Global Genetic Networks and the Genotype-to-Phenotype Relationship.

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Going wild for functional genomics: RNA interference as a tool to study gene-behavior associations in diverse species and ecological contexts.

Identifying the genetic basis of behavior has remained a challenge for biologists. A major obstacle to this goal is the difficulty of examining gene function in an ecologically relevant context. New tools such as CRISPR/Cas9, which alter the germline of an organism, have taken center stage in functional genomics in non-model organisms. However, germline modifications of this nature cannot be ethically implemented in the wild as a part of field experiments. This impediment is more than technical. Gene function is intimately tied to the environment in which the gene is expressed, especially for behavior. Most lab-based studies fail to recapitulate an organism's ecological niche, thus most published functional genomics studies of gene-behavior relationships may provide an incomplete or even inaccurate assessment of gene function. In this review, we highlight RNA interference as an especially effective experimental method to deepen our understanding of the interplay between genes, behavior, and the environment. We highlight the utility of RNAi for researchers investigating behavioral genetics, noting unique attributes of RNAi including transience of effect and the feasibility of releasing treated animals into the wild, that make it especially useful for studying the function of behavior-related genes. Furthermore, we provide guidelines for planning and executing an RNAi experiment to study behavior, including challenges to consider. We urge behavioral ecologists and functional genomicists to adopt a more fully integrated approach which we call "ethological genomics". We advocate this approach, utilizing tools such as RNAi, to study gene-behavior relationships in their natural context, arguing that such studies can provide a deeper understanding of how genes can influence behavior, as well as ecological aspects beyond the organism that houses them.

DSP variants may be associated with longitudinal change in quantitative emphysema.

BACKGROUND: Emphysema, characterized by lung destruction, is a key component of Chronic Obstructive Pulmonary Disease (COPD) and is associated with increased morbidity and mortality. Genome-wide association studies (GWAS) have identified multiple genetic factors associated with cross-sectional measures of quantitative emphysema, but the genetic determinants of longitudinal change in quantitative measures of emphysema remain largely unknown. Our study aims to identify genetic variants associated with longitudinal change in quantitative emphysema measured by computed tomography (CT) imaging. METHODS: We included current and ex-smokers from two longitudinal cohorts: COPDGene, a study of Non-Hispanic Whites (NHW) and African Americans (AA), and the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). We calculated annual change in two quantitative measures of emphysema based on chest CT imaging: percent low attenuation area (≤ - 950HU) (%LAA-950) and adjusted lung density (ALD). We conducted GWAS, separately in 3030 NHW and 1158 AA from COPDGene and 1397 Whites from ECLIPSE. We further explored effects of 360 previously reported variants and a lung function based polygenic risk score on annual change in quantitative emphysema. RESULTS: In the genome-wide association analysis, no variants achieved genome-wide significance (P < 5e-08). However, in the candidate region analysis, rs2076295 in the DSP gene, previously associated with COPD, lung function and idiopathic pulmonary fibrosis, was associated with change in %LAA-950 (ß (SE) = 0.09 (0.02), P = 3.79e-05) and in ALD (ß (SE) = - 0.06 (0.02), P = 2.88e-03). A lung function based polygenic risk score was associated with annual change in %LAA-950 (P = 4.03e-02) and with baseline measures of quantitative emphysema (P < 1e-03) and showed a trend toward association with annual change in ALD (P = 7.31e-02). CONCLUSIONS: DSP variants may be associated with longitudinal change in quantitative emphysema. Additional investigation of the DSP gene are likely to provide further insights into the disease progression in emphysema and COPD. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT00608764 , NCT00292552 .

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs.

There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations-MASS, RAREMETAL, MetaSKAT, and seqMeta-in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.

The overdue promise of short tandem repeat variation for heritability.

Short tandem repeat (STR) variation has been proposed as a major explanatory factor in the heritability of complex traits in humans and model organisms. However, we still struggle to incorporate STR variation into genotype-phenotype maps. We review here the promise of STRs in contributing to complex trait heritability and highlight the challenges that STRs pose due to their repetitive nature. We argue that STR variants are more likely than single-nucleotide variants to have epistatic interactions, reiterate the need for targeted assays to genotype STRs accurately, and call for more appropriate statistical methods in detecting STR-phenotype associations. Lastly, we suggest that somatic STR variation within individuals may serve as a read-out of disease susceptibility, and is thus potentially a valuable covariate for future association studies.

