RET mutational spectrum in Hirschsprung disease: evaluation of 601 Chinese patients

Rare (RVs) and common variants of the RET gene contribute to Hirschsprung disease (HSCR; congenital aganglionosis). While RET common variants are strongly associated with the commonest manifestation of the disease (males; short-segment aganglionosis; sporadic), rare coding sequence (CDS) variants are more frequently found in the lesser common and more severe forms of the disease (females; long/total colonic aganglionosis; familial).Here we present the screening for RVs in the RET CDS and intron/exon boundaries of 601 Chinese HSCR patients, the largest number of patients ever reported. We identified 61 different heterozygous RVs (50 novel) distributed among 100 patients (16.64%). Those include 14 silent, 29 missense, 5 nonsense, 4 frame-shifts, and one in-frame amino-acid deletion in the CDS, two splice-site deletions, 4 nucleotide substitutions and a 22-bp deletion in the intron/exon boundaries and 1 single-nucleotide substitution in the 5' untranslated region. Exonic variants were mainly clustered in RET the extracellular domain. RET RVs were more frequent among patients with the most severe phenotype (24% vs. 15% in short-HSCR). Phasing RVs with the RET HSCR-associated haplotype suggests that RVs do not underlie the undisputable association of RET common variants with HSCR. None of the variants were found in 250 Chinese controls.     

Integrative Analysis of Hereditary Nonpolyposis Colorectal Cancer: the Contribution of Allele-Specific Expression and Other Assays to Diagnostic Algorithms

The identification of germline variants predisposing to hereditary nonpolyposis colorectal cancer (HNPCC) is crucial for clinical management of carriers, but several probands remain negative for such variants or bear variants of uncertain significance (VUS). Here we describe the results of integrative molecular analyses in 132 HNPCC patients providing evidences for improved genetic testing of HNPCC with traditional or next generation methods. Patients were screened for: germline allele-specific expression (ASE), nucleotide variants, rearrangements and promoter methylation of mismatch repair (MMR) genes; germline EPCAM rearrangements; tumor microsatellite instability (MSI) and immunohistochemical (IHC) MMR protein expression. Probands negative for pathogenic variants of MMR genes were screened for germline APC and MUTYH sequence variants. Most germline defects identified were sequence variants and rearrangements of MMR genes. Remarkably, altered germline ASE of MMR genes was detected in 8/22 (36.5%) probands analyzed, including 3 cases negative at other screenings. Moreover, ASE provided evidence for the pathogenic role and guided the characterization of a VUS shared by 2 additional probands. No germline MMR gene promoter methylation was observed and only one EPCAM rearrangement was detected. In several cases, tumor IHC and MSI diverged from germline screening results. Notably, APC or biallelic MUTYH germline defects were identified in 2/19 probands negative for pathogenic variants of MMR genes. Our results show that ASE complements gDNA-based analyses in the identification of MMR defects and in the characterization of VUS affecting gene expression, increasing the number of germline alterations detected. An appreciable fraction of probands negative for MMR gene variants harbors APC or MUTYH variants. These results indicate that germline ASE analysis and screening for APC and MUTYH defects should be included in HNPCC diagnostic algorithms.     

RET and GFRA1 germline polymorphisms in medullary thyroid cancer patients

The role of RET and GFRA1 germline polymorphisms in predisposition to sporadic medullary thyroid cancer (MTC) and polymorphisms' modulation effect on clinical features of inherited and sporadic MTC were investigated. Blood samples from 67 MTC patients (22 hereditary and 45 sporadic), 3 asymptomatic mutant RET gene carriers and 178 ethnically matched healthy control individuals were tested. Screening of RET exons and portion of introns 1, 8, 10, 13, 14, 15, 16 and GFRA1 5'-UTR was performed by means of direct sequencing and PCR-RFLP. 8 polymorphic variants of RET gene (exons 11, 13, 14, 15 and introns 1, 8, 13, 14) and 4 GFRA1 polymorphisms in GFRA1 were detected. Linkage disequilibrium was found between RET variants G691S and S904S, L769L and IVS8, S836S and IVS13. In sporadic MTCs, allelic frequency of only one polymorphic RET variant, L769L, was significantly decreased versus control group. In hereditary MTCs, a significant over-representation of S836S and under-representation of S904S sequence variants were observed as compared to sporadic MTCs and controls. No co-segregation was found between individual polymorphisms and phenotype of sporadic MTC. In patients with inherited MTC whose genotype was presented with polymorphic L769L and wild-type S836S, disease onset occurred 20 years later than in individuals with polymorphic L769L and S836S or wild-type L769L (p = 0.01) suggestive of a possible protective role of L769L in MTC development and modulating effect of a combination of L769L with wild-type S836S on clinical outcome of inherited MTC.     

