Global genetic heterogeneity in adaptive traits

Understanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS in the model plant species Arabidopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, whereas others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be extremely sensitive to sampling and population structure

Body mass index and diabetes risk in fifty-seven low- and middle-income countries:a cross-sectional study of nationally representative individual-level data

BACKGROUND: Overweight, obesity, and diabetes are rising rapidly in low- and middle-income countries (LMICs) but there is scant empirical evidence about the relationship between body mass index (BMI) and diabetes in these settings. METHODS: We pooled individual-level data from nationally representative surveys across 57 LMICs, totaling 685,616 individuals aged ≥25 years. BMI categories were defined as: normal (18.5 – 22.9 kg/m(2)), upper-normal (23.0–24.9 kg/m(2)), overweight (25.0– 29.9 kg/m(2)), or obesity (≥30.0 kg/m(2)). We estimated the association between BMI and diabetes risk using multivariable Poisson regression and receiver operating curve (ROC) analyses, stratified by sex and geographic region. RESULTS: The overall prevalence of overweight was 27.2% (95% CI: 26.6, 27.8), of obesity 21.0% (19.6, 22.5), and of diabetes 9.3% (8.4, 10.2). In the pooled analysis, an increased risk of diabetes was observed at a BMI of 23 kg/m(2) or above, with a risk increase of 43% for males and 41% for females compared to a normal BMI. Diabetes risk also rose steeply in individuals 35–44 years old and men aged 25–34 years in Sub-Saharan Africa. In stratified analyses, there was regional variability in this relationship. Optimal BMI thresholds for diabetes screening ranged from 23.8 kg/m(2) among males in East/Southeast Asia to 28.3 kg/m(2) among females in the Middle East and North Africa and Latin America and the Caribbean. CONCLUSIONS: The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and younger ages than reflected in currently used cut-offs

TRY plant trait database - enhanced coverage and open access

10.1111/gcb.14904GLOBAL CHANGE BIOLOGY261119-18