Midkine is a NF-κB-inducible gene that supports prostate cancer cell survival

Background

Atherosclerotic peripheral arterial disease (PAD) affects 8–10 million people in the United States and is associated with a marked impairment in quality of life and an increased risk of cardiovascular events. Noninvasive assessment of PAD is performed by measuring the ankle-brachial index (ABI). Complex traits, such as ABI, are influenced by a large array of genetic and environmental factors and their interactions. We attempted to characterize the genetic architecture of ABI by examining the main and interactive effects of individual single nucleotide polymorphisms (SNPs) and conventional risk factors.

Methods

We applied linear regression analysis to investigate the association of 435 SNPs in 112 positional and biological candidate genes with ABI and related physiological and biochemical traits in 1046 non-Hispanic white, hypertensive participants from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. The main effects of each SNP, as well as SNP-covariate and SNP-SNP interactions, were assessed to investigate how they contribute to the inter-individual variation in ABI. Multivariable linear regression models were then used to assess the joint contributions of the top SNP associations and interactions to ABI after adjustment for covariates. We reduced the chance of false positives by 1) correcting for multiple testing using the false discovery rate, 2) internal replication, and 3) four-fold cross-validation.

Results

When the results from these three procedures were combined, only two SNP main effects in NOS3, three SNP-covariate interactions (ADRB2 Gly 16 – lipoprotein(a) and SLC4A5 – diabetes interactions), and 25 SNP-SNP interactions (involving SNPs from 29 different genes) were significant, replicated, and cross-validated. Combining the top SNPs, risk factors, and their interactions into a model explained nearly 18% of variation in ABI in the sample. SNPs in six genes (ADD2, ATP6V1B1, PRKAR2B, SLC17A2, SLC22A3, and TGFB3) were also influencing triglycerides, C-reactive protein, homocysteine, and lipoprotein(a) levels.

Conclusion

We found that candidate gene SNP main effects, SNP-covariate and SNP-SNP interactions contribute to the inter-individual variation in ABI, a marker of PAD. Our findings underscore the importance of conducting systematic investigations that consider context-dependent frameworks for developing a deeper understanding of the multidimensional genetic and environmental factors that contribute to complex diseases.     

Genome-wide prediction of functional gene-gene interactions inferred from patterns of genetic differentiation in mice and men

The human genome encodes a limited number of genes yet contributes to individual differences in a vast array of heritable traits. A possible explanation for the capacity our genome to generate this virtually unlimited range of phenotypic variation in complex traits is to assume functional interactions between genes. Therefore we searched two mammalian genomes to identify potential epistatic interactions by looking for co-adapted genes marked by excess two-locus genetic differentiation between populations/lineages using publicly available SNP genotype data. The practical motivation for this effort is to reduce the number of pair-wise tests that need to be performed in genome-wide association studies aimed at detecting GxG interactions, by focusing on pairs predicted to be more likely to jointly affect variation in complex traits. Hence, this approach generates a list of candidate interactions that can be empirically tested. In both the mouse and human data we observed two-locus genetic differentiation in excess of what can be expected from chance alone based on simulations. In an attempt to validate our hypothesis that pairs of genes showing excess genetic divergence represent potential functional interactions, we selected a small set of gene combinations postulated to be interacting based on our analyses and looked for a combined effect of the selected genes on variation in complex traits in both mice and man. In both cases the individual effect of the genes were not significant, instead we observed marginally significant interaction effects. These results show that genome wide searches for gene-gene interactions based on population genetic data are feasible and can generate interesting candidate gene pairs to be further tested for their contribution to phenotypic variation in complex traits.     

A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize

Complex quantitative traits of plants as measured on collections of genotypes across multiple environments are the outcome of processes that depend in intricate ways on genotype and environment simultaneously. For a better understanding of the genetic architecture of such traits as observed across environments, genotype-by-environment interaction should be modeled with statistical models that use explicit information on genotypes and environments. The modeling approach we propose explains genotype-by-environment interaction by differential quantitative trait locus (QTL) expression in relation to environmental variables. We analyzed grain yield and grain moisture for an experimental data set composed of 976 F(5) maize testcross progenies evaluated across 12 environments in the U.S. corn belt during 1994 and 1995. The strategy we used was based on mixed models and started with a phenotypic analysis of multi-environment data, modeling genotype-by-environment interactions and associated genetic correlations between environments, while taking into account intraenvironmental error structures. The phenotypic mixed models were then extended to QTL models via the incorporation of marker information as genotypic covariables. A majority of the detected QTL showed significant QTL-by-environment interactions (QEI). The QEI were further analyzed by including environmental covariates into the mixed model. Most QEI could be understood as differential QTL expression conditional on longitude or year, both consequences of temperature differences during critical stages of the growth.     

