A genome‐wide search for local adaptation in a terrestrial‐breeding frog reveals vulnerability to climate change

Terrestrial‐breeding amphibians are likely to be vulnerable to warming and drying climates, as their embryos require consistent moisture for successful development. Adaptation to environmental change will depend on sufficient genetic variation existing within or between connected populations. Here, we use Single Nucleotide Polymorphism (SNP) data to investigate genome‐wide patterns in genetic diversity, gene flow and local adaptation in a terrestrial‐breeding frog (Pseudophryne guentheri) subject to a rapidly drying climate and recent habitat fragmentation. The species was sampled across 12 central and range‐edge populations (192 samples), and strong genetic structure was apparent, as were high inbreeding coefficients. Populations showed differences in genetic diversity, and one population lost significant genetic diversity in a decade. More than 500 SNP loci were putatively under directional selection, and 413 of these loci were correlated with environmental variables such as temperature, rainfall, evaporation and soil moisture. One locus showed homology to a gene involved in the activation of maturation in Xenopus oocytes, which may facilitate rapid development of embryos in drier climates. The low genetic diversity, strong population structuring and presence of local adaptation revealed in this study shows why management strategies such as targeted gene flow may be necessary to assist isolated populations to adapt to future climates.     

Functional mapping of N deficiency‐induced response in wheat yield‐component traits by implementing high‐throughput phenotyping

As overfertilization leads to environmental concerns and the cost of N fertilizer increases, the issue of how to select crop cultivars that can produce high yields on N‐deficient soils has become crucially important. However, little information is known about the genetic mechanisms by which crops respond to environmental changes induced by N signaling. Here, we dissected the genetic architecture of N‐induced phenotypic plasticity in bread wheat (Triticum aestivum L.) by integrating functional mapping and semiautomatic high‐throughput phenotyping data of yield‐related canopy architecture. We identified a set of quantitative trait loci (QTLs) that determined the pattern and magnitude of how wheat cultivars responded to low N stress from normal N supply throughout the wheat life cycle. This analysis highlighted the phenological landscape of genetic effects exerted by individual QTLs, as well as their interactions with N‐induced signals and with canopy measurement angles. This information may shed light on our mechanistic understanding of plant adaptation and provide valuable information for the breeding of N‐deficiency tolerant wheat varieties.     

Evaluation of polymorphisms in microRNA‐binding sites and pancreatic cancer risk in Chinese population

As promising biomarkers and therapy targets, microRNAs (miRNAs) are involved in various physiological and tumorigenic processes. Genetic variants in miRNA‐binding sites can lead to dysfunction of miRNAs and contribute to disease. However, systematic investigation of the miRNA‐related single nucleotide polymorphisms (SNPs) for pancreatic cancer (PC) risk remains elusive. We performed integrative bioinformatics analyses to select 31 SNPs located in miRNA‐target binding sites using the miRNASNP v2.0, a solid database providing miRNA‐related SNPs for genetic research, and investigated their associations with risk of PC in two large case‐control studies totally including 1847 cases and 5713 controls. We observed that the SNP rs3802266 is significantly associated with increased risk of PC (odds ratio (OR) = 1.21, 95% confidence intervals (CI) = 1.11‐1.31, P = 1.29E‐05). Following luciferase reporter gene assays show that rs3802266‐G creates a stronger binding site for miR‐181a‐2‐3p in 3′ untranslated region (3′UTR) of the gene ZHX2. Expression quantitative trait loci (eQTL) analysis suggests that ZHX2 expression is lower in individuals carrying rs3802266‐G with increased PC risk. In conclusion, our findings highlight the involvement of miRNA‐binding SNPs in PC susceptibility and provide new clues for PC carcinogenesis.     

