The moss Bryum argenteum var. muticum Brid. is well adapted to cope with high light in continental Antarctica

The net photosynthetic rate (NP), chlorophyll fluorescence, carotenoid content and chlorophyll content of the cosmopolitan moss Bryum argenteum were measured in the field at Botany Bay, southern Victoria Land, continental Antarctica (77°S). Comparisons were made between sun- and shade-adapted forms, and changes were followed as the moss emerged from under the snow and during exposure of shade and sun forms to ambient light. Shade forms had lower light compensation and saturation values for NP but little difference in maximal NP rates. Shade forms exposed to ambient light changed rapidly (within five days) towards the performance of the sun forms. Surprisingly, this change was not by acclimation of shoots but by the production of new shoots. Chlorophyll and carotenoid levels measured on a molar chlorophyll basis showed no difference between sun and shade forms and also little change during emergence. The constant molar relationship between carotenoids and chlorophyll plus the high levels of the xanthophyll cycle pigments suggest that protection of the chlorophyll antenna was constitutive. This is an adaptation to the very high light levels that occur when the plants are active in continental Antarctica and contrasts to the situation in more temperate areas where high light is normally avoided by desiccation

Assessing the Power of Exome Chips

Genotyping chips for rare and low-frequent variants have recently gained popularity with the introduction of exome chips, but the utility of these chips remains unclear. These chips were designed using exome sequencing data from mainly American-European individuals, enriched for a narrow set of common diseases. In addition, it is well-known that the statistical power of detecting associations with rare and low-frequent variants is much lower compared to studies exclusively involving common variants. We developed a simulation program adaptable to any exome chip design to empirically evaluate the power of the exome chips. We implemented the main properties of the Illumina HumanExome BeadChip array. The simulated data sets were used to assess the power of exome chip based studies for varying effect sizes and causal variant scenarios. We applied two widely-used statistical approaches for rare and low-frequency variants, which collapse the variants into genetic regions or genes. Under optimal conditions, we found that a sample size between 20,000 to 30,000 individuals were needed in order to detect modest effect sizes (0.5% 1%) with 80% power. For small effect sizes (PAR <0.5%), 60,000–100,000 individuals were needed in the presence of non-causal variants. In conclusion, we found that at least tens of thousands of individuals are necessary to detect modest effects under optimal conditions. In addition, when using rare variant chips on cohorts or diseases they were not originally designed for, the identification of associated variants or genes will be even more challenging

QQ-plot for power analysis and null simulation, the diagonal line represent the expected value and the horizontal line the Bonferroni cut-off.

<p>(A) QQ-plot for one realization of the null simulation for 10k, SKAT is plotted in red/orange and WSS in dark green/light green. (B) QQ-plot for one realization of the null simulation for 60k, SKAT is plotted in red/orange, and WSS in dark green/light green. (C) QQ-plot of—log p-values for SKAT and WSS, given 100% causal SNVs within the causal genes, and a sample size of 10k. False negative is in lighter colors (SKAT; light green, WSS; orange) and true negative is colored in gray. (D) QQ-plot of—log p-values for SKAT and WSS, given 100% causal SNVs within the causal genes, and a sample size of 60k. False negative is in lighter colors (WSS; orange) and true negative is colored in gray.</p

Power plots for increasing PAR for SKAT and WSS for multiple testing adjusted analyses, for different sample sizes.

<p>The dashed line represent the median power, with the covered area is the inter quantile range of 25% to 75% power. (A) 100% causal SNVs within all genes, estimated with SKAT. (B) 100% causal SNVs within each gene, estimated with WSS. (C) 50% causal SNVs within each gene, estimated with SKAT. (D) 50% causal SNVs within each gene, estimated with WSS.</p

Power for increasing sample sizes and different PAR values after multiple testing adjusted analyses.

<p>(A) 100% causal SNVs within all genes, estimated with SKAT. (B) 100% causal SNVs within each gene, estimated with WSS. (C) 50% causal SNVs within each gene, estimated with SKAT. (D) 50% causal SNVs within each gene, estimated with WSS.</p

Identifying Novel Gene Variants in Coronary Artery Disease and Shared Genes With Several Cardiovascular Risk Factors.

Coronary artery disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes.We aimed to improve discovery of CAD genes and inform the pathogenic relationship between CAD and several cardiovascular disease risk factors using a shared polygenic signal-informed statistical framework.Using genome-wide association studies summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate methodology, we systematically investigated genetic overlap between CAD and 8 traits related to cardiovascular disease risk factors: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. We found significant enrichment of single-nucleotide polymorphisms associated with CAD as a function of their association with low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. Applying the conditional false discovery rate method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional false discovery rate &lt;0.01). Furthermore, we identified 53 loci with significant effects in both CAD and at least 1 of low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, systolic blood pressure, and type 1 diabetes mellitus.The observed polygenic overlap between CAD and cardiometabolic risk factors indicates a pathogenic relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD

Identifying Novel Gene Variants in Coronary Artery Disease and Shared Genes With Several Cardiovascular Risk Factors.

RationaleCoronary artery disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes.ObjectiveWe aimed to improve discovery of CAD genes and inform the pathogenic relationship between CAD and several cardiovascular disease risk factors using a shared polygenic signal-informed statistical framework.Methods and resultsUsing genome-wide association studies summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate methodology, we systematically investigated genetic overlap between CAD and 8 traits related to cardiovascular disease risk factors: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. We found significant enrichment of single-nucleotide polymorphisms associated with CAD as a function of their association with low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. Applying the conditional false discovery rate method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional false discovery rate &lt;0.01). Furthermore, we identified 53 loci with significant effects in both CAD and at least 1 of low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, systolic blood pressure, and type 1 diabetes mellitus.ConclusionsThe observed polygenic overlap between CAD and cardiometabolic risk factors indicates a pathogenic relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD

Identifying Novel Gene Variants in Coronary Artery Disease and Shared Genes With Several Cardiovascular Risk Factors

RationaleCoronary artery disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes.ObjectiveWe aimed to improve discovery of CAD genes and inform the pathogenic relationship between CAD and several cardiovascular disease risk factors using a shared polygenic signal-informed statistical framework.Methods and resultsUsing genome-wide association studies summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate methodology, we systematically investigated genetic overlap between CAD and 8 traits related to cardiovascular disease risk factors: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. We found significant enrichment of single-nucleotide polymorphisms associated with CAD as a function of their association with low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. Applying the conditional false discovery rate method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional false discovery rate &lt;0.01). Furthermore, we identified 53 loci with significant effects in both CAD and at least 1 of low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, systolic blood pressure, and type 1 diabetes mellitus.ConclusionsThe observed polygenic overlap between CAD and cardiometabolic risk factors indicates a pathogenic relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD