One hundred years of pleiotropy: a retrospective

eMERGEing progress in genomics-the first seven years

The electronic MEdical Records & GEnomics (eMERGE) network was established in 2007 by the National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) in part to explore the utility of electronic medical records (EMRs) in genome science. The initial focus was on discovery primarily using the genome-wide association paradigm, but more recently, the network has begun evaluating mechanisms to implement new genomic information coupled to clinical decision support into EMRs. Herein, we describe this evolution including the development of the individual and merged eMERGE genomic datasets, the contribution the network has made toward genomic discovery and human health, and the steps taken toward the next generation genotype-phenotype association studies and clinical implementation.

Gene pleiotropy constrains gene expression changes in fish adapted to different thermal conditions

Understanding the factors that shape the evolution of gene expression is a central goal in biology, but the molecular mechanisms behind this remain controversial. A related major goal is ascertaining how such factors may affect the adaptive potential of a species or population. Here we demonstrate that temperature-driven gene expression changes in fish adapted to differing thermal environments are constrained by the level of gene pleiotropy estimated by either the number of protein interactions or gene biological processes. Genes with low pleiotropy levels were the main drivers of both plastic and evolutionary global expression profile changes, while highly pleiotropic genes had limited expression response to temperature treatment. Our study provides critical insights into the molecular mechanisms by which natural populations can adapt to changing environments. In addition to having important implications for climate change adaptation, these results suggest that gene pleiotropy should be considered more carefully when interpreting expression profiling data.

The factors that shape the evolution of gene expression and their role in adaptation are poorly understood. Here, Papakostas et al. show that gene pleiotropy constrains protein expression evolution in freshwater salmonids adapted to different temperatures.

One gene, many neuropsychiatric disorders: lessons from Mendelian diseases

Recent human genetic studies have consistently shown that mutations in the same gene or same genomic region can increase the risk of a broad range of complex neuropsychiatric disorders. Despite the steadily increasing number of examples of such nonspecific effects on risk, the underlying biological causes remain mysterious. Here we investigate the phenomenon of such nonspecific risk by identifying Mendelian disease genes that are associated with multiple diseases and explore what is known about the underlying mechanisms in these more 'simple' examples. Our analyses make clear that there are a variety of mechanisms at work, emphasizing how challenging it will be to elucidate the causes of nonspecific risk in complex disease. Ultimately, we conclude that functional approaches will be critical for explaining the causes of nonspecific risk factors discovered by human genetic studies of neuropsychiatric disorders.

Systematic exploration of ubiquitin sequence, E1 activation efficiency, and experimental fitness in yeast

The complexity of biological interaction networks poses a challenge to understanding the function of individual connections in the overall network. To address this challenge, we developed a high-throughput reverse engineering strategy to analyze how thousands of specific perturbations (encompassing all point mutations in a central gene) impact both a specific edge (interaction to a directly connected node) and an overall network function. We analyzed the effects of ubiquitin mutations on activation by the E1 enzyme and compared these to effects on yeast growth rate. Using this approach, we delineated ubiquitin mutations that selectively impacted the ubiquitin-E1 edge. We find that the elasticity function relating the efficiency of ubiquitin-E1 interaction to growth rate is non-linear and that a greater than 50-fold decrease in E1 activation efficiency is required to reduce growth rate by 2-fold. Despite the robustness of fitness to decreases in E1 activation efficiency, the effects of most ubiquitin mutations on E1 activation paralleled the effects on growth rate. Our observations indicate that most ubiquitin mutations that disrupt E1 activation also disrupt other functions. The structurally characterized ubiquitin-E1 interface encompasses the interfaces of ubiquitin with most other known binding partners, and we propose that this enables E1 in wild-type cells to selectively activate ubiquitin protein molecules capable of binding to other partners from the cytoplasmic pool of ubiquitin protein that will include molecules with chemical damage and/or errors from transcription and translation.

Next generation sequencing techniques in neurological diseases: redefining clinical and molecular associations

The development of next-generation sequencing technologies has allowed for the identification of several new genes and genetic factors in human genetics. Common results from the application of these technologies have revealed unexpected presentations for mutations in known disease genes. In this review, we summarize the major contributions of exome sequencing to the study of neurodegenerative disorders and other neurological conditions and discuss the interface between Mendelian and complex neurological diseases with a particular focus on pleiotropic events.