Production of male flowers does not decrease with plant size in insect‐pollinated Sagittaria trifolia,contrary to predictions of size‐dependent sex allocation

Abstract It has been proposed that relative allocation to female function increases with plant size in animal‐pollinated species. Previous investigations in several monoecious Sagittaria species seem to run contrary to the prediction of size‐dependent sex allocation (SDS), throwing doubt on the generalization of SDS. Plant size, phenotypic gender, and flower production were measured in experimental populations of an aquatic, insect‐pollinated herb Sagittaria trifolia (Alismataceae) under highly different densities. The comparison of ramets produced clonally can reduce confounding effects from genetic and environmental factors. In the high‐density population, 48% of ramets were male without female flowers, but in the low‐density population all ramets were monoecious. We observed allometric growth in reproductive allocation with ramet size, as evident in biomass of reproductive structures and number of flowers. However, within both populations female and male flower production were isometric with ramet size, in contrast to an allometric growth in femaleness as predicted by SDS. Phenotypic gender was not related to ramet size in either population. The results indicated that large plants may increase both female and male function even in animal‐pollinated plants, pointing towards further studies to test the hypothesis of size‐dependent sex allocation using different allocation currencies.     

Genome‐wide association mapping for phenotypic plasticity in rice

Phenotypic plasticity of plants in response to environmental changes is important for adapting to changing climate. Less attention has been paid to exploring the advantages of phenotypic plasticity in resource‐rich environments to enhance the productivity of agricultural crops. Here, we examined genetic variation for phenotypic plasticity in indica rice (Oryza sativa L.) across two diverse panels: (1) a Phenomics of Rice Adaptation and Yield (PRAY) population comprising 301 accessions; and (2) a Multi‐parent Advanced Generation Inter‐Cross (MAGIC) indica population comprising 151 accessions. Altered planting density was used as a proxy for elevated atmospheric CO₂ response. Low planting density significantly increased panicle weight per plant compared with normal density, and the magnitude of the increase ranged from 1.10 to 2.78 times among accessions for the PRAY population and from 1.05 to 2.45 times for the MAGIC population. Genome‐wide‐association studies validate three E nvironmental R esponsiveness (ER) candidate alleles (qER1–3) that were associated with relative response of panicle weight to low density. Two of these alleles were tested in 13 genotypes to clarify their biomass responses during vegetative growth under elevated CO₂ in Japan. Our study provides evidence for polymorphisms that control rice phenotypic plasticity in environments that are rich in resources such as light and CO₂.     

Environmental change drives long‐term recruitment and growth variation in an estuarine fish

How individuals respond to environmental change determines the strength and direction of biological processes like recruitment and growth that underpin population productivity. Ascertaining the relative importance of environmental factors can, however, be difficult given the numerous mechanisms through which they affect individuals. This is especially true in dynamic and complex estuarine environments. Here, we develop long‐term otolith‐based indices of recruitment and growth for estuary perch Percalates colonorum (Bemm River, Australia), to explore the importance of intrinsic (individual, demographic) and extrinsic (hydrologic, climatic, density‐dependent) factors in driving estuarine fish productivity. Analyses involved a novel zero‐inflated specification of catch curve regression and mixed effects modelling. The 39 years of recruitment and 46 years of growth data, spanning a period of environmental change including severe drought, displayed considerable inter‐annual variation. Recruitment success was strongly related to high freshwater inflows during the spawning season, suggesting that these conditions act as spawning cues for adults and potentially provide favourable conditions for larvae. Individuals displayed age‐dependent growth, with highest rates observed at younger ages in years characterized by warm temperatures, and to a lesser degree, greater magnitude base inflow conditions. We detected systematic among‐year‐class growth differences, but these were not attributable to year class strength, suggesting that environmental conditions experienced by individuals as juveniles can have long‐lasting effects of greater importance to population productivity than density‐dependent growth responses. The primacy of temperature in driving growth variation highlights that under‐appreciated climatic variation can affect estuarine fish productivity through direct physiological and indirect food web mechanisms. We predict that climatic warming will promote individual growth in southerly populations of P. colonorum but concurrently limit recruitment due to forecast reductions in spawning season river discharge. Disparate trait responses are likely in other fishes as they respond to multiple and changing environmental drivers, making predictions of future population productivity challenging.     

Non‐replication of genome‐wide‐based associations of efficient food conversion in dairy cows

Animal growth relative to food energy input is of key importance to agricultural production. Several recent studies highlighted genetic markers associated with food conversion efficiency in beef cattle, and there is now a requirement to validate these associations in additional populations and to assess their potential utility for selecting animals with enhanced food‐use efficiency. The current analysis tested a population of dairy cattle using 138 DNA markers previously associated with food intake and growth in a whole‐genome association analysis of beef animals. Although seven markers showed point‐wise significance at P < 0.05, none of the single‐nucleotide polymorphisms tested were significantly associated with food conversion efficiency after correction for multiple testing. These data do not support the involvement of this subset of previously implicated markers in the food conversion efficiency of the physiologically distinct New Zealand Holstein‐Friesian dairy breed.     