Use of genotype × environment interactions to understand rooting depth and the ability of wheat to penetrate hard soils

Background

Root systems are well-recognized as complex and a variety of traits have been identified as contributing to plant adaptation to the environment. A significant proportion of soil in south-western Australia is prone to the formation of hardpans of compacted soil that limit root exploration and thus access to nutrients and water for plant growth. Genotypic variation has been reported for root-penetration ability of wheat in controlled conditions, which has been related to field performance in these environments. However, research on root traits in field soil is recognized as difficult and labour intensive. Pattern analysis of genotype × environment (G × E) interactions is one approach that enables interpretation of these complex relationships, particularly when undertaken with probe genotypes with well-documented traits, in this case, for the ability to penetrate a wax layer. While the analytical approach is well-established in the scientific literature, there are very few examples of pattern analysis for G × E interactions applied to root traits of cereal crops.

Scope

In this viewpoint, we aim to review the approach of pattern analysis for G × E interaction and the importance of environment and genotype characterization, with a focus on root traits. We draw on our research on G × E interaction for root depth and related studies on genotypic evaluation for root-penetration ability. In doing so, we wish to explore how pattern analysis can aid in the interpretation of complex root traits and their interaction with the environment and how this may explain patterns of adaptation and inform future research.

Conclusions

With appropriate characterization of environments and genotypes, the G × E approach can be used to aid in the interpretation of the complex interactions of root systems with the environment, inform future research and therefore provide supporting evidence for selecting specific root traits for target environments in a crop breeding programme.     

Data and theory point to mainly additive genetic variance for complex traits

The relative proportion of additive and non-additive variation for complex traits is important in evolutionary biology, medicine, and agriculture. We address a long-standing controversy and paradox about the contribution of non-additive genetic variation, namely that knowledge about biological pathways and gene networks imply that epistasis is important. Yet empirical data across a range of traits and species imply that most genetic variance is additive. We evaluate the evidence from empirical studies of genetic variance components and find that additive variance typically accounts for over half, and often close to 100%, of the total genetic variance. We present new theoretical results, based upon the distribution of allele frequencies under neutral and other population genetic models, that show why this is the case even if there are non-additive effects at the level of gene action. We conclude that interactions at the level of genes are not likely to generate much interaction at the level of variance.     

Evidence for the genetic basis and epistatic interactions underlying ocean‐ and river‐maturing ecotypes of Pacific Lamprey (Entosphenus tridentatus) returning to the Klamath River,California

Surveys of genomic variation have improved our understanding of the relationship between fitness‐related phenotypes and their underlying genetic basis. In some cases, single large‐effect genes have been found to underlie important traits; however, complex traits are expected to be under polygenic control and elucidation of multiple gene interactions may be required to fully understand the genetic basis of the trait. In this study, we investigated the genetic basis of the ocean‐ and river‐maturing ecotypes in anadromous Pacific lamprey (Entosphenus tridentatus). In Pacific lamprey, the ocean‐maturing ecotype is distinguished by advanced maturity of females (e.g., large egg mass) at the onset of freshwater migration relative to immature females of the river‐maturing ecotype. We examined a total of 219 adult Pacific lamprey that were collected at‐entry to the Klamath River over a 12‐month period. Each individual was genotyped at 308 SNPs representing known neutral and adaptive loci and measured at morphological traits, including egg mass as an indicator of ocean‐ and river‐maturing ecotype for females. The two ecotypes did not exhibit genetic structure at 148 neutral loci, indicating that ecotypic diversity exists within a single population. In contrast, we identified the genetic basis of maturation ecotypes in Pacific lamprey as polygenic, involving two unlinked gene regions that have a complex epistatic relationship. Importantly, these gene regions appear to show stronger effects when considered in gene interaction models than if just considered additive, illustrating the importance of considering epistatic effects and gene networks when researching the genetic basis of complex traits in Pacific lamprey and other species.     

