Maximizing the Power of Principal-Component Analysis of Correlated Phenotypes in Genome-wide Association Studies

Many human traits are highly correlated. This correlation can be leveraged to improve the power of genetic association tests to identify markers associated with one or more of the traits. Principal component analysis (PCA) is a useful tool that has been widely used for the multivariate analysis of correlated variables. PCA is usually applied as a dimension reduction method: the few top principal components (PCs) explaining most of total trait variance are tested for association with a predictor of interest, and the remaining components are not analyzed. In this study we review the theoretical basis of PCA and describe the behavior of PCA when testing for association between a SNP and correlated traits. We then use simulation to compare the power of various PCA-based strategies when analyzing up to 100 correlated traits. We show that contrary to widespread practice, testing only the top PCs often has low power, whereas combining signal across all PCs can have greater power. This power gain is primarily due to increased power to detect genetic variants with opposite effects on positively correlated traits and variants that are exclusively associated with a single trait. Relative to other methods, the combined-PC approach has close to optimal power in all scenarios considered while offering more flexibility and more robustness to potential confounders. Finally, we apply the proposed PCA strategy to the genome-wide association study of five correlated coagulation traits where we identify two candidate SNPs that were not found by the standard approach.     

Analysis of HPC1, HPCX, and PCaP in Icelandic hereditary prostate cancer

Putative prostate cancer susceptibility loci have recently been identified by genetic linkage analysis on chromosomes 1q24-25 (HPC1). 1q44.243 (PCaP), and Xq27-28 (HPCX). In order to estimate the genetic linkage in Icelandic prostate cancer families, we genotyped 241 samples from 87 families with eleven markers in the HPC1 region, six markers at PCaP, and eight at HPCX. Concurrently, we assessed allelic imbalance at the HPC1 and PCaP loci in selected tumors from the patients. For each of the candidate regions, the combined parametric and non-parametric LOD scores were strongly negative. Evidence for linkage allowing for genetic heterogeneity was also insignificant for all the regions. The results were negative irrespective of whether calculations were performed for the whole material or for a selected set of early age at onset families. The prevalence of allelic imbalance was relatively low in both the HPC1 (0%-9%) and PCaP (5%-20%) regions and was not elevated in tumors from positively linked families. Our studies indicate that the putative cancer susceptibility genes at chromosomes 1q24-25, 1q44.2-43, and Xq27-28 are unlikely to contribute significantly to hereditary prostate cancer in Iceland and that selective loss of the HPC1 and PCaP loci is a relatively rare somatic event in prostate cancers.     

Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn''s Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.     

The likely impact of elevated [CO2], nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: a literature review

Temperate and boreal forest ecosystems contain a large part of the carbon stored on land, in the form of both biomass and soil organic matter. Increasing atmospheric [CO2], increasing temperature, elevated nitrogen deposition and intensified management will change this C store. Well documented single-factor responses of net primary production are: higher photosynthetic rate (the main [CO2] response); increasing length of growing season (the main temperature response); and higher leaf-area index (the main N deposition and partly [CO2] response). Soil organic matter will increase with increasing litter input, although priming may decrease the soil C stock initially, but litter quality effects should be minimal (response to [CO2], N deposition, and temperature); will decrease because of increasing temperature; and will increase because of retardation of decomposition with N deposition, although the rate of decomposition of high-quality litter can be increased and that of low-quality litter decreased. Single-factor responses can be misleading because of interactions between factors, in particular those between N and other factors, and indirect effects such as increased N availability from temperature-induced decomposition. In the long term the strength of feedbacks, for example the increasing demand for N from increased growth, will dominate over short-term responses to single factors. However, management has considerable potential for controlling the C store.     

Interspecific Introgression in Cetaceans: DNA Markers Reveal Post-F1 Status of a Pilot Whale

Visual species identification of cetacean strandings is difficult, especially when dead specimens are degraded and/or species are morphologically similar. The two recognised pilot whale species (Globicephala melas and Globicephala macrorhynchus) are sympatric in the North Atlantic Ocean. These species are very similar in external appearance and their morphometric characteristics partially overlap; thus visual identification is not always reliable. Genetic species identification ensures correct identification of specimens. Here we have employed one mitochondrial (D-Loop region) and eight nuclear loci (microsatellites) as genetic markers to identify six stranded pilot whales found in Galicia (Northwest Spain), one of them of ambiguous phenotype. DNA analyses yielded positive amplification of all loci and enabled species identification. Nuclear microsatellite DNA genotypes revealed mixed ancestry for one individual, identified as a post-F1 interspecific hybrid employing two different Bayesian methods. From the mitochondrial sequence the maternal species was Globicephala melas. This is the first hybrid documented between Globicephala melas and G. macrorhynchus, and the first post-F1 hybrid genetically identified between cetaceans, revealing interspecific genetic introgression in marine mammals. We propose to add nuclear loci to genetic databases for cetacean species identification in order to detect hybrid individuals.     

