Controlling for polygenic genetic confounding in epidemiologic association studies.

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically-unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.

Pervasive biases in proxy GWAS based on parental history of Alzheimer's disease.

Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and non-random participation of parental illness survey, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that current AD GWAX provide highly misleading genetic correlations between AD risk and higher education which subsequently affects a variety of genetic epidemiologic applications involving AD and cognition. Our study sheds important light on the design and analysis of mid-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

Occupational Exposure among Electronic Repair Workers in Ghana.

Electronic repair workers may be exposed to lead, mercury, cadmium and other elements including rare earth elements used in electronic equipment. In this study, repair work took place in small repair shops where, e.g., televisions, radios, video players, compact discs and computers were repaired. Personal full-shift air samples of particulate matter were collected among 64 electronic repair workers in Kumasi (Ghana) and analysed for 29 elements by inductively coupled plasma mass spectrometry. Results showed that air concentrations of all elements were low. The highest air concentration was measured for iron with a geometric mean concentration and geometric standard deviation of 6.3 ± 0.001 µg/m3. The corresponding concentration of Pb and Hg were 157 ± 3 ng/m3 and 0.2 ± 2.7 ng/m3, respectively. The cerium concentration of 5 ± 2 ng/m3 was the highest among the rare earth elements. Source apportionment with ranked principal component analysis indicated that 63% of the variance could be explained by the repair and soldering of electronic components such as batteries, magnets, displays and printed circuit boards. An association between concentrations of lead in the workroom air and lead in whole blood was found (Pearson's correlation coefficient r = 0.42, p < 0.001). There was, however, no statistically significant difference between whole blood lead concentrations in the workers and references indicating that lead did not exclusively originate from occupational exposure.