Cloning and characterization of the human GFRA2 locus and investigation of the gene in Hirschsprung disease

The glial-cell-line-derived neurotrophic factor (GDNF) family receptors alpha (GFRalpha) are cell surface bound glycoproteins that mediate interactions of the GDNF ligand family with the RET receptor. These interactions are crucial to the development of the kidney and some peripheral nerve lineages. In humans, mutations of RET or RET ligands are associated with the congenital abnormality Hirschsprung disease (HSCR) in which nerves and ganglia of the hind gut are absent. As the GFRalpha family are required for normal activation of the RET receptor, they are also candidates for a role in HSCR. The GFRA2 gene, which is required for the development of the myenteric nerve plexus, is an excellent candidate gene for HSCR. In this study, we cloned the human GFRA2 locus, characterized the gene structure, and compared it with other GFRA family members. We further investigated the GFRA2 gene for mutations in a panel of HSCR patients. GFRA2 has nine coding exons that are similar in size and organization to those of other GFRA family genes. We identified six sequence variants of GFRA2, four of which did not affect the amino acid sequence of the GFRalpha-2 protein. Two further changes that resulted in amino acid substitutions were found in exon 9 and were predicted to lie in the amino acid sequence encoding the glycosylphosphatidylinositol-linkage signal of GFRalpha-2. There was no difference in frequency of any of the sequence variants between control and HSCR populations. Our data indicate that members of the GFRA gene family are closely related in intron/exon structure and in sequence. We have not detected any correlation between sequence variants of GFRA2 and the HSCR phenotype.     

Single nucleotide polymorphisms in theRET gene and their correlations with Hirschsprung disease phenotype

Hirschsprung disease (HSCR) is a congenital, heterogeneous disorder, characterized by the absence of intestinal ganglion cells. Recent advances show that the RET gene is a major locus involved in the pathogenesis of HSCR. The aim of this study was to analyse if the HSCR phenotype in the Polish population is associated with the presence of polymorphisms in exons 2, 3, 7, 11, 13, 14 and 15 of the RET gene. Molecular results were compared with clinical and long-term follow-up data in 70 Polish patients with HSCR (84.3% with a short segment and 15.7% with a long segment of aganglionic gut). Single-nucleotide polymorphisms were analysed by using the minisequencing SNaPshot multiplex method. The 135G>A polymorphism in RET exon 2 was overrepresented in HSCR patients, compared with a healthy control group. Moreover, the 135G>A variant was shown to be associated with the severe HSCR phenotype. Two other polymorphisms, 2071G>A in exon 11 and 2712C>G in exon 15, were underrepresented in the patients. The results confirm that these RET polymorphisms play a role in the aetiology of HSCR.     

Pleiotropy informed adaptive association test of multiple traits using genome‐wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large‐scale genome‐wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease‐related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans‐Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the “single trait” association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well‐characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome‐phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual‐level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P‐value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta‐analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta‐analysis. All the proposed methods are implemented in a publicly available R package.     

A rare haplotype of the RET proto-oncogene is a risk-modifying allele in hirschsprung disease

Hirschsprung disease (HSCR) is a common genetic disorder characterized by intestinal obstruction secondary to enteric aganglionosis. HSCR demonstrates a complex pattern of inheritance, with the RET proto-oncogene acting as a major gene and with several additional susceptibility loci related to the Ret-signaling pathway or to other developmental programs of neural crest cells. To test how the HSCR phenotype may be affected by the presence of genetic variants, we investigated the role of a single-nucleotide polymorphism (SNP), 2508C-->T (S836S), in exon 14 of the RET gene, characterized by low frequency among patients with HSCR and overrepresentation in individuals affected by sporadic medullary thyroid carcinoma. Typing of several different markers across the RET gene demonstrated that a whole conserved haplotype displayed anomalous distribution and nonrandom segregation in families with HSCR. We provide genetic evidence about a protective role of this low-penetrant haplotype in the pathogenesis of HSCR and demonstrate a possible functional effect linked to RET messenger RNA expression.     

