Stochastic simulation of clonal growth in the tall goldenrod,Solidago altissima

Summary As clonal plants grow they move through space. The movement patterns that result can be complex and difficult to interpret without the aid of models. We developed a stochastic simulation model of clonal growth in the tall goldenrod, Solidago altissima. Our model was calibrated with field data on the clonal expansion of both seedlings and established clones, and model assumptions were verified by statistical analyses.When simulations were based on empirical distributions with long rhizome lengths, there was greater dispersal, less leaf overlap, and less spatial aggregation than when simulations were based on distributions with comparatively short rhizome lengths. For the field data that we utilized, variation in rhizome lengths had a greater effect than variation for either branching angles or rhizome initiation points (see text). We also found that observed patterns of clonal growth in S. altissima did not cause the formation of fairy rings. However, simulations with an artificial distribution of branching angles demonstrate that fairy rings can result solely from a plant's clonal morphology.Stochastic simulation models that incorporated variation in rhizome lengths, branching angles, and rhizome initiation points produced greater dispersal and less leaf overlap than deterministic models. Thus, variation for clonal growth parameters may increase the efficiency of substrate exploration by increasing the area covered and by decreasing the potential for intraclonal competition. We also demonstrated that ramet displacements were slightly, but consistently lower in stochastic simulation models than in random-walk models. This difference was due to the incorporation of details on rhizome bud initiation into stochastic simulation models, but not random-walk models. We discuss the advantages and disadvantages of deterministic, stochastic simulation, and random-walk models of clonal growth.     

Neighborhood predictors of plant performance

Summary We developed models of inter-individual interference to predict the fecundity of individuals in populations of the annual plant species, Arabidopsis thaliana. An individual plant is modeled as having a neighborhood which is a circular area of fixed radius with the plant at its center. Other plants which share the circle with the focal plant are termed neighbors of the focal plant. We developed an index of neighborhood interference which is the independent variable in a non-linear regression model that predicts individual plant fecundity. We present methods of exploratory data analysis that are useful in determining a best neighborhood radius, defined as that radius which minimizes residual sum of squares, and in deciding on the functional form of the interference index. In developing the interference index for Arabidopsis, we focus on aspects of the spatial distribution of neighbors: their number, distance and angular dispersion.We found that a best (or optimal) neighborhood radius can be resolved, which provides the best predictor of plant performance. Fecundity predictors based on adult neighbors were noticeably better than those based on neighbors at the seedling stage. Rosettes of Arabidopsis may change location during development (they fall over) and the new fallen positions do provide some improvement in the predictor. Taking into account distance to neighbors within the neighborhood provided only negligible improvement in the model. Finally, the incorporation of angular dispersion in the crowding index produced a considerably better fit. The fecundity predictor that included number of neighbors and angular dispersion in the crowding index explained about 70% of the variation in individual seed set.     

Combined effects of thrombosis pathway gene variants predict cardiovascular events

The genetic background of complex diseases is proposed to consist of several low-penetrance risk loci. Addressing this complexity likely requires both large sample size and simultaneous analysis of different predisposing variants. We investigated the role of four thrombosis genes: coagulation factor V (F5), intercellular adhesion molecule 1 (ICAM1), protein C (PROC), and thrombomodulin (THBD) in cardiovascular diseases. Single allelic gene variants and their pair-wise combinations were analyzed in two independently sampled population cohorts from Finland. From among 14,140 FINRISK participants (FINRISK-92, n = 5,999 and FINRISK-97, n = 8,141), we selected for genotyping a sample of 2,222, including 528 incident cardiovascular disease (CVD) cases and random subcohorts totaling 786. To cover all known common haplotypes (>10%), 54 single nucleotide polymorphisms (SNPs) were genotyped. Classification-tree analysis identified 11 SNPs that were further analyzed in Cox's proportional hazard model as single variants and pair-wise combinations. Multiple testing was controlled by use of two independent cohorts and with false-discovery rate. Several CVD risk variants were identified: In women, the combination of F5 rs7542281 x THBD rs1042580, together with three single F5 SNPs, was associated with CVD events. Among men, PROC rs1041296, when combined with either ICAM1 rs5030341 or F5 rs2269648, was associated with total mortality. As a single variant, PROC rs1401296, together with the F5 Leiden mutation, was associated with ischemic stroke events. Our strategy to combine the classification-tree analysis with more traditional genetic models was successful in identifying SNPs-acting either in combination or as single variants--predisposing to CVD, and produced consistent results in two independent cohorts. These results suggest that variants in these four thrombosis genes contribute to arterial cardiovascular events at population level.     