Molecular network analysis enhances understanding of the biology of mental disorders

We provide an introduction to network theory, evidence to support a connection between molecular network structure and neuropsychiatric disease, and examples of how network approaches can expand our knowledge of the molecular bases of these diseases. Without systematic methods to derive their biological meanings and inter-relatedness, the many molecular changes associated with neuropsychiatric disease, including genetic variants, gene expression changes, and protein differences, present an impenetrably complex set of findings. Network approaches can potentially help integrate and reconcile these findings, as well as provide new insights into the molecular architecture of neuropsychiatric diseases. Network approaches to neuropsychiatric disease are still in their infancy, and we discuss what might be done to improve their prospects.

Template-directed chemical ligation to obtain 3'-3' and 5'-5' phosphodiester DNA linkages

Up to now, the direct ligation of two DNA fragments with opposite directions to obtain 3'-3' or 5'-5' phosphate ester bonds is still challenging. The only way to obtain DNA oligonucleotides containing a 3'-3' or 5'-5' inversion of polarity sites is based on professional DNA chemical synthesis. Herein, we demonstrate a convenient template-directed chemical ligation that enables 3'-3' and 5'-5' linkages of two DNA oligonucleotides. This method is based on the assembly of two oligonucleotides on a template in opposite directions through forming antiparallel and parallel duplexes simultaneously, followed by coupling with N-Cyanoimidazole under mild condition. Moreover, on the basis of DNA oligonucleotides with 5'-5' linkage obtained through our template-directed chemical ligation, we developed a new cDNA display technique for in vitro selection of functional polypeptides.

The nature and extent of mutational pleiotropy in gene expression of male Drosophila serrata

The nature and extent of mutational pleiotropy remain largely unknown, despite the central role that pleiotropy plays in many areas of biology, including human disease, agricultural production, and evolution. Here, we investigate the variation in 11,604 gene expression traits among 41 mutation accumulation (MA) lines of Drosophila serrata. We first confirmed that these expression phenotypes were heritable, detecting genetic variation in 96% of them in an outbred, natural population of D. serrata. Among the MA lines, 3385 (29%) of expression traits were variable, with a mean mutational heritability of 0.0005. In most traits, variation was generated by mutations of relatively small phenotypic effect; putative mutations with effects of greater than one phenotypic standard deviation were observed for only 8% of traits. With most (71%) traits unaffected by any mutation, our data provide no support for universal pleiotropy. We further characterized mutational pleiotropy in the 3385 variable traits, using sets of 5, randomly assigned, traits. Covariance among traits chosen at random with respect to their biological function is expected only if pleiotropy is extensive. Taking an analytical approach in which the variance unique to each trait in the random 5-trait sets was partitioned from variance shared among traits, we detected significant (at 5% false discovery rate) mutational covariance in 21% of sets. This frequency of statistically supported covariance implied that at least some mutations must pleiotropically affect a substantial number of traits (>70; 0.6% of all measured traits).

Quantification of amylose, amylopectin, and β-glucan in search for genes controlling the three major quality traits in barley by genome-wide association studies

Genome-wide association studies (GWAS) for amylose, amylopectin and β-glucan concentration in a collection of 254 European spring barley varieties allowed to identify 20, 17, and 21 single nucleotide polymorphic (SNP) markers, respectively, associated with these important grain quality traits. Negative correlations between the content of amylose and β-glucan (R = -0.62, P < 0.01) and amylopectin and β-glucan (R = -0.487, P < 0.01) were found in this large collection of spring barley varieties. Besides HvCslF6, amo1 and AGPL2, sex6, and waxy were identified among the major genes responsible for β-glucan, amylose and amylopectin content, respectively. Several minor genes like HvGSL4, HvGSL3, and HvCesA6, PWD were also detected by GWAS for the first time. Furthermore, the gene encoding β-fructofuranosidase, located on the short arm of chromosome 7H at 1.49 cM, and SRF6, encoding "leucine-rich repeat receptor kinase protein" on chromosome 2 H, are proposed to be new candidate genes for amylopectin formation in barley endosperm. Several of the associated SNPs on chromosome 1, 5, 6, and 7H mapped to overlapping regions containing QTLs and genes controlling the three grain constituents. In particular chromosomes 5 and 7H carry many QTLs controlling barley grain quality. Amylose, amylopectin and β-glucan were interacted among each other through a metabolic network connected by UDP showing pleiotropic effects. Taken together, these results showed that cereal quality traits related each other and regulated through an interaction network, the identified major genes and genetic regions for amylose, amylopectin and β-glucan is a helpful for further research on carbohydrates and barley breeding.

Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene

With the rapid increase in the quality and quantity of data generated by modern high-throughput sequencing techniques, there has been a need for innovative methods able to convert this tremendous amount of data into more accessible forms. Networks have been a corner stone of this movement, as they are an intuitive way of representing interaction data, yet they offer a full set of sophisticated statistical tools to analyze the phenomena they model. We propose a novel approach to reveal and analyze pleiotropic and epistatic effects at the genome-wide scale using a bipartite network composed of human diseases, phenotypic traits, and several types of predictive elements (i.e. SNPs, genes, or pathways). We take advantage of publicly available GWAS data, gene and pathway databases, and more to construct networks different levels of granularity, from common genetic variants to entire biological pathways. We use the connections between the layers of the network to approximate the pleiotropy and epistasis effects taking place between the traits and the predictive elements. The global graph-theory based quantitative methods reveal that the levels of pleiotropy and epistasis are comparable for all types of predictive element. The results of the magnified "glaucoma" region of the network demonstrate the existence of well documented interactions, supported by overlapping genes and biological pathway, and more obscure associations. As the amount and complexity of genetic data increases, bipartite, and more generally multipartite networks that combine human diseases and other physical attributes with layers of genetic information, have the potential to become ubiquitous tools in the study of complex genetic and phenotypic interactions.

The bottom-up approach to defining life: deciphering the functional organization of biological cells via multi-objective representation of biological complexity from molecules to cells

In silico representation of cellular systems needs to represent the adaptive dynamics of biological cells, recognizing a cell's multi-objective topology formed by temporally cohesive intracellular structures. The design of these models needs to address the hierarchical and concurrent nature of cellular functions and incorporate the ability to self-organize in response to transitions between healthy and pathological phases, and adapt accordingly. The functions of biological systems are constantly progressing, due to the ever changing demands of their environment. Biological systems meet these demands by pursuing objectives, aided by their constituents, giving rise to biological functions. A biological cell is organized into an objective/task hierarchy. These objective hierarchy corresponds to the nested nature of temporally cohesive structures and representing them will facilitate in studying pleiotropy and polygeny by modeling causalities propagating across multiple interconnected intracellular processes. Although biological adaptations occur in physiological, developmental and reproductive timescales, the paper is focused on adaptations that occur within physiological timescales, where the biomolecular activities contributing to functional organization, play a key role in cellular physiology. The paper proposes a multi-scale and multi-objective modeling approach from the bottom-up by representing temporally cohesive structures for multi-tasking of intracellular processes. Further the paper characterizes the properties and constraints that are consequential to the adaptive dynamics in biological cells.

Heritability of chronic obstructive pulmonary disease and related phenotypes in smokers

RATIONALE

Previous studies of chronic obstructive pulmonary disease (COPD) have suggested that genetic factors play an important role in the development of disease. However, single-nucleotide polymorphisms that are associated with COPD in genome-wide association studies have been shown to account for only a small percentage of the genetic variance in phenotypes of COPD, such as spirometry and imaging variables. These phenotypes are highly predictive of disease, and family studies have shown that spirometric phenotypes are heritable.

OBJECTIVES

To assess the heritability and coheritability of four major COPD-related phenotypes (measurements of FEV1, FEV1/FVC, percent emphysema, and percent gas trapping), and COPD affection status in smokers of non-Hispanic white and African American descent using a population design.

METHODS

Single-nucleotide polymorphisms from genome-wide association studies chips were used to calculate the relatedness of pairs of individuals and a mixed model was adopted to estimate genetic variance and covariance.

MEASUREMENTS AND MAIN RESULTS

In the non-Hispanic whites, estimated heritabilities of FEV1 and FEV1/FVC were both about 37%, consistent with estimates in the literature from family-based studies. For chest computed tomography scan phenotypes, estimated heritabilities were both close to 25%. Heritability of COPD affection status was estimated as 37.7% in both populations.

CONCLUSIONS

This study suggests that a large portion of the genetic risk of COPD is yet to be discovered and gives rationale for additional genetic studies of COPD. The estimates of coheritability (genetic covariance) for pairs of the phenotypes suggest considerable overlap of causal genetic loci.

A rice lectin receptor-like kinase that is involved in innate immune responses also contributes to seed germination

Seed germination and innate immunity both have significant effects on plant life spans because they control the plant's entry into the ecosystem and provide defenses against various external stresses, respectively. Much ecological evidence has shown that seeds with high vigor are generally more tolerant of various environmental stimuli in the field than those with low vigor. However, there is little genetic evidence linking germination and immunity in plants. Here, we show that the rice lectin receptor-like kinase OslecRK contributes to both seed germination and plant innate immunity. We demonstrate that knocking down the OslecRK gene depresses the expression of α-amylase genes, reducing seed viability and thereby decreasing the rate of seed germination. Moreover, it also inhibits the expression of defense genes, and so reduces the resistance of rice plants to fungal and bacterial pathogens as well as herbivorous insects. Yeast two-hybrid and co-immunoprecipitation experiments revealed that OslecRK interacts with an actin-depolymerizing factor (ADF) in vivo via its kinase domain. Moreover, the rice adf mutant exhibited a reduced seed germination rate due to the suppression of α-amylase gene expression. This mutant also exhibited depressed immune responses and reduced resistance to biotic stresses. Our results thus provide direct genetic evidence for a common physiological pathway connecting germination and immunity in plants. They also partially explain the common observation that high-vigor seeds often perform well in the field. The dual effects of OslecRK may be indicative of progressive adaptive evolution in rice.

APOE modulates the correlation between triglycerides, cholesterol, and CHD through pleiotropy, and gene-by-gene interactions

Relationship loci (rQTL) exist when the correlation between multiple traits varies by genotype. rQTL often occur due to gene-by-gene (G × G) or gene-by-environmental interactions, making them a powerful tool for detecting G × G. Here we present an empirical analysis of apolipoprotein E (APOE) with respect to lipid traits and incident CHD leading to the discovery of loci that interact with APOE to affect these traits. We found that the relationship between total cholesterol (TC) and triglycerides (ln TG) varies by APOE isoform genotype in African-American (AA) and European-American (EA) populations. The e2 allele is associated with strong correlation between ln TG and TC while the e4 allele leads to little or no correlation. This led to a priori hypotheses that APOE genotypes affect the relationship of TC and/or ln TG with incident CHD. We found that APOE*TC was significant (P = 0.016) for AA but not EA while APOE*ln TG was significant for EA (P = 0.027) but not AA. In both cases, e2e2 and e2e3 had strong relationships between TC and ln TG with CHD while e2e4 and e4e4 results in little or no relationship between TC and ln TG with CHD. Using ARIC GWAS data, scans for loci that significantly interact with APOE produced four loci for African Americans (one CHD, one TC, and two HDL). These interactions contribute to the rQTL pattern. rQTL are a powerful tool to identify loci that modify the relationship between risk factors and disease and substantially increase statistical power for detecting G × G.

Restricted pleiotropy facilitates mutational erosion of major life-history traits

Radical shifts to new natural and human made niches can make some functions unneeded and thus exposed to genetic degeneration. Here we ask not about highly specialized and rarely used functions but those relating to major life-history traits, rate of growth, and resistance to prolonged starvation. We found that in yeast each of the two traits was visibly impaired by at least several hundred individual gene deletions. There were relatively few deletions affecting negatively both traits and likely none harming one but improving the other. Functional profiles of gene deletions affecting either growth or survival were strikingly different: the first related chiefly to synthesis of macromolecules whereas the second to maintenance and recycling of cellular structures. The observed pattern of gene indispensability corresponds to that of gene induction, providing a rather rare example of agreement between the results of deletion and expression studies. We conclude that transitions to new environments in which the ability to grow at possibly fastest rate or survive under very long starvation become practically unnecessary can result in rapid erosion of these vital functions because they are coded by many genes constituting large mutational targets and because restricted pleiotropy is unlikely to constrain this process.

Pleiotropy in complex traits: challenges and strategies

Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.

Coat colour in mouse populations selected for weight gain: support for hitchhiking, not pleiotropy

With many molecular markers in many species, research efforts in quantitative genetics have focused on dissecting these traits and understanding the importance of factors such as correlated response due to hitchhiking or pleiotropy. Here, in an examination of long-term selection experiments in mice, the evidence strongly supports the primary importance of hitchhiking on the coat colour loci brown and dilute in mice selected for high weight gain. First, the amount of observed change in coat colour allele frequency could not be explained by genetic drift alone, implying that selection was of high importance. Second, the allele frequency changes included reversals in the direction change, but there were still positive correlations in the early generations with differences in weight gain between the phenotypes. Third, the correlation between the change in allele frequencies and phenotypic difference in weight gain declined over time, consistent with the decay expected from linkage associations. Fourth, the changes at both loci in a short-term selection experiment for low weight gain were in the opposite direction than the changes in the contemporaneous related population selected for high weight gain.

