Finding genes that underlie complex traits

Primary Pediatric Hypertension: Current Understanding and Emerging Concepts

The rising prevalence of primary pediatric hypertension and its tracking into adult hypertension point to the importance of determining its pathogenesis to gain insights into its current and emerging management. Considering that the intricate control of BP is governed by a myriad of anatomical, molecular biological, biochemical, and physiological systems, multiple genes are likely to influence an individual's BP and susceptibility to develop hypertension. The long-term regulation of BP rests on renal and non-renal mechanisms. One renal mechanism relates to sodium transport. The impaired renal sodium handling in primary hypertension and salt sensitivity may be caused by aberrant counter-regulatory natriuretic and anti-natriuretic pathways. The sympathetic nervous and renin-angiotensin-aldosterone systems are examples of antinatriuretic pathways. An important counter-regulatory natriuretic pathway is afforded by the renal autocrine/paracrine dopamine system, aberrations of which are involved in the pathogenesis of hypertension, including that associated with obesity. We present updates on the complex interactions of these two systems with dietary salt intake in relation to obesity, insulin resistance, inflammation, and oxidative stress. We review how insults during pregnancy such as maternal and paternal malnutrition, glucocorticoid exposure, infection, placental insufficiency, and treatments during the neonatal period have long-lasting effects in the regulation of renal function and BP. Moreover, these effects have sex differences. There is a need for early diagnosis, frequent monitoring, and timely management due to increasing evidence of premature target organ damage. Large controlled studies are needed to evaluate the long-term consequences of the treatment of elevated BP during childhood, especially to establish the validity of the current definition and treatment of pediatric hypertension.

Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis

BACKGROUND

The burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease. We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent coronary heart disease.

METHODS AND RESULTS

We studied a total of 25 109 European ancestry and African ancestry participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52 869 participants with common carotid intima-media thickness measured by ultrasonography within the CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology). Participants were genotyped for 247 870 DNA sequence variants (231 539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and carotid intima-media thickness. APOB p.Arg3527Gln was associated with 4-fold excess CAC (P=3×10^-10). The APOE ε2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P=1×10^-12) and 1.4% reduced carotid intima-media thickness (P=4×10^-14) in carriers compared with noncarriers. In secondary analyses conditioning on low-density lipoprotein cholesterol concentration, the ε2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of ε2 was associated with reduced risk for coronary heart disease (odds ratio 0.77; P=1×10^-11).

CONCLUSIONS

Exome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE ε2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities, as well as clinical coronary heart disease.

A meta-analysis of public microarray data identifies gene regulatory pathways deregulated in peripheral blood mononuclear cells from individuals with Systemic Lupus Erythematosus compared to those without

BACKGROUND

Systemic Lupus Erythematosus (SLE) is a complex, multi-systemic, autoimmune disease for which the underlying aetiological mechanisms are poorly understood. The genetic and molecular processes underlying lupus have been extensively investigated using a variety of -omics approaches, including genome-wide association studies, candidate gene studies and microarray experiments of differential gene expression in lupus samples compared to controls.

METHODS

This study analyses a combination of existing microarray data sets to identify differentially regulated genetic pathways that are dysregulated in human peripheral blood mononuclear cells from SLE patients compared to unaffected controls. Two statistical approaches, quantile discretisation and scaling, are used to combine publicly available expression microarray datasets and perform a meta-analysis of differentially expressed genes.

RESULTS

Differentially expressed genes implicated in interferon signaling were identified by the meta-analysis, in agreement with the findings of the individual studies that generated the datasets used. In contrast to the individual studies, however, the meta-analysis and subsequent pathway analysis additionally highlighted TLR signaling, oxidative phosphorylation and diapedesis and adhesion regulatory networks as being differentially regulated in peripheral blood mononuclear cells (PBMCs) from SLE patients compared to controls.

CONCLUSION

Our analysis demonstrates that it is possible to derive additional information from publicly available expression data using meta-analysis techniques, which is particularly relevant to research into rare diseases where sample numbers can be limiting.

SNP Marker Discovery in Pima Cotton (Gossypium barbadense L.) Leaf Transcriptomes

The objective of this study was to explore the known narrow genetic diversity and discover single-nucleotide polymorphic (SNP) markers for marker-assisted breeding within Pima cotton (Gossypium barbadense L.) leaf transcriptomes. cDNA from 25-day plants of three diverse cotton genotypes [Pima S6 (PS6), Pima S7 (PS7), and Pima 3-79 (P3-79)] was sequenced on Illumina sequencing platform. A total of 28.9 million reads (average read length of 138 bp) were generated by sequencing cDNA libraries of these three genotypes. The de novo assembly of reads generated transcriptome sets of 26,369 contigs for PS6, 25,870 contigs for PS7, and 24,796 contigs for P3-79. A Pima leaf reference transcriptome was generated consisting of 42,695 contigs. More than 10,000 single-nucleotide polymorphisms (SNPs) were identified between the genotypes, with 100% SNP frequency and a minimum of eight sequencing reads. The most prevalent SNP substitutions were C-T and A-G in these cotton genotypes. The putative SNPs identified can be utilized for characterizing genetic diversity, genotyping, and eventually in Pima cotton breeding through marker-assisted selection.

