Progress and promise of genome-wide association studies for human complex trait genetics

The promises of neurodegenerative disease modeling

The rise in the prevalence of neurodegenerative diseases parallels the rapid increase in human lifespan. Despite intensive research, the molecular and cellular mechanisms underlying the onset and progression of these devastating diseases with age are still poorly understood. Many aspects of these diseases have been modelled successfully in experimental animals such as the mouse, the zebrafish Brachydanio rero, the nematode worm Caenorhaditis elegans and the fruit fly Drosophila melanogaster. This review will focus on the advantages offered by the genetic tools available in Drosophila for combining powerful strategies in order to tackle the causative factors of these complex pathologies and help to elaborate efficient drugs to treat them.

Potential of promotion of alleles by genome editing to improve quantitative traits in livestock breeding programs

Background

Genome editing (GE) is a method that enables specific nucleotides in the genome of an individual to be changed. To date, use of GE in livestock has focussed on simple traits that are controlled by a few quantitative trait nucleotides (QTN) with large effects. The aim of this study was to evaluate the potential of GE to improve quantitative traits that are controlled by many QTN, referred to here as promotion of alleles by genome editing (PAGE).

Methods

Multiple scenarios were simulated to test alternative PAGE strategies for a quantitative trait. They differed in (i) the number of edits per sire (0 to 100), (ii) the number of edits per generation (0 to 500), and (iii) the extent of use of PAGE (i.e. editing all sires or only a proportion of them). The base line scenario involved selecting individuals on true breeding values (i.e., genomic selection only (GS only)-genomic selection with perfect accuracy) for several generations. Alternative scenarios complemented this base line scenario with PAGE (GS + PAGE). The effect of different PAGE strategies was quantified by comparing response to selection, changes in allele frequencies, the number of distinct QTN edited, the sum of absolute effects of the edited QTN per generation, and inbreeding.

Results

Response to selection after 20 generations was between 1.08 and 4.12 times higher with GS + PAGE than with GS only. Increases in response to selection were larger with more edits per sire and more sires edited. When the total resources for PAGE were limited, editing a few sires for many QTN resulted in greater response to selection and inbreeding compared to editing many sires for a few QTN. Between the scenarios GS only and GS + PAGE, there was little difference in the average change in QTN allele frequencies, but there was a major difference for the QTN with the largest effects. The sum of the effects of the edited QTN decreased across generations.

Conclusions

This study showed that PAGE has great potential for application in livestock breeding programs, but inbreeding needs to be managed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0135-3) contains supplementary material, which is available to authorized users.

The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease

The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available.

Functional annotation of HOT regions in the human genome: implications for human disease and cancer

Advances in genome-wide association studies (GWAS) and large-scale sequencing studies have resulted in an impressive and growing list of disease- and trait-associated genetic variants. Most studies have emphasised the discovery of genetic variation in coding sequences, however, the noncoding regulatory effects responsible for human disease and cancer biology have been substantially understudied. To better characterise the cis-regulatory effects of noncoding variation, we performed a comprehensive analysis of the genetic variants in HOT (high-occupancy target) regions, which are considered to be one of the most intriguing findings of recent large-scale sequencing studies. We observed that GWAS variants that map to HOT regions undergo a substantial net decrease and illustrate development-specific localisation during haematopoiesis. Additionally, genetic risk variants are disproportionally enriched in HOT regions compared with LOT (low-occupancy target) regions in both disease-relevant and cancer cells. Importantly, this enrichment is biased toward disease- or cancer-specific cell types. Furthermore, we observed that cancer cells generally acquire cancer-specific HOT regions at oncogenes through diverse mechanisms of cancer pathogenesis. Collectively, our findings demonstrate the key roles of HOT regions in human disease and cancer and represent a critical step toward further understanding disease biology, diagnosis, and therapy.

Shared genetic etiology underlying Alzheimer's disease and type 2 diabetes

Epidemiological evidence supports the observation that subjects with type 2 diabetes (T2D) are at higher risk to develop Alzheimer's disease (AD). However, whether and how these two conditions are causally linked is unknown. Possible mechanisms include shared genetic risk factors, which were investigated in this study based on recent genome wide association study (GWAS) findings. In order to achieve our goal, we retrieved single nucleotide polymorphisms (SNPs) associated with T2D and AD from large-scale GWAS meta-analysis consortia and tested for overlap among the T2D- and AD-associated SNPs at various p-value thresholds. We then explored the function of the shared T2D/AD GWAS SNPs by leveraging expressional quantitative trait loci, pathways, gene ontology data, and co-expression networks. We found 927 SNPs associated with both AD and T2D with p-value ≤0.01, an overlap significantly larger than random chance (overlapping p-value of 6.93E-28). Among these, 395 of the shared GWAS SNPs have the same risk allele for AD and T2D, suggesting common pathogenic mechanisms underlying the development of both AD and T2D. Genes influenced by shared T2D/AD SNPs with the same risk allele were first identified using a SNP annotation variation (ANNOVAR) software, followed by using Association Protein-Protein Link Evaluator (DAPPLE) software to identify additional proteins that are known to physically interact with the ANNOVAR gene annotations. We found that gene annotations from ANNOVAR and DAPPLE significantly enriched specific KEGG pathways pertaining to immune responses, cell signaling and neuronal plasticity, cellular processes in which abnormalities are known to contribute to both T2D and AD pathogenesis. Thus, our observation suggests that among T2D subjects with common genetic predispositions (e.g., SNPs with consistent risk alleles for T2D and AD), dysregulation of these pathogenic pathways could contribute to the elevated risks for AD in subjects. Interestingly, we found that 532 of the shared T2D/AD GWAS SNPs had divergent risk alleles in the two diseases. For individual shared T2D/AD SNPs with divergent alleles, one of the allelic forms may contribute to one of the diseases (e.g., T2D), but not necessarily to the other (e.g., AD), or vice versa. Collectively, our GWAS studies tentatively support the epidemiological observation of disease concordance between T2D and AD. Moreover, the studies provide the much needed information for the design of future novel therapeutic approaches, for a subpopulation of T2D subjects with genetic disposition to AD, that could benefit T2D and reduce the risk for subsequent development of AD.