A protocol for high-throughput phenotyping, suitable for quantitative trait analysis in mice

Whole-genome genetic association studies in outbred mouse populations represent a novel approach to identifying the molecular basis of naturally occurring genetic variants, the major source of quantitative variation between inbred strains of mice. Measuring multiple phenotypes in parallel on each mouse would make the approach cost effective, but protocols for phenotyping on a large enough scale have not been developed. In this article we describe the development and deployment of a protocol to collect measures on three models of human disease (anxiety, type II diabetes, and asthma) as well as measures of mouse blood biochemistry, immunology, and hematology. We report that the protocol delivers highly significant differences among the eight inbred strains (A/J, AKR/J, BALBc/J, CBA/J, C3H/HeJ, C57BL/6 J, DBA/2 J, and LP/J), the progenitors of a genetically heterogeneous stock (HS) of mice. We report the successful collection of multiple phenotypes from 2000 outbred HS animals. The phenotypes measured in the protocol form the basis of a large-scale investigation into the genetic basis of complex traits in mice designed to examine interactions between genes and between genes and environment, as well as the main effects of genetic variants on phenotypes.     

Floral plasticity in an iteroparous plant: the interactive effects of genotype, environment, and ontogeny in Campanula rapunculoides (Campanulaceae)

Phenotypic variation in 11 floral and reproductive traits was studied in cloned plants of Campanula rapunculoides replicated in three discrete environments. Using an ANOVA approach, we determined the relative influence of genotype (G), environment (E), G × E interaction, and ontogeny (position on the raceme) on the 11 traits. Two traits, duration of flowering and pollen size, showed no significant variation. All nine remaining traits had significant genotypic variation, accounting for 21-38% of the total phenotypic variation. Correlations among variant traits in seven genotypes were predominantly positive, but several significant correlations in one environment changed sign or were nonsignificant in another environment. Ovule number was negatively correlated with most male function traits: the negative correlation between ovule and pollen number was particularly strong and consistent across environments. Six traits varied significantly across environments, including number of flowers, number of ovules per flower, and duration of the male phase, but pollen traits did not show a significant environmental main effect. The G × E interaction was significant for flower number, corolla size, nectar quality, duration of the male phase, pollen viability, and ovule number. The contribution of interaction variance to the total phenotypic variation (5-14%) was comparable to that of the environment alone (7-21%). Ovule number decreased in flowers on the upper part of the raceme by nearly 25%, but other traits did not vary significantly by floral position. These results suggest that (1) pollen traits are buffered against environmental change more than ovule number or other floral characters, (2) a male-female trade-off exists and is complicated by ontogenic factors, (3) G × E interactions are common but may have small effects, and (4) specific correlation patterns among floral traits can be dependent upon the environment under which they develop.     

Inheritance, ecology and the evolution of the canoes of east Oceania

We consider patterns in the evolution of canoe technology in the eastern Pacific relative to three general processes: movement of canoe traits along the Polynesian settlement sequence, adaptations to local island environment, and post-settlement interaction between island groups. Using model selection methods on the distributions of canoe technology, we show that social and ecological covariates together consistently outperform each considered individually, though knowledge of island area and post-settlement trading spheres does not add explanatory power. In particular, decorative canoe traits are not effectively explained by either our ecological or transmission models. We also estimate negative effects from both settlement sequence and island geomorphology, consistent with the die-off of particular canoe designs on resource-rich high island groups such as Hawaii and New Zealand. This decline in measured traits may be owing to the lifting of ecological constraints on population size or building materials.     

Age-specific changes in epistatic effects on mortality rate in Drosophila melanogaster

Models for the evolution of senescence assume that genes with age-specific effects act independently of one another. Although recent empirical data show that longevity is influenced in part by interactions between genes, there are currently few data on whether epistasis influences age-specific components of mortality. To gauge if and how interactions affect age-specific traits, we incorporated the Drosophila visible marker mutations ebony, forked, and purple into seven wild-caught strains of D. melanogaster to examine gene x genetic background interactions. We found significant natural genetic variation for longevity and baseline mortality rates. Gene x genetic background interactions were prevalent not only for longevity but also for baseline mortality rates and age-specific mortality rates. We conclude that gene x genetic background epistasis is prevalent for aging-related traits and could play a significant role in the evolution of aging. These results suggest that future genetic models for the evolution of aging should incorporate the effects of epistasis.     

A reaction norm model for genomic selection using high-dimensional genomic and environmental data

Key message

New methods that incorporate the main and interaction effects of high-dimensional markers and of high-dimensional environmental covariates gave increased prediction accuracy of grain yield in wheat across and within environments.