A millennial‐scale chronicle of evolutionary responses to cultural eutrophication in Daphnia

For an accurate assessment of the anthropogenic impacts on evolutionary change in natural populations, we need long‐term environmental, genetic and phenotypic data that predate human disturbances. Analysis of c. 1600 years of history chronicled in the sediments of South Center Lake, Minnesota, USA, revealed major environmental changes beginning c. 120 years ago coinciding with the initiation of industrialised agriculture in the catchment area. Population genetic structure, analysed using DNA from dormant eggs of the keystone aquatic herbivore, Daphnia pulicaria, suggested no change for c. 1500 years prior to striking shifts associated with anthropogenic environmental alterations. Furthermore, phenotypic assays on the oldest resurrected metazoan genotypes (potentially as old as c. 700 years) indicate significant shifts in phosphorus utilisation rates compared to younger genotypes. Younger genotypes show steeper reaction norms with high growth under high phosphorus (P), and low growth under low P, while ‘ancient’ genotypes show flat reaction norms, yet higher growth efficiency under low P. Using this resurrection ecology approach, environmental, genetic and phenotypic data spanning pre‐ and post‐industrialised agricultural eras clearly reveal the evolutionary consequences of anthropogenic environmental change.     

Parameter estimation for discretely observed linear birth‐and‐death processes

Birth‐and‐death processes are widely used to model the development of biological populations. Although they are relatively simple models, their parameters can be challenging to estimate, as the likelihood can become numerically unstable when data arise from the most common sampling schemes, such as annual population censuses. A further difficulty arises when the discrete observations are not equi‐spaced, for example, when census data are unavailable for some years. We present two approaches to estimating the birth, death, and growth rates of a discretely observed linear birth‐and‐death process: via an embedded Galton‐Watson process and by maximizing a saddlepoint approximation to the likelihood. We study asymptotic properties of the estimators, compare them on numerical examples, and apply the methodology to data on monitored populations.     

Structural insights of Mycobacterium GTPase‐Obg and anti‐sigma‐F factor Usfx interaction

An essential protein for bacterial growth, GTPase‐Obg (Obg), is known to play an unknown but crucial role in stress response as its expression increases in Mycobacterium under stress conditions. It is well reported that Obg interacts with anti‐sigma‐F factor Usfx; however, a detailed analysis and structural characterization of their physical interaction remain undone. In view of above‐mentioned points, this study was conceptualized for performing binding analysis and structural characterization of Obg‐Usfx interaction. The binding studies were performed by surface plasmon resonance, while in silico docking analysis was done to identify crucial residues responsible for Obg‐Usfx interaction. Surface plasmon resonance results clearly suggest that N‐terminal and G domains of Obg mainly contribute to Usfx binding. Also, binding constants display strong affinity that was further evident by intermolecular hydrogen bonds and hydrophobic interactions in the predicted complex. Strong interaction between Obg and Usfx supports the view that Obg plays an important role in stress response, essentially required for Mycobacterium survival. As concluded by various studies that Obg is crucial for Mycobacterium survival under stress, this structural information may help us in designing novel and potential inhibitors against resistant Mycobacterium strains.     

Pleiotropy informed adaptive association test of multiple traits using genome‐wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large‐scale genome‐wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease‐related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans‐Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the “single trait” association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well‐characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome‐phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual‐level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P‐value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta‐analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta‐analysis. All the proposed methods are implemented in a publicly available R package.     

Environmentally induced dispersal‐related life‐history syndrome in the tropical butterfly,Bicyclus anynana

