Memorial Session for Professor C. R. Rao

C. R. Rao's work was always inspired by some practical problems. A problem of estimation of linkage parameters arose while Rao was working in R.A. Fisher's genetics laboratory on mice experiments involving mapping chromosomes. Rao wrote and published two papers based on this work. The first paper dealt with the general problem of testing simple and composite hypotheses concerning a vector parameter using scores, derivatives of the log-likelihood function with respect to the individual parameters. (We will call this the Rao Score (RS) test.) The other paper detailed the steps for analyzing the data involving the segregation of several factors in mating of different genotypes and using the RS test for the meta-analysis of testing the equality of parameters coming from different experimental data sets.
The RS test was well ahead of its time. It went unnoticed for very many years. It is fair to say that econometricians can claim major credit in applying RS test in several useful contexts and coming up with neat and elegant formulae test statistics. More recently, statisticians are catching up with innovative applications of RS test.
 

Diversity in genetic research plays a critical role in uncovering variations that contribute to disease risk, constructing accurate risk prediction models for disease prevention and management, and mitigating healthcare disparities, thus enhancing health outcomes in underserved communities. Nonetheless, the abundance of genetic data across diverse populations presents significant challenges in analysis and interpretation. In this presentation, I will delve into these challenges and explore current methodologies for mapping complex traits within multi-ethnic samples. Additionally, I will highlight the seminal work of Prof. C.R. Rao in quantifying diversity and pioneering statistical techniques for gene detection. Prof. Rao's groundbreaking contributions have left an indelible mark on statistical genetics, shaping its trajectory and methodologies. This discussion will serve as a tribute to his profound impact on the field of statistical genetics. 

Meta-analysis is a statistical method that combines the results of multiple high quality published studies investigating similar research questions. It involves systematically searching, appraising, and selecting studies for synthesizing of the results. However, studies with positive findings are more likely to be published (publication bias) that can potentially skew the findings. My 1991 thesis under Dr. C.R. Rao addressed this and sparked my passion for biostatistics. I'll provide a review of the progress in this field over the last thirty years and demonstrate how it could be used for informing policy and countering misinformation. Inspired by Dr. Rao's curiosity, I'll also discuss the importance of constantly exploring new methods and applications.