The basis of antagonistic pleiotropy in hfq mutations that have opposite effects on fitness at slow and fast growth rates

Mutations beneficial in one environment may cause costs in different environments, resulting in antagonistic pleiotropy. Here, we describe a novel form of antagonistic pleiotropy that operates even within the same environment, where benefits and deleterious effects exhibit themselves at different growth rates. The fitness of hfq mutations in Escherichia coli affecting the RNA chaperone involved in small-RNA regulation is remarkably sensitive to growth rate. E. coli populations evolving in chemostats under nutrient limitation acquired beneficial mutations in hfq during slow growth (0.1 h(-1)) but not in populations growing sixfold faster. Four identified hfq alleles from parallel populations were beneficial at 0.1 h(-1) and deleterious at 0.6 h(-1). The hfq mutations were beneficial, deleterious or neutral at an intermediate growth rate (0.5 h(-1)) and one changed from beneficial to deleterious within a 36 min difference in doubling time. The benefit of hfq mutations was due to the greater transport of limiting nutrient, which diminished at higher growth rates. The deleterious effects of hfq mutations at 0.6 h(-1) were less clear, with decreased viability a contributing factor. The results demonstrate distinct pleiotropy characteristics in the alleles of the same gene, probably because the altered residues in Hfq affected the regulation of expression of different genes in distinct ways. In addition, these results point to a source of variation in experimental measurement of the selective advantage of a mutation; estimates of fitness need to consider variation in growth rate impacting on the magnitude of the benefit of mutations and on their fitness distributions.

FTO at rs9939609, food responsiveness, emotional control and symptoms of ADHD in preschool children

The FTO minor allele at rs9939609 has been associated with body mass index (BMI: weight (kg)/height (m)²) in children from 5 years onwards, food intake, and eating behaviour. The high expression of FTO in the brain suggests that this gene may also be associated with behavioural phenotypes, such as impulsivity and control. We examined the effect of the FTO minor allele (A) at rs9939609 on eating behaviour, impulsivity and control in young children, thus before the BMI effect becomes apparent. This study was embedded in the Generation R Study, a population-based cohort from fetal life onwards. 1,718 children of European descent were genotyped for FTO at rs9939609. With logistic regression assuming an additive genetic model, we examined the association between the FTO minor allele and eating behaviour, impulsivity and control in preschool children. There was no relation between FTO at rs9939609 and child BMI at this age. The A allele at rs9939609 was associated with increased food responsiveness (OR 1.21, p = 0.03). Also, children with the A allele were less likely to have symptoms of ADHD (OR 0.74, p = 0.01) and showed more emotional control (OR 0.64, p = 0.01) compared to children without the A allele. Our findings suggest that before the association between FTO and BMI becomes apparent, the FTO minor allele at rs9939609 leads to increased food responsiveness, a decreased risk for symptoms of ADHD and better emotional control. Future studies are needed to investigate whether these findings represent one single mechanism or reflect pleiotropic effects of FTO.

The many faces of pleiotropy

Pleiotropy is the well-established phenomenon of a single gene affecting multiple traits. It has long played a central role in theoretical, experimental, and clinical research in genetics, development, molecular biology, evolution, and medicine. In recent years, genomic techniques have brought data to bear on fundamental questions about the nature and extent of pleiotropy. However, these efforts are plagued by conceptual difficulties derived from disparate meanings and interpretations of pleiotropy. Here, we describe distinct uses of the pleiotropy concept and explain the pitfalls associated with applying empirical data to them. We conclude that, for any question about the nature or extent of pleiotropy, the appropriate answer is always 'What do you mean?'.