Systems genetics reveals key genetic elements of drought induced gene regulation in diploid potato

In plants, tolerance to drought stress is a result of numerous minor effect loci in which transcriptional regulation contributes significantly to the observed phenotypes. Under severe drought conditions, a major expression quantitative trait loci hotspot was identified on chromosome five in potato. A putative Nuclear factor y subunit C4 was identified as key candidate in the regulatory cascade in response to drought. Further investigation of the eQTL hotspots suggests a role for a putative Homeobox leucine zipper protein 12 in relation to drought in potato. Genes strongly co-expressed with Homeobox leucine zipper protein 12 were plant growth regulators responsive to water deficit stress in Arabidopsis thaliana, implying a possible conserved mechanism. Integrative analysis of genetic, genomic, phenotypic and transcriptomic data provided insights in the downstream functional components of the drought response. The abscisic acid- and environmental stress-inducible protein TAS14 was highly induced by severe drought in potato and acts as a reliable biomarker for the level of stress perceived by the plant. The systems genetics approach supported a role for multiple genes responsive to severe drought stress of Solanum tuberosum. The combination of gene regulatory networks, expression quantitative trait loci mapping and phenotypic analysis proved useful for candidate gene selection.

Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information

BACKGROUND

Diabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.

RESULTS

In this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.

CONCLUSIONS

The results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.

Genetic determinants of swallowing impairment, recovery and responsiveness to treatment

Purpose of review

Here we review the latest literature and evidence in the field of genetics and determinants of swallowing and its treatments—specifically, this is a very recent concept in the field of oropharyngeal dysphagia, with only now an emerging research interest in the relationship between our genetic makeup and the effect this has on swallowing function and dysfunction. As such our review will look at preclinical, clinical and hypothesis generating research covering all aspects of the genetics of swallowing, giving new importance to the genotype-phenotype influences pertaining to dysphagia and its recovery.

Recent findings

There appear to be a number of candidate gene systems that interact with swallowing or its neurophysiology, which include brain-derived neurotrophic factor, apolipoprotein E and catechol-O-methyltransferase, that have been shown to impact on either swallowing function or the brain’s ability to respond to neurostimulation and induce plasticity. In addition, a number of genetic disorders, where dysphagia is a clinical phenomenon, have given us clues as to how multiple genes or the polygenetics of dysphagia might interact with our swallowing phenotype.

Summary

There is currently limited research in the field of genetic factors that influence (human) swallowing and oropharyngeal dysphagia, but this is an emerging science and one which, in the future, may herald a new era in precision medicine and better targeting of therapies for dysphagia based on an individual’s genetic makeup.

Cichlid fishes as a model to understand normal and clinical craniofacial variation

We have made great strides towards understanding the etiology of craniofacial disorders, especially for 'simple' Mendelian traits. However, the facial skeleton is a complex trait, and the full spectrum of genetic, developmental, and environmental factors that contribute to its final geometry remain unresolved. Forward genetic screens are constrained with respect to complex traits due to the types of genes and alleles commonly identified, developmental pleiotropy, and limited information about the impact of environmental interactions. Here, we discuss how studies in an evolutionary model - African cichlid fishes - can complement traditional approaches to understand the genetic and developmental origins of complex shape. Cichlids exhibit an unparalleled range of natural craniofacial morphologies that model normal human variation, and in certain instances mimic human facial dysmorphologies. Moreover, the evolutionary history and genomic architecture of cichlids make them an ideal system to identify the genetic basis of these phenotypes via quantitative trait loci (QTL) mapping and population genomics. Given the molecular conservation of developmental genes and pathways, insights from cichlids are applicable to human facial variation and disease. We review recent work in this system, which has identified lbh as a novel regulator of neural crest cell migration, determined the Wnt and Hedgehog pathways mediate species-specific bone morphologies, and examined how plastic responses to diet modulate adult facial shapes. These studies have not only revealed new roles for existing pathways in craniofacial development, but have identified new genes and mechanisms involved in shaping the craniofacial skeleton. In all, we suggest that combining work in traditional laboratory and evolutionary models offers significant potential to provide a more complete and comprehensive picture of the myriad factors that are involved in the development of complex traits.

Dissecting the Genetic Basis of Resistance to Soybean Cyst Nematode Combining Linkage and Association Mapping

A set of 585 informative single-nucleotide polymorphism (SNP) loci was used to genotype both a panel of diverse accessions and a set of recombinant inbred lines (RILs) bred from the cross Zhongpin03-5373 (ZP; resistant to SCN) × Zhonghuang13 (ZH; susceptible). The SNP loci are mostly sited within genic sequence in regions of the soybean [ (L.) Merr.] genome thought to harbor genes determining resistance to the soybean cyst nematode (SCN, Ichinohe). The three strongest quantitative trait nucleotides (QTNs) identified by association mapping (AM) involved the genes (a component of the multigene locus ), and (an paralog), as well as some other loci with smaller effects. The linkage mapping (LM) analysis performed using the RILs revealed two putative quantitative trait loci (QTL): one mapping to and the other to an paralog; both of these loci were also identified by AM. The former locus explained 25.5% of the phenotypic variance for SCN resistance and the latter 5.8%. In combination, the two major loci acted nonadditively, providing a high level of SCN resistance.

Gut microbiota in hypertension

PURPOSE OF REVIEW

Hypertension, which is present in about one quarter of the world's population, is responsible for about 41% of the number one cause of death - cardiovascular disease. Not included in these statistics is the effect of sodium intake on blood pressure, even though an increase or a marked decrease in sodium intake can increase blood pressure. This review deals with the interaction of gut microbiota and the kidney with genetics and epigenetics in the regulation of blood pressure and salt sensitivity.

RECENT FINDINGS

The abundance of the gut microbes, Firmicutes and Bacteroidetes, is associated with increased blood pressure in several models of hypertension, including the spontaneously hypertensive and Dahl salt-sensitive rats. Decreasing gut microbiota by antibiotics can increase or decrease blood pressure that is influenced by genotype. The biological function of probiotics may also be a consequence of epigenetic modification, related, in part, to microRNA. Products of the fermentation of nutrients by gut microbiota can influence blood pressure by regulating expenditure of energy, intestinal metabolism of catecholamines, and gastrointestinal and renal ion transport, and thus, salt sensitivity.

SUMMARY

The beneficial or deleterious effect of gut microbiota on blood pressure is a consequence of several variables, including genetics, epigenetics, lifestyle, and intake of antibiotics. These variables may influence the ultimate level of blood pressure and control of hypertension.

Polygenic analysis and targeted improvement of the complex trait of high acetic acid tolerance in the yeast Saccharomyces cerevisiae

BACKGROUND

Acetic acid is one of the major inhibitors in lignocellulose hydrolysates used for the production of second-generation bioethanol. Although several genes have been identified in laboratory yeast strains that are required for tolerance to acetic acid, the genetic basis of the high acetic acid tolerance naturally present in some Saccharomyces cerevisiae strains is unknown. Identification of its polygenic basis may allow improvement of acetic acid tolerance in yeast strains used for second-generation bioethanol production by precise genome editing, minimizing the risk of negatively affecting other industrially important properties of the yeast.

RESULTS

Haploid segregants of a strain with unusually high acetic acid tolerance and a reference industrial strain were used as superior and inferior parent strain, respectively. After crossing of the parent strains, QTL mapping using the SNP variant frequency determined by pooled-segregant whole-genome sequence analysis revealed two major QTLs. All F1 segregants were then submitted to multiple rounds of random inbreeding and the superior F7 segregants were submitted to the same analysis, further refined by sequencing of individual segregants and bioinformatics analysis taking into account the relative acetic acid tolerance of the segregants. This resulted in disappearance in the QTL mapping with the F7 segregants of a major F1 QTL, in which we identified HAA1, a known regulator of high acetic acid tolerance, as a true causative allele. Novel genes determining high acetic acid tolerance, GLO1, DOT5, CUP2, and a previously identified component, VMA7, were identified as causative alleles in the second major F1 QTL and in three newly appearing F7 QTLs, respectively. The superior HAA1 allele contained a unique single point mutation that significantly improved acetic acid tolerance under industrially relevant conditions when inserted into an industrial yeast strain for second-generation bioethanol production.

CONCLUSIONS

This work reveals the polygenic basis of high acetic acid tolerance in S. cerevisiae in unprecedented detail. It also shows for the first time that a single strain can harbor different sets of causative genes able to establish the same polygenic trait. The superior alleles identified can be used successfully for improvement of acetic acid tolerance in industrial yeast strains.

An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies

Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn’s disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of pervasive small but replicating effects in CD and SZ on genomic control and power. Finally, we conclude that, despite having very similar estimates of variance explained by genotyped SNPs, CD and SZ have a broadly dissimilar genetic architecture, due to differing mean effect size and proportion of non-null loci.

We describe in detail the implications of a particular mixture model (a scale mixture of two normals) for effect size distributions from genome-wide genotyping data. Parameters from this model can be used for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, power for detecting non-null loci, and proportion of variance explained from additive effects. Here, we fit this model by minimizing discrepancies with nonparametric estimates from a resampling-based algorithm. We examine the effects of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We validate this approach using meta-analysis test statistics (“z-scores”) from two large GWAS, one for Crohn’s disease and the other for schizophrenia. We demonstrate that for these studies a scale mixture of two normal distributions generally fits empirical replication effect sizes well, providing an excellent fit for the schizophrenia effect sizes but underestimating the tails of the distribution for Crohn’s disease.

A forest-based feature screening approach for large-scale genome data with complex structures

Background

Genome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits. When the number of SNPs dramatically increases to half million but the sample size is still limited to thousands, the traditional p-value based statistical approaches suffer from unprecedented limitations. Feature screening has proved to be an effective and powerful approach to handle ultrahigh dimensional data statistically, yet it has not received much attention in GWAS. Feature screening reduces the feature space from millions to hundreds by removing non-informative noise. However, the univariate measures used to rank features are mainly based on individual effect without considering the mutual interactions with other features. In this article, we explore the performance of a random forest (RF) based feature screening procedure to emphasize the SNPs that have complex effects for a continuous phenotype.

Results

Both simulation and real data analysis are conducted to examine the power of the forest-based feature screening. We compare it with five other popular feature screening approaches via simulation and conclude that RF can serve as a decent feature screening tool to accommodate complex genetic effects such as nonlinear, interactive, correlative, and joint effects. Unlike the traditional p-value based Manhattan plot, we use the Permutation Variable Importance Measure (PVIM) to display the relative significance and believe that it will provide as much useful information as the traditional plot.

