Experience of a multidisciplinary task force with exome sequencing for Mendelian disorders

BACKGROUND

In order to optimally integrate the use of high-throughput sequencing (HTS) as a tool in clinical diagnostics of likely monogenic disorders, we have created a multidisciplinary "Genome Clinic Task Force" at the University Hospitals of Geneva, which is composed of clinical and molecular geneticists, bioinformaticians, technicians, bioethicists, and a coordinator.

METHODS AND RESULTS

We have implemented whole exome sequencing (WES) with subsequent targeted bioinformatics analysis of gene lists for specific disorders. Clinical cases of heterogeneous Mendelian disorders that could potentially benefit from HTS are presented and discussed during the sessions of the task force. Debate concerning the interpretation of identified variants and the content of the final report constitutes a major part of the task force's work. Furthermore, issues related to bioethics, genetic counseling, quality control, and reimbursement are also addressed.

CONCLUSIONS

This multidisciplinary task force has enabled us to create a platform for regular exchanges between all involved experts in order to deal with the multiple complex issues related to HTS in clinical practice and to continuously improve the diagnostic use of HTS. In addition, this task force was instrumental to formally approve the reimbursement of HTS for molecular diagnosis of Mendelian disorders in Switzerland.

CATCHing putative causative variants in consanguineous families

Background

Consanguinity is an important risk factor for autosomal recessive (AR) disorders. Extended genomic regions identical by descent (IBD) in the offspring of consanguineous parents give rise to recessive disorders with identical (homozygous) pathogenic variants in both alleles. However, many clinical phenotypes presenting in the offspring of consanguineous couples are still of unknown etiology. Nowadays advances in High Throughput Sequencing provide an excellent opportunity to achieve a molecular diagnosis or to identify novel candidate genes.

Results

To exploit all available information from the family structure we developed CATCH, an algorithm that combines genotyped SNPs of all family members for the optimal detection of Runs Of Homozygosity (ROH) and exome sequencing data from one affected individual to identify putative causative variants in consanguineous families.

Conclusions

CATCH proved to be effective in discovering known or putative new causative variants in 43 out of 50 consanguineous families. Among them, novel variants causative of familial thrombocytopenia, sclerosis bone dysplasia and the first homozygous loss-of-function mutation in FGFR3 in human causing severe skeletal deformities, tall stature and hearing impairment were identified.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0727-5) contains supplementary material, which is available to authorized users.

EMdeCODE: a novel algorithm capable of reading words of epigenetic code to predict enhancers and retroviral integration sites and to identify H3R2me1 as a distinctive mark of coding versus non-coding genes

Existence of some extra-genetic (epigenetic) codes has been postulated since the discovery of the primary genetic code. Evident effects of histone post-translational modifications or DNA methylation over the efficiency and the regulation of DNA processes are supporting this postulation. EMdeCODE is an original algorithm that approximate the genomic distribution of given DNA features (e.g. promoter, enhancer, viral integration) by identifying relevant ChIPSeq profiles of post-translational histone marks or DNA binding proteins and combining them in a supermark. EMdeCODE kernel is essentially a two-step procedure: (i) an expectation-maximization process calculates the mixture of epigenetic factors that maximize the Sensitivity (recall) of the association with the feature under study; (ii) the approximated density is then recursively trimmed with respect to a control dataset to increase the precision by reducing the number of false positives. EMdeCODE densities improve significantly the prediction of enhancer loci and retroviral integration sites with respect to previous methods. Importantly, it can also be used to extract distinctive factors between two arbitrary conditions. Indeed EMdeCODE identifies unexpected epigenetic profiles specific for coding versus non-coding RNA, pointing towards a new role for H3R2me1 in coding regions.

Deciphering the code for retroviral integration target site selection

Upon cell invasion, retroviruses generate a DNA copy of their RNA genome and integrate retroviral cDNA within host chromosomal DNA. Integration occurs throughout the host cell genome, but target site selection is not random. Each subgroup of retrovirus is distinguished from the others by attraction to particular features on chromosomes. Despite extensive efforts to identify host factors that interact with retrovirion components or chromosome features predictive of integration, little is known about how integration sites are selected. We attempted to identify markers predictive of retroviral integration by exploiting Precision-Recall methods for extracting information from highly skewed datasets to derive robust and discriminating measures of association. ChIPSeq datasets for more than 60 factors were compared with 14 retroviral integration datasets. When compared with MLV, PERV or XMRV integration sites, strong association was observed with STAT1, acetylation of H3 and H4 at several positions, and methylation of H2AZ, H3K4, and K9. By combining peaks from ChIPSeq datasets, a supermarker was identified that localized within 2 kB of 75% of MLV proviruses and detected differences in integration preferences among different cell types. The supermarker predicted the likelihood of integration within specific chromosomal regions in a cell-type specific manner, yielding probabilities for integration into proto-oncogene LMO2 identical to experimentally determined values. The supermarker thus identifies chromosomal features highly favored for retroviral integration, provides clues to the mechanism by which retrovirus integration sites are selected, and offers a tool for predicting cell-type specific proto-oncogene activation by retroviruses.

When HIV-1, murine leukemia virus (MLV), or other retroviruses infect a cell, the virus generates a DNA copy of the viral RNA genome and ligates the cDNA within host chromosomal DNA. This integration reaction occurs at sites throughout the host cell genome, but little is known about how integration sites are selected. We attempted to identify markers predictive of retroviral integration by comparing the genome-wide binding sites for more than 60 factors with 14 retroviral integration datasets. We borrowed Precision-Recall methods from the Information Retrieval field for extracting information from highly skewed datasets such as these. For MLV and other gammaretroviruses, strong association was observed with STAT1, acetylation of H3 and H4 at several positions, and methylation of H2AZ, H3K4, and K9. We generated a supermarker by combining high scoring markers. The supermarker localized within 2 kB of 75% of MLV proviruses and predicted the likelihood of integration within specific chromosomal regions in a cell-type specific manner. This study identified chromosomal features highly favored for retroviral integration. It also provides clues to the mechanism by which retrovirus integration sites are selected, and offers a tool for predicting cell-type specific proto-oncogene activation by retroviruses.