Abstract

In most agricultural crops the effects of genes on traits are modulated by environmental conditions, leading to genetic by environmental interaction (G × E). Modern genotyping technologies allow characterizing genomes in great detail and modern information systems can generate large volumes of environmental data. In principle, G × E can be accounted for using interactions between markers and environmental covariates (ECs). However, when genotypic and environmental information is high dimensional, modeling all possible interactions explicitly becomes infeasible. In this article we show how to model interactions between high-dimensional sets of markers and ECs using covariance functions. The model presented here consists of (random) reaction norm where the genetic and environmental gradients are described as linear functions of markers and of ECs, respectively. We assessed the proposed method using data from Arvalis, consisting of 139 wheat lines genotyped with 2,395 SNPs and evaluated for grain yield over 8 years and various locations within northern France. A total of 68 ECs, defined based on five phases of the phenology of the crop, were used in the analysis. Interaction terms accounted for a sizable proportion (16 %) of the within-environment yield variance, and the prediction accuracy of models including interaction terms was substantially higher (17–34 %) than that of models based on main effects only. Breeding for target environmental conditions has become a central priority of most breeding programs. Methods, like the one presented here, that can capitalize upon the wealth of genomic and environmental information available, will become increasingly important.     

Alcohol dehydrogenase activities and ethanol tolerance in Anastrepha (Diptera, Tephritidae) fruit-fly species and their hybrids

The ADH (alcohol dehydrogenase) system is one of the earliest known models of molecular evolution, and is still the most studied in Drosophila. Herein, we studied this model in the genus Anastrepha (Diptera, Tephritidae). Due to the remarkable advantages it presents, it is possible to cross species with different Adh genotypes and with different phenotype traits related to ethanol tolerance. The two species studied here each have a different number of Adh gene copies, whereby crosses generate polymorphisms in gene number and in composition of the genetic background. We measured certain traits related to ethanol metabolism and tolerance. ADH specific enzyme activity presented gene by environment interactions, and the larval protein content showed an additive pattern of inheritance, whilst ADH enzyme activity per larva presented a complex behavior that may be explained by epistatic effects. Regression models suggest that there are heritable factors acting on ethanol tolerance, which may be related to enzymatic activity of the ADHs and to larval mass, although a pronounced environmental effect on ethanol tolerance was also observed. By using these data, we speculated on the mechanisms of ethanol tolerance and its inheritance as well as of associated traits.     

The Sensitivity of Evapotranspiration to Inter-Specific Plant Neighbor Interactions: Implications for Models

Evapotranspiration (ET) is an important water loss flux in ecosystem water cycles, and quantifying the spatial and temporal variation of ET can improve ecohydrological models in arid ecosystems. Plant neighbor interactions may be a source of spatial and temporal variation in ET due to their effects on the above- and belowground microclimate and increased water demand for transpiration. Over longer timescales (annual to multiple years), adjustments in plant physiological traits may occur in response to neighbor environments, potentially affecting the transpiration (T) component of ET. We used a dynamic soil water model to assess the sensitivity of ET and T estimates to neighbor effects on soil moisture via competition for water, aboveground microclimate effects via canopy shading, and physiological adjustments (specifically, root distribution, stomatal behavior, and canopy leaf area). We focus on a common desert shrub (Larrea tridentata) under different inter-specific neighbor environments and precipitation regimes. Neighbors impacted T of Larrea by as much as 75% at the patch scale (plant and surrounding soil) and 30% at the stand scale. Annual T estimates were highly sensitive to changes in soil moisture associated with competition for water, and the inclusion of physiological adjustments to neighbor environments significantly impacted seasonal T. Plant neighbor interactions can significantly influence ET and soil moisture, and their inclusion in models can help explain spatial and temporal variation in water fluxes in arid ecosystems. Furthermore, physiological adjustments to neighbor environments may be an important source of variation to include in models that operate over seasonal timescales or in studies focused on plant responses to precipitation under climate change.     

An epistatic genetic basis for physical activity traits in mice

We recently identified several (4-8) quantitative trait loci (QTL) for 3 physical activity traits (daily distance, duration, and speed voluntarily run) in an F(2) population of mice derived from an original intercross of 2 strains that exhibited large differences in activity. These QTL cumulatively explained from 11% to 34% of the variation in these traits, but this was considerably less than their total genetic variability estimated from differences among inbred strains. We therefore decided to test whether epistatic interactions might account for additional genetic variation in these traits in this same population of mice. We conducted a full genome epistasis scan for all possible interactions of QTL between each pair of 20 chromosomes. The results of this scan revealed an abundance of epistasis, with QTL throughout the genome being involved in significant interactions. Overall, epistatic effects contributed an average of 26% of the total variation among the 3 activity traits. These results suggest that epistatic interactions of genes may play as important a role in the genetic architecture of physical activity traits as single-locus effects and need to be considered in future candidate gene identification studies.