The Finland-United States investigation of non-insulin-dependent diabetes mellitus genetics (FUSION) study. I. An autosomal genome scan for genes that predispose to type 2 diabetes

We performed a genome scan at an average resolution of 8 cM in 719 Finnish sib pairs with type 2 diabetes. Our strongest results are for chromosome 20, where we observe a weighted maximum LOD score (MLS) of 2.15 at map position 69.5 cM from pter and secondary weighted LOD-score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. Our next largest MLS is for chromosome 11 (MLS = 1.75 at 84.0 cM), followed by chromosomes 2 (MLS = 0.87 at 5.5 cM), 10 (MLS = 0.77 at 75.0 cM), and 6 (MLS = 0.61 at 112.5 cM), all under an additive model. When we condition on chromosome 2 at 8.5 cM, the MLS for chromosome 20 increases to 5.50 at 69.0 cM (P=.0014). An ordered-subsets analysis based on families with high or low diabetes-related quantitative traits yielded results that support the possible existence of disease-predisposing genes on chromosomes 6 and 10. Genomewide linkage-disequilibrium analysis using microsatellite marker data revealed strong evidence of association for D22S423 (P=.00007). Further analyses are being carried out to confirm and to refine the location of these putative diabetes-predisposing genes.     

Variation in the selenoprotein S gene locus is associated with coronary heart disease and ischemic stroke in two independent Finnish cohorts

Selenoprotein S (SEPS1) is a novel candidate gene involved in the regulation of inflammatory response and protection from oxidative damage. This study explored the genetic variation in the SEPS1 locus for an association with CVD as well as with quantitative phenotypes related to obesity and inflammation. We used the case-cohort design and time-to-event analysis in two separate prospectively followed population-based cohorts FINRISK 92 and 97 (n = 999 and 1,223 individuals, respectively) to study the associations of five single nucleotide polymorphisms with the risk for coronary heart disease (CHD) and ischemic stroke events. We found a significant association with increased CHD risk in females carrying the minor allele of rs8025174 in the combined analysis of both cohorts [hazard ratio (HR) 2.95 (95% confidence interval: 1.37–6.39)]. Another variant, rs7178239, increased the risk for ischemic stroke significantly in females [HR: 3.35 (1.66–6.76)] and in joint analysis of both sexes and both cohorts [HR: 1.75 (1.17–2.64)]. These results indicate that variation in the SEPS1 locus may have an effect on CVD morbidity, especially in females. This observation should stimulate further investigations of the role of this gene and protein in the pathogenesis of CVD. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Forecasting phenology under global warming

As a consequence of warming temperatures around the world, spring and autumn phenologies have been shifting, with corresponding changes in the length of the growing season. Our understanding of the spatial and interspecific variation of these changes, however, is limited. Not all species are responding similarly, and there is significant spatial variation in responses even within species. This spatial and interspecific variation complicates efforts to predict phenological responses to ongoing climate change, but must be incorporated in order to build reliable forecasts. Here, we use a long-term dataset (1953–2005) of plant phenological events in spring (flowering and leaf out) and autumn (leaf colouring and leaf fall) throughout Japan and South Korea to build forecasts that account for these sources of variability. Specifically, we used hierarchical models to incorporate the spatial variability in phenological responses to temperature to then forecast species'' overall and site-specific responses to global warming. We found that for most species, spring phenology is advancing and autumn phenology is getting later, with the timing of events changing more quickly in autumn compared with the spring. Temporal trends and phenological responses to temperature in East Asia contrasted with results from comparable studies in Europe, where spring events are changing more rapidly than are autumn events. Our results emphasize the need to study multiple species at many sites to understand and forecast regional changes in phenology.     

Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance

Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway.     

Disentangling the paradox of insect phenology: are temporal trends reflecting the response to warming?

The strength and direction of phenological responses to changes in climate have been shown to vary significantly both among species and among populations of a species, with the overall patterns not fully resolved. Here, we studied the temporal and spatial variability associated with the response of several insect species to recent global warming. We use hierarchical models within a model comparison framework to analyze phenological data gathered over 40 years by the Japan Meteorological Agency on the emergence dates of 14 insect species at sites across Japan. Contrary to what has been predicted with global warming, temporal trends of annual emergence showed a later emergence day for some species and sites over time, even though temperatures are warming. However, when emergence data were analyzed as a function of temperature and precipitation, the overall response pointed out an earlier emergence day with warmer conditions. The apparent contradiction between the response to temperature and trends over time indicates that other factors, such as declining populations, may be affecting the date phenological events are being recorded. Overall, the responses by insects were weaker than those found for plants in previous work over the same time period in these ecosystems, suggesting the potential for ecological mismatches with deleterious effects for both suites of species. And although temperature may be the major driver of species phenology, we should be cautious when analyzing phenological datasets as many other factors may also be contributing to the variability in phenology.     

Genetic Markers Enhance Coronary Risk Prediction in Men: The MORGAM Prospective Cohorts

Background

More accurate coronary heart disease (CHD) prediction, specifically in middle-aged men, is needed to reduce the burden of disease more effectively. We hypothesised that a multilocus genetic risk score could refine CHD prediction beyond classic risk scores and obtain more precise risk estimates using a prospective cohort design.

Methods

Using data from nine prospective European cohorts, including 26,221 men, we selected in a case-cohort setting 4,818 healthy men at baseline, and used Cox proportional hazards models to examine associations between CHD and risk scores based on genetic variants representing 13 genomic regions. Over follow-up (range: 5–18 years), 1,736 incident CHD events occurred. Genetic risk scores were validated in men with at least 10 years of follow-up (632 cases, 1361 non-cases). Genetic risk score 1 (GRS1) combined 11 SNPs and two haplotypes, with effect estimates from previous genome-wide association studies. GRS2 combined 11 SNPs plus 4 SNPs from the haplotypes with coefficients estimated from these prospective cohorts using 10-fold cross-validation. Scores were added to a model adjusted for classic risk factors comprising the Framingham risk score and 10-year risks were derived.

Results

Both scores improved net reclassification (NRI) over the Framingham score (7.5%, p = 0.017 for GRS1, 6.5%, p = 0.044 for GRS2) but GRS2 also improved discrimination (c-index improvement 1.11%, p = 0.048). Subgroup analysis on men aged 50–59 (436 cases, 603 non-cases) improved net reclassification for GRS1 (13.8%) and GRS2 (12.5%). Net reclassification improvement remained significant for both scores when family history of CHD was added to the baseline model for this male subgroup improving prediction of early onset CHD events.

Conclusions

Genetic risk scores add precision to risk estimates for CHD and improve prediction beyond classic risk factors, particularly for middle aged men.     

Further evidence for the role of ENPP1 in obesity: association with morbid obesity in Finns

The aim of this study was to investigate a series of single-nucleotide polymorphisms (SNPs) in the genes MC2R, MC3R, MC4R, MC5R, POMC, and ENPP1 for association with obesity. Twenty-five SNPs (2-7 SNPs/gene) were genotyped in 246 Finns with extreme obesity (BMI > or = 40 kg/m2) and in 481 lean subjects (BMI 20-25 kg/m2). Of the obese subjects, 23% had concomitant type 2 diabetes. SNPs and SNP haplotypes were tested for association with obesity and type 2 diabetes. Allele frequencies differed between obese and lean subjects for two SNPs in the ENPP1 gene, rs1800949 (P = 0.006) and rs943003 (P = 0.0009). These SNPs are part of a haplotype (rs1800949 C-rs943003 A), which was observed more frequently in lean subjects compared to obese subjects (P = 0.0007). Weaker associations were detected between the SNPs rs1541276 in the MC5R, rs1926065 in the MC3R genes and obesity (P = 0.04 and P = 0.03, respectively), and between SNPs rs2236700 in the MC5R, rs2118404 in the POMC, rs943003 in the ENPP1 genes and type 2 diabetes (P = 0.03, P = 0.02 and P = 0.02, respectively); these associations did not, however, remain significant after correction for multiple testing. In conclusion, a previously unexplored ENPP1 haplotype composed of SNPs rs1800949 and rs943003 showed suggestive evidence for association with adult-onset morbid obesity in Finns. In this study, we did not find association between the frequently studied ENPP1 K121Q variant, nor SNPs in the MCR or POMC genes and obesity or type 2 diabetes.