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning

Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.     

Genetic polymorphism of vitamin D receptor gene affects the phenotype of PCOS

Aims

Polycystic ovary syndrome (PCOS), a common female endocrine disorder, represents a wide range of clinical manifestations and disease severity. Recent studies suggest an association between gene variants involved in vitamin D metabolism and common metabolic disturbances in PCOS. We aimed to examine the association of vitamin D receptor (VDR) gene variant with PCOS susceptibility and the severity of disease phenotype.

Methods

All participants, including 260 PCOS women (cases) and 221 normoovulatory women (controls), were recruited from a reproductive endocrinology clinic. Cases were divided into the severe and mild PCOS phenotype groups, based on their clinical and paraclinical features. An adenosine to guanine single nucleotide polymorphism of VDR gene (rs757343) was genotyped using the PCR–RFLP method.

Results

Distributions of genotypes and alleles did not differ between cases and controls, indicating that this SNP is not associated with increased risk for PCOS. However, this SNP was found to be associated with the severity of the PCOS phenotype. In particular, presence of the A allele is associated with a 74% increased risk of severe phenotype development (OR, 1.74; 95% CI, 1.07–2.82).

Conclusion

The genetic variant of the VDR was found to have an association with severity of clinical features of PCOS, but none with disease risk.     

Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.     

GREEN-DB: a framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

Non-coding variants have long been recognized as important contributors to common disease risks, but with the expansion of clinical whole genome sequencing, examples of rare, high-impact non-coding variants are also accumulating. Despite recent advances in the study of regulatory elements and the availability of specialized data collections, the systematic annotation of non-coding variants from genome sequencing remains challenging. Here, we propose a new framework for the prioritization of non-coding regulatory variants that integrates information about regulatory regions with prediction scores and HPO-based prioritization. Firstly, we created a comprehensive collection of annotations for regulatory regions including a database of 2.4 million regulatory elements (GREEN-DB) annotated with controlled gene(s), tissue(s) and associated phenotype(s) where available. Secondly, we calculated a variation constraint metric and showed that constrained regulatory regions associate with disease-associated genes and essential genes from mouse knock-outs. Thirdly, we compared 19 non-coding impact prediction scores providing suggestions for variant prioritization. Finally, we developed a VCF annotation tool (GREEN-VARAN) that can integrate all these elements to annotate variants for their potential regulatory impact. In our evaluation, we show that GREEN-DB can capture previously published disease-associated non-coding variants as well as identify additional candidate disease genes in trio analyses.     

Clinical interpretation and recommendations for patients with a variant of uncertain significance in BRCA1 or BRCA2: a survey of genetic counseling practice

The intent of this study was to document current practices in breast cancer genetic counseling and identify areas of variability for patients with a variant of uncertain significance (VUS) in the BRCA1 or BRCA2 gene. Registered members of the National Society of Genetic Counselors (NSGC) Cancer Special Interest Group (SIG) were sent an invitation via electronic mail to participate in an online questionnaire. The questionnaire was divided into three sections: clinical experience, clinical meaning, and risk perceptions and clinical recommendations for clinical situations involving a VUS. Fifty-seven of the eligible members responded. During the pre-test counseling session for a BRCA risk assessment patient, the vast majority of counselors (80.7%) mention VUS as a possible test result. Nearly half, 49.1%, report having given such a result to their patients at least one to four times. However, only 63.2% felt as though their patients understood the meaning of a VUS result. When asked to conclude the implication of a VUS and make medical management recommendations, the responses were varied. Nevertheless, a good proportion of counselors expressed the importance of testing other family members to help clarify the proband's risk and aid in medical management issues. Although the recent recommendations by the American College of Medical Genetics suggest standards for the interpretation of sequence variations, they do not provide guidelines for making clinical recommendations based on these variations. The results of this study reveal significant diversity in the personal interpretation of a VUS result, leading to various clinical recommendations, and suggest a need for clinical management recommendations as well.     