The Rate of Change in Alcohol Misuse Across Adolescence is Heritable.

BACKGROUND: Alcohol use typically begins during adolescence and escalates into young adulthood. This represents an important period for the establishment of alcohol use and misuse patterns, which can have psychosocial and medical consequences. Although changes in alcohol use during this time have been phenotypically characterized, their genetic nature is poorly understood. METHODS: Participants of the Avon Longitudinal Study of Parents and Children completed the Alcohol Use Disorders Identification Test (AUDIT) 4 times from age 16 to 20. We used Mplus to construct a growth model characterizing changes in AUDIT scores across time (N = 4,545, where data were available for at least 2 time points). The slope of the model was used as the phenotype in a genomewide association study (N = 3,380), followed by secondary genetic analyses. RESULTS: No individual marker met genomewide significance criteria. Top markers mapped to biologically plausible candidate genes. The slope term was moderately heritable (h2SNP = 0.26, p = 0.009), and replication attempts using a meta-analysis of independent samples provided support for implicated variants at the aggregate level. Nominally significant (p < 0.00001) markers mapped to putatively active genomic regions in brain tissue more frequently than expected by chance. CONCLUSIONS: These results build on prior studies by demonstrating that common genetic variation impacts alcohol misuse trajectories. Influential loci map to genes that merit additional research, as well as to intergenic regions with regulatory functions in the central nervous system. These findings underscore the complex biological nature of alcohol misuse across development.

The future of model organisms in human disease research.

Model organisms have played a huge part in the history of studies of human genetic disease, both in identifying disease genes and characterizing their normal and abnormal functions. But is the importance of model organisms diminishing? The direct discovery of disease genes and variants in humans has been revolutionized, first by genome-wide association studies and now by whole-genome sequencing. Not only is it now much easier to directly identify potential disease genes in humans, but the genetic architecture that is being revealed in many cases is hard to replicate in model organisms. Furthermore, disease modelling can be done with increasing effectiveness using human cells. Where does this leave non-human models of disease?

Genome-wide genetic marker discovery and genotyping using next-generation sequencing.

The advent of next-generation sequencing (NGS) has revolutionized genomic and transcriptomic approaches to biology. These new sequencing tools are also valuable for the discovery, validation and assessment of genetic markers in populations. Here we review and discuss best practices for several NGS methods for genome-wide genetic marker development and genotyping that use restriction enzyme digestion of target genomes to reduce the complexity of the target. These new methods -- which include reduced-representation sequencing using reduced-representation libraries (RRLs) or complexity reduction of polymorphic sequences (CRoPS), restriction-site-associated DNA sequencing (RAD-seq) and low coverage genotyping -- are applicable to both model organisms with high-quality reference genome sequences and, excitingly, to non-model species with no existing genomic data.

New Genes Causing Hereditary Parkinson's Disease or Parkinsonism.

PURPOSE OF REVIEW: This article reviews was to review genes where putative or confirmed pathogenic mutations causing Parkinson's disease or Parkinsonism have been identified since 2012, and summarizes the clinical and pathological picture of the associated disease subtypes. RECENT FINDINGS: Newly reported genes for dominant Parkinson's disease are DNAJC13, CHCHD2, and TMEM230. However, the evidence for a disease-causing role is not conclusive, and further genetic and functional studies are warranted. RIC3 mutations have been reported from one family but not yet encountered in other patients. New genes for autosomal recessive disease include SYNJ1, DNAJC6, VPS13C, and PTRHD1. Deletions of a region on chromosome 22 (22q11.2del) are also associated with early-onset PD, but the mode of inheritance and the underlying causative gene remain unclear. PODXL mutations were reported in autosomal recessive PD, but their roles remain to be confirmed. Mutations in RAB39B cause an X-linked Parkinsonian disorder. Mutations in the new dominant PD genes have generally been found in medium- to late-onset Parkinson's disease. Many mutations in the new recessive and X-chromosomal genes cause severe atypical juvenile Parkinsonism, but less devastating mutations in these genes may cause PD.

Drosophila and Caenorhabditis elegans as Discovery Platforms for Genes Involved in Human Alcohol Use Disorder.

