Case‐control data analysis for randomly pooled biomarkers

Pooled study designs, where individual biospecimens are combined prior to measurement via a laboratory assay, can reduce lab costs while maintaining statistical efficiency. Analysis of the resulting pooled measurements, however, often requires specialized techniques. Existing methods can effectively estimate the relation between a binary outcome and a continuous pooled exposure when pools are matched on disease status. When pools are of mixed disease status, however, the existing methods may not be applicable. By exploiting characteristics of the gamma distribution, we propose a flexible method for estimating odds ratios from pooled measurements of mixed and matched status. We use simulation studies to compare consistency and efficiency of risk effect estimates from our proposed methods to existing methods. We then demonstrate the efficacy of our method applied to an analysis of pregnancy outcomes and pooled cytokine concentrations. Our proposed approach contributes to the toolkit of available methods for analyzing odds ratios of a pooled exposure, without restricting pools to be matched on a specific outcome.     

A community‐level test of the leaf‐height‐seed ecology strategy scheme in relation to grazing conditions

Question: Is the assumption of trait independence implied in Westoby's (1998) leaf‐height‐seed (LHS) ecology strategy scheme upheld in a Mediterranean grazing system dominated by annuals? Is the LHS approach applicable at the community level? Location: Northern Israel. Methods: LHS traits (specific leaf area [SLA], plant height and seed mass), and additional leaf traits (leaf dry matter content [LDMC], leaf area, and leaf content of nitrogen [LNC], carbon [LCC], and phosphorus [LPC]), were analyzed at the species and community levels. Treatments included manipulations of grazing intensity (moderate and heavy) and protection from grazing. We focused on species comprising 80% of biomass over all treatments, assuming that these species drive trait relationships and ecosystem processes. Results: At the species level, SLA and seed mass were negatively correlated, and plant height was positively correlated to LCC. SLA, seed mass, and LPC increased with protection from grazing. At the community level, redundancy analysis revealed one principal gradient of variation: SLA, correlated to grazing, versus seed mass and plant height, associated with protection from grazing. We divided community functional parameters into two groups according to grazing response: (1) plant height, seed mass, LDMC, and LCC, associated with protection from grazing, and (2) SLA, associated with grazing. Conclusions: The assumption of independence between LHS traits was not upheld at the species level in this Mediterranean grazing system. At the community level, the LHS approach captured most of the variation associated with protection from grazing, reflecting changes in dominance within the plant community.     

Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling

Aim Robust and reliable predictions of the effects of climate change on biodiversity are required in formulating conservation and management strategies that best retain biodiversity into the future. Significant challenges in modelling climate change impacts arise from limitations in our current knowledge of biodiversity. Community‐level modelling can complement species‐level approaches in overcoming these limitations and predicting climate change impacts on biodiversity as a whole. However, the community‐level approaches applied to date have been largely correlative, ignoring the key processes that influence change in biodiversity over space and time. Here, we suggest that the development of new ‘semi‐mechanistic’ community‐level models would substantially increase our capacity to predict climate change impacts on biodiversity. Location Global. Methods Drawing on an expansive review of biodiversity modelling approaches and recent advances in semi‐mechanistic modelling at the species level, we outline the main elements of a new semi‐mechanistic community‐level modelling approach. Results Our quantitative review revealed a sharp divide between mechanistic and non‐mechanistic biodiversity modelling approaches, with very few semi‐mechanistic models developed to date. Main conclusions We suggest that the conceptual framework presented here for combining mechanistic and non‐mechanistic community‐level approaches offers a promising means of incorporating key processes into predictions of climate change impacts on biodiversity whilst working within the limits of our current knowledge.     

Genome‐wide association studies on the phyllosphere microbiome: Embracing complexity in host–microbe interactions

Environmental sequencing shows that plants harbor complex communities of microbes that vary across environments. However, many approaches for mapping plant genetic variation to microbe‐related traits were developed in the relatively simple context of binary host–microbe interactions under controlled conditions. Recent advances in sequencing and statistics make genome‐wide association studies (GWAS) an increasingly promising approach for identifying the plant genetic variation associated with microbes in a community context. This review discusses early efforts on GWAS of the plant phyllosphere microbiome and the outlook for future studies based on human microbiome GWAS. A workflow for GWAS of the phyllosphere microbiome is then presented, with particular attention to how perspectives on the mechanisms, evolution and environmental dependence of plant–microbe interactions will influence the choice of traits to be mapped.     

