Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease

Absence of Dystrophin Related Protein-2 disrupts Cajal bands in a patient with Charcot-Marie-Tooth disease

Using exome sequencing in an individual with Charcot-Marie-Tooth disease (CMT) we have identified a mutation in the X-linked dystrophin-related protein 2 (DRP2) gene. A 60-year-old gentleman presented to our clinic and underwent clinical, electrophysiological and skin biopsy studies. The patient had clinical features of a length dependent sensorimotor neuropathy with an age of onset of 50 years. Neurophysiology revealed prolonged latencies with intermediate conduction velocities but no conduction block or temporal dispersion. A panel of 23 disease causing genes was sequenced and ultimately was uninformative. Whole exome sequencing revealed a stop mutation in DRP2, c.805C>T (Q269*). DRP2 interacts with periaxin and dystroglycan to form the periaxin-DRP2-dystroglycan complex which plays a role in the maintenance of the well-characterized Cajal bands of myelinating Schwann cells. Skin biopsies from our patient revealed a lack of DRP2 in myelinated dermal nerves by immunofluorescence. Furthermore electron microscopy failed to identify Cajal bands in the patient's dermal myelinated axons in keeping with ultrastructural pathology seen in the Drp2 knockout mouse. Both the electrophysiologic and dermal nerve twig pathology support the interpretation that this patient's DRP2 mutation causes characteristic morphological abnormalities recapitulating the Drp2 knockout model and potentially represents a novel genetic cause of CMT.

Genetics of Inflammatory Bowel Diseases

In this review, we provide an update on genome-wide association studies (GWAS) in inflammatory bowel disease (IBD). In addition, we summarize progress in defining the functional consequences of associated alleles for coding and noncoding genetic variation. In the small minority of loci where major association signals correspond to nonsynonymous variation, we summarize studies defining their functional effects and implications for therapeutic targeting. Importantly, the large majority of GWAS-associated loci involve noncoding variation, many of which modulate levels of gene expression. Recent expression quantitative trait loci (eQTL) studies have established that the expression of most human genes is regulated by noncoding genetic variations. Significant advances in defining the epigenetic landscape have demonstrated that IBD GWAS signals are highly enriched within cell-specific active enhancer marks. Studies in European ancestry populations have dominated the landscape of IBD genetics studies, but increasingly, studies in Asian and African-American populations are being reported. Common variation accounts for only a modest fraction of the predicted heritability and the role of rare genetic variation of higher effects (ie, odds ratios markedly deviating from 1) is increasingly being identified through sequencing efforts. These sequencing studies have been particularly productive in more severe very early onset cases. A major challenge in IBD genetics will be harnessing the vast array of genetic discovery for clinical utility through emerging precision medical initiatives. In this article, we discuss the rapidly evolving area of direct-to-consumer genetic testing and the current utility of clinical exome sequencing, especially in very early onset, severe IBD cases. We summarize recent progress in the pharmacogenetics of IBD with respect to partitioning patient responses to anti-TNF and thiopurine therapies. Highly collaborative studies across research centers and across subspecialties and disciplines will be required to fully realize the promise of genetic discovery in IBD.

Identification of mutations in Korean patients with amyotrophic lateral sclerosis using multigene panel testing

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease involving motor neurons. Because a growing number of genes have been identified as the genetic etiology of ALS, simultaneous screening of mutations in multiple genes is likely to be more efficient than gene-by-gene testing. In this study, we performed a multigene panel testing by using targeted capture of 18 ALS-related genes followed by next-generation sequencing. Using this technique, we tried to identify mutations in 4 index patients with familial ALS and 148 sporadic ALS in Korean population and identified 4 known mutations in SOD1, ALS2, MAPT, and SQSTM1 genes, respectively, and 28 variants of uncertain significance in 9 genes. Among the 28 variants of uncertain significance, 6 missense variants were found in highly conserved residues and were consistently predicted to be deleterious by in silico analyses. These results suggest that multigene panel testing is an effective approach for mutation screening in ALS-related genes. Moreover, the relatively low frequency of mutations in known ALS genes implies marked genetic heterogeneity at least in Korean patients with ALS.

Regulatory variant in FZD6 gene contributes to nonsyndromic cleft lip and palate in an African-American family

Nonsyndromic cleft lip with or without cleft palate (NSCLP) is a common birth defect affecting 135,000 newborns worldwide each year. While a multifactorial etiology has been suggested as the cause, despite decades of research, the genetic underpinnings of NSCLP remain largely unexplained. In our previous genome-wide linkage study of a large NSCLP African-American family, we identified a candidate locus at 8q21.3-24.12 (LOD = 2.98). This region contained four genes, Frizzled-6 (FZD6), Matrilin-2 (MATN2), Odd-skipped related 2 (OSR2) and Solute Carrier Family 25, Member 32 (SLC25A32). FZD6 was located under the maximum linkage peak. In this study, we sequenced the coding and noncoding regions of these genes in two affected family members, and identified a rare variant in intron 1 of FZD6 (rs138557689; c.-153 + 432A>C). The variant C allele segregated with NSCLP in this family, through affected and unaffected individuals, and was found in one other NSCLP African-American family. Functional assays showed that this allele creates an allele-specific protein-binding site and decreases promoter activity. We also observed that loss and gain of fzd6 in zebrafish contributes to craniofacial anomalies. FZD6 regulates the WNT signaling pathway, which is involved in craniofacial development, including midfacial formation and upper labial fusion. We hypothesize, therefore, that alteration in FZD6 expression contributes to NSCLP in this family by perturbing the WNT signaling pathway.

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward.