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data

Microarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient's samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation.

A Candidate-Pathway Approach to Identify Gene-Environment Interactions: Analyses of Colon Cancer Risk and Survival

BACKGROUND

Genetic association studies have traditionally focused on associations between individual single nucleotide polymorphisms (SNPs) and disease. Standard analysis ignores interactions between multiple SNPs and environmental exposures explaining a small portion of disease heritability: the often-cited issue of "missing heritability."

METHODS

We present a novel three-step analytic framework for modeling gene-environment interactions (GEIs) between an angiogenesis candidate-gene pathway and three lifestyle exposures (dietary protein, smoking, and alcohol consumption) on colon cancer risk and survival. Logic regression was used to summarize the gene-pathway effects, and GEIs were modeled using logistic regression and Cox proportional hazards models. We analyzed data from 1541 colon cancer case patients and 1934 control subjects in the Diet, Activity and Lifestyle as a Risk Factor for Colon Cancer Study.

RESULTS

We identified five statistically significant GEIs for colon cancer risk. For risk interaction, odds ratios (ORINT) and 95% confidence intervals (CIs) were FLT1(rs678714) and BMP4(rs17563) and smoking (ORINT = 1.64, 95% CI = 1.11 to 2.41 and ORINT = 1.60, 95% CI = 1.10 to 2.32, respectively); FLT1(rs2387632 OR rs9513070) and protein intake (ORINT = 1.69, 95% CI = 1.03 to 2.77); KDR(rs6838752) and TLR2(rs3804099) and alcohol (ORINT = 1.53, 95% CI = 1.10 to 2.13 and ORINT = 1.59, 95% CI = 1.05 to 2.38, respectively). Three GEIs between TNF, BMP1, and BMPR2 genes and the three exposures were statistically significant at the 5% level in relation to colon cancer survival but not after multiple-testing adjustment.

CONCLUSIONS

Adopting a comprehensive biologically informed candidate-pathway approach identified GEI effects on colon cancer. Findings may have important implications for public health and personalized medicine targeting prevention and therapeutic strategies. Findings from this study need to be validated in other studies.

Genetic contributions to urgency urinary incontinence in women

PURPOSE

We identify genetic variants associated with urgency urinary incontinence in postmenopausal women.

MATERIALS AND METHODS

A 2-stage genome-wide association analysis was conducted to identify variants associated with urgency urinary incontinence. The WHI GARNET substudy with 4,894 genotyped post-reproductive white women was randomly split into independent discovery and replication cohorts. Genome-wide imputation was performed using IMPUTE2 with the 1000 Genomes ALL Phase I integrated variant set as a reference. Controls reported no urgency urinary incontinence at enrollment or followup. Cases reported monthly or greater urgency urinary incontinence and leaked sufficiently to wet/soak underpants/clothes. Logistic regression models were used to predict urgency urinary incontinence case vs control status based on genotype, assuming additive inheritance. Age, obesity, diabetes and depression were included in the models as covariates.

RESULTS

Following quality control, 975,508 single nucleotide polymorphisms in 2,241 cases (discovery 1,102; replication 1,133) and 776 controls (discovery 405, replication 371) remained. Genotype imputation resulted in 9,077,347 single nucleotide polymorphisms and insertions/deletions with minor allele frequency greater than 0.01 available for analysis. Meta-analysis of the discovery and replication samples identified 6 loci on chromosomes 5, 10, 11, 12 and 18 associated with urgency urinary incontinence at p <10(-6). Of the loci 3 were within genes, the zinc finger protein 521 (ZFP521) gene on chromosome 18q11, the ADAMTS16 gene on chromosome 5p15 and the CIT gene on chromosome 12q24. The other 3 loci were intergenic.

CONCLUSIONS

Although environmental factors also likely contribute, this first exploratory genome-wide association study for urgency urinary incontinence suggests that genetic variants in the ZFP521, CIT and ADAMTS16 genes might account for some of the observed heritability of the condition.

Inference of Evolutionary Forces Acting on Human Biological Pathways

Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald-Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects and evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their two-dimensional null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of nonsynonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures.

Genome-wide association studies in biliary atresia

Biliary atresia (BA) is a model complex disease resulting from interactions between multiple susceptibility loci and environmental factors. This perception is based on a heterogeneous phenotype extending beyond an absent extrahepatic bile duct to include gut and cardiovascular anomalies, and the association of BA with viral infections. Refractory jaundice and progression to cirrhosis shortly after birth can be fatal without surgical correction, and further suggests a pathogenesis during liver and bile duct development. Conclusive proof for a developmental origin would require documentation of disease progression in the perinatal or fetal liver, an impossible task for obvious reasons. We review three different sets of genome-wide association studies (GWAS) from three different cohorts of BA patients by three different groups of investigators, which address this knowledge gap. Knockdown of each susceptibility gene identified by GWAS in zebrafish embryos impairs excretion of bile from the liver, duplicating the characteristic diagnostic finding seen in affected children. This finding is associated with impaired intrahepatic biliary network formation in zebrafish morphants. Although distinct, these susceptibility genes share several functions including roles in mechanisms for organogenesis (glypican 1 or GPC1, and adenosine diphosphate ribosylation factor 6, or ARF6) or a greater expression in fetal liver than in adult liver (adducin 3 or ADD3). Together, these studies emphasize the importance of the human evidence, and present opportunities to map novel pathways which explain the phenotypic heterogeneity of BA.

Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans

Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry

Background

Today computational molecular evolution is a vibrant research field that benefits from the availability of large and complex new generation sequencing data – ranging from full genomes and proteomes to microbiomes, metabolomes and epigenomes. The grounds for this progress were established long before the discovery of the DNA structure. Specifically, Darwin’s theory of evolution by means of natural selection not only remains relevant today, but also provides a solid basis for computational research with a variety of applications. But a long-term progress in biology was ensured by the mathematical sciences, as exemplified by Sir R. Fisher in early 20th century. Now this is true more than ever: The data size and its complexity require biologists to work in close collaboration with experts in computational sciences, modeling and statistics.

Results

Natural selection drives function conservation and adaptation to emerging pathogens or new environments; selection plays key role in immune and resistance systems. Here I focus on computational methods for evaluating selection in molecular sequences, and argue that they have a high potential for applications. Pharma and biotech industries can successfully use this potential, and should take the initiative to enhance their research and development with state of the art bioinformatics approaches.

Conclusions

This review provides a quick guide to the current computational approaches that apply the evolutionary principles of natural selection to real life problems – from drug target validation, vaccine design and protein engineering to applications in agriculture, ecology and conservation.

Genome-wide association analysis reveals new targets for carotenoid biofortification in maize

KEY MESSAGE

Genome-wide association analysis in CIMMYT's association panel revealed new favorable native genomic variations in/nearby important genes such as hydroxylases and CCD1 that have potential for carotenoid biofortification in maize. Genome-wide association studies (GWAS) have been used extensively to identify allelic variation for genes controlling important agronomic and nutritional traits in plants. Provitamin A (proVA) enhancing alleles of lycopene epsilon cyclase (LCYE) and β-carotene hydroxylase 1 (CRTRB1), previously identified through candidate-gene based GWAS, are currently used in CIMMYT's maize breeding program. The objective of this study was to identify genes or genomic regions controlling variation for carotenoid concentrations in grain for CIMMYT's carotenoid association mapping panel of 380 inbred maize lines, using high-density genome-wide platforms with ~476,000 SNP markers. Population structure effects were minimized by adjustments using principal components and kinship matrix with mixed models. Genome-wide linkage disequilibrium (LD) analysis indicated faster LD decay (3.9 kb; r (2) = 0.1) than commonly reported for temperate germplasm, and therefore the possibility of achieving higher mapping resolution with our mostly tropical diversity panel. GWAS for various carotenoids identified CRTRB1, LCYE and other key genes or genomic regions that govern rate-critical steps in the upstream pathway, such as DXS1, GGPS1, and GGPS2 that are known to play important roles in the accumulation of precursor isoprenoids as well as downstream genes HYD5, CCD1, and ZEP1, which are involved in hydroxylation and carotenoid degradation. SNPs at or near all of these regions were identified and may be useful target regions for carotenoid biofortification breeding efforts in maize; for example a genomic region on chromosome 2 explained ~16% of the phenotypic variance for β-carotene independently of CRTRB1, and a variant of CCD1 that resulted in reduced β-cryptoxanthin degradation was found in lines that have previously been observed to have low proVA degradation rates.

Candidate genes for hypertension: insights from the Dahl S rat

Human genetic linkage and association studies have nominated many genes as possible contributors to disease. Mutating or deleting these genes in a relevant disease model can validate their association with disease and potentially uncover novel mechanisms of pathogenesis. Targeted genetic mutagenesis has only recently been developed in the rat, and this technique has been applied in the Dahl salt-sensitive (S) rat to investigate human candidate genes associated with hypertension. This mini-review communicates the findings of these studies and displays how targeted genetic mutagenesis may contribute to the discovery of novel therapies for patients.

Distribution of the most Common Genetic Variants Associated with a Variable Drug Response in the Population of the Republic of Macedonia

Genetic variation in the regulation, expression and activity of genes coding for Phase I, Phase II drug metabolizing enzymes (DMEs) and drug targets, can be defining factors for the variability in both the effectiveness and occurrence of drug therapy side effects. Information regarding the geographic structure and multi-ethnic distribution of clinically relevant genetic variations is becoming increasingly useful for improving drug therapy and explaining inter-individual and inter-ethnic differences in drug response.

This study summarizes our current knowledge about the frequency distribution of the most common allelic variants in three broad gene categories: the Phase I oxidation-cytochrome P450 (CYP450) family (CYP2C9, CYP2C19, CYP3A5, CYP2D6); the Phase II conjugation (GSTT1, SULT1A1; UGT1A1) and drug target (TYMS-TSER, MTHFR and VKORC1) in the population of the Republic of Macedonia and compares the information obtained with data published for other indigenous European populations.

Our findings define the population of the Republic of Macedonia as an ethnic group with a highly polymorphic genetic profile. These results add to the evidence regarding the distribution of clinically important variant alleles in DME and drug target genes in populations of European ancestry.

Relative performance of gene- and pathway-level methods as secondary analyses for genome-wide association studies

BACKGROUND

Despite the success of genome-wide association studies (GWAS), there still remains "missing heritability" for many traits. One contributing factor may be the result of examining one marker at a time as opposed to a group of markers that are biologically meaningful in aggregate. To address this problem, a variety of gene- and pathway-level methods have been developed to identify putative biologically relevant associations. A simulation was conducted to systematically assess the performance of these methods. Using genetic data from 4,500 individuals in the Wellcome Trust Case Control Consortium (WTCCC), case-control status was simulated based on an additive polygenic model. We evaluated gene-level methods based on their sensitivity, specificity, and proportion of false positives. Pathway-level methods were evaluated on the relationship between proportion of causal genes within the pathway and the strength of association.

RESULTS

The gene-level methods had low sensitivity (20-63%), high specificity (89-100%), and low proportion of false positives (0.1-6%). The gene-level program VEGAS using only the top 10% of associated single nucleotide polymorphisms (SNPs) within the gene had the highest sensitivity (28.6%) with less than 1% false positives. The performance of the pathway-level methods depended on their reliance upon asymptotic distributions or if significance was estimated in a competitive manner. The pathway-level programs GenGen, GSA-SNP and MAGENTA had the best performance while accounting for potential confounders.