Ecological genomics meets community‐level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation

Local adaptation is a central feature of most species occupying spatially heterogeneous environments, and may factor critically in responses to environmental change. However, most efforts to model the response of species to climate change ignore intraspecific variation due to local adaptation. Here, we present a new perspective on spatial modelling of organism–environment relationships that combines genomic data and community‐level modelling to develop scenarios regarding the geographic distribution of genomic variation in response to environmental change. Rather than modelling species within communities, we use these techniques to model large numbers of loci across genomes. Using balsam poplar (Populus balsamifera) as a case study, we demonstrate how our framework can accommodate nonlinear responses of loci to environmental gradients. We identify a threshold response to temperature in the circadian clock gene GIGANTEA‐5 (GI5), suggesting that this gene has experienced strong local adaptation to temperature. We also demonstrate how these methods can map ecological adaptation from genomic data, including the identification of predicted differences in the genetic composition of populations under current and future climates. Community‐level modelling of genomic variation represents an important advance in landscape genomics and spatial modelling of biodiversity that moves beyond species‐level assessments of climate change vulnerability.     

Using the Formozov–Malyshev–Pereleshin formula to convert mammal spoor counts into density estimates for long‐term community‐level monitoring

Finding an appropriate method to monitor a wide range of mammal species simultaneously is notoriously difficult, as each method has its limitations. Here, we examine a formula, known as the Formozov–Malyshev–Pereleshin (FMP), which uses mean daily travel distances (day ranges) to convert spoor counts into density estimates. Availability of accurate estimates of day ranges is a limitation of the FMP formula. Here, we used allometry to estimate day ranges for those species that lacked empirical movement data and general additive models (GAM) to assess trends in density estimates. With this approach, we derived density estimates for 10 mammal species, regardless of whether they were abundant, or rare and elusive (e.g. carnivores). General additive models suggest that six species are stable or increasing, and four declining, although all nonsignificantly. Use of allometric estimates in lieu of empirical estimates led to falsely increased precision in density estimates, highlighting the need to fill the knowledge gap in movement ecology for certain species. Simulations were used to examine error introduced into trend estimates by this bias. We conclude that the FMP formula, when properly employed, can be an efficient method for simultaneous monitoring of multispecies in different functional groups.     

Genome‐wide association study for the level of serum electrolytes in Italian Large White pigs

Calcium, magnesium and phosphorus are essential electrolytes involved in a large number of biological processes. Imbalance of these minerals in blood may indicate clinically relevant conditions and are important in inferring acute or chronic pathologies in humans and animals. In this work, we carried out a genome‐wide association study (GWAS) for the level of these three electrolytes in the serum of 843 performance‐tested Italian Large White pigs. All pigs were genotyped with the Illumina PorcineSNP60 BeadChip, and GWAS was carried out using genome‐wide efficient mixed‐model association. For the level of Ca²⁺, eight single nucleotide polymorphisms (SNPs) were significant, considering a false discovery rate (FDR) < 0.05, and another eight were above the moderate association threshold (P_{nominal value} < 5.00E‐05). These SNPs are distributed in four porcine chromosomes (SSC): SSC8, SSC11, SSC12 and SSC13. In particular, a few putative different signals of association detected on SSC13 and one on SSC12 were in genes or close to genes involved in calcium metabolism (P2RY1, RAP2B, SLC9A9, C3orf58, TSC22D2, PLCH1 and CACNB1). Only one SNP (on SSC7) and six SNPs (on SSC2 and SSC7) showed moderate association with the level of magnesium and phosphorus respectively. The association signals for these two latter minerals might identify genes not known thus far for playing a role in their biological functions and regulations. In conclusion, our GWAS contributed to increased knowledge on the role that calcium, magnesium and phosphorus may play in the genetically determined physiological mechanisms affecting the natural variability of mineral levels in mammalian blood.     

New insights into the genetic basis of natural chilling and cold shock tolerance in rice by genome‐wide association analysis

In order to understand cold adaptability and explore additional genetic resources for the cold tolerance improvement of rice, we investigated the genetic variation of 529 rice accessions under natural chilling and cold shock stress conditions at the seedling stage using genome‐wide association studies; a total of 132 loci were identified. Among them, 12 loci were common for both chilling and cold shock tolerance, suggesting that rice has a distinct and overlapping genetic response and adaptation to the two stresses. Haplotype analysis of a known gene OsMYB2, which is involved in cold tolerance, revealed indica–japonica differentiation and latitude tendency for the haplotypes of this gene. By checking the subpopulation and geographical distribution of accessions with tolerance or sensitivity under these two stress conditions, we found that the chilling tolerance group, which mainly consisted of japonica accessions, has a wider latitudinal distribution than the chilling sensitivity group. We conclude that the genetic basis of natural chilling stress tolerance in rice is distinct from that of cold shock stress frequently used for low‐temperature treatment in the laboratory and the cold adaptability of rice is associated with the subpopulation and latitudinal distribution.     