Conclusion

Most complex traits are found to be regulated by epistatic and polygenic variants. The forest-based feature screening is proven to be an efficient, easily implemented, and accurate approach to cope whole genome data with complex structures. Our explorations should add to a growing body of enlargement of feature screening better serving the demands of contemporary genome data.

Human GRK4γ142V Variant Promotes Angiotensin II Type I Receptor-Mediated Hypertension via Renal Histone Deacetylase Type 1 Inhibition

The influence of a single gene on the pathogenesis of essential hypertension may be difficult to ascertain, unless the gene interacts with other genes that are germane to blood pressure regulation. G-protein-coupled receptor kinase type 4 (GRK4) is one such gene. We have reported that the expression of its variant hGRK4γ(142V) in mice results in hypertension because of impaired dopamine D1 receptor. Signaling through dopamine D1 receptor and angiotensin II type I receptor (AT1R) reciprocally modulates renal sodium excretion and blood pressure. Here, we demonstrate the ability of the hGRK4γ(142V) to increase the expression and activity of the AT1R. We show that hGRK4γ(142V) phosphorylates histone deacetylase type 1 and promotes its nuclear export to the cytoplasm, resulting in increased AT1R expression and greater pressor response to angiotensin II. AT1R blockade and the deletion of the Agtr1a gene normalize the hypertension in hGRK4γ(142V) mice. These findings illustrate the unique role of GRK4 by targeting receptors with opposite physiological activity for the same goal of maintaining blood pressure homeostasis, and thus making the GRK4 a relevant therapeutic target to control blood pressure.

Associations between temperament and gene polymorphisms in the brain dopaminergic system and the adrenal gland of sheep

Sheep of calm or nervous temperament differ in their physiological (cortisol secretion) and behavioural (motor activity) responses to stressors, perhaps due to variation in genes that regulate glucocorticoid synthesis or brain dopamine activity. Using ewes that had been selected over 20 generations for nervous (n=58) or calm (n=59) temperament, we confirmed the presence of a polymorphism in a gene specifically involved in cortisol production (CYP17), and identified polymorphisms in three genes specifically associated with personality and behavioural traits: dopamine receptors 2 and 4 (DRD2, DRD4), and monoamine oxidase A (MAOA). The calm and nervous lines differed in their frequencies of CYP17 SNP628 (single nucleotide A-G mutation at position 628) and DRD2 SNP939 (single nucleotide T-C mutation at position 939), but not for other SNPs detected in DRD2 or MAOA. In a second experiment, we then genotyped a large, non-selected flock of ewes for DRD2 SNP939 and CYP17 SNP628. Responses to the 'arena' and 'isolation box' challenges were associated with the DRD2 SNP939 genotype and the response to ACTH challenge was associated with the CYP17 SNP628 genotype. We conclude that, for sheep, a combination of the DRD2 SNP939 C allele and the CYP17 SNP628 A/A genotype could be used as a genetic marker for nervous temperament, and that a combination of DRD2 SNP939 T/T and CYP17 SNP628 G/G could be used as a genetic marker for calm temperament.

Epigenetics and a New Look on Metabolic Syndrome

The incidence of metabolic syndrome increases in the developed countries, therefore biomedical research is focused on the understanding of its etiology. The study of exact mechanisms is very complicated because both genetic and environmental factors contribute to this complex disease. The ability of environmental factors to promote phenotype changes by epigenetic DNA modifications (i.e. DNA methylation, histone modifications) was demonstrated to play an important role in the development and predisposition to particular symptoms of metabolic syndrome. There is no doubt that the early life, such as the fetal and perinatal periods, is critical for metabolic syndrome development and therefore critical for prevention of this disease. Moreover, these changes are visible not only in individuals exposed to environmental factors but also in the subsequent progeny for multiple generations and this phenomenon is called transgenerational inheritance. The knowledge of molecular mechanisms, by which early minor environmental stimuli modify the expression of genetic information, might be the desired key for the understanding of mechanisms leading to the change of phenotype in adulthood. This review provides a short overview of metabolic syndrome epigenetics.

The Genetic Architecture of Domestication in Animals

Domestication has been essential to the progress of human civilization, and the process itself has fascinated biologists for hundreds of years. Domestication has led to a series of remarkable changes in a variety of plants and animals, in what is termed the “domestication phenotype.” In domesticated animals, this general phenotype typically consists of similar changes in tameness, behavior, size/morphology, color, brain composition, and adrenal gland size. This domestication phenotype is seen in a range of different animals. However, the genetic basis of these associated changes is still puzzling. The genes for these different traits tend to be grouped together in clusters in the genome, though it is still not clear whether these clusters represent pleiotropic effects, or are in fact linked clusters. This review focuses on what is currently known about the genetic architecture of domesticated animal species, if genes of large effect (often referred to as major genes) are prevalent in driving the domestication phenotype, and whether pleiotropy can explain the loci underpinning these diverse traits being colocated.

