VLDB Panel Summary

Database education is at an inflection point. With the surge of interest in all things "data", enrollments in traditional database courses are at an all time high. At the same time, the rise of Data Science as a discipline has led to the creation of new courses whose content significantly overlaps that of an introductory database course (e.g. data preparation, cleaning, SQL). Students from all across campus aspire to take data science courses, even with limited Computer Science backgrounds. This juxtaposition of content and proliferation of audiences is causing many database educators to question what we should be teaching in our data-oriented courses, and what resources we should use to teach them.

Evaluating the estimation of genetic correlation and heritability using summary statistics

While novel statistical methods quantifying the shared heritability of traits and diseases between ancestral distinct populations have been recently proposed, a thorough evaluation of these approaches under differing circumstances remain elusive. Brown et al.2016 proposed the method Popcorn to estimate the shared heritability, i.e. genetic correlation, using only summary statistics. Here, we evaluate Popcorn under several parameters and circumstances: sample size, number of SNPs, sample size of external reference panel, various population pairs, inappropriate external reference panel, and admixed population involved. Our results determined the minimum sample size of the external reference panel, summary statistics, and number of SNPs required to accurately estimate both the genetic correlation and heritability. Moreover, the number of individuals and SNPs required to produce accurate and stable estimates was directly proportional with heritability in Popcorn. Misrepresentation of the reference panel overestimated the genetic correlation by 20% and heritability by 60%. Lastly, applying Popcorn to homogeneous (EUR) and admixed (ASW) populations underestimated the genetic correlation by 15%. Although statistical approaches estimating the shared heritability between ancestral populations will provide novel etiologic insight, caution is required ensuring results are based on the appropriate sample size, number of SNPs, and the generalizability of the reference panel to the discovery populations.