BACKGROUND: Despite the profound clinical significance and strong heritability of alcohol use disorder (AUD), we do not yet have a comprehensive understanding of the naturally occurring genetic variance within the human genome that drives its development. This lack of understanding is likely to be due in part to the large phenotypic and genetic heterogeneities that underlie human AUD. As a complement to genetic studies in humans, many laboratories are using the invertebrate model organisms (iMOs) Drosophila melanogaster (fruit fly) and Caenorhabditis elegans (nematode worm) to identify genetic mechanisms that influence the effects of alcohol (ethanol) on behavior. While these extremely powerful models have identified many genes that influence the behavioral responses to alcohol, in most cases it has remained unclear whether results from behavioral-genetic studies in iMOs are directly applicable to understanding the genetic basis of human AUD. METHODS: In this review, we critically evaluate the utility of the fly and worm models for identifying genes that influence AUD in humans. RESULTS: Based on results published through early 2015, studies in flies and worms have identified 91 and 50 genes, respectively, that influence 1 or more aspects of behavioral responses to alcohol. Collectively, these fly and worm genes correspond to 293 orthologous genes in humans. Intriguingly, 51 of these 293 human genes have been implicated in AUD by at least 1 study in human populations. CONCLUSIONS: Our analyses strongly suggest that the Drosophila and C. elegans models have considerable utility for identifying orthologs of genes that influence human AUD.

Genetics of Guillain-Barré syndrome (GBS) and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP): current knowledge and future directions.

Guillain-Barré syndrome (GBS) and chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) are thought to be autoimmune diseases. There have been many attempts to find a human leukocyte antigen (HLA) association with GBS and CIDP with little success. There have been studies of other plausible genes in GBS and CIDP and the role of these genes in GBS and CIDP and the data from these genetic studies is reviewed. Some of the genes that have been studied are immune related and some others have nervous system effects. The studies are limited by small numbers. Some of the genes show association with disease severity rather than disease susceptibility. The need for more detailed molecular studies of the role of HLA molecules and the need for modern genetic approaches to GBS and CIDP are explained.

Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record.

Large-scale DNA databanks linked to electronic medical record (EMR) systems have been proposed as an approach for rapidly generating large, diverse cohorts for discovery and replication of genotype-phenotype associations. However, the extent to which such resources are capable of delivering on this promise is unknown. We studied whether an EMR-linked DNA biorepository can be used to detect known genotype-phenotype associations for five diseases. Twenty-one SNPs previously implicated as common variants predisposing to atrial fibrillation, Crohn disease, multiple sclerosis, rheumatoid arthritis, or type 2 diabetes were successfully genotyped in 9483 samples accrued over 4 mo into BioVU, the Vanderbilt University Medical Center DNA biobank. Previously reported odds ratios (OR(PR)) ranged from 1.14 to 2.36. For each phenotype, natural language processing techniques and billing-code queries were used to identify cases (n = 70-698) and controls (n = 808-3818) from deidentified health records. Each of the 21 tests of association yielded point estimates in the expected direction. Previous genotype-phenotype associations were replicated (p < 0.05) in 8/14 cases when the OR(PR) was > 1.25, and in 0/7 with lower OR(PR). Statistically significant associations were detected in all analyses that were adequately powered. In each of the five diseases studied, at least one previously reported association was replicated. These data demonstrate that phenotypes representing clinical diagnoses can be extracted from EMR systems, and they support the use of DNA resources coupled to EMR systems as tools for rapid generation of large data sets required for replication of associations found in research cohorts and for discovery in genome science.

The golden era of ocular disease gene discovery: race to the finish.

Within the last decade, technological advances have led to amazing genetic insights into Mendelian and multifactorial ocular diseases. We provide a perspective of the progress in gene discovery and discuss the implications. We believe that the time has come to redefine the goals and begin utilizing the genetic knowledge for clinical management and treatment design. The unbelievable opportunities now exist for those nimble enough to seize them.

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement.

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

[Epilepsy pharmacogenetics : science or fiction?]. / Pharmacogénétique de l'épilepsie - Science ou fiction ?

Pharmacogenetics (PGX) is the study of how genetic variants influence individual responses to drugs. Although numerous candidate gene studies in epilepsy PGX have been published, to date only two validated associations exist: the association of the *2 and *3 alleles of CYP2C9 with phenytoin metabolism and the association of HLA-B*1502 with serious hypersensitivity reactions to carbamazepine. The advent of novel technologies such as genomewide association studies and next generation sequencing will likely lead to the identification of additional genetic biomarkers. The potential benefits of epilepsy PGX are multiple: epilepsy treatment in individual patients would become more rationalized, clinical trials could be stratified according to patients' genetic profiles and novel therapeutic pathways may be uncovered. Ultimately, it is hoped that PGX will improve the quality of life for people suffering from epilepsy worldwide.

[Advances on gene-based association analysis].

Genome-wide association studies (GWAS) have been widely used for hunting the susceptibility genes for common diseases in the past years; however, the abundant information for the disease mechanism based on the GWAS data has not been fully mined. Recently, some researchers focused on the biological network and pathway analysis for the GWAS data to explore the potential disease mechanism. Since genes are the basic units for the biological network and pathway, the genetic effects from all or part of single nucleotide polymorphisms (SNPs) in the genes should be integrated into genetic scores, which are so-called "gene-based association analysis". Gene-based association analysis takes into account some important factors such as genetic effects of the SNPs, the number of the SNPs in the genes and the linkage disequilibrium structure of the SNPs. In this review, we will focus on the progress, principle and application of gene-based association analysis.

From mouse to humans: discovery of the CACNG2 pain susceptibility gene.

Chronic pain is a major healthcare problem affecting the daily lives of millions with enormous financial costs. The notorious variability and lack of efficient pain relief pharmaceuticals provide both genetic and therapeutic challenge. There are several genetic approaches that aim to uncover the molecular nature of pain phenotypes into their genetic components. Gene mapping using model organisms for various pain phenotypes has led to the identification of novel genes affecting susceptibility and response to pain stimuli. Translational studies have succeeded to tie those genes to human pain syndromes, thus suggesting new targets for drug discovery. In this short review, a perspective on pain genetics and the trajectory from pain phenotype to pain gene involving fine-mapping strategies, bioinformatic analysis and microarray profiling alongside human association analysis will be introduced. This integrated approach has led to identification of CACNG2 as a novel neuropathic pain gene affecting pain susceptibility both in mice and humans. It also serves as a prototype for efficient and economic discovery of pain genes. Comparisons to other methods as well as future directions of pain genetics will be discussed as well.

Future directions in vitamin D and cardiovascular research.

AIMS: Evidence is accumulating that vitamin D status may influence the risk of cardiovascular disease (CVD). Final confirmation for a causal relationship between vitamin D and CVD is however still lacking. The present viewpoint article outlines several future research directions to close this gap. DATA SYNTHESIS: Future directions include the need of performing large randomised controlled supplementation trials with vitamin D in specific risk groups. In addition, large register sets of data on vitamin D supplementation can be used, provided that adequate statistical methods such as propensity score modelled analysis are applied. To better understand vitamin D-mediated effects on CVD risk, the routine measurement of circulating levels of the hormonal vitamin D form, 1,25-dihydroxyvitamin D, is also necessary, in addition to the determination of its precursor 25-hydroxyvitamin D. Further, genetic association studies may help in clarifying the contribution of vitamin D to the development of CVD. Finally, the interrelationship of vitamin D with physical activity should be considered when studying CVD risk. CONCLUSIONS: Overall, it can be expected that the next 10-15 years will provide an increased clarity concerning the role of vitamin D in CVD.

PON1 55 and 192 gene polymorphisms in type 2 diabetes mellitus patients in a Turkish population.

Diabetes mellitus is a multifactorial metabolic disease, caused by the complete or relative absence of insulin hormone, which results in the deterioration of carbohydrate, protein, and lipid metabolism. The PON1 55 and 192 polymorphisms have been reported to be associated with type 2 diabetes and its complications. In this study, the involvement of the PON1 55 and 192 polymorphisms and paraoxonase enzyme activity in diabetic complications was assessed. The MM and QQ genotypes were the most frequent in complications of type 2 diabetes in both of the polymorphisms. PON enzyme activity was lower in the type 2 diabetes group with respect to the control group. Regarding both genotypes and enzyme activity, correlations were found between the PON1 55 and 192 genotypes and diabetic complications. This study thus helps to outline a genotype-phenotype relation for the PON1 gene in a Turkish population.