ABSTRACT
AIMS/HYPOTHESIS: Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk. METHODS: We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study. RESULTS: The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10-21 and p = 9.6 × 10-31, respectively) and a 4.4-fold (p = 6.8 × 10-33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. CONCLUSIONS/INTERPRETATION: This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.
Subject(s)
Diabetes Complications , Diabetes Mellitus, Type 2 , Multifactorial Inheritance , Blood Glucose , Blood Pressure/genetics , Diabetes Complications/complications , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/genetics , Genome-Wide Association Study , Humans , Risk FactorsABSTRACT
How the genetic composition of a population changes through stochastic processes, such as genetic drift, in combination with deterministic processes, such as selection, is critical to understanding how phenotypes vary in space and time. Here, we show how evolutionary forces affecting selection, including recombination and effective population size, drive genomic patterns of allele-specific expression (ASE). Integrating tissue-specific genotypic and transcriptomic data from 1500 individuals from two different cohorts, we demonstrate that ASE is less often observed in regions of low recombination, and loci in high or normal recombination regions are more efficient at using ASE to underexpress harmful mutations. By tracking genetic ancestry, we discriminate between ASE variability due to past demographic effects, including subsequent bottlenecks, versus local environment. We observe that ASE is not randomly distributed along the genome and that population parameters influencing the efficacy of natural selection alter ASE levels genome wide.
Subject(s)
Genetic Variation , Selection, Genetic , Alleles , Genetic Drift , Humans , Recombination, GeneticABSTRACT
DNA extracted from fecal samples contains DNA from the focal species, food, bacteria and pathogens. Most DNA quantification methods measure total DNA and cannot differentiate among sources. Despite the desirability of noninvasive fecal sampling for studying wildlife populations, low amounts of focal species DNA make it difficult to use for next-generation sequencing (NGS), where accurate DNA quantification is critical for normalization. Two factors are required prior to using fecal samples in NGS libraries: (1) an accurate quantification method for the amount of target DNA and (2) a determination of the relative amount of target DNA needed for successful single nucleotide polymorphism genotyping assays. Here, we address these needs by developing primers to amplify a 101 bp region of the nuclear F2 gene and a quantitative PCR (qPCR) assay that allows the accurate quantification of the amount of polar bear (Ursus maritimus) DNA in fecal extracts. We test the assay on pure polar bear DNA extracted from muscle tissue and find a high correlation between fluorometric and qPCR quantifications. The qPCR assay was also successfully used to quantify the amount of DNA derived from polar bears in fecal extractions. Orthologs of the F2 gene have been identified across vertebrates; thus, similar qPCR assays could be developed for other species to enable noninvasive studies.