Early evolutionary history and genomic features of gene duplicates in the human genome

BACKGROUND

Human gene duplicates have been the focus of intense research since the development of array-based and targeted next-generation sequencing approaches in the last decade. These studies have primarily concentrated on determining the extant copy-number variation from a population-genomic perspective but lack a robust evolutionary framework to elucidate the early structural and genomic characteristics of gene duplicates at emergence and their subsequent evolution with increasing age.

RESULTS

We analyzed 184 gene duplicate pairs comprising small gene families in the draft human genome with 10% or less synonymous sequence divergence. Human gene duplicates primarily originate from DNA-mediated events, taking up genomic residence as intrachromosomal copies in direct or inverse orientation. The distribution of paralogs on autosomes follows random expectations in contrast to their significant enrichment on the sex chromosomes. Furthermore, human gene duplicates exhibit a skewed gradient of distribution along the chromosomal length with significant clustering in pericentromeric regions. Surprisingly, despite the large average length of human genes, the majority of extant duplicates (83%) are complete duplicates, wherein the entire ORF of the ancestral copy was duplicated. The preponderance of complete duplicates is in accord with an extremely large median duplication span of 36 kb, which enhances the probability of capturing ancestral ORFs in their entirety. With increasing evolutionary age, human paralogs exhibit declines in (i) the frequency of intrachromosomal paralogs, and (ii) the proportion of complete duplicates. These changes may reflect lower survival rates of certain classes of duplicates and/or the role of purifying selection. Duplications arising from RNA-mediated events comprise a small fraction (11.4%) of all human paralogs and are more numerous in older evolutionary cohorts of duplicates.

CONCLUSIONS

The degree of structural resemblance, genomic location and duplication span appear to influence the long-term maintenance of paralogs in the human genome. The median duplication span in the human genome far exceeds that in C. elegans and yeast and likely contributes to the high prevalence of complete duplicates relative to structurally heterogeneous duplicates (partial and chimeric). The relative roles of regulatory sequence versus exon-intron structure changes in the acquisition of novel function by human paralogs remains to be determined.

Aetiological coding sequence variants in non-syndromic premature ovarian failure: From genetic linkage analysis to next generation sequencing

Premature ovarian failure (POF) is a frequent pathology affecting 1-1.5% of women under 40 years old. Despite advances in diagnosing and treating human infertility, POF is still classified as being idiopathic in 50-80% of cases, strongly suggesting a genetic origin for the disease. Different types of autosomal and X-linked genetic anomalies can originate the phenotype in syndromic and non-syndromic POF cases. Particular interest has been focused on research into non-syndromic POF causative coding variants during the past two decades. This has been based on the assumption that amino acid substitutions might modify the intrinsic physicochemical properties of functional proteins, thereby inducing pathological phenotypes. In this case, a restricted number of mutations might originate the disease. However, like other complex pathologies, POF might result from synergistic/compensatory effects caused by several low-to-mildly drastic mutations which have frequently been classified as non-functional SNPs. Indeed, reproductive phenotypes can be considered as quantitative traits resulting from the subtle interaction of many genes. Although numerous sequencing projects have involved candidate genes, only a few coding mutations explaining a low percentage of cases have been described. Such apparent failure to identify aetiological coding sequence variations might have been due to the inherent molecular complexity of mammalian reproduction and to the difficulty of simultaneously analysing large genomic regions by Sanger sequencing. The purpose of this review is to present the molecular and cellular effects caused by non-synonymous mutations which have been formally associated, by functional tests, with the aetiology of hypergonadotropic non-syndromic POF. Considerations have also been included regarding the polygenic nature of reproduction and POF, as well as future approaches for identifying novel aetiological genes based on next generation sequencing (NGS).

ExScalibur: A High-Performance Cloud-Enabled Suite for Whole Exome Germline and Somatic Mutation Identification

Whole exome sequencing has facilitated the discovery of causal genetic variants associated with human diseases at deep coverage and low cost. In particular, the detection of somatic mutations from tumor/normal pairs has provided insights into the cancer genome. Although there is an abundance of publicly-available software for the detection of germline and somatic variants, concordance is generally limited among variant callers and alignment algorithms. Successful integration of variants detected by multiple methods requires in-depth knowledge of the software, access to high-performance computing resources, and advanced programming techniques. We present ExScalibur, a set of fully automated, highly scalable and modulated pipelines for whole exome data analysis. The suite integrates multiple alignment and variant calling algorithms for the accurate detection of germline and somatic mutations with close to 99% sensitivity and specificity. ExScalibur implements streamlined execution of analytical modules, real-time monitoring of pipeline progress, robust handling of errors and intuitive documentation that allows for increased reproducibility and sharing of results and workflows. It runs on local computers, high-performance computing clusters and cloud environments. In addition, we provide a data analysis report utility to facilitate visualization of the results that offers interactive exploration of quality control files, read alignment and variant calls, assisting downstream customization of potential disease-causing mutations. ExScalibur is open-source and is also available as a public image on Amazon cloud.

ProSim: A Method for Prioritizing Disease Genes Based on Protein Proximity and Disease Similarity

Predicting disease genes for a particular genetic disease is very challenging in bioinformatics. Based on current research studies, this challenge can be tackled via network-based approaches. Furthermore, it has been highlighted that it is necessary to consider disease similarity along with the protein's proximity to disease genes in a protein-protein interaction (PPI) network in order to improve the accuracy of disease gene prioritization. In this study we propose a new algorithm called proximity disease similarity algorithm (ProSim), which takes both of the aforementioned properties into consideration, to prioritize disease genes. To illustrate the proposed algorithm, we have conducted six case studies, namely, prostate cancer, Alzheimer's disease, diabetes mellitus type 2, breast cancer, colorectal cancer, and lung cancer. We employed leave-one-out cross validation, mean enrichment, tenfold cross validation, and ROC curves to evaluate our proposed method and other existing methods. The results show that our proposed method outperforms existing methods such as PRINCE, RWR, and DADA.