Comparison of detergent‐based sample preparation workflows for LTQ‐Orbitrap analysis of the Escherichia coli proteome

This work presents a comparative evaluation of several detergent‐based sample preparation workflows for the MS‐based analysis of bacterial proteomes, performed using the model organism Escherichia coli. Initially, RapiGest‐ and SDS‐based buffers were compared for their protein extraction efficiency and quality of the MS data generated. As a result, SDS performed best in terms of total protein yields and overall number of MS identifications, mainly due to a higher efficiency in extracting high molecular weight (MW) and membrane proteins, while RapiGest led to an enrichment in periplasmic and fimbrial proteins. Then, SDS extracts underwent five different MS sample preparation workflows, including: detergent removal by spin columns followed by in‐solution digestion (SC), protein precipitation followed by in‐solution digestion in ammonium bicarbonate or urea buffer, filter‐aided sample preparation (FASP), and 1DE separation followed by in‐gel digestion. On the whole, about 1000 proteins were identified upon LC‐MS/MS analysis of all preparations (>1100 with the SC workflow), with FASP producing more identified peptides and a higher mean sequence coverage. Each protocol exhibited specific behaviors in terms of MW, hydrophobicity, and subcellular localization distribution of the identified proteins; a comparative assessment of the different outputs is presented.     

SERS‐based biosensor for Alzheimer disease evaluation through the fast analysis of human serum

Alzheimer disease (AD) is the most common form of dementia in the elderly, progressively affecting the cognitive functions with a complex diagnostic procedure that limits the time for a prompt intervention. In this study we optimized a reliable protocol for the analysis of AD patients and healthy subjects' serum using the Surface Enhanced Raman Spectroscopy (SERS), taking into consideration the effect of different variables on the final spectra, analyzed and compared through multivariate analysis and correlated with hippocampus volume. As results, we demonstrated a statistical difference between the spectra collected from the two investigated groups, with an accuracy, precision and specificity of respectively 83%, 86%, and 86%. The correlation of these data with those obtained from MRI, demonstrated a direct correlation between Raman spectra and hippocampus degeneration showing the Raman Spectroscopy (RS) as a potential tool for the monitoring of AD progression and rehabilitation treatments.     

Comparison of adjustment methods for stratified two‐sample tests in the context of ROC analysis

We compare several nonparametric and parametric weighting methods for the adjustment of the effect of strata. In particular, we focus on the adjustment methods in the context of receiver‐operating characteristic (ROC) analysis. Nonparametrically, rank‐based van Elteren's test and inverse‐variance (IV) weighting using the area under the ROC curve (AUC) are examined. Parametrically, the stratified t‐test and IV AUC weighted method are applied based on a binormal monotone transformation model. Stratum‐specific, pooled, and adjusted estimates are obtained. The pooled and adjusted AUCs are estimated. We illustrate and compare these weighting methods on a multi‐center diagnostic trial and through extensive Monte‐Carlo simulations.     

Analytic power and sample size calculation for the genotypic transmission/disequilibrium test in case‐parent trio studies

Case‐parent trio studies considering genotype data from children affected by a disease and their parents are frequently used to detect single nucleotide polymorphisms (SNPs) associated with disease. The most popular statistical tests for this study design are transmission/disequilibrium tests (TDTs). Several types of these tests have been developed, for example, procedures based on alleles or genotypes. Therefore, it is of great interest to examine which of these tests have the highest statistical power to detect SNPs associated with disease. Comparisons of the allelic and the genotypic TDT for individual SNPs have so far been conducted based on simulation studies, since the test statistic of the genotypic TDT was determined numerically. Recently, however, it has been shown that this test statistic can be presented in closed form. In this article, we employ this analytic solution to derive equations for calculating the statistical power and the required sample size for different types of the genotypic TDT. The power of this test is then compared with the one of the corresponding score test assuming the same mode of inheritance as well as the allelic TDT based on a multiplicative mode of inheritance, which is equivalent to the score test assuming an additive mode of inheritance. This is, thus, the first time the power of these tests are compared based on equations, yielding instant results and omitting the need for time‐consuming simulation studies. This comparison reveals that these tests have almost the same power, with the score test being slightly more powerful.