Connectivity in a pond system influences migration and genetic structure in threespine stickleback

Neutral genetic structure of natural populations is primarily influenced by migration (the movement of individuals and, subsequently, their genes) and drift (the statistical chance of losing genetic diversity over time). Migration between populations is influenced by several factors, including individual behavior, physical barriers, and environmental heterogeneity among populations. However, drift is expected to be stronger in populations with low immigration rate and small effective population size. With the technological advancement in geological information systems and spatial analysis tools, landscape genetics now allows the development of realistic migration models and increased insight to important processes influencing diversity of natural populations. In this study, we investigated the relationship between landscape connectivity and genetic distance of threespine stickleback (Gasterosteus aculeatus) inhabiting a pond complex in Belgjarskógur, Northeast Iceland. We used two landscape genetic approaches (i.e., least-cost-path and isolation-by-resistance) and asked whether gene flow, as measured by genetic distance, was more strongly associated with Euclidean distance (isolation-by-distance) or with landscape connectivity provided by areas prone to flooding (as indicated by Carex sp. cover)? We found substantial genetic structure across the study area, with pairwise genetic distances among populations (D_PS) ranging from 0.118 to 0.488. Genetic distances among populations were more strongly correlated with least-cost-path and isolation-by-resistance than with Euclidean distance, whereas the relative contribution of isolation-by-resistance and Euclidian distance could not be disentangled. These results indicate that migration among stickleback populations occurs via periodically flooded areas. Overall, this study highlights the importance of transient landscape elements influencing migration and genetic structure of populations at small spatial scales.     

Plant invasion is associated with higher plant–soil nutrient concentrations in nutrient‐poor environments

Plant invasion is an emerging driver of global change worldwide. We aimed to disentangle its impacts on plant–soil nutrient concentrations. We conducted a meta‐analysis of 215 peer‐reviewed articles and 1233 observations. Invasive plant species had globally higher N and P concentrations in photosynthetic tissues but not in foliar litter, in comparison with their native competitors. Invasive plants were also associated with higher soil C and N stocks and N, P, and K availabilities. The differences in N and P concentrations in photosynthetic tissues and in soil total C and N, soil N, P, and K availabilities between invasive and native species decreased when the environment was richer in nutrient resources. The results thus suggested higher nutrient resorption efficiencies in invasive than in native species in nutrient‐poor environments. There were differences in soil total N concentrations but not in total P concentrations, indicating that the differences associated to invasive plants were related with biological processes, not with geochemical processes. The results suggest that invasiveness is not only a driver of changes in ecosystem species composition but that it is also associated with significant changes in plant–soil elemental composition and stoichiometry.     

Effect of elevated [CO2] on stem wood properties of mature Norway spruce grown at different soil nutrient availability

The objective of the present study was to investigate the interactive effects of elevated [CO₂] and soil nutrient availability on secondary xylem structure and chemical composition of 41‐year‐old Norway spruce (Picea abies (L.) Karst.) trees. The nonfertilized and irrigated‐fertilized trees were, for 3 years, continuously exposed to elevated [CO₂] in whole‐tree chambers. Elevated [CO₂] decreased concentrations of soluble sugars, acid‐soluble lignin and nitrogen in stem wood, but the effects were not consistent between sampling height and/or fertilization. The effect of 2*ambient [CO₂] on wood structure depended on the exposure year and/or fertilization. Radial lumen diameter decreased and annual ring width increased in the second year of exposure (1999) in elevated [CO₂]. In the latter, the CO₂ effect was significant only in the nonfertilized trees. Stem wood chemistry and structure were significantly affected by fertilization. Fertilization increased the concentrations of nitrogen and gravimetric lignin, annual ring width, and radial lumen diameter. Fertilization decreased C/N ratio, mean ring density, earlywood density, latewood density, cell wall thickness, cell wall index, and latewood percentage. We conclude that elevated [CO₂] had only minor effects on wood properties while fertilization had more marked effects and thus may affect ecosystem processes and suitability of wood for different end‐use purposes.