Genetic variation within transcriptional regulatory elements and its implications for human disease

Common human pathologies have a complicated etiology involving both genetic and environmental risk factors. Moreover, the genetic basis of these disorders is also complex, with multiple and weak genetic variants contributing to disease susceptibility. In addition, most of these risk genetic variants occur outside genes, within the vast non-coding human genomic space. In this review I first illustrate how large-scale genomic studies aimed at mapping cis-regulatory elements in the human genome are facilitating the identification of disease-causative non-coding genetic variation. I then discuss some of the challenges that remain to be solved before the pathological consequences of non-coding genetic variation can be fully appreciated. Ultimately, revealing the genetics of human complex disease can be a critical step towards more personalized and effective diagnosis and treatments.

The mathematical limits of genetic prediction for complex chronic disease

BACKGROUND

Attempts at predicting individual risk of disease based on common germline genetic variation have largely been disappointing. The present paper formalises why genetic prediction at the individual level is and will continue to have limited utility given the aetiological architecture of most common complex diseases.

METHODS

Data were simulated on one million populations with 10 000 individuals in each populations with varying prevalences of a genetic risk factor, an interacting environmental factor and the background rate of disease. The determinant risk ratio and risk difference magnitude for the association between a gene variant and disease is a function of the prevalence of the interacting factors that activate the gene, and the background rate of disease.

RESULTS

The risk ratio and total excess cases due to the genetic factor increase as the prevalence of interacting factors increase, and decrease as the background rate of disease increases. Germline genetic variations have high predictive capacity for individual disease only under conditions of high heritability of particular genetic sequences, plausible only under rare variant hypotheses.

CONCLUSIONS

Under a model of common germline genetic variants that interact with other genes and/or environmental factors in order to cause disease, the predictive capacity of common genetic variants is determined by the prevalence of the factors that interact with the variant and the background rate. A focus on estimating genetic associations for the purpose of prediction without explicitly grounding such work in an understanding of modifiable (including environmentally influenced) factors will be limited in its ability to yield important insights about the risk of disease.

Scoring the correlation of genes by their shared properties using OScal, an improved overlap quantification model

Scoring the correlation between two genes by their shared properties is a common and basic work in biological study. A prospective way to score this correlation is to quantify the overlap between the two sets of homogeneous properties of the two genes. However the proper model has not been decided, here we focused on studying the quantification of overlap and proposed a more effective model after theoretically compared 7 existing models. We defined three characteristic parameters (d, R, r) of an overlap, which highlight essential differences among the 7 models and grouped them into two classes. Then the pros and cons of the two groups of model were fully examined by their solution space in the (d, R, r) coordinate system. Finally we proposed a new model called OScal (Overlap Score calculator), which was modified on Poisson distribution (one of 7 models) to avoid its disadvantages. Tested in assessing gene relation using different data, OScal performs better than existing models. In addition, OScal is a basic mathematic model, with very low computation cost and few restrictive conditions, so it can be used in a wide-range of research areas to measure the overlap or similarity of two entities.

Searching High-Order SNP Combinations for Complex Diseases Based on Energy Distribution Difference

Single nucleotide polymorphisms, a dominant type of genetic variants, have been used successfully to identify defective genes causing human single gene diseases. However, most common human diseases are complex diseases and caused by gene-gene and gene-environment interactions. Many SNP-SNP interaction analysis methods have been introduced but they are not powerful enough to discover interactions more than three SNPs. The paper proposes a novel method that analyzes all SNPs simultaneously. Different from existing methods, the method regards an individual's genotype data on a list of SNPs as a point with a unit of energy in a multi-dimensional space, and tries to find a new coordinate system where the energy distribution difference between cases and controls reaches the maximum. The method will find different multiple SNPs combinatorial patterns between cases and controls based on the new coordinate system. The experiment on simulated data shows that the method is efficient. The tests on the real data of age-related macular degeneration (AMD) disease show that it can find out more significant multi-SNP combinatorial patterns than existing methods.

Non-syndromic hearing loss gene identification: A brief history and glimpse into the future

From the first identified non-syndromic hearing loss gene in 1995, to those discovered in present day, the field of human genetics has witnessed an unparalleled revolution that includes the completion of the Human Genome Project in 2003 to the $1000 genome in 2014. This review highlights the classical and cutting-edge strategies for non-syndromic hearing loss gene identification that have been used throughout the twenty year history with a special emphasis on how the innovative breakthroughs in next generation sequencing technology have forever changed candidate gene approaches. The simplified approach afforded by next generation sequencing technology provides a second chance for the many linked loci in large and well characterized families that have been identified by linkage analysis but have presently failed to identify a causative gene. It also discusses some complexities that may restrict eventual candidate gene discovery and calls for novel approaches to answer some of the questions that make this simple Mendelian disorder so intriguing.