Association of Amine-Receptor DNA Sequence Variants with Associative Learning in the Honeybee

Octopamine- and dopamine-based neuromodulatory systems play a critical role in learning and learning-related behaviour in insects. To further our understanding of these systems and resulting phenotypes, we quantified DNA sequence variations at six loci coding octopamine-and dopamine-receptors and their association with aversive and appetitive learning traits in a population of honeybees. We identified 79 polymorphic sequence markers (mostly SNPs and a few insertions/deletions) located within or close to six candidate genes. Intriguingly, we found that levels of sequence variation in the protein-coding regions studied were low, indicating that sequence variation in the coding regions of receptor genes critical to learning and memory is strongly selected against. Non-coding and upstream regions of the same genes, however, were less conserved and sequence variations in these regions were weakly associated with between-individual differences in learning-related traits. While these associations do not directly imply a specific molecular mechanism, they suggest that the cross-talk between dopamine and octopamine signalling pathways may influence olfactory learning and memory in the honeybee.

African Indigenous Cattle: Unique Genetic Resources in a Rapidly Changing World

At least 150 indigenous African cattle breeds have been named, but the majority of African cattle populations remain largely uncharacterized. As cattle breeds and populations in Africa adapted to various local environmental conditions, they acquired unique features. We know now that the history of African cattle was particularly complex and while several of its episodes remain debated, there is no doubt that African cattle population evolved dramatically over time. Today, we find a mosaic of genetically diverse population from the purest Bos taurus to the nearly pure Bos indicus. African cattle are now found all across the continent, with the exception of the Sahara and the river Congo basin. They are found on the rift valley highlands as well as below sea level in the Afar depression. These unique livestock genetic resources are in danger to disappear rapidly following uncontrolled crossbreeding and breed replacements with exotic breeds. Breeding improvement programs of African indigenous livestock remain too few while paradoxically the demand of livestock products is continually increasing. Many African indigenous breeds are endangered now, and their unique adaptive traits may be lost forever. This paper reviews the unique known characteristics of indigenous African cattle populations while describing the opportunities, the necessity and urgency to understand and utilize these resources to respond to the needs of the people of the continent and to the benefit of African farmers.

Second generation physical and linkage maps of yellowtail (Seriola quinqueradiata) and comparison of synteny with four model fish

Background

Physical and linkage maps are important aids for the assembly of genome sequences, comparative analyses of synteny, and to search for candidate genes by quantitative trait locus analysis. Yellowtail, Seriola quinqueradiata, is an economically important species in Japanese aquaculture, and genetic information will be useful for DNA-assisted breeding. We report the construction of a second generation radiation hybrid map, its synteny analysis, and a second generation linkage map containing SNPs (single nucleotide polymorphisms) in yellowtail.

Results

Approximately 1.4 million reads were obtained from transcriptome sequence analysis derived from 11 tissues of one individual. To identify SNPs, cDNA libraries were generated from a pool of 500 whole juveniles, and the gills and kidneys of 100 adults. 9,356 putative SNPs were detected in 6,025 contigs, with a minor allele frequency ≥25%. The linkage and radiation hybrid maps were constructed based on these contig sequences. 2,081 markers, including 601 SNPs markers, were mapped onto the linkage map, and 1,532 markers were mapped in the radiation hybrid map.

Conclusions

The second generation linkage and physical maps were constructed using 6,025 contigs having SNP markers. These maps will aid the de novo assembly of sequencing reads, linkage studies and the identification of candidate genes related to important traits. The comparison of marker contigs in the radiation hybrid map indicated that yellowtail is evolutionarily closer to medaka than to green-spotted pufferfish, three-spined stickleback or zebrafish. The synteny analysis may aid studies of chromosomal evolution in yellowtail compared with model fish.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1600-7) contains supplementary material, which is available to authorized users.

Contrasting genetic architectures in different mouse reference populations used for studying complex traits

Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.

Bayesian semiparametric copula estimation with application to psychiatric genetics

This paper proposes a semiparametric methodology for modeling multivariate and conditional distributions. We first build a multivariate distribution whose dependence structure is induced by a Gaussian copula and whose marginal distributions are estimated nonparametrically via mixtures of B-spline densities. The conditional distribution of a given variable is obtained in closed form from this multivariate distribution. We take a Bayesian approach, using Markov chain Monte Carlo methods for inference. We study the frequentist properties of the proposed methodology via simulation and apply the method to estimation of conditional densities of summary statistics, used for computing conditional local false discovery rates, from genetic association studies of schizophrenia and cardiovascular disease risk factors.

A comparative study of disease genes and drug targets in the human protein interactome

BACKGROUND

Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear.

RESULTS

In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes.

CONCLUSIONS

The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

Common variants of the G protein-coupled receptor type 4 are associated with human essential hypertension and predict the blood pressure response to angiotensin receptor blockade

Non-synonymous GRK4 variants, R65L, A142V and A486V, are associated with essential hypertension in diverse populations. This study replicated the association of GRK4 variants, including GRK4(142V), with human essential hypertension in a Japanese population (n=588; hypertensive, n=486 normotensive controls) and determined whether the presence of GRK4 variants predicted the blood pressure (BP) response to angiotensin receptor blockers (ARBs) in patients with essential hypertension. We analyzed 829 patients and compared the response to ARBs between individuals with no GRK4 variants (n=136) and those with variants at one or any of the three loci (n=693). Carriers of hGRK4(142V) had a greater decrease in systolic BP in response to ARBs than non-carrier hypertensive patients. By contrast, those with variants only at GRK4(486V) were less likely to achieve the BP goal in response to an ARB than those with no variants. These studies showed for the first time the association between GRK4(142V) and a larger decrease in BP with ARBs in hypertensive patients.

