Population-based risk factors for elevated alanine aminotransferase in a South Texas Mexican-American population

Background and aims: Elevated alanine aminotransferase (ALT \u3e40 IU/mL) is a marker of liver injury but provides little insight into etiology. We aimed to identify and stratify risk factors associated with elevated ALT in a randomly selected population with a high prevalence of elevated ALT (39%), obesity (49%) and diabetes (30%). Methods: Two machine learning methods, the support vector machine (SVM) and Bayesian logistic regression (BLR), were used to capture risk factors in a community cohort of 1532 adults from the Cameron County Hispanic Cohort (CCHC). A total of 28 predictor variables were used in the prediction models. The recently identified genetic marker rs738409 on the PNPLA3 gene was genotyped using the Sequenom iPLEX assay. Results: The four major risk factors for elevated ALT were fasting plasma insulin level and insulin resistance, increased BMI and total body weight, plasma triglycerides and non-HDL cholesterol, and diastolic hypertension. In spite of the highly significant association of rs738409 in females, the role of rs738409 in the prediction model is minimal, compared to other epidemiological risk factors. Age and drug and alcohol consumption were not independent determinants of elevated ALT in this analysis. Conclusions: The risk factors most strongly associated with elevated ALT in this population are components of the metabolic syndrome and point to nonalcoholic fatty liver disease (NAFLD). This population-based model identifies the likely cause of liver disease without the requirement of individual pathological diagnosis of liver diseases. Use of such a model can greatly contribute to a population-based approach to prevention of liver disease

A Genome-Wide association Study Discovers 46 Loci of the Human Metabolome in the Hispanic Community Health Study/Study of Latinos

Variation in levels of the human metabolome reflect changes in homeostasis, providing a window into health and disease. The genetic impact on circulating metabolites in Hispanics, a population with high cardiometabolic disease burden, is largely unknown. We conducted genome-wide association analyses on 640 circulating metabolites in 3,926 Hispanic Community Health Study/Study of Latinos participants. The estimated heritability for 640 metabolites ranged between 0%-54% with a median at 2.5%. We discovered 46 variant-metabolite pairs (p value \u3c 1.2 × 1

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution

Continental-scale homogenization of residential lawn plant communities

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Landscape and Urban Planning 165 (2017): 54-63, doi:10.1016/j.landurbplan.2017.05.004.Residential lawns are highly managed ecosystems that occur in urbanized landscapes across the United States. Because they are ubiquitous, lawns are good systems in which to study the potential homogenizing effects of urban land use and management together with the continental-scale effects of climate on ecosystem structure and functioning. We hypothesized that similar homeowner preferences and management in residential areas across the United States would lead to low plant species diversity in lawns and relatively homogeneous vegetation across broad geographical regions. We also hypothesized that lawn plant species richness would increase with regional temperature and precipitation due to the presence of spontaneous, weedy vegetation, but would decrease with household income and fertilizer use. To test these predictions, we compared plant species composition and richness in residential lawns in seven U.S. metropolitan regions. We also compared species composition in lawns with understory vegetation in minimally-managed reference areas in each city. As expected, the composition of cultivated turfgrasses was more similar among lawns than among reference areas, but this pattern also held among spontaneous species. Plant species richness and diversity varied more among lawns than among reference areas, and more diverse lawns occurred in metropolitan areas with higher precipitation. Native forb diversity increased with precipitation and decreased with income, driving overall lawn diversity trends with these predictors as well. Our results showed that both management and regional climate shaped lawn species composition, but the overall homogeneity of species regardless of regional context strongly suggested that management was a more important driver.This research was supported by the Macrosystems Biology Program in the Emerging Frontiers Division of the Biological Sciences Directorate at the National Science Foundation (NSF) under grants EF-1065548, 1065737, 1065740, 1065741, 1065772, 1065785, 1065831, and 121238320

Climate and lawn management interact to control C4 plant distribution in residential lawns across seven U.S. cities.