Bayesian methods for multivariate modeling of pleiotropic SNP associations and genetic risk prediction

Genome-wide association studies (GWAS) have identified numerous associations between genetic loci and individual phenotypes; however, relatively few GWAS have attempted to detect pleiotropic associations, in which loci are simultaneously associated with multiple distinct phenotypes. We show that pleiotropic associations can be directly modeled via the construction of simple Bayesian networks, and that these models can be applied to produce single or ensembles of Bayesian classifiers that leverage pleiotropy to improve genetic risk prediction. The proposed method includes two phases: (1) Bayesian model comparison, to identify Single-Nucleotide Polymorphisms (SNPs) associated with one or more traits; and (2) cross-validation feature selection, in which a final set of SNPs is selected to optimize prediction. To demonstrate the capabilities and limitations of the method, a total of 1600 case-control GWAS datasets with two dichotomous phenotypes were simulated under 16 scenarios, varying the association strengths of causal SNPs, the size of the discovery sets, the balance between cases and controls, and the number of pleiotropic causal SNPs. Across the 16 scenarios, prediction accuracy varied from 90 to 50%. In the 14 scenarios that included pleiotropically associated SNPs, the pleiotropic model search and prediction methods consistently outperformed the naive model search and prediction. In the two scenarios in which there were no true pleiotropic SNPs, the differences between the pleiotropic and naive model searches were minimal. To further evaluate the method on real data, a discovery set of 1071 sickle cell disease (SCD) patients was used to search for pleiotropic associations between cerebral vascular accidents and fetal hemoglobin level. Classification was performed on a smaller validation set of 352 SCD patients, and showed that the inclusion of pleiotropic SNPs may slightly improve prediction, although the difference was not statistically significant. The proposed method is robust, computationally efficient, and provides a powerful new approach for detecting and modeling pleiotropic disease loci.

Reward value determines memory consolidation in parasitic wasps

Animals can store learned information in their brains through a series of distinct memory forms. Short-lasting memory forms can be followed by longer-lasting, consolidated memory forms. However, the factors determining variation in memory consolidation encountered in nature have thus far not been fully elucidated. Here, we show that two parasitic wasp species belonging to different families, Cotesia glomerata (Hymenoptera: Braconidae) and Trichogramma evanescens (Hymenoptera; Trichogrammatidae), similarly adjust the memory form they consolidate to a fitness-determining reward: egg-laying into a host-insect that serves as food for their offspring. Protein synthesis-dependent long-term memory (LTM) was consolidated after single-trial conditioning with a high-value host. However, single-trial conditioning with a low-value host induced consolidation of a shorter-lasting memory form. For Cotesia glomerata, we subsequently identified this shorter-lasting memory form as anesthesia-resistant memory (ARM) because it was not sensitive to protein synthesis inhibitors or anesthesia. Associative conditioning using a single reward of different value thus induced a physiologically different mechanism of memory formation in this species. We conclude that the memory form that is consolidated does not only change in response to relatively large differences in conditioning, such as the number and type of conditioning trials, but is also sensitive to more subtle differences, such as reward value. Reward-dependent consolidation of exclusive ARM or LTM provides excellent opportunities for within-species comparison of mechanisms underlying memory consolidation.

Bayesian mapping of multiple traits in maize: the importance of pleiotropic effects in studying the inheritance of quantitative traits

Pleiotropy has played an important role in understanding quantitative traits. However, the extensiveness of this effect in the genome and its consequences for plant improvement have not been fully elucidated. The aim of this study was to identify pleiotropic quantitative trait loci (QTLs) in maize using Bayesian multiple interval mapping. Additionally, we sought to obtain a better understanding of the inheritance, extent and distribution of pleiotropic effects of several components in maize production. The design III procedure was used from a population derived from the cross of the inbred lines L-14-04B and L-08-05F. Two hundred and fifty plants were genotyped with 177 microsatellite markers and backcrossed to both parents giving rise to 500 backcrossed progenies, which were evaluated in six environments for grain yield and its components. The results of this study suggest that mapping isolated traits limits our understanding of the genetic architecture of quantitative traits. This architecture can be better understood by using pleiotropic networks that facilitate the visualization of the complexity of quantitative inheritance, and this characterization will help to develop new selection strategies. It was also possible to confront the idea that it is feasible to identify QTLs for complex traits such as grain yield, as pleiotropy acts prominently on its subtraits and as this "trait" can be broken down and predicted almost completely by the QTLs of its components. Additionally, pleiotropic QTLs do not necessarily signify pleiotropy of allelic interactions, and this indicates that the pervasive pleiotropy does not limit the genetic adaptability of plants.