Dispersal is a key process for understanding the persistence of populations as well as the capacity of organisms to respond to environmental change. Therefore, understanding factors that may facilitate or constrain the evolution of dispersal is of crucial interest. Assessments of phenotypic variation in various behavioural, physiological and morphological traits related to insect dispersal and flight performance are common, yet very little is known about the genetic associations among these traits. We have used experiments on the butterfly Bicyclus anynana to estimate genetic variation and covariation in seven behavioural, physiological and morphological traits related to flight potential and hence dispersal. Our goal was to characterize the heritabilities and genetic correlations among these traits and thus to understand more about the evolution of dispersal‐related life‐history syndromes in butterflies. Using a version of the animal model, we showed that all of the traits varied between the sexes, and most were either positively or negatively (phenotypically and/or genetically) correlated with body size. Heritable variation was present in most traits, with the highest heritabilities estimated for body mass and thorax ratio. The variance in flight activity among multiple measurements for the same individual was high even after controlling for the prevailing environmental conditions, indicating the importance of behavioural switching and/or inherent randomness associated with this type of movement. A number of dispersal‐related traits showed phenotypic correlations among one another, but only a few of these were associated with significant genetic correlations indicating that covariances between these traits in Bicyclus anynana are mainly environmentally induced.     

r‐ and K‐selection in fluctuating populations is determined by the evolutionary trade‐off between two fitness measures: Growth rate and lifetime reproductive success

In a stable environment, evolution maximizes growth rates in populations that are not density regulated and the carrying capacity in the case of density regulation. In a fluctuating environment, evolution maximizes a function of growth rate, carrying capacity and environmental variance, tending to r‐selection and K‐selection under large and small environmental noise, respectively. Here we analyze a model in which birth and death rates depend on density through the same function but with independent strength of density dependence. As a special case, both functions may be linear, corresponding to logistic dynamics. It is shown that evolution maximizes a function of the deterministic growth rate r₀ and the lifetime reproductive success (LRS) R₀, both defined at small densities, as well as the environmental variance. Under large noise this function is dominated by r₀ and average lifetimes are small, whereas R₀ dominates and lifetimes are larger under small noise. Thus, K‐selection is closely linked to selection for large R₀ so that evolution tends to maximize LRS in a stable environment. Consequently, different quantities (r₀ and R₀) tend to be maximized at low and high densities, respectively, favoring density‐dependent changes in the optimal life history.     

RNA‐guided Cas9 as an in vivo desired‐target mutator in maize

The RNA‐guided Cas9 system is a versatile tool for genome editing. Here, we established a RNA‐guided endonuclease (RGEN) system as an in vivo desired‐target mutator (DTM) in maize to reduce the linkage drag during breeding procedure, using the LIGULELESS1 (LG1) locus as a proof‐of‐concept. Our system showed 51.5%–91.2% mutation frequency in T0 transgenic plants. We then crossed the T1 plants stably expressing DTM with six diverse recipient maize lines and found that 11.79%–28.71% of the plants tested were mutants induced by the DTM effect. Analysis of successive F2 plants indicated that the mutations induced by the DTM effect were largely heritable. Moreover, DTM‐generated hybrids had significantly smaller leaf angles that were reduced more than 50% when compared with that of the wild type. Planting experiments showed that DTM‐generated maize plants can be grown with significantly higher density and hence greater yield potential. Our work demonstrate that stably expressed RGEN could be implemented as an in vivoDTM to rapidly generate and spread desired mutations in maize through hybridization and subsequent backcrossing, and hence bypassing the linkage drag effect in convention introgression methodology. This proof‐of‐concept experiment can be a potentially much more efficient breeding strategy in crops employing the RNA‐guided Cas9 genome editing.     

Investigating the genomic basis of discrete phenotypes using a Pool‐Seq‐only approach: New insights into the genetics underlying colour variation in diverse taxa

While large‐scale genomic approaches are increasingly revealing the genetic basis of polymorphic phenotypes such as colour morphs, such approaches are almost exclusively conducted in species with high‐quality genomes and annotations. Here, we use Pool‐Seq data for both genome assembly and SNP frequency estimation, followed by scanning for F_ST outliers to identify divergent genomic regions. Using paired‐end, short‐read sequencing data from two groups of individuals expressing divergent phenotypes, we generate a de novo rough‐draft genome, identify SNPs and calculate genomewide F_ST differences between phenotypic groups. As genomes generated by Pool‐Seq data are highly fragmented, we also present an approach for super‐scaffolding contigs using existing protein‐coding data sets. Using this approach, we reanalysed genomic data from two recent studies of birds and butterflies investigating colour pattern variation and replicated their core findings, demonstrating the accuracy and power of a Pool‐Seq‐only approach. Additionally, we discovered new regions of high divergence and new annotations that together suggest novel parallels between birds and butterflies in the origins of their colour pattern variation.