifies regions of the genome containing distinct causal variants. This method shows enhanced power and localization compared to traditional methods, providing a robust framework for identifying genetic influences on traits (Bates et al., 2020).

Genetic Correlation and Mendelian Randomization Studies Kraft, Chen, and Lindström (2020) discussed the use of genome-wide genetic correlation and Mendelian Randomization (MR) studies to explore relationships between complex traits. Despite their potential, these approaches require careful consideration of their assumptions. The development of novel methods that are less sensitive to these assumptions is a key area of ongoing research (Kraft et al., 2020).

Multiple-trait Adaptive Fisher's Method for GWAS
Deng and Song (2020) proposed the Multiple-trait Adaptive Fisher's (MTAF) method, which tests associations between a genetic variant and multiple traits simultaneously by aggregating evidence from each trait. This method accommodates both continuous and binary traits and demonstrates reliability under various scenarios, providing a competitive alternative to existing methods in terms of type I error control and statistical power (Deng & Song, 2020).

Design Efficiency in Genetic Association Studies
Gjerdevik et al. (2020) explored the selection of optimal designs for genetic association studies, emphasizing the importance of achieving high statistical power at the lowest cost. They introduced the concept of relative efficiency to compare different study designs and demonstrated the effectiveness of case-parent triad designs for identifying parent-of-origin effects (Gjerdevik et al., 2020).

Conclusion
The advancements in genetic association methods in 2020 highlight the field's progress towards more accurate and powerful genetic analyses. These new methodologies, ranging from causal inference to design efficiency and multiple-trait analysis, offer promising avenues for unraveling the genetic underpinnings of complex traits and diseases. As these methods continue to evolve, they will undoubtedly contribute to our understanding of genetics, paving the way for personalized medicine and targeted therapeutic interventions.

Reference:
Bates, S., Sesia, M., Sabatti, C., & Candès, E. (2020). Causal inference in genetic trio studies. Proceedings of the National Academy of Sciences of the United States of America, 117, 24117 - 24126.
Kraft, P., Chen, H., & Lindström, S. (2020). The Use of Genetic Correlation and Mendelian Randomization Studies to Increase Our Understanding of Relationships between Complex Traits. Current Epidemiology Reports, 7, 104-112.
Deng, Q., & Song, C. (2020). Multiple-trait Adaptive Fisher's Method for Genome-wide Association Studies. arXiv: Methodology.
Gjerdevik, M., Gjessing, H., Romanowska, J., Haaland, Ø., Jugessur, A., Czajkowski, N., & Lie, R. (2020). Design efficiency in genetic association studies. Statistics in Medicine, 39, 1292 - 1310.