Life-history evolution and density-dependent growth in experimental populations of yeast

We studied the evolution of the correlation between growth rate r and yield K in experimental lineages of the yeast Saccharomyces cerevisiae. First, we isolated a single clone every approximately 250 generations from each of eight populations selected in a glucose-limited medium for 5000 generations at approximately 6.6 population doublings per day (20 clones per line × 8 lines) and measured its growth rate and yield in a new, galactose-limited medium (with ∼1.3 doubling per day). For most lines, r on galactose increased throughout the 5000 generations of selection on glucose whereas K on galactose declined. Next, we selected these 160 glucose-adapted clones in the galactose environment for approximately 120 generations and measured changes in r and K in galactose. In general, growth rate increased and yield declined, and clones that initially grew slowly on galactose improved more than did faster clones. We found a negative correlation between r and K among clones both within each line and across all clones. We provide evidence that this relationship is not heritable and is a negative environmental correlation rather than a genetic trade-off.

How Can the Comorbidity with ADHD Aid Understanding of Language and Speech Disorders?

This paper serves to provide a background for the topic of comorbidity than extends through this issue. Comorbidity is common within developmental disorders. It is shown that there are many possible reasons for comorbidity. Some of these can be viewed as artifacts as simple as chance occurrence or because of the way that the research participants were sampled. If these artifacts are eliminated, then comorbidity can be informative with respect to possible causes of the disorders that are comorbid. Several possible etiologic models are presented along with a general framework for considering levels of causality in developmental disorders.

A model of developmental evolution: selection, pleiotropy and compensation

Development and physiology translate genetic variation into phenotypic variation and determine the genotype-phenotype map, such as which gene affects which character (pleiotropy). Any genetic change in this mapping reflects a change in development. Here, we discuss evidence for variation in pleiotropy and propose the selection, pleiotropy and compensation model (SPC) for adaptive evolution. It predicts that adaptive change in one character is associated with deleterious pleiotropy in others and subsequent selection to compensate for these pleiotropic effects. The SPC model provides a unifying perspective for a variety of puzzling phenomena, including developmental systems drift and character homogenization. The model suggests that most adaptive signatures detected in genome scans could be the result of compensatory changes, rather than of progressive character adaptations.

HDL cholesterol and bone mineral density: is there a genetic link?

Overwhelming evidence has linked cardiovascular disease and osteoporosis, but the shared root cause of these two diseases of the elderly remains unknown. Low levels of high density lipoprotein cholesterol (HDL) and bone mineral density (BMD) are risk factors for cardiovascular disease and osteoporosis respectively. A number of correlation studies have attempted to determine if there is a relationship between serum HDL and BMD but these studies are confounded by a number of variables including age, diet, genetic background, gender and hormonal status. Collectively, these data suggest that there is a relationship between these two phenotypes, but that the nature of this relationship is context specific. Studies in mice plainly demonstrate that genetic loci for BMD and HDL co-map and transgenic mouse models have been used to show that a single gene can affect both serum HDL and BMD. Work completed to date has demonstrated that HDL can interact directly with both osteoblasts and osteoclasts, but no direct evidence links bone back to the regulation of HDL levels. Understanding the genetic relationship between BMD and HDL has huge implications for understanding the clinical relationship between CVD and osteoporosis and for the development of safe treatment options for both diseases.

On the pleiotropic structure of the genotype-phenotype map and the evolvability of complex organisms

Analyses of effects of mutants on many traits have enabled estimates to be obtained of the magnitude of pleiotropy, and in reviews of such data others have concluded that the degree of pleiotropy is highly restricted, with implications on the evolvability of complex organisms. We show that these conclusions are highly dependent on statistical assumptions, for example significance levels. We analyze models with pleiotropic effects on all traits at all loci but by variable amounts, considering distributions of numbers of traits declared significant, overall pleiotropic effects, and extent of apparent modularity of effects. We demonstrate that these highly pleiotropic models can give results similar to those obtained in analyses of experimental data and that conclusions on limits to evolvability through pleiotropy are not robust.

Abundant pleiotropy in human complex diseases and traits

We present a systematic review of pleiotropy among SNPs and genes reported to show genome-wide association with common complex diseases and traits. We find abundant evidence of pleiotropy; 233 (16.9%) genes and 77 (4.6%) SNPs show pleiotropic effects. SNP pleiotropic status was associated with gene location (p = 0.024; pleiotropic SNPs more often exonic [14.5% versus 4.9% for nonpleiotropic, trait-associated SNPs] and less often intergenic [15.8% versus 23.6%]), "predicted transcript consequence" (p = 0.001; pleiotropic SNPs more often predicted to be structurally deleterious [5% versus 0.4%] but not more often in regulatory sequences), and certain disease classes. We develop a method to calculate the likelihood that pleiotropic links between traits occurred more often than expected and demonstrate that this approach can identify etiological links that are already known (such as between fetal hemoglobin and malaria risk) and those that are not yet established (e.g., between plasma campesterol levels and gallstones risk; and between immunoglobulin A and juvenile idiopathic arthritis). Examples of pleiotropy will accumulate over time, but it is already clear that pleiotropy is a common property of genes and SNPs associated with disease traits, and this will have implications for identification of molecular targets for drug development, future genetic risk-profiling, and classification of diseases.