Genetic Determinants of Epigenetic Patterns: Providing Insight into Disease

The field of epigenetics and our understanding of the mechanisms that regulate the establishment, maintenance and heritability of epigenetic patterns continue to grow at a remarkable rate. This information is providing increased understanding of the role of epigenetic changes in disease, insight into the underlying causes of these epigenetic changes and revealing new avenues for therapeutic intervention. Epigenetic modifiers are increasingly being pursued as therapeutic targets in a range of diseases, with a number of agents targeting epigenetic modifications already proving effective in diseases such as cancer. Although it is well established that DNA mutations and aberrant expression of epigenetic modifiers play a key role in disease, attention is now turning to the interplay between genetic and epigenetic factors in complex disease etiology. The role of genetic variability in determining epigenetic profiles, which can then be modified by environmental and stochastic factors, is becoming more apparent. Understanding the interplay between genetic and epigenetic factors is likely to aid in identifying individuals most likely to benefit from epigenetic therapies. This goal is coming closer to realization because of continual advances in laboratory and statistical tools enabling improvements in the integration of genomic, epigenomic and phenotypic data.

Lessons learned from whole exome sequencing in multiplex families affected by a complex genetic disorder, intracranial aneurysm

Genetic risk factors for intracranial aneurysm (IA) are not yet fully understood. Genomewide association studies have been successful at identifying common variants; however, the role of rare variation in IA susceptibility has not been fully explored. In this study, we report the use of whole exome sequencing (WES) in seven densely-affected families (45 individuals) recruited as part of the Familial Intracranial Aneurysm study. WES variants were prioritized by functional prediction, frequency, predicted pathogenicity, and segregation within families. Using these criteria, 68 variants in 68 genes were prioritized across the seven families. Of the genes that were expressed in IA tissue, one gene (TMEM132B) was differentially expressed in aneurysmal samples (n=44) as compared to control samples (n=16) (false discovery rate adjusted p-value=0.023). We demonstrate that sequencing of densely affected families permits exploration of the role of rare variants in a relatively common disease such as IA, although there are important study design considerations for applying sequencing to complex disorders. In this study, we explore methods of WES variant prioritization, including the incorporation of unaffected individuals, multipoint linkage analysis, biological pathway information, and transcriptome profiling. Further studies are needed to validate and characterize the set of variants and genes identified in this study.

Nano-enabled bioanalytical approaches to ultrasensitive detection of low abundance single nucleotide polymorphisms

A survey of the recent, significant developments on nanomaterials enabled ultrasensitive DNA and gene mutation assays is presented.

Single nucleotide polymorphisms (SNPs) constitute the most common types of genetic variations in the human genome. A number of SNPs have been linked to the development of life threatening diseases including cancer, cardiovascular diseases and neurodegenerative diseases. The ability for ultrasensitive and accurate detection of low abundant disease-related SNPs in bodily fluids (e.g. blood, serum, etc.) holds a significant value in the development of non-invasive future biodiagnostic tools. Over the past two decades, nanomaterials have been utilized in a myriad of biosensing applications due to their ability of detecting extremely low quantities of biologically important biomarkers with high sensitivity and accuracy. Of particular interest is the application of such technologies in the detection of SNPs. The use of various nanomaterials, coupled with different powerful signal amplification strategies, has paved the way for a new generation of ultrasensitive SNP biodiagnostic assays. Over the past few years, several ultrasensitive SNP biosensors capable of detecting specific targets down to the ultra-low regimes (ca. aM and below) and therefore holding great promises for early clinical diagnosis of diseases have been developed. This mini review will highlight some of the most recent, significant advances in nanomaterial-based ultrasensitive SNP sensing technologies capable of detecting specific targets on the attomolar (10^–18 M) regime or below. In particular, the design of novel, powerful signal amplification strategies that hold the key to the ultrasensitivity is highlighted.

Pinpointing disease genes through phenomic and genomic data fusion

Background

Pinpointing genes involved in inherited human diseases remains a great challenge in the post-genomics era. Although approaches have been proposed either based on the guilt-by-association principle or making use of disease phenotype similarities, the low coverage of both diseases and genes in existing methods has been preventing the scan of causative genes for a significant proportion of diseases at the whole-genome level.

Results

To overcome this limitation, we proposed a rigorous statistical method called pgFusion to prioritize candidate genes by integrating one type of disease phenotype similarity derived from the Unified Medical Language System (UMLS) and seven types of gene functional similarities calculated from gene expression, gene ontology, pathway membership, protein sequence, protein domain, protein-protein interaction and regulation pattern, respectively. Our method covered a total of 7,719 diseases and 20,327 genes, achieving the highest coverage thus far for both diseases and genes. We performed leave-one-out cross-validation experiments to demonstrate the superior performance of our method and applied it to a real exome sequencing dataset of epileptic encephalopathies, showing the capability of this approach in finding causative genes for complex diseases. We further provided the standalone software and online services of pgFusion at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgfusion.

Conclusions

pgFusion not only provided an effective way for prioritizing candidate genes, but also demonstrated feasible solutions to two fundamental questions in the analysis of big genomic data: the comparability of heterogeneous data and the integration of multiple types of data. Applications of this method in exome or whole genome sequencing studies would accelerate the finding of causative genes for human diseases. Other research fields in genomics could also benefit from the incorporation of our data fusion methodology.

Evolutionary Diagnosis of non-synonymous variants involved in differential drug response

Background

Many pharmaceutical drugs are known to be ineffective or have negative side effects in a substantial proportion of patients. Genomic advances are revealing that some non-synonymous single nucleotide variants (nsSNVs) may cause differences in drug efficacy and side effects. Therefore, it is desirable to evaluate nsSNVs of interest in their ability to modulate the drug response.