The genetics of osteoporosis

INTRODUCTION

Osteoporosis is the commonest metabolic bone disease worldwide. The clinical hallmark of osteoporosis is low trauma fracture, with the most devastating being hip fracture, resulting in significant effects on both morbidity and mortality.

SOURCES OF DATA

Data for this review have been gathered from the published literature and from a range of web resources.

AREAS OF AGREEMENT

Genome-wide association studies in the field of osteoporosis have led to the identification of a number of loci associated with both bone mineral density and fracture risk and further increased our understanding of disease.

AREAS OF CONTROVERSY

The early strategies for mapping osteoporosis disease genes reported only isolated associations, with replication in independent cohorts proving difficult. Neither candidate gene or linkage studies showed association at genome-wide level of significance.

GROWING POINTS

The advent of massive parallel sequencing technologies has proved extremely successful in mapping monogenic diseases and thus leading to the utilization of this new technology in complex disease genetics.

AREAS TIMELY FOR DEVELOPING RESEARCH

The identification of novel genes and pathways will potentially lead to the identification of novel therapeutic options for patients with osteoporosis.

Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization

Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request.

A pleiotropy-informed Bayesian false discovery rate adapted to a shared control design finds new disease associations from GWAS summary statistics

Genome-wide association studies (GWAS) have been successful in identifying single nucleotide polymorphisms (SNPs) associated with many traits and diseases. However, at existing sample sizes, these variants explain only part of the estimated heritability. Leverage of GWAS results from related phenotypes may improve detection without the need for larger datasets. The Bayesian conditional false discovery rate (cFDR) constitutes an upper bound on the expected false discovery rate (FDR) across a set of SNPs whose p values for two diseases are both less than two disease-specific thresholds. Calculation of the cFDR requires only summary statistics and have several advantages over traditional GWAS analysis. However, existing methods require distinct control samples between studies. Here, we extend the technique to allow for some or all controls to be shared, increasing applicability. Several different SNP sets can be defined with the same cFDR value, and we show that the expected FDR across the union of these sets may exceed expected FDR in any single set. We describe a procedure to establish an upper bound for the expected FDR among the union of such sets of SNPs. We apply our technique to pairwise analysis of p values from ten autoimmune diseases with variable sharing of controls, enabling discovery of 59 SNP-disease associations which do not reach GWAS significance after genomic control in individual datasets. Most of the SNPs we highlight have previously been confirmed using replication studies or larger GWAS, a useful validation of our technique; we report eight SNP-disease associations across five diseases not previously declared. Our technique extends and strengthens the previous algorithm, and establishes robust limits on the expected FDR. This approach can improve SNP detection in GWAS, and give insight into shared aetiology between phenotypically related conditions.

Many diseases have a significant hereditary component, only part of which has been explained by analysis of genome-wide association studies (GWAS). Shared aetiology, treatment protocols, and overlapping results from existing GWAS suggest similarities in genetic susceptibility between related diseases, which may be exploited to detect more disease-associated SNPs without the need for further data. We extend an existing method for detecting SNPs associated with a given disease by conditioning on association with another disease. Our extension allows GWAS for the two conditions to share control samples, enabling larger overall control groups and application to the common case when GWAS for related diseases pool control samples. We demonstrate that our technique limits the expected overall false discovery rate at a threshold dependent on the two diseases. We apply our technique to genotype data from ten immune mediated diseases. Overall pleiotropy between phenotypes is demonstrated graphically. We are able to declare several SNPs significant at a genome-wide level whilst controlling at a lower false-discovery rate than would be possible using a conventional approach, identifying eight previously unknown disease associations. This technique can improve SNP detection in GWAS by re-analysing existing data, and gives insight into the shared genetic bases of autoimmune diseases.

Human genetics of diabetic nephropathy

Diabetic vascular complications (DVCs) affecting several important organ systems of human body such as cardiovascular system contribute a major public health problem. Genetic factors contribute to the risk of diabetic nephropathy (DN). Genetics variants, structural variants (copy number variation) and epigenetic changes play important roles in the development of DN. Apart from nucleus genome, mitochondrial DNA (mtDNA) plays critical roles in regulation of development of DN. Epigenetic studies have indicated epigenetic changes in chromatin affecting gene transcription in response to environmental stimuli, which provided a large body of evidence of regulating development of diabetes mellitus. This review focused on the current knowledge of the genetic and epigenetic basis of DN. Ultimately, identification of genes or genetic loci, structural variants and epigenetic changes contributed to risk or protection of DN will benefit uncovering the complex mechanism underlying DN, with crucial implications for the development of personalized medicine to diabetes mellitus and its complications.

Human genetics of diabetic retinopathy

There is evidence demonstrating that genetic factors contribute to the risk of diabetic retinopathy (DR). Genetics variants, structural variants (copy number variation, CNV) and epigenetic changes play important roles in the development of DR. Genetic linkage and association studies have uncovered a number of genetic loci and common genetic variants susceptibility to DR. CNV and interactions of gene by environment have also been detected by association analysis. Apart from nucleus genome, mitochondrial DNA plays critical roles in regulation of development of DR. Epigenetic studies have indicated epigenetic changes in chromatin affecting gene transcription in response to environmental stimuli, which provided a large body of evidence of regulating development of diabetes mellitus. Identification of genetic variants and epigenetic changes contributed to risk or protection of DR will benefit uncovering the complex mechanism underlying DR. This review focused on the current knowledge of the genetic and epigenetic basis of DR.