Functional variants in the lipoprotein lipase gene and risk cardiovascular disease.

The current report is a quantitative review of the relationship between lipoprotein lipase gene variants and cardiovascular disease based on published population-based studies. Sixteen studies, representing 17,630 individuals, report allelic distribution for lipoprotein lipase gene variants among patients and control individuals. Patient outcomes included clinical cardiovascular disease events, documented coronary disease based on angiography, or intimal media thickening by B-mode ultrasonography. Mantel-Haenszel stratified analysis was used to compute a summary odds ratio and 95% confidence intervals for the association between rare allele in the lipoprotein lipase gene and disease status. Because of potential differing effects associated with different lipoprotein lipase variants, each lipoprotein lipase mutant allele was considered separately. The lipoprotein lipase D9N/-93G to T allele has a summary odds ratio of 2.03 (95% confidence interval 1.30-3.18), indicating a twofold increase in risk of coronary disease for carriers with this allelic variant. The summary odds ratio for the relationship of the rare lipoprotein lipase G188E variant with cardiovascular disease is 5.25 (95% confidence interval 1.54-24.29). The lipoprotein lipase N291S allele is associated with a marginal increase in cardiovascular disease (summary odds ratio 1.25, 95% confidence interval 0.99-1.60, P = 0.07). However, there is stronger evidence for a positive association in certain populations. The summary odds ratio for lipoprotein lipase S447X allele is 0.81 (95% confidence interval 0.65-1.0), which indicates a cardioprotective effect of this lipoprotein lipase gene variant. Thus, lipoprotein lipase gene variants are associated with differential susceptibility to cardiovascular disease.     

A large family with hereditary MTC: role of RET genetic analysis in differential diagnosis between MEN 2A and FMTC.

Germline mutations of the RET proto-oncogene cause three different cancer syndromes: multiple endocrine neoplasia type 2A (MEN 2A), multiple endocrine neoplasia type 2B (MEN 2B) and familial medullary thyroid carcinoma (FMTC). In the absence of biochemical and/or clinical evidence of pheochromocytoma and hyperparathyroidism, patients with MEN 2A disease display the same phenotype of FMTC disease, although prognosis and clinical management in both affected and unaffected familial members are quite different. We studied a family with hereditary MTC, whose proband was referred to us because of enlarged cervical nodes and increased calcitonin serum levels 28 years after the total thyroidectomy for MTC. Cervical node dissection was carried out and subsequently the presence of MTC metastasis was histologically confirmed. A RET genomic mutation at codon 634 (TGC-->TTC) was identified in the proband and in seven out of 19 familial members studied. Accordingly, a hereditary disease was suggested. However, the strong association of RET mutation at codon 634 with the presence of pheochromocytoma in MEN 2 disease suggested a more rigorous management in all gene carriers. Indeed, during the follow-up pheochromocytoma was subsequently identified in the proband. This finding suggests that all families with a pedigree suggestive of FMTC should be regarded at risk from MEN 2A disease, at least when a critical mutation in the RET cysteine domain is detected.     

Fine mapping of the NRG1 Hirschsprung's disease locus

The primary pathology of Hirschsprung's disease (HSCR, colon aganglionosis) is the absence of ganglia in variable lengths of the hindgut, resulting in functional obstruction. HSCR is attributed to a failure of migration of the enteric ganglion precursors along the developing gut. RET is a key regulator of the development of the enteric nervous system (ENS) and the major HSCR-causing gene. Yet the reduced penetrance of RET DNA HSCR-associated variants together with the phenotypic variability suggest the involvement of additional genes in the disease. Through a genome-wide association study, we uncovered a ～350 kb HSCR-associated region encompassing part of the neuregulin-1 gene (NRG1). To identify the causal NRG1 variants contributing to HSCR, we genotyped 243 SNPs variants on 343 ethnic Chinese HSCR patients and 359 controls. Genotype analysis coupled with imputation narrowed down the HSCR-associated region to 21 kb, with four of the most associated SNPs (rs10088313, rs10094655, rs4624987, and rs3884552) mapping to the NRG1 promoter. We investigated whether there was correlation between the genotype at the rs10088313 locus and the amount of NRG1 expressed in human gut tissues (40 patients and 21 controls) and found differences in expression as a function of genotype. We also found significant differences in NRG1 expression levels between diseased and control individuals bearing the same rs10088313 risk genotype. This indicates that the effects of NRG1 common variants are likely to depend on other alleles or epigenetic factors present in the patients and would account for the variability in the genetic predisposition to HSCR.     