CONCLUSIONS

Novel genes and pathways can be identified using the gene and pathway-level methods. These methods may provide valuable insight into the "missing heritability" of traits and provide biological interpretations to GWAS findings.

Towards the identification of the loci of adaptive evolution

1. Establishing the genetic and molecular basis underlying adaptive traits is one of the major goals of evolutionary geneticists in order to understand the connection between genotype and phenotype and elucidate the mechanisms of evolutionary change. Despite considerable effort to address this question, there remain relatively few systems in which the genes shaping adaptations have been identified. 2. Here, we review the experimental tools that have been applied to document the molecular basis underlying evolution in several natural systems, in order to highlight their benefits, limitations and suitability. In most cases, a combination of DNA, RNA and functional methodologies with field experiments will be needed to uncover the genes and mechanisms shaping adaptation in nature.

Genome-wide association study (GWAS) for growth rate and age at sexual maturation in Atlantic salmon (Salmo salar)

Early sexual maturation is considered a serious drawback for Atlantic salmon aquaculture as it retards growth, increases production times and affects flesh quality. Although both growth and sexual maturation are thought to be complex processes controlled by several genetic and environmental factors, selection for these traits has been continuously accomplished since the beginning of Atlantic salmon selective breeding programs. In this genome-wide association study (GWAS) we used a 6.5K single-nucleotide polymorphism (SNP) array to genotype ∼480 individuals from the Cermaq Canada broodstock program and search for SNPs associated with growth and age at sexual maturation. Using a mixed model approach we identified markers showing a significant association with growth, grilsing (early sexual maturation) and late sexual maturation. The most significant associations were found for grilsing, with markers located in Ssa10, Ssa02, Ssa13, Ssa25 and Ssa12, and for late maturation with markers located in Ssa28, Ssa01 and Ssa21. A lower level of association was detected with growth on Ssa13. Candidate genes, which were linked to these genetic markers, were identified and some of them show a direct relationship with developmental processes, especially for those in association with sexual maturation. However, the relatively low power to detect genetic markers associated with growth (days to 5 kg) in this GWAS indicates the need to use a higher density SNP array in order to overcome the low levels of linkage disequilibrium observed in Atlantic salmon before the information can be incorporated into a selective breeding program.

Familial periodontal disease in the Cayo Santiago rhesus macaques

Substantial ongoing research continues to explore the contribution of genetics and environment to the onset, extent and severity of periodontal disease(s). Existing evidence supports that periodontal disease appears to have an increased prevalence in family units with a member having aggressive periodontitis. We have been using the nonhuman primate as a model of periodontal disease for over 25 years with these species demonstrating naturally occurring periodontal disease that increases with age. This report details our findings from evaluation of periodontal disease in skulls from 97 animals (5-31 years of age) derived from the skeletons of the rhesus monkeys (Macaca mulatta) on Cayo Santiago. Periodontal disease was evaluated by determining the distance from the base of the alveolar bone defect to the cemento-enamel junction on 1st/2nd premolars and 1st/2nd molars from all four quadrants. The results demonstrated an increasing extent and severity of periodontitis with aging across the population of animals beyond only compensatory eruption. Importantly, irrespective of age, extensive heterogeneity in disease expression was observed among the animals. Linking these variations to multi-generational matriarchal family units supported familial susceptibility of periodontitis. As the current generations of animals that are descendants from these matrilines are alive, studies can be conducted to explore an array of underlying factors that could account for susceptibility or resistance to periodontal disease.

Development, validation and genetic analysis of a large soybean SNP genotyping array

Cultivated soybean (Glycine max) suffers from a narrow germplasm relative to other crop species, probably because of under-use of wild soybean (Glycine soja) as a breeding resource. Use of a single nucleotide polymorphism (SNP) genotyping array is a promising method for dissecting cultivated and wild germplasms to identify important adaptive genes through high-density genetic mapping and genome-wide association studies. Here we describe a large soybean SNP array for use in diversity analyses, linkage mapping and genome-wide association analyses. More than four million high-quality SNPs identified from high-depth genome re-sequencing of 16 soybean accessions and low-depth genome re-sequencing of 31 soybean accessions were used to select 180,961 SNPs for creation of the Axiom(®) SoyaSNP array. Validation analysis for a set of 222 diverse soybean lines showed that 170,223 markers were of good quality for genotyping. Phylogenetic and allele frequency analyses of the validation set data indicated that accessions showing an intermediate morphology between cultivated and wild soybeans collected in Korea were natural hybrids. More than 90 unanchored scaffolds in the current soybean reference sequence were assigned to chromosomes using this array. Finally, dense average spacing and preferential distribution of the SNPs in gene-rich chromosomal regions suggest that this array may be suitable for genome-wide association studies of soybean germplasm. Taken together, these results suggest that use of this array may be a powerful method for soybean genetic analyses relating to many aspects of soybean breeding.

Detection of genomic loci associated with environmental variables using generalized linear mixed models

We tested the use of Generalized Linear Mixed Models to detect associations between genetic loci and environmental variables, taking into account the population structure of sampled individuals. We used a simulation approach to generate datasets under demographically and selectively explicit models. These datasets were used to analyze and optimize GLMM capacity to detect the association between markers and selective coefficients as environmental data in terms of false and true positive rates. Different sampling strategies were tested, maximizing the number of populations sampled, sites sampled per population, or individuals sampled per site, and the effect of different selective intensities on the efficiency of the method was determined. Finally, we apply these models to an Arabidopsis thaliana SNP dataset from different accessions, looking for loci associated with spring minimal temperature. We identified 25 regions that exhibit unusual correlations with the climatic variable and contain genes with functions related to temperature stress.

Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies

Background

Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this “missing heritability” problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network. It was applied here to both simulated and real data sets.

Results

Simulation studies indicated that the iBVS method was advantageous in its performance with highest AUC in both variable selection and outcome prediction, when compared to Stepwise and LASSO based strategies. In an analysis of a leprosy case–control study, iBVS selected 94 SNPs as predictors, while LASSO selected 100 SNPs. The Stepwise regression yielded a more parsimonious model with only 3 SNPs. The prediction results demonstrated that the iBVS method had comparable performance with that of LASSO, but better than Stepwise strategies.