Results

We found that the available data on the link between drug response and nsSNV is rather modest. There were only 31 distinct drug response-altering (DR-altering) and 43 distinct drug response-neutral (DR-neutral) nsSNVs in the whole Pharmacogenomics Knowledge Base (PharmGKB). However, even with this modest dataset, it was clear that existing bioinformatics tools have difficulties in correctly predicting the known DR-altering and DR-neutral nsSNVs. They exhibited an overall accuracy of less than 50%, which was not better than random diagnosis. We found that the underlying problem is the markedly different evolutionary properties between positions harboring nsSNVs linked to drug responses and those observed for inherited diseases. To solve this problem, we developed a new diagnosis method, Drug-EvoD, which was trained on the evolutionary properties of nsSNVs associated with drug responses in a sparse learning framework. Drug-EvoD achieves a TPR of 84% and a TNR of 53%, with a balanced accuracy of 69%, which improves upon other methods significantly.

Conclusions

The new tool will enable researchers to computationally identify nsSNVs that may affect drug responses. However, much larger training and testing datasets are needed to develop more reliable and accurate tools.

Building Integrated Ontological Knowledge Structures with Efficient Approximation Algorithms

The integration of ontologies builds knowledge structures which brings new understanding on existing terminologies and their associations. With the steady increase in the number of ontologies, automatic integration of ontologies is preferable over manual solutions in many applications. However, available works on ontology integration are largely heuristic without guarantees on the quality of the integration results. In this work, we focus on the integration of ontologies with hierarchical structures. We identified optimal structures in this problem and proposed optimal and efficient approximation algorithms for integrating a pair of ontologies. Furthermore, we extend the basic problem to address the integration of a large number of ontologies, and correspondingly we proposed an efficient approximation algorithm for integrating multiple ontologies. The empirical study on both real ontologies and synthetic data demonstrates the effectiveness of our proposed approaches. In addition, the results of integration between gene ontology and National Drug File Reference Terminology suggest that our method provides a novel way to perform association studies between biomedical terms.

Autosomal dominant lateral temporal epilepsy (ADLTE): novel structural and single-nucleotide LGI1 mutations in families with predominant visual auras

PURPOSE

Autosomal dominant lateral temporal epilepsy (ADLTE) is a genetic focal epilepsy syndrome characterized by prominent auditory or aphasic symptoms. Mutations in LGI1 account for less than 50% of ADLTE families. We assessed the impact of LGI1 microrearrangements in a collection of ADLTE families and sporadic lateral temporal epilepsy (LTE) patients, and investigated novel ADLTE and LTE patients.

METHODS

Twenty-four ADLTE families and 140 sporadic LTE patients with no evidence of point mutations in LGI1 were screened for copy number alterations using multiplex ligation-dependent probe amplification (MLPA). Newly ascertained familial and sporadic LTE patients were clinically investigated, and interictal EEG and MRI findings were obtained; probands were tested for LGI1 mutations by direct exon sequencing or denaturing high performance liquid chromatography.

RESULTS

We identified a novel microdeletion spanning LGI1 exon 2 in a family with two affected members, both presenting focal seizures with visual symptoms. Also, we identified a novel LGI1 missense mutation (c.1118T > C; p.L373S) in a newly ascertained family with focal seizures with prominent visual auras, and another missense mutation (c.856T > C; p.C286R) in a sporadic patient with auditory seizures.

CONCLUSIONS

We describe two novel ADLTE families with predominant visual auras segregating pathogenic LGI1 mutations. These findings support the notion that, in addition to auditory symptoms, other types of auras can be found in patients carrying LGI1 mutations. The identification of a novel microdeletion in LGI1, the second so far identified, suggests that LGI1 microrearrangements may not be exceptional.

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.

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

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

Host genetic studies in adult pulmonary tuberculosis

Early observations, candidate gene studies and, more recently, genome-wide association studies have shown that susceptibility to tuberculosis has a host genetic component. Because the value of candidate gene studies has been doubted due to major limitations such as lack of sufficient power and small study groups, lack of reproducibility in independent groups and, often, ambiguous or even contrasting results in attempts of replication, much hope and expectancy has been put on the progress the genome-wide association approach has created. However, much less than initially expected became clear by the results obtained in genome-wide studies, emphasizing the need of increasing sample sizes, e.g. through meta-analyses, and of increasing the density of genetic variants studied across the human genome. A further reason why a rather low number of associated genetic variants were identified to date in infectious diseases in general and tuberculosis in particular might be the fact that selection acts strongly in diseases that affect the reproductive success. As in most genome-wide association studies performed so far, significant signals, often most likely surrogate marker only, have been found in non-coding regions of genomes, the identification of truly causative genetic variation and of the functionality of associated factors needs urgent attention. In the following we briefly discuss genetic studies in tuberculosis and describe new technologies that are currently employed in the search for responsible genetic elements involved in tuberculosis susceptibility.

Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing

The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, pre-processing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.

The humankind genome: from genetic diversity to the origin of human diseases

Genome-wide association studies have failed to establish common variant risk for the majority of common human diseases. The underlying reasons for this failure are explained by recent studies of resequencing and comparison of over 1200 human genomes and 10 000 exomes, together with the delineation of DNA methylation patterns (epigenome) and full characterization of coding and noncoding RNAs (transcriptome) being transcribed. These studies have provided the most comprehensive catalogues of functional elements and genetic variants that are now available for global integrative analysis and experimental validation in prospective cohort studies. With these datasets, researchers will have unparalleled opportunities for the alignment, mining, and testing of hypotheses for the roles of specific genetic variants, including copy number variations, single nucleotide polymorphisms, and indels as the cause of specific phenotypes and diseases. Through the use of next-generation sequencing technologies for genotyping and standardized ontological annotation to systematically analyze the effects of genomic variation on humans and model organism phenotypes, we will be able to find candidate genes and new clues for disease's etiology and treatment. This article describes essential concepts in genetics and genomic technologies as well as the emerging computational framework to comprehensively search websites and platforms available for the analysis and interpretation of genomic data.