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks

Identification of disease-causing genes among a large number of candidates is a fundamental challenge in human disease studies. However, it is still time-consuming and laborious to determine the real disease-causing genes by biological experiments. With the advances of the high-throughput techniques, a large number of protein-protein interactions have been produced. Therefore, to address this issue, several methods based on protein interaction network have been proposed. In this paper, we propose a shortest path-based algorithm, named SPranker, to prioritize disease-causing genes in protein interaction networks. Considering the fact that diseases with similar phenotypes are generally caused by functionally related genes, we further propose an improved algorithm SPGOranker by integrating the semantic similarity of GO annotations. SPGOranker not only considers the topological similarity between protein pairs in a protein interaction network but also takes their functional similarity into account. The proposed algorithms SPranker and SPGOranker were applied to 1598 known orphan disease-causing genes from 172 orphan diseases and compared with three state-of-the-art approaches, ICN, VS and RWR. The experimental results show that SPranker and SPGOranker outperform ICN, VS, and RWR for the prioritization of orphan disease-causing genes. Importantly, for the case study of severe combined immunodeficiency, SPranker and SPGOranker predict several novel causal genes.

Gene by Social-Environment Interaction for Youth Delinquency and Violence: Thirty-Nine Aggression-related Genes

Complex human traits are likely to be affected by many environmental and genetic factors, and the interactions among them. However, previous gene-environment interaction (G×E) studies have typically focused on one or only a few genetic variants at a time. To provide a broader view of G×E, this study examines the relationship between 403 genetic variants from 39 genes and youth delinquency and violence. We find evidence that low social control is associated with greater genetic risk for delinquency and violence and high/moderate social control with smaller genetic risk for delinquency and violence. Our findings are consistent with prior G×E studies based on a small number of genetic variants, and, more importantly, we show that these findings still hold when a large number of genetic variants are considered simultaneously. A key implication of these findings is that the expression of multiple genes related to delinquency depends on the social environment: gene expression is likely to be amplified in low-social-control environments but, tends to be suppressed in high/moderate-social-control environments. This study not only deepens our understanding of how the social environment shapes individual behavior, but also provides important conceptual and methodological insights for future G×E research on complex human traits.

The revolution in human monogenic disease mapping

The successful completion of the Human Genome Project (HGP) was an unprecedented scientific advance that has become an invaluable resource in the search for genes that cause monogenic and common (polygenic) diseases. Prior to the HGP, linkage analysis had successfully mapped many disease genes for monogenic disorders; however, the limitations of this approach were particularly evident for identifying causative genes in rare genetic disorders affecting lifespan and/or reproductive fitness, such as skeletal dysplasias. In this review, we illustrate the challenges of mapping disease genes in such conditions through the ultra-rare disorder fibrodysplasia ossificans progressiva (FOP) and we discuss the advances that are being made through current massively parallel (“next generation”) sequencing (MPS) technologies.

Identifying and prioritizing disease-related genes based on the network topological features

Identifying and prioritizing disease-related genes are the most important steps for understanding the pathogenesis and discovering the therapeutic targets. The experimental examination of these genes is very expensive and laborious, and usually has a higher false positive rate. Therefore, it is highly desirable to develop computational methods for the identification and prioritization of disease-related genes. In this study, we develop a powerful method to identify and prioritize candidate disease genes. The novel network topological features with local and global information are proposed and adopted to characterize genes. The performance of these novel features is verified based on the 10-fold cross-validation test and leave-one-out cross-validation test. The proposed features are compared with the published features, and fused strategy is investigated by combining the current features with the published features. And, these combination features are also utilized to identify and prioritize Parkinson's disease-related genes. The results indicate that identified genes are highly related to some molecular process and biological function, which provides new clues for researching pathogenesis of Parkinson's disease. The source code of Matlab is freely available on request from the authors.

Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases

Motivation: Whole-exome sequencing (WES) has opened up previously unheard of possibilities for identifying novel disease genes in Mendelian disorders, only about half of which have been elucidated to date. However, interpretation of WES data remains challenging.

Results: Here, we analyze protein–protein association (PPA) networks to identify candidate genes in the vicinity of genes previously implicated in a disease. The analysis, using a random-walk with restart (RWR) method, is adapted to the setting of WES by developing a composite variant-gene relevance score based on the rarity, location and predicted pathogenicity of variants and the RWR evaluation of genes harboring the variants. Benchmarking using known disease variants from 88 disease-gene families reveals that the correct gene is ranked among the top 10 candidates in ≥50% of cases, a figure which we confirmed using a prospective study of disease genes identified in 2012 and PPA data produced before that date. We implement our method in a freely available Web server, ExomeWalker, that displays a ranked list of candidates together with information on PPAs, frequency and predicted pathogenicity of the variants to allow quick and effective searches for candidates that are likely to reward closer investigation.