Conclusions

The proposed iBVS strategy is a novel and valid method for Genome-wide Association Studies, with the additional advantage in that it produces more interpretable posterior probabilities for each variable unlike LASSO and other penalized regression methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-014-0130-7) contains supplementary material, which is available to authorized users.

Genome-wide mapping in a house mouse hybrid zone reveals hybrid sterility loci and Dobzhansky-Muller interactions

Mapping hybrid defects in contact zones between incipient species can identify genomic regions contributing to reproductive isolation and reveal genetic mechanisms of speciation. The house mouse features a rare combination of sophisticated genetic tools and natural hybrid zones between subspecies. Male hybrids often show reduced fertility, a common reproductive barrier between incipient species. Laboratory crosses have identified sterility loci, but each encompasses hundreds of genes. We map genetic determinants of testis weight and testis gene expression using offspring of mice captured in a hybrid zone between M. musculus musculus and M. m. domesticus. Many generations of admixture enables high-resolution mapping of loci contributing to these sterility-related phenotypes. We identify complex interactions among sterility loci, suggesting multiple, non-independent genetic incompatibilities contribute to barriers to gene flow in the hybrid zone.

Joint GWAS Analysis: Comparing similar GWAS at different genomic resolutions identifies novel pathway associations with six complex diseases

We show here that combining two existing genome wide association studies (GWAS) yields additional biologically relevant information, beyond that obtained by either GWAS separately. We propose Joint GWAS Analysis, a method that compares a pair of GWAS for similarity among the top SNP associations, top genes identified, gene functional clusters, and top biological pathways. We show that Joint GWAS Analysis identifies additional enriched biological pathways that would be missed by traditional Single-GWAS analysis. Furthermore, we examine the similarities of six complex genetic disorders at the SNP-level, gene-level, gene-cluster-level, and pathway-level. We make concrete hypotheses regarding novel pathway associations for several complex disorders considered, based on the results of Joint GWAS Analysis. Together, these results demonstrate that common complex disorders share substantially more genomic architecture than has been previously realized and that the meta-analysis of GWAS needs not be limited to GWAS of the same phenotype to be informative.

Heritability of liver enzyme levels estimated from genome-wide SNP data

Variation in the liver enzyme levels in humans is moderately heritable, as indicated by twin-family studies. At present, genome-wide association studies have traced <2% of the variance back to genome-wide significant single-nucleotide polymorphisms (SNPs). We estimated the SNP-based heritability of levels of three liver enzymes (gamma-glutamyl transferase (GGT); alanine aminotransferase (ALT); and aspartate aminotransferase (AST)) using genome-wide SNP data in a sample of 5421 unrelated Dutch individuals. Two estimation methods for SNP-based heritability were compared, one based on the distant genetic relatedness among all subjects as summarized in a Genetic Relatedness Matrix (GRM), and the other one based on density estimation (DE). The DE method was also applied to meta-analysis results on GGT and ALT. GRM-derived SNP-based heritability estimates were significant for GGT (16%) and AST (11%), but not for ALT (6%). DE estimates in the same sample varied as a function of pruning and were around 23% for all liver enzymes. Application of the DE approach to meta-analysis results for GGT and ALT gave SNP-based heritability estimates of 6 and 3%. The significant results in the Dutch sample indicate that genome-wide SNP platforms contain substantial information regarding the underlying genetic variation in the liver enzyme levels. A major part of this genetic variation remains however undetected. SNP-based heritability estimates, based on meta-analysis results, may point at substantial heterogeneity among cohorts contributing to the meta-analysis. This type of analysis may provide useful information to guide future gene searches.

QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies

Background

The identification of genetic markers associated with complex traits that are expensive to record such as feed intake or feed efficiency would allow these traits to be included in selection programs. To identify large-effect QTL, we performed a series of genome-wide association studies and functional analyses using 50 K and 770 K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations (Cycle VII, Angus, Hereford and Simmental × Angus) with phenotypes for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake.

Results

A total of 5, 6, 11 and 10 significant QTL (defined as 1-Mb genome windows with Bonferroni-corrected P-value <0.05) were identified for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake, respectively. The identified QTL were population-specific and had little overlap across the 4 populations. The pleiotropic or closely linked QTL on BTA 7 at 23 Mb identified in the Angus population harbours a promising candidate gene ACSL6 (acyl-CoA synthetase long-chain family member 6), and was the largest effect QTL associated with dry matter intake and mid-test body weight explaining 10.39% and 14.25% of the additive genetic variance, respectively. Pleiotropic or closely linked QTL associated with average daily gain and mid-test body weight were detected on BTA 6 at 38 Mb and BTA 7 at 93 Mb confirming previous reports. No QTL for residual feed intake explained more than 2.5% of the additive genetic variance in any population. Marker-based estimates of heritability ranged from 0.21 to 0.49 for residual feed intake across the 4 populations.

Conclusions

This GWAS study, which is the largest performed for feed efficiency and its component traits in beef cattle to date, identified several large-effect QTL that cumulatively explained a significant percentage of additive genetic variance within each population. Differences in the QTL identified among the different populations may be due to differences in power to detect QTL, environmental variation, or differences in the genetic architecture of trait variation among breeds. These results enhance our understanding of the biology of growth, feed intake and utilisation in beef cattle.

