Alpine environments are particularly susceptible to environmental changes associated with global warming but there is potential for alpine plants to adapt to warming if local adaptation occurs and gene flow allows genotypes adapted to low altitudes to colonize higher altitude sites. Here we examine the adaptive potential of a common alpine grass, Poa hiemata, within the restricted alpine habitat of Australian mountains, across a narrow altitudinal gradient replicated in three areas. Grasses at high altitude sites had shorter leaf lengths and larger circumferences than those at lower sites. Transplant experiments with clonal material and plants grown from seed indicated that these differences were partly genetic, with environmental and genetic factors both contributing to the differences between altitudes. Differences in altitudinal forms were also evident in a common garden experiment. Plants showed a home-site advantage in terms of survival. A fitness analysis indicated that at high altitude sites, selection favored plants with short leaves and larger circumferences, whereas these traits were selected in the opposite direction at the low altitude sites.These findings indicate cogradient selection and potential for both plastic and genotypic shifts in response to climate change in P.hiemata.

Are humans currently evolving? This question can be answered using data on lifetime reproductive success, multiple traits and genetic variation and covariation in those traits. Such data are available in existing long-term, multigeneration studies - both clinical and epidemiological - but they have not yet been widely used to address contemporary human evolution. Here we review methods to predict evolutionary change and attempts to measure selection and inheritance in humans. We also assemble examples of long-term studies in which additional measurements of evolution could be made. The evidence strongly suggests that we are evolving and that our nature is dynamic, not static.

AimsGenetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores.Methods and resultsWe generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61–1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18–1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5–1.6%, P < 0.001), particularly for individuals ≥60 years old (meta-analysis C-index: +4.6–5.1%, P < 0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12–18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking.ConclusionsA GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.

Our aims were to demonstrate that natural selection is operating on contemporary humans, predict future evolutionary change for specific traits with medical significance, and show that for some traits we can make short-term predictions about our future evolution. To do so, we measured the strength of selection, estimated genetic variation and covariation, and predicted the response to selection for women in the Framingham Heart Study, a project of the National Heart, Lung, and Blood Institute and Boston University that began in 1948. We found that natural selection is acting to cause slow, gradual evolutionary change. The descendants of these women are predicted to be on average slightly shorter and stouter, to have lower total cholesterol levels and systolic blood pressure, to have their first child earlier, and to reach menopause later than they would in the absence of evolution. Selection is tending to lengthen the reproductive period at both ends. To better understand and predict such changes, the design of planned large, long-term, multicohort studies should include input from evolutionary biologists.evolutionary rates | heritability | Homo sapiens | medical traits A re contemporary humans experiencing natural selection and evolving in response to it? The answer to that question depends on whom one asks. A long tradition in the medical community (1) holds that natural selection does not operate on contemporary human populations because medicine keeps "alive many who otherwise would have perished" (2). No evolutionary biologist would now agree with that claim, for natural selection works through differential reproductive success rather than simple differential survival, and individuals in contemporary human populations vary in lifetime reproductive success (LRS). Selection operates on any trait that varies and is correlated with LRS, and traits respond to selection with change across generations if they vary genetically. But what traits is selection operating on? Do they include the traits treated by physicians? Previous work (e.g., ref.3) has shown that human life history traits, most significantly age at first reproduction, are currently under selection, but evidence for selection operating on traits of medical importance is scarce. Here, we report estimates of natural selection, and the potential genetic response to selection, in the women of the first two generations of the Framingham Heart Study (FHS) population. The traits we analyzed include traits of medical significance: total cholesterol (TC), systolic blood pressure (SBP), diastolic blood pressure (DBP), and blood glucose (GLU). We had three general aims: first, to correct the still widespread misconception that natural selection is not operating on contemporary humans; second, to make quantitative predictions about future evolutionary change for specific traits with medical significance; and third, to register firmly a point of general cultural interest that follows directly from our first two aims: We are still evolving, and for some traits we c...

BackgroundComplex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies.ResultsWe developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package.ConclusionMergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3198-9) contains supplementary material, which is available to authorized users.