Availability and implementation: http://compbio.charite.de/ExomeWalker

Contact: peter.robinson@charite.de

Core and specific network markers of carcinogenesis from multiple cancer samples

Cancer is the leading cause of death worldwide and is generally caused by mutations in multiple proteins or the dysregulation of pathways. Understanding the causes and the underlying carcinogenic mechanisms can help fight this disease. In this study, a systems biology approach was used to construct the protein-protein interaction (PPI) networks of four cancers and the non-cancers by their corresponding microarray data, PPI modeling and database-mining. By comparing PPI networks between cancer and non-cancer samples to find significant proteins with large PPI changes during carcinogenesis process, core and specific network markers were identified by the intersection and difference of significant proteins, respectively, with carcinogenesis relevance values (CRVs) for each cancer. A total of 28 significant proteins were identified as core network markers in the carcinogenesis of four types of cancer, two of which are novel cancer-related proteins (e.g., UBC and PSMA3). Moreover, seven crucial common pathways were found among these cancers based on their core network markers, and some specific pathways were particularly prominent based on the specific network markers of different cancers (e.g., the RIG-I-like receptor pathway in bladder cancer, the proteasome pathway and TCR pathway in liver cancer, and the HR pathway in lung cancer). Additional validation of these network markers using the literature and new tested datasets could strengthen our findings and confirm the proposed method. From these core and specific network markers, we could not only gain an insight into crucial common and specific pathways in the carcinogenesis, but also obtain a high promising PPI target for cancer therapy.

Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors

In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions.

Regulatory and coding genome regions are enriched for trait associated variants in dairy and beef cattle

BACKGROUND

In livestock, as in humans, the number of genetic variants that can be tested for association with complex quantitative traits, or used in genomic predictions, is increasing exponentially as whole genome sequencing becomes more common. The power to identify variants associated with traits, particularly those of small effects, could be increased if certain regions of the genome were known a priori to be enriched for associations. Here, we investigate whether twelve genomic annotation classes were enriched or depleted for significant associations in genome wide association studies for complex traits in beef and dairy cattle. We also describe a variance component approach to determine the proportion of genetic variance captured by each annotation class.

RESULTS

P-values from large GWAS using 700K SNP in both dairy and beef cattle were available for 11 and 10 traits respectively. We found significant enrichment for trait associated variants (SNP significant in the GWAS) in the missense class along with regions 5 kilobases upstream and downstream of coding genes. We found that the non-coding conserved regions (across mammals) were not enriched for trait associated variants. The results from the enrichment or depletion analysis were not in complete agreement with the results from variance component analysis, where the missense and synonymous classes gave the greatest increase in variance explained, while the upstream and downstream classes showed a more modest increase in the variance explained.

CONCLUSION

Our results indicate that functional annotations could assist in prioritization of variants to a subset more likely to be associated with complex traits; including missense variants, and upstream and downstream regions. The differences in two sets of results (GWAS enrichment depletion versus variance component approaches) might be explained by the fact that the variance component approach has greater power to capture the cumulative effect of mutations of small effect, while the enrichment or depletion approach only captures the variants that are significant in GWAS, which is restricted to a limited number of common variants of moderate effects.

Approaches for recognizing disease genes based on network

Diseases are closely related to genes, thus indicating that genetic abnormalities may lead to certain diseases. The recognition of disease genes has long been a goal in biology, which may contribute to the improvement of health care and understanding gene functions, pathways, and interactions. However, few large-scale gene-gene association datasets, disease-disease association datasets, and gene-disease association datasets are available. A number of machine learning methods have been used to recognize disease genes based on networks. This paper states the relationship between disease and gene, summarizes the approaches used to recognize disease genes based on network, analyzes the core problems and challenges of the methods, and outlooks future research direction.

Hunting for immune response regulatory genes: vaccination studies in infant twins

The contribution of twin studies towards unraveling the complex mechanisms of multifactorial diseases is increasingly recognized. Recent twin studies using infant vaccination as a model for infectious diseases have confirmed the importance of host genetic factors as major regulators of the immune response. A combination of twin-based family studies and population-based association studies should lead to the identification of the specific genes involved. These genes and their products have the potential to be developed as targets for novel therapeutic and prophylactic agents against infectious diseases.

Boosting the power of schizophrenia genetics by leveraging new statistical tools

Genome-wide association studies (GWAS) have identified a large number of gene variants associated with schizophrenia, but these variants explain only a small portion of the heritability. It is becoming increasingly clear that schizophrenia is influenced by many genes, most of which have effects too small to be identified using traditional GWAS statistical methods. By applying recently developed Empirical Bayes statistical approaches, we have demonstrated that functional genic elements show differential contribution to phenotypic variance, with some elements (regulatory regions and exons) showing strong enrichment for association with schizophrenia. Applying related methods, we also showed abundant genetic overlap (pleiotropy) between schizophrenia and other phenotypes, including bipolar disorder, cardiovascular disease risk factors, and multiple sclerosis. We estimated the number of gene variants with effects in schizophrenia and bipolar disorder to be approximately 1.2%. By applying our novel statistical framework, we dramatically improved gene discovery and detected a large number of new gene loci associated with schizophrenia that have not yet been identified with standard GWAS methods. Utilizing independent schizophrenia substudies, we showed that these new loci have high replication rates in de novo samples, indicating that they likely represent true schizophrenia risk genes. The new statistical tools provide a powerful approach for uncovering more of the missing heritability of schizophrenia and other complex disorders. In conclusion, the highly polygenic architecture of schizophrenia strongly suggests the utility of research approaches that recognize schizophrenia neuropathology as a complex dynamic system, with many small gene effects integrated in functional networks.