Comparing partial least square approaches in a gene- or region-based association study for multiple quantitative phenotypes

On thinking quantitatively of complex diseases, there are at least three statistical strategies for association studies: one single-nucleotide polymorphism (SNP) on a single trait, gene or region (with multiple SNPs) on a single trait, and gene or region on multiple traits. The third approach is the most general in dissecting genetic mechanisms underlying complex diseases underpinning multiple quantitative traits. Gene or region association methods based on partial least square (PLS) approaches have been shown to have apparent power advantage. However, few approaches have been developed for multiple quantitative phenotypes or traits underlying a condition or disease, and the performance of various PLS approaches used in association studies for multiple quantitative traits have not been assessed. Here we exploit association between multiple SNPs and multiple phenotypes or traits, from a regression perspective, through exhaustive scan statistics (sliding window) using PLS and sparse PLS regressions. Simulations were conducted to assess the performance of the proposed scan statistics and compare them with existing methods. The proposed methods were applied to 12 regions of genome-wide association study data from the European Prospective Investigation of Cancer-Norfolk study.

Statistical power for identifying nucleotide markers associated with quantitative traits in genome-wide association analysis using a mixed model

Use of mixed models is in the spotlight as an emerging method for genome-wide association studies (GWASs). This study investigated the statistical power for identifying nucleotide variants associated with quantitative traits using the mixed model methodology. Quantitative traits were simulated through design of heritability, the number of causal variants (NCV), the number of polygenic variants, and genetic variance ratio of causal to polygenic variants (VRCTP). Statistical power estimates were influenced not only by individual factors of heritability, NCV, and VRCTP, but also by their interactions (P < 0.05). As the genetic variance ratio decreased, the difference in power between heritabilities of 0.3 and 0.5 increased with the use of 20 causal variants, but decreased when there were 100 causal variants (P < 0.05). The power empirically estimated from the simulation study would be applicable to the design of GWAS for quantitative traits with known genetic parameters by predicting the degree of false negative associations.

GSTA1*-69C/T and GSTO2*N142D as asthma- and allergy-related risk factors in Italian adult patients

1. Asthma and allergies are characterized by variable and subjective symptoms influenced by many genes, molecular mechanisms and environmental factors. The presence of inflammation and oxidative stress in the airways are important biochemical features of asthma and respiratory allergies. Glutathione S-transferase (GSTs) enzymes play an important role in cellular protection against inflammation, and functional genetic polymorphisms in GST genes show a significant association with asthma and allergy risk. Specifically, our previous study on asthmatic children highlighted GSTA1 and GSTO2 as novel susceptibility loci for asthma. 2. In the present study we focused our attention on GSTA1*-69C/T (rs3957357) and GSTO2*N142D (rs156697) polymorphisms to confirm our previous results in an independent adult study population and to clarify whether GSTA1 and GSTO2 gene polymorphisms are involved in a non-discriminative pathway towards asthma and respiratory allergy. 3. To accomplish this, we recruited 103 patients with respiratory allergies, 199 patients with asthma and 200 healthy controls. Genomic DNA extracted from buccal cells was screened for GSTA1*-69C/T and GSTO2*N142D single nucleotide polymorphisms. 4. The GSTA1*-69T and GSTO2*D142 variants are both associated with a significantly increased risk of asthma, whereas only GSTA1*-69C/T is significantly associated with allergies. These outcomes confirm the involvement of GSTO2 loci in asthma and suggest that GSTA1 is a common risk factor for asthma and allergies.

Unbiased approaches to biomarker discovery in neurodegenerative diseases

Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and frontotemporal dementia have several important features in common. They are progressive, they affect a relatively inaccessible organ, and we have no disease-modifying therapies for them. For these brain-based diseases, current diagnosis and evaluation of disease severity rely almost entirely on clinical examination, which may be only a rough approximation of disease state. Thus, the development of biomarkers-objective, relatively easily measured, and precise indicators of pathogenic processes-could improve patient care and accelerate therapeutic discovery. Yet existing, rigorously tested neurodegenerative disease biomarkers are few, and even fewer biomarkers have translated into clinical use. To find new biomarkers for these diseases, an unbiased, high-throughput screening approach may be needed. In this review, I will describe the potential utility of such an approach to biomarker discovery, using Parkinson's disease as a case example.

Case-only exome sequencing and complex disease susceptibility gene discovery: study design considerations

Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.

Using genomics to characterize evolutionary potential for conservation of wild populations

Genomics promises exciting advances towards the important conservation goal of maximizing evolutionary potential, notwithstanding associated challenges. Here, we explore some of the complexity of adaptation genetics and discuss the strengths and limitations of genomics as a tool for characterizing evolutionary potential in the context of conservation management. Many traits are polygenic and can be strongly influenced by minor differences in regulatory networks and by epigenetic variation not visible in DNA sequence. Much of this critical complexity is difficult to detect using methods commonly used to identify adaptive variation, and this needs appropriate consideration when planning genomic screens, and when basing management decisions on genomic data. When the genomic basis of adaptation and future threats are well understood, it may be appropriate to focus management on particular adaptive traits. For more typical conservations scenarios, we argue that screening genome-wide variation should be a sensible approach that may provide a generalized measure of evolutionary potential that accounts for the contributions of small-effect loci and cryptic variation and is robust to uncertainty about future change and required adaptive response(s). The best conservation outcomes should be achieved when genomic estimates of evolutionary potential are used within an adaptive management framework.

Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations

Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.

Genome-wide association study identifies three novel genetic markers associated with elite endurance performance

To investigate the association between multiple single-nucleotide polymorphisms (SNPs), aerobic performance and elite endurance athlete status in Russians. By using GWAS approach, we examined the association between 1,140,419 SNPs and relative maximal oxygen consumption rate (V.O₂max) in 80 international-level Russian endurance athletes (46 males and 34 females). To validate obtained results, we further performed case-control studies by comparing the frequencies of the most significant SNPs (with P < 10⁻⁵-10⁻⁸) between 218 endurance athletes and opposite cohorts (192 Russian controls, 1367 European controls, and 230 Russian power athletes). Initially, six ‘endurance alleles’ were identified showing discrete associations with V.O₂max both in males and females. Next, case-control studies resulted in remaining three SNPs (NFIA-AS2 rs1572312, TSHR rs7144481, RBFOX1 rs7191721) associated with endurance athlete status. The C allele of the most significant SNP, rs1572312, was associated with high values of V.O₂max (males: P = 0.0051; females: P = 0.0005). Furthermore, the frequency of the rs1572312 C allele was significantly higher in elite endurance athletes (95.5%) in comparison with non-elite endurance athletes (89.8%, P = 0.0257), Russian (88.8%, P = 0.007) and European (90.6%, P = 0.0197) controls and power athletes (86.2%, P = 0.0005). The rs1572312 SNP is located on the nuclear factor I A antisense RNA 2 (NFIA-AS2) gene which is supposed to regulate the expression of the NFIA gene (encodes transcription factor involved in activation of erythropoiesis and repression of the granulopoiesis). Our data show that the NFIA-AS2 rs1572312, TSHR rs7144481 and RBFOX1 rs7191721 polymorphisms are associated with aerobic performance and elite endurance athlete status.