The emergence of complexity and restricted pleiotropy in adapting networks

Background

The emergence of organismal complexity has been a difficult subject for researchers because it is not readily amenable to investigation by experimental approaches. Complexity has a myriad of untested definitions and our understanding of its evolution comes primarily from static snapshots gleaned from organisms ranked on an intuitive scale. Fisher's geometric model of adaptation, which defines complexity as the number of phenotypes an organism exposes to natural selection, provides a theoretical framework to study complexity. Yet investigations of this model reveal phenotypic complexity as costly and therefore unlikely to emerge.

Results

We have developed a computational approach to study the emergence of complexity by subjecting neural networks to adaptive evolution in environments exacting different levels of demands. We monitored complexity by a variety of metrics. Top down metrics derived from Fisher's geometric model correlated better with the environmental demands than bottom up ones such as network size. Phenotypic complexity was found to increase towards an environment-dependent level through the emergence of restricted pleiotropy. Such pleiotropy, which confined the action of mutations to only a subset of traits, better tuned phenotypes in challenging environments. However, restricted pleiotropy also came at a cost in the form of a higher genetic load, as it required the maintenance by natural selection of more independent traits. Consequently, networks of different sizes converged in complexity when facing similar environment.

Conclusions

Phenotypic complexity evolved as a function of the demands of the selective pressures, rather than the physical properties of the network architecture, such as functional size. Our results show that complexity may be more predictable, and understandable, if analyzed from the perspective of the integrated task the organism performs, rather than the physical architecture used to accomplish such tasks. Thus, top down metrics emphasizing selection may be better for describing biological complexity than bottom up ones representing size and other physical attributes.

The pleiotropic structure of the genotype-phenotype map: the evolvability of complex organisms

It was first noticed 100 years ago that mutations tend to affect more than one phenotypic characteristic, a phenomenon that was called 'pleiotropy'. Because pleiotropy was found so frequently, the notion arose that pleiotropy is 'universal'. However, quantitative estimates of pleiotropy have not been available until recently. These estimates show that pleiotropy is highly restricted and are more in line with the notion of variational modularity than with universal pleiotropy. This finding has major implications for the evolvability of complex organisms and the mapping of disease-causing mutations.

PRIMe: a method for characterization and evaluation of pleiotropic regions from multiple genome-wide association studies

MOTIVATION

The concept of pleiotropy was proposed a century ago, though up to now there have been insufficient efforts to design robust statistics and software aimed at visualizing and evaluating pleiotropy at a regional level. The Pleiotropic Region Identification Method (PRIMe) was developed to evaluate potentially pleiotropic loci based upon data from multiple genome-wide association studies (GWAS).

METHODS

We first provide a software tool to systematically identify and characterize genomic regions where low association P-values are observed with multiple traits. We use the term Pleiotropy Index to denote the number of traits with low association P-values at a particular genomic region. For GWAS assumed to be uncorrelated, we adopted the binomial distribution to approximate the statistical significance of the Pleiotropy Index. For GWAS conducted on traits with known correlation coefficients, simulations are performed to derive the statistical distribution of the Pleiotropy Index under the null hypothesis of no genotype-phenotype association. For six hematologic and three blood pressure traits where full GWAS results were available from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, we estimated the trait correlations and applied the simulation approach to examine genomic regions with statistical evidence of pleiotropy. We then applied the approximation approach to explore GWAS summarized in the National Human Genome Research Institute (NHGRI) GWAS Catalog.

RESULTS

By simulation, we identified pleiotropic regions including SH2B3 and BRAP (12q24.12) for hematologic and blood pressure traits. By approximation, we confirmed the genome-wide significant pleiotropy of these two regions based on the GWAS Catalog data, together with an exploration on other regions which highlights the FTO, GCKR and ABO regions.

AVAILABILITY AND IMPLEMENTATION

The Perl and R scripts are available at http://www.framinghamheartstudy.org/research/gwas_pleiotropictool.html.