Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research.

INTRODUCTION: A major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension. METHODS: We collected genotyped data from a novel DTC platform where participants upload their genotype data files and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation, and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case-control setting using the BASIL algorithm. RESULTS: We collected data on N = 4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D and N = 4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC = 0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers. DISCUSSION: DTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly. CONCLUSIONS: The genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.

Variation in the Plasma Levels of Polyunsaturated Fatty Acids in Control vis-à-vis Nonalcoholic Fatty Liver Disease Subjects and Its Possible Association with Gut Microbiome.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) are becoming a major cause of chronic liver diseases globally. Polyunsaturated fatty acids (PUFA) have been postulated as a potential treatment for NAFLD. They can be obtained from diet and are also endogenously synthesized by enzymes delta-6-desaturase (D6D), delta-5-desaturase, and elongases. The current study is aimed at investigating the differences in the intake and levels of plasma PUFA between NAFLD patients and controls in Asian Indians. This correlation further propelled a pilot study to check for the differences in the gut microbiome of NASH subjects versus controls. METHODS: One hundred forty-seven subjects were recruited and were grouped into healthy controls and cases. Subjects filled a food frequency questionnaire to assess PUFA intake. Plasma samples were subjected to gas chromatography analysis. For characterizing the gut microbiome, fecal samples of 20 NASH and healthy controls were analyzed by 16s rRNA gene sequencing. Alpha and beta diversity metrics and taxonomic analysis were carried out. RESULTS: Plasma levels of eicosapentaenoic acid, γ-linoleic acid and D6D enzyme activity were significantly lower in cases. Dietary intake of total n-6 and n-3 PUFA did not differ between the two groups. Relative abundance of Streptococcus and Clostridium ramosum was significantly higher in NASH patients compared with healthy controls. CONCLUSION: This study demonstrates, for the first time, decreased D6D enzyme activity and plasma PUFA levels in NAFLD patients. Furthermore, it demonstrates gut dysbiosis in histologically proven NASH patients vis-à-vis healthy controls.

Progesterone utilizes the PI3K-AKT pathway in human spermatozoa to regulate motility and hyperactivation but not acrosome reaction.

Progesterone is a physiologic regulator of sperm hyperactivation and acrosome reaction and it does so by activating a range of kinases present in the spermatozoa. In the present study, the involvement of the AKT- phosphatidylinositol 3-kinase (PI3K) signaling pathway in mediating progesterone response in human spermatozoa was investigated. In capacitated spermatozoa, progesterone transiently and concentration dependently lead to phosphorylation of AKT at both Thr 308 and Ser 473 in the tail region. This phosphorylation was inhibited by the PI3K inhibitor wortmannin, suggesting that progesterone leads to activation of PI3K-AKT pathway. The activation of AKT in response to progesterone is calcium dependent and the CatSper channel inhibitor mibefradil significantly reduced progesterone mediated AKT phosphorylation. Preincubation of spermatozoa with wortmannin inhibited the progesterone mediated increase in tyrosine phosphorylation and also attenuated the increase in number of motile, progressively motile and hyperactive spermatozoa but not the number of acrosome reacted spermatozoa. These observations imply that progesterone via CatSper activates the PI3K-AKT pathway required for motility and hyperactivation but not for acrosome reaction.