Accurate estimation of haplotype frequency from pooled sequencing data and cost-effective identification of rare haplotype carriers by overlapping pool sequencing

MOTIVATION

A variety of hypotheses have been proposed for finding the missing heritability of complex diseases in genome-wide association studies. Studies have focused on the value of haplotype to improve the power of detecting associations with disease. To facilitate haplotype-based association analysis, it is necessary to accurately estimate haplotype frequencies of pooled samples.

RESULTS

Taking advantage of databases that contain prior haplotypes, we present Ehapp based on the algorithm for solving the system of linear equations to estimate the frequencies of haplotypes from pooled sequencing data. Effects of various factors in sequencing on the performance are evaluated using simulated data. Our method could estimate the frequencies of haplotypes with only about 3% average relative difference for pooled sequencing of the mixture of 10 haplotypes with total coverage of 50×. When unknown haplotypes exist, our method maintains excellent performance for haplotypes with actual frequencies >0.05. Comparisons with present method on simulated data in conjunction with publicly available Illumina sequencing data indicate that our method is state of the art for many sequencing study designs. We also demonstrate the feasibility of applying overlapping pool sequencing to identify rare haplotype carriers cost-effectively.

AVAILABILITY AND IMPLEMENTATION

Ehapp (in Perl) for the Linux platforms is available online (http://bioinfo.seu.edu.cn/Ehapp/).

CONTACT

xsun@seu.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Targeted exon capture and sequencing in sporadic amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that results in progressive degeneration of motor neurons, ultimately leading to paralysis and death. Approximately 10% of ALS cases are familial, with the remaining 90% of cases being sporadic. Genetic studies in familial cases of ALS have been extremely informative in determining the causative mutations behind ALS, especially as the same mutations identified in familial ALS can also cause sporadic disease. However, the cause of ALS in approximately 30% of familial cases and in the majority of sporadic cases remains unknown. Sporadic ALS cases represent an underutilized resource for genetic information about ALS; therefore, we undertook a targeted sequencing approach of 169 known and candidate ALS disease genes in 242 sporadic ALS cases and 129 matched controls to try to identify novel variants linked to ALS. We found a significant enrichment in novel and rare variants in cases versus controls, indicating that we are likely identifying disease associated mutations. This study highlights the utility of next generation sequencing techniques combined with functional studies and rare variant analysis tools to provide insight into the genetic etiology of a heterogeneous sporadic disease.

Amyotrophic lateral sclerosis (ALS), also known as Charcot disease or Lou Gehrig's disease, is one of the most common neuromuscular diseases worldwide. This disease is characterized by a progressive degeneration of motor neurons, leading to patient death within a few years after onset. Despite the fact that most ALS cases are sporadic, most of the ALS genetic studies have focused on familial forms, leading to the genetic determination of cause for 70% of cases of familial ALS but for only 10% of sporadic ALS cases. This, coupled with the dearth of families available for study, suggests that researchers should begin tapping into the relatively untouched reservoir of available sporadic samples to identify novel genetic causes of sporadic ALS. Here we take advantage of high-throughput target sequencing techniques to test four different hypotheses about the genetic causes of ALS in sporadic ALS and uncover new candidate genes and pathways implicated in ALS.

Epigenome: biosensor of cumulative exposure to chemical and nonchemical stressors related to environmental justice

Understanding differential disease susceptibility requires new tools to quantify the cumulative effects of environmental stress. Evidence suggests that social, physical, and chemical stressors can influence disease through the accumulation of epigenetic modifications. Geographically stable epigenetic alterations could identify plausible mechanisms for health disparities among the disadvantaged and poor. Relations between neighborhood-specific epigenetic markers and disease would identify the most appropriate targets for medical and environmental intervention. Complex interactions among genes, the environment, and disease require the examination of how epigenetic changes regulate susceptibility to environmental stressors. Progress in understanding disparities in disease susceptibility may depend on assessing the cumulative effect of environmental stressors on genetic substrates. We highlight key concepts regarding the interface between environmental stress, epigenetics, and chronic disease.

Discovery in genetic skin disease: the impact of high throughput genetic technologies

The last decade has seen considerable advances in our understanding of the genetic basis of skin disease, as a consequence of high throughput sequencing technologies including next generation sequencing and whole exome sequencing. We have now determined the genes underlying several monogenic diseases, such as harlequin ichthyosis, Olmsted syndrome, and exfoliative ichthyosis, which have provided unique insights into the structure and function of the skin. In addition, through genome wide association studies we now have an understanding of how low penetrance variants contribute to inflammatory skin diseases such as psoriasis vulgaris and atopic dermatitis, and how they contribute to underlying pathophysiological disease processes. In this review we discuss strategies used to unravel the genes underlying both monogenic and complex trait skin diseases in the last 10 years and the implications on mechanistic studies, diagnostics, and therapeutics.

Incorporation of personal single nucleotide polymorphism (SNP) data into a national level electronic health record for disease risk assessment, part 1: an overview of requirements

BACKGROUND

Personalized medicine approaches provide opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world including Turkey.

