Host genetics and population structure effects on parasitic disease

Host genetic factors exert significant influences on differential susceptibility to many infectious diseases. In addition, population structure of both host and parasite may influence disease distribution patterns. In this study, we assess the effects of population structure on infectious disease in two populations in which host genetic factors influencing susceptibility to parasitic disease have been extensively studied. The first population is the Jirel population of eastern Nepal that has been the subject of research on the determinants of differential susceptibility to soil-transmitted helminth infections. The second group is a Brazilian population residing in an area endemic for Trypanosoma cruzi infection that has been assessed for genetic influences on differential disease progression in Chagas disease. For measures of Ascaris worm burden, within-population host genetic effects are generally more important than host population structure factors in determining patterns of infectious disease. No significant influences of population structure on measures associated with progression of cardiac disease in individuals who were seropositive for T. cruzi infection were found

Gene-by-Environment Expression and Calculation of the Frailty Index

Background: Frailty can be described as a phenotype (e.g., sarcopenia, reduced grip strength, decreased VO2 max) or as a ratio of deficits, i.e., a Frailty Index (FI). FI predicts survival, death, cognitive impairment, falls, and hospitalizations. Frailty is influenced by both genes and environment. We calculated the FI as the sum of measured deficits divided by the total number of items assessed in a pedigree-based sample of 1,029 Mexican Americans participants in the San Antonio Family Heart Study. We performed a novel search for genotype-by-environment interactions (GXE) influencing FI. Such interactions lead to heritable differences between individuals in their responses to the environment. Methods: We investigated a panel of 34 measured environmental factors to look for GXE influencing frailty. We employed a powerful polygenic approach to genotype-by-environment modeling, allowing for both dichotomous and continuous environmental measures. We performed likelihood-based estimation of parameters and tests for the presence of GXE. Results: GXE interactions influencing frailty were observed for the following environments: obesity (P=7.9E-10), hypertriglyceridemia (P=2.74E-09), low HDL (P=2.15E-06), impaired glucose status (P=.002), hypertension (P=0.01), and diabetes (P=0.02), Additionally, GXE interactions were detected for a number of quantitative dietary components: carbohydrates (P=5.73E-07), fats (P=2.01E-06), fiber (P=2.76E-05), dietary cholesterol (P=0.01), and protein ( P=0.006). These results document substantial statistical evidence for the interactive effects of genes and environmental factors on frailty. Conclusion: Our results support the presence of substantive gene-by-environmental interactions influencing frailty. This finding documents the presence of heritable differences between individuals that lead to differential response to environmental challenges

Human iPSC derived cardiomyocyte model reveals the transcriptomic bases of COVID-19 associated myocardial injury

Background: Multi-organ complications have been the hallmark of severe COVID-19; cardiac injuries were reported in 20% to 30% of hospitalized COVID-19 patients, although the disease etiology remains poorly understood. This study leveraged genome-wide RNA-sequence data generated using induced pluripotent stem cell (iPSC) differentiated cardiomyocytes (CMs) and in vitro modeling of SARS-CoV-2 infection in CMs, to understand the molecular mechanisms of COVID-19 myocardial injuries for novel diagnostic and therapeutic development. Methods: Raw RNA-sequence data sets, GSE165242 and GSE150392 were aligned to human genome assembly GRCh38 and gene expressions were quantified. Differentially expressed (DE) genes between experimental groups were identified using moderated t-statistics (FDR-corrected p-value ≤ 0.05) and Fold-Change analysis (FC absolute ≥ 2.0). Results: A total of 2,148 genes were significantly DE between SARS-CoV-2 infected and vehicle treated CMs and showed significant enrichment in cytokine signaling pathways (p-value=4.89E-25) and regulation of heart contraction (p-value=2.51E-19) gene-ontology biological processes. 606 of these DE genes were significantly upregulated during iPSC to CM differentiation. Disease and function annotation analysis of these 606 genes showed significant enrichment and activation of angiogenesis (p-value=4.04E-23; activation Z-score=3.7) and downregulation of heart contraction and related functions (p-value=4.24E-29; activation Z-score=-2.2) in SARS-CoV-2 infected CMs. The upstream regulator analysis identified upregulation of AGT associated proinflammatory genes and significant downregulation of TBX5 and MYOCD transcription factors and their gene networks, suggesting remodeling of CM contractility architecture. Conclusions: This study identified several AGT associated proinflammatory genes and TBX5 and MYOCD gene networks as potential targets for drug development to address COVID-19 associated cardiac injury

Longitudinal familial analysis of blood pressure involving parametric (co)variance functions

BACKGROUND: For analyzing longitudinal familial data we adopted a log-linear form to incorporate heterogeneity in genetic variance components over the time, and additionally a serial correlation term in the genetic effects at different levels of ages. Due to the availability of multiple measures on the same individual, we permitted environmental correlations that may change across time. RESULTS: Systolic blood pressure from family members from the first and second cohort was used in the current analysis. Measures of subjects receiving hypertension treatment were set as censored values and they were corrected. An initial check of the variance and covariance functions proposed for analyzing longitudinal familial data, using empirical semi-variogram plots, indicated that the observed trait dispersion pattern follows the assumptions adopted. CONCLUSION: The corrections for censored phenotypes based on ordinary linear models may be an appropriate simple model to correct the data, ensuring that the original variability in the data was retained. In addition, empirical semi-variogram plots are useful for diagnosis of the (co)variance model adopted

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even data sets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper we re-examine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (6 CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1357 individuals in 124 pedigrees) takes less than 2 minutes and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 minutes and 1.5 GB of memory

