A comprehensive analysis comparing linear and generalized linear models in detecting adaptive SNPs

A novel sand cat swarm optimization algorithm-based SVM for diagnosis imaging genomics in Alzheimer's disease

In recent years, brain imaging genomics has advanced significantly in revealing underlying pathological mechanisms of Alzheimer's disease (AD) and providing early diagnosis. In this paper, we present a framework for diagnosing AD that integrates magnetic resonance imaging (fMRI) genetic preprocessing, feature selection, and a support vector machine (SVM) model. In particular, a novel sand cat swarm optimization (SCSO) algorithm, named SS-SCSO, which integrates the spiral search strategy and alert mechanism from the sparrow search algorithm, is proposed to optimize the SVM parameters. The optimization efficacy of the SS-SCSO algorithm is evaluated using CEC2017 benchmark functions, with results compared with other metaheuristic algorithms (MAs). The proposed SS-SCSO-SVM framework has been effectively employed to classify different stages of cognitive impairment in Alzheimer's Disease using imaging genetic datasets from the Alzheimer's Disease Neuroimaging Initiative. It has demonstrated excellent classification accuracies for four typical cases, including AD, early mild cognitive impairment, late mild cognitive impairment, and healthy control. Furthermore, experiment results indicate that the SS-SCSO-SVM algorithm has a stronger exploration capability for diagnosing AD compared to other well-established MAs and machine learning techniques.

Oryza CLIMtools: A genome-environment association resource reveals adaptive roles for heterotrimeric G proteins in the regulation of rice agronomic traits

Modern crop varieties display a degree of mismatch between their current distributions and the suitability of the local climate for their productivity. To address this issue, we present Oryza CLIMtools (https://gramene.org/CLIMtools/oryza_v1.0/), the first resource for pan-genome prediction of climate-associated genetic variants in a crop species. Oryza CLIMtools consists of interactive web-based databases that enable the user to (1) explore the local environments of traditional rice varieties (landraces) in South-East Asia and (2) investigate the environment by genome associations for 658 Indica and 283 Japonica rice landrace accessions collected from georeferenced local environments and included in the 3K Rice Genomes Project. We demonstrate the value of these resources by identifying an interplay between flowering time and temperature in the local environment that is facilitated by adaptive natural variation in OsHD2 and disrupted by a natural variant in OsSOC1. Prior quantitative trait locus analysis has suggested the importance of heterotrimeric G proteins in the control of agronomic traits. Accordingly, we analyzed the climate associations of natural variants in the different heterotrimeric G protein subunits. We identified a coordinated role of G proteins in adaptation to the prevailing potential evapotranspiration gradient and revealed their regulation of key agronomic traits, including plant height and seed and panicle length. We conclude by highlighting the prospect of targeting heterotrimeric G proteins to produce climate-resilient crops.

A cosmopolitan inversion facilitates seasonal adaptation in overwintering Drosophila

Fluctuations in the strength and direction of natural selection through time are a ubiquitous feature of life on Earth. One evolutionary outcome of such fluctuations is adaptive tracking, wherein populations rapidly adapt from standing genetic variation. In certain circumstances, adaptive tracking can lead to the long-term maintenance of functional polymorphism despite allele frequency change due to selection. Although adaptive tracking is likely a common process, we still have a limited understanding of aspects of its genetic architecture and its strength relative to other evolutionary forces such as drift. Drosophila melanogaster living in temperate regions evolve to track seasonal fluctuations and are an excellent system to tackle these gaps in knowledge. By sequencing orchard populations collected across multiple years, we characterized the genomic signal of seasonal demography and identified that the cosmopolitan inversion In(2L)t facilitates seasonal adaptive tracking and shows molecular footprints of selection. A meta-analysis of phenotypic studies shows that seasonal loci within In(2L)t are associated with behavior, life history, physiology, and morphological traits. We identify candidate loci and experimentally link them to phenotype. Our work contributes to our general understanding of fluctuating selection and highlights the evolutionary outcome and dynamics of contemporary selection on inversions.

Genomic analyses indicate resilience of a commercially and culturally important marine gastropod snail to climate change

Genomic vulnerability analyses are being increasingly used to assess the adaptability of species to climate change and provide an opportunity for proactive management of harvested marine species in changing oceans. Southeastern Australia is a climate change hotspot where many marine species are shifting poleward. The turban snail, Turbo militaris is a commercially and culturally harvested marine gastropod snail from eastern Australia. The species has exhibited a climate-driven poleward range shift over the last two decades presenting an ongoing challenge for sustainable fisheries management. We investigate the impact of future climate change on T. militaris using genotype-by-sequencing to project patterns of gene flow and local adaptation across its range under climate change scenarios. A single admixed, and potentially panmictic, demographic unit was revealed with no evidence of genetic subdivision across the species range. Significant genotype associations with heterogeneous habitat features were observed, including associations with sea surface temperature, ocean currents, and nutrients, indicating possible adaptive genetic differentiation. These findings suggest that standing genetic variation may be available for selection to counter future environmental change, assisted by widespread gene flow, high fecundity and short generation time in this species. We discuss the findings of this study in the content of future fisheries management and conservation.