Whole-genome association studies on alcoholism comparing different phenotypes using single-nucleotide polymorphisms and microsatellites

Alcoholism is a complex disease. As with other common diseases, genetic variants underlying alcoholism have been illusive, possibly due to the small effect from each individual susceptible variant, gene x environment and gene x gene interactions and complications in phenotype definition. We conducted association tests, the family-based association tests (FBAT) and the backward haplotype transmission association (BHTA), on the Collaborative Study of the Genetics of Alcoholism (COGA) data provided by Genetic Analysis Workshop (GAW) 14. Efron's local false discovery rate method was applied to control the proportion of false discoveries. For FBAT, we compared the results based on different types of genetic markers (single-nucleotide polymorphisms (SNPs) versus microsatellites) and different phenotype definitions (clinical diagnoses versus electrophysiological phenotypes). Significant association results were found only between SNPs and clinical diagnoses. In contrast, significant results were found only between microsatellites and electrophysiological phenotypes. In addition, we obtained the association results for SNPs and microsatellites using COGA diagnosis as phenotype based on BHTA. In this case, the results for SNPs and microsatellites are more consistent. Compared to FBAT, more significant markers are detected with BHTA.     

Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions

In modern genetic epidemiology studies, the association between the disease and a genomic region, such as a candidate gene, is often investigated using multiple SNPs. We propose a multilocus test of genetic association that can account for genetic effects that might be modified by variants in other genes or by environmental factors. We consider use of the venerable and parsimonious Tukey's 1-degree-of-freedom model of interaction, which is natural when individual SNPs within a gene are associated with disease through a common biological mechanism; in contrast, many standard regression models are designed as if each SNP has unique functional significance. On the basis of Tukey's model, we propose a novel but computationally simple generalized test of association that can simultaneously capture both the main effects of the variants within a genomic region and their interactions with the variants in another region or with an environmental exposure. We compared performance of our method with that of two standard tests of association, one ignoring gene-gene/gene-environment interactions and the other based on a saturated model of interactions. We demonstrate major power advantages of our method both in analysis of data from a case-control study of the association between colorectal adenoma and DNA variants in the NAT2 genomic region, which are well known to be related to a common biological phenotype, and under different models of gene-gene interactions with use of simulated data.     

Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network

Background

Polygenic diseases are usually caused by the dysfunction of multiple genes. Unravelling such disease genes is crucial to fully understand the genetic landscape of diseases on molecular level. With the advent of ‘omic’ data era, network-based methods have prominently boosted disease gene discovery. However, how to make better use of different types of data for the prediction of disease genes remains a challenge.

Results

In this study, we improved the performance of disease gene prediction by integrating the similarity of disease phenotype, biological function and network topology. First, for each phenotype, a phenotype-specific network was specially constructed by mapping phenotype similarity information of given phenotype onto the protein-protein interaction (PPI) network. Then, we developed a gene gravity-like algorithm, to score candidate genes based on not only topological similarity but also functional similarity. We tested the proposed network and algorithm by conducting leave-one-out and leave-10%-out cross validation and compared them with state-of-art algorithms. The results showed a preference to phenotype-specific network as well as gene gravity-like algorithm. At last, we tested the predicting capacity of proposed algorithms by test gene set derived from the DisGeNET database. Also, potential disease genes of three polygenic diseases, obesity, prostate cancer and lung cancer, were predicted by proposed methods. We found that the predicted disease genes are highly consistent with literature and database evidence.

Conclusions

The good performance of phenotype-specific networks indicates that phenotype similarity information has positive effect on the prediction of disease genes. The proposed gene gravity-like algorithm outperforms the algorithm of Random Walk with Restart (RWR), implicating its predicting capacity by combing topological similarity with functional similarity. Our work will give an insight to the discovery of disease genes by fusing multiple similarities of genes and diseases.