OBJECTIVE

The objective of this study was to concentrate on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluate the performance of various predictive models for prostate cancer cases. We present our work as a miniseries containing three parts: (1) an overview of requirements, (2) the incorporation of SNP into the NHIS-T, and (3) an evaluation of SNP incorporated NHIS-T for prostate cancer.

METHODS

For the first article of this miniseries, the scientific literature is reviewed and the requirements of SNP data integration into EMRs/EHRs are extracted and presented.

RESULTS

In the literature, basic requirements of genomic-enabled EMRs/EHRs are listed as incorporating genotype data and its clinical interpretation into EMRs/EHRs, developing accurate and accessible clinicogenomic interpretation resources (knowledge bases), interpreting and reinterpreting of variant data, and immersing of clinicogenomic information into the medical decision processes. In this section, we have analyzed these requirements under the subtitles of terminology standards, interoperability standards, clinicogenomic knowledge bases, defining clinical significance, and clinicogenomic decision support.

CONCLUSIONS

In order to integrate structured genotype and phenotype data into any system, there is a need to determine data components, terminology standards, and identifiers of clinicogenomic information. Also, we need to determine interoperability standards to share information between different information systems of stakeholders, and develop decision support capability to interpret genomic variations based on the knowledge bases via different assessment approaches.

Genetic variants at the IFNL3 locus and their association with hepatitis C virus infections reveal novel insights into host-virus interactions

Human genetic variation plays a critical role in both spontaneous clearance of and response to interferon (IFN)-based therapies against hepatitis C virus (HCV) as shown by the success of recent genome-wide association studies (GWAS). Several GWAS and later validation studies have shown that single nucleotide polymorphisms (SNPs) at the IFNL3 (formerly IL28B) locus on chromosome 19 are involved in eliminating HCV in human patients. No doubt that this information is helping clinicians worldwide in making better clinical decisions in anti-HCV therapy, but the biological mechanisms involving the SNPs leading to differential responses to therapy and spontaneous clearance of HCV remain elusive. Recent reports including the discovery of a novel IFN (IFN-λ4) gene at the IFNL3 locus and in vitro functional studies implicating 2 SNPs as causal variants lead to novel conclusions and perhaps to new directions in research. An attempt is made in this review to summarize the major findings of the GWAS, the efforts involved in the discovery of causal SNPs; and to explain the biological basis for spontaneous clearance and response to treatment in HCV infections.

Laboratory evolution of adenylyl cyclase independent learning in Drosophila and missing heritability

Gene interactions are acknowledged to be a likely source of missing heritability in large-scale genetic studies of complex neurological phenotypes. However, involvement of rare variants, de novo mutations, genetic lesions that are not easily detected with commonly used methods and epigenetic factors also are possible explanations. We used a laboratory evolution study to investigate the modulatory effects of background genetic variation on the phenotypic effect size of a null mutation with known impact on olfactory learning. To accomplish this, we first established a population that contained variation at just 23 loci and used selection to evolve suppression of the learning defect seen with null mutations in the rutabaga adenylyl cyclase. We thus biased the system to favor relatively simplified outcomes by choosing a Mendelian trait and by restricting the genetic variation segregating in the population. This experimental design also assures that the causal effects are among the known 23 segregating loci. We observe a robust response to selection that requires the presence of the 23 variants. Analyses of the underlying genotypes showed that interactions between more than two loci are likely to be involved in explaining the selection response, with implications for the missing heritability problem.

The genetic basis of music ability

Music is an integral part of the cultural heritage of all known human societies, with the capacity for music perception and production present in most people. Researchers generally agree that both genetic and environmental factors contribute to the broader realization of music ability, with the degree of music aptitude varying, not only from individual to individual, but across various components of music ability within the same individual. While environmental factors influencing music development and expertise have been well investigated in the psychological and music literature, the interrogation of possible genetic influences has not progressed at the same rate. Recent advances in genetic research offer fertile ground for exploring the genetic basis of music ability. This paper begins with a brief overview of behavioral and molecular genetic approaches commonly used in human genetic analyses, and then critically reviews the key findings of genetic investigations of the components of music ability. Some promising and converging findings have emerged, with several loci on chromosome 4 implicated in singing and music perception, and certain loci on chromosome 8q implicated in absolute pitch and music perception. The gene AVPR1A on chromosome 12q has also been implicated in music perception, music memory, and music listening, whereas SLC6A4 on chromosome 17q has been associated with music memory and choir participation. Replication of these results in alternate populations and with larger samples is warranted to confirm the findings. Through increased research efforts, a clearer picture of the genetic mechanisms underpinning music ability will hopefully emerge.

The genome in three dimensions: a new frontier in human brain research

Less than 1.5% of the human genome encodes protein. However, vast portions of the human genome are subject to transcriptional and epigenetic regulation, and many noncoding regulatory DNA elements are thought to regulate the spatial organization of interphase chromosomes. For example, chromosomal "loopings" are pivotal for the orderly process of gene expression, by enabling distal regulatory enhancer or silencer elements to directly interact with proximal promoter and transcription start sites, potentially bypassing hundreds of kilobases of interspersed sequence on the linear genome. To date, however, epigenetic studies in the human brain are mostly limited to the exploration of DNA methylation and posttranslational modifications of the nucleosome core histones. In contrast, very little is known about the regulation of supranucleosomal structures. Here, we show that chromosome conformation capture, a widely used approach to study higher-order chromatin, is applicable to tissue collected postmortem, thereby informing about genome organization in the human brain. We introduce chromosome conformation capture protocols for brain and compare higher-order chromatin structures at the chromosome 6p22.2-22.1 schizophrenia and bipolar disorder susceptibility locus, and additional neurodevelopmental risk genes, (DPP10, MCPH1) in adult prefrontal cortex and various cell culture systems, including neurons derived from reprogrammed skin cells. We predict that the exploration of three-dimensional genome architectures and function will open up new frontiers in human brain research and psychiatric genetics and provide novel insights into the epigenetic risk architectures of regulatory noncoding DNA.