Thyroid disrupting effects of multiple metals exposure: Comprehensive investigation from the thyroid parenchyma to hormonal function in a prospective cohort study

This study aimed to investigate the thyroid disrupting effects of multiple metals exposure with comprehensive investigation from the thyroid parenchyma to hormonal function. In this prospective cohort study of in-service staff of the Baoding Power Supply, we found that arsenic was negatively associated with total thyroxine (TT4) [βAs = -0.075, 95% confidence interval (CI): -0.129, -0.020, Padj = 0.04]. Similarly, selenium was negatively correlated with TT4 (βSe = -0.134, 95% CI: -0.211, -0.058, Padj < 0.01) and peripheral deiodinase activity (GT) (βSe = -0.133, 95% CI: -0.210, -0.056, Padj = 0.01). With respect to strontium, there were positive associations of strontium with thyroid-stimulating hormone (βSr = 0.263, 95% CI: 0.112, 0.414, Padj = 0.01), and negative associations of strontium with TT4 (βSr = -0.099, 95% CI: -0.150, -0.048, Padj < 0.01) and GT (βSr = -0.102, 95% CI: -0.153, -0.050, Padj < 0.01). We also observed negative associations of metal mixtures with TT4 and GT and potential interactions. Increased risks of thyroid nodule associated with aluminum, cobalt and nickel were also observed. Our findings suggest that multiple metals exposure leads to a multi-pronged assault to thyroid from the thyroid parenchyma to hormonal function. Future large-scale prospective cohort studies of general population and experimental studies were warranted.

On generalized latent factor modeling and inference for high-dimensional binomial data

We explore a hierarchical generalized latent factor model for discrete and bounded response variables and in particular, binomial responses. Specifically, we develop a novel two-step estimation procedure and the corresponding statistical inference that is computationally efficient and scalable for the high dimension in terms of both the number of subjects and the number of features per subject. We also establish the validity of the estimation procedure, particularly the asymptotic properties of the estimated effect size and the latent structure, as well as the estimated number of latent factors. The results are corroborated by a simulation study and for illustration, the proposed methodology is applied to analyze a dataset in a gene-environment association study.

Host association, environment, and geography underlie genomic differentiation in a major forest pest

Diverse geographic, environmental, and ecological factors affect gene flow and adaptive genomic variation within species. With recent advances in landscape ecological modelling and high-throughput DNA sequencing, it is now possible to effectively quantify and partition their relative contributions. Here, we use landscape genomics to identify determinants of genomic differentiation in the forest tent caterpillar, Malacosoma disstria, a widespread and irruptive pest of numerous deciduous tree species in North America. We collected larvae from multiple populations across Eastern Canada, where the species experiences a diversity of environmental gradients and feeds on a number of different host tree species, including trembling aspen (Populus tremuloides), sugar maple (Acer saccharum), red oak (Quercus rubra), and white birch (Betula papyrifera). Using a combination of reciprocal causal modelling (RCM) and distance-based redundancy analyses (dbRDA), we show that differentiation of thousands of genome-wide single nucleotide polymorphisms (SNPs) among individuals is best explained by a combination of isolation by distance, isolation by environment (spatial variation in summer temperatures and length of the growing season), and differences in host association. Configuration of suitable habitat inferred from ecological niche models was not significantly related to genomic differentiation, suggesting that M. disstria dispersal is agnostic with respect to habitat quality. Although population structure was not discretely related to host association, our modelling framework provides the first molecular evidence of host-associated differentiation in M. disstria, congruent with previous documentation of reduced growth and survival of larvae moved between natal host species. We conclude that ecologically mediated selection is contributing to variation within M. disstria, and that divergent adaptation related to both environmental conditions and host association should be considered in ongoing research and management of this important forest pest.

The effect of urinary essential and non-essential elements on serum albumin: Evidence from a community-based study of the elderly in Beijing

Background & aims

Few epidemiological studies have investigated the relationships of urinary essential and non-essential elements with serum albumin, an indicator of nutritional status, especially for the elderly in China.

Methods

A community-based study among elderly participants (n = 275) was conducted in Beijing from November to December 2016. We measured 15 urinary elements concentrations and serum albumin levels. Three statistical methods including the generalized linear model (GLM), quantile g-computation model (qgcomp) and bayesian kernel machine regression (BKMR) were adapted.

Results

In GLM analysis, we observed decreased serum albumin levels associated with elevated urinary concentrations of aluminum, arsenic, barium, cobalt, chromium, copper, iron, manganese, selenium, strontium, and zinc. Compared with the lowest tertile, the highest tertile of cadmium and cesium was also negatively associated with serum albumin. Urinary selenium concentration had the most significant negative contribution (30.05%) in the qgcomp analysis. The negative correlations of element mixtures with serum albumin were also observed in BKMR analysis.

Conclusions

Our findings suggested the negative associations of essential and non-essential elements with serum albumin among the elderly. Large-scare cohort studies among the general population are required to validate our findings and elucidate the relevant underlying mechanisms.

Introgression among North American wild grapes (Vitis) fuels biotic and abiotic adaptation

BACKGROUND

Introgressive hybridization can reassort genetic variants into beneficial combinations, permitting adaptation to new ecological niches. To evaluate evolutionary patterns and dynamics that contribute to introgression, we investigate six wild Vitis species that are native to the Southwestern United States and useful for breeding grapevine (V. vinifera) rootstocks.

RESULTS

By creating a reference genome assembly from one wild species, V. arizonica, and by resequencing 130 accessions, we focus on identifying putatively introgressed regions (pIRs) between species. We find six species pairs with signals of introgression between them, comprising up to ~ 8% of the extant genome for some pairs. The pIRs tend to be gene poor, located in regions of high recombination and enriched for genes implicated in disease resistance functions. To assess potential pIR function, we explore SNP associations to bioclimatic variables and to bacterial levels after infection with the causative agent of Pierce's disease (Xylella fastidiosa). pIRs are enriched for SNPs associated with both climate and bacterial levels, suggesting that introgression is driven by adaptation to biotic and abiotic stressors.

CONCLUSIONS

Altogether, this study yields insights into the genomic extent of introgression, potential pressures that shape adaptive introgression, and the evolutionary history of economically important wild relatives of a critical crop.