Non-alcoholic Fatty Liver Disease and Depression: Evidence for Genotype × Environment Interaction in Mexican Americans

This study examines the impact of G × E interaction effects on non-alcoholic fatty liver disease (NAFLD) among Mexican Americans in the Rio Grande Valley (RGV) of South Texas. We examined potential G × E interaction using variance components models and likelihood-based statistical inference in the phenotypic expression of NAFLD, including hepatic steatosis and hepatic fibrosis (identified using vibration controlled transient elastography and controlled attenuation parameter measured by the FibroScan Device). We screened for depression using the Beck Depression Inventory-II (BDI-II). We identified significant G × E interactions for hepatic fibrosis × BDI-II. These findings provide evidence that genetic factors interact with depression to influence the expression of hepatic fibrosis

Gene by Environment interaction and metabolic-associated fatty liver disease in Mexican American patients with depression

Knowledge of genetic and environmental (G x E) interaction effects on metabolic-associated fatty liver disease (MAFLD) is limited. The purpose of this study was to examine the impact of G x E interaction effects on MAFLD in Mexican Americans in the Rio Grande Valley (RGV). The environment examined was depression as measured by the Beck Depression Inventory-II (BDI-II). We examined potential G x E interaction in the phenotypic expression of MAFLD, including hepatic steatosis and hepatic fibrosis, using variance component models and likelihood-based statistical inference. Significant G x E interactions were identified for hepatic fibrosis x BDI-II. These findings provide evidence that genetic factors interact with depression to influence expression of hepatic fibrosis. A better understanding of these genetic interactions are necessary to develop strategies and interventions to reduce the bi-directional relationship of hepatic fibrosis and depression

Modeling methylation data as an additional genetic variance component

High-throughput platforms allow the characterization of thousands of previously known methylation sites. These platforms have great potential for investigating the epigenetic effects that are partially responsible for gene expression control. Methylation sites provide a bridge for the investigation of real-time environmental contributions on genomic events by the alteration of methylation status of those sites. Using the data provided by GAW20’s organization committee, we calculated the heritability estimates of each cytosine-phosphate-guanine (CpG) island before and after the use of fenofibrate, a lipid-control drug. Surprisingly, we detected substantially high heritability estimates before drug usage. This somewhat unexpected high sample correlation was corrected by the use of principal components and the distributions of heritability estimates before and after fenofibrate treatment, which made the distributions comparable. The methylation sites located near a gene were collected and a genetic relationship matrix estimated to represent the overall correlation between samples. We implemented a randomeffect association test to screen genes whose methylation patterns partially explain the observable high-density lipoprotein (HDL) heritability. Our leading association was observed for the TMEM52 gene that encodes a transmembrane protein, and is largely expressed in the liver, had not been previously associated with HDL until this manuscript. Using a variance component decomposition framework with the linear mixed model allows the integration of data from different sources, such as methylation, gene expression, metabolomics, and proteomics. The decomposition of the genetic variance component decomposition provides a flexible analytical approach for the challenges of this new omics era

Frailty Index in the Colonias on the US-Mexico Border: A Special Report

Frailty is the age-related decline in well-being. The Frailty index (FI) measures the accumulation of health deficits and reflects biopsychosocial and cultural determinants of well-being. Frailty is measured as a static phenotype or as a Frailty Index comprising a ratio of suffered health deficits and total deficits. We report a Frailty Index calculated from routinely measured clinical variables gathered from residents of two Colonias (neighborhoods) in South Texas. A Colonia is a predominantly Hispanic, economically distressed, unincorporated neighborhood. We analyzed retrospective data from 894 patients that live in two Colonias located on the Texas-Mexico border. We calculated the FI with seven physiological variables, PHQ-9 score, and the 11 domain-specific Duke Profile scores, for a total of 19 possible health deficits. FI against age separately in males (n = 272) and females (n = 622) was regressed. Females had a significantly higher starting frailty, and males had a significantly greater change rate with age. FI against age for Cameron Park Colonia and Indian Hills Colonia was regressed. We calculated a significantly higher starting FI in Indian Hills and a significantly greater change rate in Cameron Park residents. Frailty\u27s contributors are complex, especially in neighborhoods of poverty, immigration, low education level, and high prevalence of chronic disease. We report baseline Frailty Index data from two Colonias in South Texas and the clinical and research implications

Modeling methylation data as an additional genetic variance component

High-throughput platforms allow the characterization of thousands of previously known methylation sites. These platforms have great potential for investigating the epigenetic effects that are partially responsible for gene expression control. Methylation sites provide a bridge for the investigation of real-time environmental contributions on genomic events by the alteration of methylation status of those sites. Using the data provided by GAW20’s organization committee, we calculated the heritability estimates of each cytosine-phosphate-guanine (CpG) island before and after the use of fenofibrate, a lipid-control drug. Surprisingly, we detected substantially high heritability estimates before drug usage. This somewhat unexpected high sample correlation was corrected by the use of principal components and the distributions of heritability estimates before and after fenofibrate treatment, which made the distributions comparable. The methylation sites located near a gene were collected and a genetic relationship matrix estimated to represent the overall correlation between samples. We implemented a randomeffect association test to screen genes whose methylation patterns partially explain the observable high-density lipoprotein (HDL) heritability. Our leading association was observed for the TMEM52 gene that encodes a transmembrane protein, and is largely expressed in the liver, had not been previously associated with HDL until this manuscript. Using a variance component decomposition framework with the linear mixed model allows the integration of data from different sources, such as methylation, gene expression, metabolomics, and proteomics. The decomposition of the genetic variance component decomposition provides a flexible analytical approach for the challenges of this new omics era