A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness

The relationship between genotype mutations and phenotype variations determines health in the short term and evolution over the long term, and it hinges on the action of mutations on fitness. A fundamental difficulty in determining this action, however, is that it depends on the unique context of each mutation, which is complex and often cryptic. As a result, the effect of most genome variations on molecular function and overall fitness remains unknown and stands apart from population genetics theories linking fitness effect to polymorphism frequency. Here, we hypothesize that evolution is a continuous and differentiable physical process coupling genotype to phenotype. This leads to a formal equation for the action of coding mutations on fitness that can be interpreted as a product of the evolutionary importance of the mutated site with the difference in amino acid similarity. Approximations for these terms are readily computable from phylogenetic sequence analysis, and we show mutational, clinical, and population genetic evidence that this action equation predicts the effect of point mutations in vivo and in vitro in diverse proteins, correlates disease-causing gene mutations with morbidity, and determines the frequency of human coding polymorphisms, respectively. Thus, elementary calculus and phylogenetics can be integrated into a perturbation analysis of the evolutionary relationship between genotype and phenotype that quantitatively links point mutations to function and fitness and that opens a new analytic framework for equations of biology. In practice, this work explicitly bridges molecular evolution with population genetics with applications from protein redesign to the clinical assessment of human genetic variations.

A hidden Markov model for haplotype inference for present-absent data of clustered genes using identified haplotypes and haplotype patterns

The majority of killer cell immunoglobin-like receptor (KIR) genes are detected as either present or absent using locus-specific genotyping technology. Ambiguity arises from the presence of a specific KIR gene since the exact copy number (one or two) of that gene is unknown. Therefore, haplotype inference for these genes is becoming more challenging due to such large portion of missing information. Meantime, many haplotypes and partial haplotype patterns have been previously identified due to tight linkage disequilibrium (LD) among these clustered genes thus can be incorporated to facilitate haplotype inference. In this paper, we developed a hidden Markov model (HMM) based method that can incorporate identified haplotypes or partial haplotype patterns for haplotype inference from present-absent data of clustered genes (e.g., KIR genes). We compared its performance with an expectation maximization (EM) based method previously developed in terms of haplotype assignments and haplotype frequency estimation through extensive simulations for KIR genes. The simulation results showed that the new HMM based method outperformed the previous method when some incorrect haplotypes were included as identified haplotypes and/or the standard deviation of haplotype frequencies were small. We also compared the performance of our method with two methods that do not use previously identified haplotypes and haplotype patterns, including an EM based method, HPALORE, and a HMM based method, MaCH. Our simulation results showed that the incorporation of identified haplotypes and partial haplotype patterns can improve accuracy for haplotype inference. The new software package HaploHMM is available and can be downloaded at http://www.soph.uab.edu/ssg/files/People/KZhang/HaploHMM/haplohmm-index.html.

Genotype phenotype associations across the voltage-gated sodium channel family

Mutations in genes encoding voltage-gated sodium channels have emerged as the most clinically relevant genes associated with epilepsy, cardiac conduction defects, skeletal muscle channelopathies and peripheral pain disorders. Geneticists in partnership with neurologists and cardiologists are often asked to comment on the clinical significance of specific mutations. We have reviewed the evidence relating to genotype phenotype associations among the best known voltage-gated sodium channel related disorders. Comparing over 1300 sodium channel mutations in central and peripheral nervous system, heart and muscle, we have identified many similarities in the genetic and clinical characteristics across the voltage-gated sodium channel family. There is evidence, that the level of impairment a specific mutation causes can be anticipated by the underlying physico-chemical property change of that mutation. Across missense mutations those with higher Grantham scores are associated with more severe phenotypes and truncating mutations underlie the most severe phenotypes. Missense mutations are clustered in specific areas and are associated with distinct phenotypes according to their position in the protein. Inherited mutations tend to be less severe than de novo mutations which are usually associated with greater physico-chemical difference. These findings should lead to a better understanding of the clinical significance of specific voltage-gated sodium channel mutations, aiding geneticists and physicians in the interpretation of genetic variants and counselling individuals and their families.

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

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

A Comprehensive In Silico Analysis of the Functional and Structural Impact of Nonsynonymous SNPs in the ABCA1 Transporter Gene

Disease phenotypes and defects in function can be traced to nonsynonymous single nucleotide polymorphisms (nsSNPs), which are important indicators of action sites and effective potential therapeutic approaches. Identification of deleterious nsSNPs is crucial to characterize the genetic basis of diseases, assess individual susceptibility to disease, determinate molecular and therapeutic targets, and predict clinical phenotypes. In this study using PolyPhen2 and MutPred in silico algorithms, we analyzed the genetic variations that can alter the expression and function of the ABCA1 gene that causes the allelic disorders familial hypoalphalipoproteinemia and Tangier disease. Predictions were validated with published results from in vitro, in vivo, and human studies. Out of a total of 233 nsSNPs, 80 (34.33%) were found deleterious by both methods. Among these 80 deleterious nsSNPs found, 29 (12.44%) rare variants resulted highly deleterious with a probability >0.8. We have observed that mostly variants with verified functional effect in experimental studies are correctly predicted as damage variants by MutPred and PolyPhen2 tools. Still, the controversial results of experimental approaches correspond to nsSNPs predicted as neutral by both methods, or contradictory predictions are obtained for them. A total of seventeen nsSNPs were predicted as deleterious by PolyPhen2, which resulted neutral by MutPred. Otherwise, forty two nsSNPs were predicted as deleterious by MutPred, which resulted neutral by PolyPhen2.