Author Posting. © Ecological Society of America, 2019. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Trammell, T. L. E., Pataki, D. E., Still, C. J., Ehleringer, J. R., Avolio, M. L., Bettez, N., Cavender-Bares, J., Groffman, P. M., Grove, M., Hall, S. J., Heffernan, J., Hobbie, S. E., Larson, K. L., Morse, J. L., Neill, C., Nelson, K. C., O'Neil-Dunne, J., Pearse, W. D., Chowdhury, R. R., Steele, M., & Wheeler, M. M. Climate and lawn management interact to control C4 plant distribution in residential lawns across seven U.S. cities. Ecological Applications, 29(4), (2019): e01884, doi: 10.1002/eap.1884.In natural grasslands, C4 plant dominance increases with growing season temperatures and reflects distinct differences in plant growth rates and water use efficiencies of C3 vs. C4 photosynthetic pathways. However, in lawns, management decisions influence interactions between planted turfgrass and weed species, leading to some uncertainty about the degree of human vs. climatic controls on lawn species distributions. We measured herbaceous plant carbon isotope ratios (δ13C, index of C3/C4 relative abundance) and C4 cover in residential lawns across seven U.S. cities to determine how climate, lawn plant management, or interactions between climate and plant management influenced C4 lawn cover. We also calculated theoretical C4 carbon gain predicted by a plant physiological model as an index of expected C4 cover due to growing season climatic conditions in each city. Contrary to theoretical predictions, plant δ13C and C4 cover in urban lawns were more strongly related to mean annual temperature than to growing season temperature. Wintertime temperatures influenced the distribution of C4 lawn turf plants, contrary to natural ecosystems where growing season temperatures primarily drive C4 distributions. C4 cover in lawns was greatest in the three warmest cities, due to an interaction between climate and homeowner plant management (e.g., planting C4 turf species) in these cities. The proportion of C4 lawn species was similar to the proportion of C4 species in the regional grass flora. However, the majority of C4 species were nonnative turf grasses, and not of regional origin. While temperature was a strong control on lawn species composition across the United States, cities differed as to whether these patterns were driven by cultivated lawn grasses vs. weedy species. In some cities, biotic interactions with weedy plants appeared to dominate, while in other cities, C4 plants were predominantly imported and cultivated. Elevated CO2 and temperature in cities can influence C3/C4 competitive outcomes; however, this study provides evidence that climate and plant management dynamics influence biogeography and ecology of C3/C4 plants in lawns. Their differing water and nutrient use efficiency may have substantial impacts on carbon, water, energy, and nutrient budgets across cities.This research was funded by a series of collaborative grants from the U.S. National Science Foundation Macrosystems Biology Program (EF‐1065548, 1065737, 1065740, 1065741, 1065772, 1065785, 1065831, 121238320). The authors thank La'Shaye Ervin, William Borrowman, Moumita Kundu, and Barbara Uhl for field and laboratory assistance

Climate and Lawn Management Interact to Control C\u3csub\u3e4\u3c/sub\u3e Plant Distribution in Residential Lawns Across Seven U.S. Cities

In natural grasslands, C4 plant dominance increases with growing season temperatures and reflects distinct differences in plant growth rates and water use efficiencies of C3 vs. C4 photosynthetic pathways. However, in lawns, management decisions influence interactions between planted turfgrass and weed species, leading to some uncertainty about the degree of human vs. climatic controls on lawn species distributions. We measured herbaceous plant carbon isotope ratios (δ13C, index of C3/C4 relative abundance) and C4 cover in residential lawns across seven U.S. cities to determine how climate, lawn plant management, or interactions between climate and plant management influenced C4 lawn cover. We also calculated theoretical C4carbon gain predicted by a plant physiological model as an index of expected C4 cover due to growing season climatic conditions in each city. Contrary to theoretical predictions, plant δ13C and C4 cover in urban lawns were more strongly related to mean annual temperature than to growing season temperature. Wintertime temperatures influenced the distribution of C4 lawn turf plants, contrary to natural ecosystems where growing season temperatures primarily drive C4 distributions. C4 cover in lawns was greatest in the three warmest cities, due to an interaction between climate and homeowner plant management (e.g., planting C4 turf species) in these cities. The proportion of C4 lawn species was similar to the proportion of C4 species in the regional grass flora. However, the majority of C4 species were nonnative turf grasses, and not of regional origin. While temperature was a strong control on lawn species composition across the United States, cities differed as to whether these patterns were driven by cultivated lawn grasses vs. weedy species. In some cities, biotic interactions with weedy plants appeared to dominate, while in other cities, C4 plants were predominantly imported and cultivated. Elevated CO2 and temperature in cities can influence C3/C4competitive outcomes; however, this study provides evidence that climate and plant management dynamics influence biogeography and ecology of C3/C4plants in lawns. Their differing water and nutrient use efficiency may have substantial impacts on carbon, water, energy, and nutrient budgets across cities

Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach

Background The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain. Methods and Findings We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination. Conclusions We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases

Deleterious Heteroplasmic Mitochondrial Mutations are associated With an increased Risk of Overall and Cancer-Specific Mortality

Mitochondria carry their own circular genome and disruption of the mitochondrial genome is associated with various aging-related diseases. Unlike the nuclear genome, mitochondrial DNA (mtDNA) can be present at 1000 s to 10,000 s copies in somatic cells and variants may exist in a state of heteroplasmy, where only a fraction of the DNA molecules harbors a particular variant. We quantify mtDNA heteroplasmy in 194,871 participants in the UK Biobank and find that heteroplasmy is associated with a 1.5-fold increased risk of all-cause mortality. Additionally, we functionally characterize mtDNA single nucleotide variants (SNVs) using a constraint-based score, mitochondrial local constraint score sum (MSS) and find it associated with all-cause mortality, and with the prevalence and incidence of cancer and cancer-related mortality, particularly leukemia. These results indicate that mitochondria may have a functional role in certain cancers, and mitochondrial heteroplasmic SNVs may serve as a prognostic marker for cancer, especially for leukemia

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition