Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci

The exotic shrub Rosa rubiginosa as a nurse plant. Implications for the restoration of disturbed temperate forests in Patagonia, Argentina

Facilitation of forest native species under exotic nurse plants may differ between climatic regions and microsites. Recruitment of other exotic species should be taken into account when areas invaded by exotic shrubs are considered for forest restoration. Natural regeneration of native and exotic species and survival of planted native saplings under the deciduous exotic Rosa rubiginosa (shrub microsite = SM) and in open microsites (OM) were studied in preexisting shrublands of mesic and wet regions in North Patagonia. Light levels, soil chemical composition and seasonal variation of soil moisture were analyzed in SM and OM and the content of N and C was compared between mature and senescent R. rubiginosa leaves. In the SM, native species received less light and soils had higher C:N rate and moisture in spring than in the OM. R. rubiginosa reabsorbs this nutrient before leaves fall. Natural native forest species recruitment occurred only in the SM. In shrublands of the mesic region native species richness and abundance increased under bigger nurse plants. In the wet region, where herbivory was higher, moderate climatic conditions allowed greater species richness and abundance than in the mesic region, independently of the nurse plant volume. The height of the exotic shrub and the protected species showed a positive and negative relationship in the mesic and wet region, respectively. Exotic species grew under 5-15% of the nurse plants (n= 60). Survival of planted saplings, shoot resprouting and herbivore-related mortality were highest in the SM and in wet regions. Sapling mortality due to drying out was highest in the OM of the mesic region. It is possible for forest restoration in areas previously invaded by R. rubiginosa to achieve highly positive results in mesic regions where plants are protected from desiccation. In areas with moderate climatic conditions, facilitation against herbivores has beneficial initial effects, but as the nurse plant competes with taller native individuals, forest restoration would depend on effective control of the nurse plant biomass. In both areas other exotic species would be well represented in the long term. © 2012 Elsevier B.V.Fil: Svriz, Maya. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario; ArgentinaFil: Damascos, Maria Angélica. Universidad Nacional del Comahue. Centro Regional Universitario; ArgentinaFil: Schaumberg, Heike. Martin Luther University; AlemaniaFil: Hensen, Isabell. Martin Luther University; Alemani

The effect of travel modes on children’s mental health, cognitive and social development; a systematic review

Systematic map of the research undertaken on the effects of travel on children. The study is original both in terms of developing the method of systematic mapping and being the first systematic overview of this field that examines the breadth of the effects of travel on children. Its policy significance was in relation to the effects on children of different modes of transport from home to school

Average χ² statistics for LT versus other approaches in simulated data.

<p>For each statistic we display average results across 1,000,000 simulations, for various effect sizes <i>γ</i>. All statistics are χ²(1 dof). Logistic regression with an interaction term (G+GxE) values been converted from χ²(2 dof) to the equivalent χ²(1 dof) value. At an effect size of 0 all statistics give the expected value under the null. OR LBMI is the odds ratio computed from cases with BMI = 24. OR HBMI is the odds ratio for cases with BMI = 35.</p

Inferred covariates and effect sizes on the liability scale.

<p>LT model is the liability threshold model for each disease with parameters estimated using the LTPub method. For diseases with multiple covariates, models with all covariates and each covariate separately are given. %Variance Explained is the fraction of variance explained on the liability scale in the study data for each of the covariates in each of the diseases when all covariates are used in the model, and is specific to the distribution of covariates in each particular study. BMI30 is a binary variable, which is 1 if an individual's BMI is greater than 30 and 0 otherwise. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p

Summary statistics across all datasets.

<p>The sum of each of the test statistics across all of the SNPs in each of the diseases. LTPub vs LogR is the % increase of LTPub compared to LogR. It has a median value of 16%. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p

Power calculations for LogR, G+GxE, and LT approaches in simulated data.

<p>For each statistic we display power to attain P<5<b>×</b>10⁻⁸ based on 1,000,000 simulations of 3000 cases and 3000 controls, for various effect sizes <i>γ</i>. The increase in power (ratio of y-axis values) for LT versus LogR is 22.8% for <i>γ</i> = 0.1, and 23.0% when computing average power across all values of <i>γ</i>. For γ = 0 the power was 5.0% for all statistics when the P-value threshold is 0.05. G+GxE performs worse due to an extra degree of freedom.</p

Illustration of liability threshold model: simulated T2D example.

<p>The posterior mean of <i>ε</i> for low-BMI and high-BMI cases is the expected value of <i>ε</i> given that it exceeds <u>c(t</u>−)+m. High-BMI cases have a lower posterior mean relative to low-BMI cases since they require a smaller contribution from genetics to exceed the threshold in the liability threshold model.</p