Single nucleotide polymorphisms other than factor V Leiden are associated with coagulopathy and osteonecrosis of the femoral head in Chinese patients

Single nucleotide polymorphisms (SNPs) of factor V Leiden have been associated with osteonecrosis of the femoral head (ONFH) in Caucasians but remains controversial in Asians. We used an SNP microarray to screen 55 loci of factor V gene in patients with ONFH of Chinese. Significantly different candidate SNPs at 14 loci were analyzed in 146 patients and 116 healthy controls using MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry and gene sequencing. The factor V Leiden (rs6025) was not found in all participants. Six SNP loci (rs9332595, rs6020, rs9332647, rs3766110, rs10919186, and rs12040141) were confirmed with significant differences in patients but not in controls. The rs6020 G-to-A polymorphism was found in 88.9% of the patients. In addition, a high percentage (87.6%) of the patients had an abnormal coagulation profile that included hyperfibrinogen, elevated fibrinogen degradation products, elevated D-dimer, abnormal protein S, abnormal protein C, or a decrease in anti-thrombin III. Patients with the rs6020 G-to-A polymorphism (mutation) had a higher risk (odds ratio: 4.62; 95% confidence interval: 1.44-14.8) of having coagulation abnormalities than did those without the mutation (wild-type) (χ(2) p  =  0.006). Our findings suggested that the rs6020 polymorphism might be the genetic trait that accounts for the higher prevalence of ONFH in the Chinese population than in Westerners. Exposure to risk factors such as alcohol and steroids in patients with the rs6020 polymorphism causes coagulation abnormalities and, subsequently, thromboembolisms in the femoral head.

Gene hunting in the genomic era: approaches to diagnostic dilemmas in patients with primary immunodeficiencies

There are more than 180 different genetic causes of primary immunodeficiencies identified to date. Approaches for identifying causative mutations can be broadly classified into 3 strategies: (1) educated guesses based on known signaling pathways essential for immune cell development and function, (2) similarity of clinical phenotypes to mouse models, and (3) unbiased genetic approaches. Next-generation DNA sequencing permits efficient sequencing of whole genomes or exomes but also requires strategies for filtering vast amounts of data. Recent studies have identified ways to solve difficult cases, such as diseases with autosomal dominant inheritance, incomplete penetrance, or mutations in noncoding regions. This review focuses on recently identified primary immunodeficiencies to illustrate the strategies, technologies, and potential pitfalls in finding novel causes of these diseases.

Jumping on the Train of Personalized Medicine: A Primer for Non-Geneticist Clinicians: Part 2. Fundamental Concepts in Genetic Epidemiology

With the decrease in sequencing costs, personalized genome sequencing will eventually become common in medical practice. We therefore write this series of three reviews to help non-geneticist clinicians to jump into the fast-moving field of personalized medicine. In the first article of this series, we reviewed the fundamental concepts in molecular genetics. In this second article, we cover the key concepts and methods in genetic epidemiology including the classification of genetic disorders, study designs and their implementation, genetic marker selection, genotyping and sequencing technologies, gene identification strategies, data analyses and data interpretation. This review will help the reader critically appraise a genetic association study. In the next article, we will discuss the clinical applications of genetic epidemiology in the personalized medicine area.

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

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

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

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

Contact: peter.robinson@charite.de

Linkage analysis and the study of Mendelian disease in the era of whole exome and genome sequencing

Whole exome and whole genome sequencing are now routinely used in the study of inherited disease, and some of their major successes have been the identification of genes involved in disease predisposition in pedigrees where disease seems to follow Mendelian inheritance patterns. These successes include scenarios where only a single individual was sequenced and raise the question whether linkage analysis has become superfluous. Linkage analysis requires genome-wide genotyping on family-based data, and traditionally the linkage analysis was performed before the targeting sequencing stage. However, methods are emerging that seek to exploit the capability of linkage analysis to integrate data both across individuals and across pedigrees. This ability has been exploited to select samples used for sequencing studies and to identify among the variants uncovered by sequencing those mapping to regions likely to contain the gene of interest and, more generally, to improve variant detection. So, although the formal isolated linkage analysis stage is less commonly seen, when uncovering the genetic basis of Mendelian disease, methods relying heavily on genetic linkage analysis principles are being integrated directly into the whole mapping process ranging from sample selection to variant calling and filtering.

Whole genome and exome sequencing of monozygotic twins discordant for Crohn's disease

Background

Crohn’s disease (CD) is an inflammatory bowel disease caused by genetic and environmental factors. More than 160 susceptibility loci have been identified for IBD, yet a large part of the genetic variance remains unexplained. Recent studies have demonstrated genetic differences between monozygotic twins, who were long thought to be genetically completely identical.

Results

We aimed to test if somatic mutations play a role in CD etiology by sequencing the genomes and exomes of directly affected tissue from the bowel and blood samples of one and the blood-derived exomes of two further monozygotic discordant twin pairs. Our goal was the identification of mutations present only in the affected twins, pointing to novel candidates for CD susceptibility loci. We present a thorough genetic characterization of the sequenced individuals but detected no consistent differences within the twin pairs. An estimate of the CD susceptibility based on known CD loci however hinted at a higher mutational load in all three twin pairs compared to 1,920 healthy individuals.

Conclusion

Somatic mosaicism does not seem to play a role in the discordance of monozygotic CD twins. Our study constitutes the first to perform whole genome sequencing for CD twins and therefore provides a valuable reference dataset for future studies. We present an example framework for mosaicism detection and point to the challenges in these types of analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-564) contains supplementary material, which is available to authorized users.

Population genetics for target identification

In this review, we address how to best use data from the Human Genome Project to discover new drug targets for common disease. We focus on population genetic approaches to identify variants associated with disease and how these can illuminate new targets and pathways for intervention. We discuss new insights into patterns of human genetic variation, evolving strategies for genome-wide case-control design, and developments in bioinformatic technologies. Hypothesis versus non-hypothesis-driven approaches to target identification are considered.:

Visualization of haplotype sharing patterns in pedigree samples

OBJECTIVES

A particular approach to the visualization of descent of founder DNA copies in a pedigree has been suggested, which helps to understand haplotype sharing patterns among subjects of interest. However, the approach does not provide the information in an ideal format to show haplotype sharing patterns. Therefore, we aimed to find an efficient way to visualize such sharing patterns and to demonstrate that our tool provides useful information for finding an informative subset of subjects for a sequence study.

METHODS

The visualization package, SharedHap, computes and visualizes a novel metric, the SharedHap proportion, which quantifies haplotype sharing among a set of subjects of interest. We applied SharedHap to simulated and real pedigree datasets to illustrate the approach.

RESULTS

SharedHap successfully represents haplotype sharing patterns that contribute to linkage signals in both simulated and real datasets. Using the visualizations we were also able to find ideal sets of subjects for sequencing studies.

CONCLUSIONS

Our novel metric that can be computed using the SharedHap package provides useful information about haplotype sharing patterns among subjects of interest. The visualization of the SharedHap proportion provides useful information in pedigree studies, allowing for a better selection of candidate subjects for use in further sequencing studies.

Human symptoms-disease network

In the post-genomic era, the elucidation of the relationship between the molecular origins of diseases and their resulting phenotypes is a crucial task for medical research. Here, we use a large-scale biomedical literature database to construct a symptom-based human disease network and investigate the connection between clinical manifestations of diseases and their underlying molecular interactions. We find that the symptom-based similarity of two diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interact. Moreover, the diversity of the clinical manifestations of a disease can be related to the connectivity patterns of the underlying protein interaction network. The comprehensive, high-quality map of disease-symptom relations can further be used as a resource helping to address important questions in the field of systems medicine, for example, the identification of unexpected associations between diseases, disease etiology research or drug design.

Down-regulation of pancreatic and duodenal homeobox-1 by somatostatin receptor subtype 5: a novel mechanism for inhibition of cellular proliferation and insulin secretion by somatostatin

Somatostatin (SST) is a regulatory peptide and acts as an endogenous inhibitory regulator of the secretory and proliferative responses of target cells. SST’s actions are mediated by a family of seven transmembrane domain G protein-coupled receptors that comprise five distinct subtypes (SSTR1-5). SSTR5 is one of the major SSTRs in the islets of Langerhans. Homeodomain-containing transcription factor pancreatic and duodenal homeobox-1 (PDX-1) is essential for pancreatic development, β cell differentiation, maintenance of normal β cell functions in adults and tumorigenesis. Recent studies show that SSTR5 acts as a negative regulator for PDX-1 expression and that SSTR5 mediates somatostatin’s inhibitory effect on cell proliferation and insulin expression/excretion through down-regulating PDX-1 expression. SSTR5 exerts its inhibitory effect on PDX-1 expression at both the transcriptional level by down-regulating PDX-1 mRNA and the post-translational level by enhancing PDX-1 ubiquitination. Identification of PDX-1 as a transcriptional target for SSTR5 may help in guiding the choice of therapeutic cancer treatments.

The development of genome-wide association studies and their application to complex diseases, including lupus

In this review, we explain the motivation for carrying out genome-wide association studies (GWAS), contrasting the achievements of linkage-based experiments for Mendelian traits with the difficulties found when applying that type of experiment to complex diseases. We explain the technical and organizational developments that were required to make GWAS feasible, as well as some of the theoretical concerns that were raised during the design of these studies. We describe the impressive achievements of GWAS in lupus, and compare them with the experiences in three other genetically complex disorders: rheumatoid arthritis, type 1 diabetes and coronary heart disease. GWAS have been successful in identifying many new susceptibility loci for these four diseases, and have provided the motivation for novel immunological work. We conclude by describing preliminary steps that have been taken towards translating the results of GWAS into improvements in patient care, explaining some of the difficulties involved, as well as successes that have already been achieved.

Genomics meets proteomics: identifying the culprits in disease

Genome-wide association studies (GWAS) revealed genomic risk loci that potentially have an impact on disease and phenotypic traits. This extensive resource holds great promise in providing novel directions for personalized medicine, including disease risk prediction, prevention and targeted medication. One of the major challenges that researchers face on the path between the initial identification of an association and precision treatment of patients is the comprehension of the biological mechanisms that underlie these associations. Currently, the focus to solve these questions lies on the integrative analysis of system-wide data on global genome variation, gene expression, transcription factor binding, epigenetic profiles and chromatin conformation. The generation of this data mainly relies on next-generation sequencing. However, due to multiple recent developments, mass spectrometry-based proteomics now offers additional, by the GWAS field so far hardly recognized possibilities for the identification of functional genome variants and, in particular, for the identification and characterization of (differentially) bound protein complexes as well as physiological target genes. In this review, we introduce these proteomics advances and suggest how they might be integrated in post-GWAS workflows. We argue that the combination of highly complementary techniques is powerful and can provide an unbiased, detailed picture of GWAS loci and their mechanistic involvement in disease.