Unravelling the genetic basis of Mayer-Rokitansky- Küster-Hauser syndrome through whole exome sequencing

Transcription

1 RESEARCH PROJECTS 2014 Unravelling the genetic basis of Mayer-Rokitansky- Küster-Hauser syndrome through whole exome sequencing Dr Antigone Dimas, Postdoctoral Research Fellow, BSRC Al. Fleming Dr Klelia Salpea, Affiliated Postdoctoral Research Fellow, BSRC Al. Fleming Dr Stavros Glentis Postdoctoral Research Fellow, BSRC Al. Fleming Dr Alexandros Dimopoulos Postdoctoral Research Fellow, BSRC Al. Fleming December 2014

2 SUMMARY Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is a congenital disorder in which the Mu llerian ducts fail to develop, leading to aplasia of the uterus and vagina. MRKH syndrome affects 1/4,500 female live births, results in infertility, and may be accompanied by severe malformations of the urinary tract and spine. Evidence for a genetic basis of this disorder has accumulated, but variants linked to MRKH syndrome remain elusive. In our study we applied whole exome sequencing (WES) to 44 individuals from 25 families with an MRKH individual. WES is a powerful strategy to investigate the protein-coding fraction of the genome which represents a highly enriched subset in which to search for variants with large effect size. Following a carefully designed analytical strategy that maximized: a) informative samples, b) data quality, and c) data analysis, we present an indicative list (based on eight individuals) of shortlisted variants that fulfill criteria rendering them likely drivers of the MRKH phenotype. Highlighted variants reside in genes with a function in development, cell adhesion, sexual reproduction and transport. These findings bring us a step closer to understanding the biological pathways guiding development of the female reproductive system, but also to improved genetic counselling for MRKH patients and family members. REPORT Project aim The aim of this project was to address the genetic basis of MRKH syndrome through comprehensive investigation of the protein-coding fraction of the genome (WES) in families with an affected member. Generation and analysis of these type of data for a substantial number of individuals also drive longer term aims, that are out of the scope of this specific project. Briefly, one of the future directions involves a better understanding of the biological pathways that guide normal development of the female reproductive system. Detecting causal genetic variants and genes that result in developmental failure of the Mu llerian ducts and to congenital aplasia of the uterus and of the vagina, will shed light on biological mechanisms controlling normal development of the female reproductive system. Similarly, in cases of MRKH syndrome accompanied by further symptoms (e.g. renal aplasia), detecting causal variants will most likely build on our understanding of the development of vital organs (e.g. kidneys). Additionally, we will be able to explore the genetic aetiology underlying phenotypic variation in MRKH syndrome. Given that this disorder can exhibit extensive variability, pinning down causal genetic factors will serve as a starting point to link specific phenotypic manifestations to defined genetic causes. Finally, studies of this type also aim to enable better medical support of MRKH patients and family members. Pinpointing tangible genetic causes for MRKH syndrome is likely to aid patients in their psychological adjustment and future decisions in treatment (e.g. creation of neovagina), as well as in management of sterility. Furthermore, expected results from this and similar projects also include increased value of genetic counselling for phenotypically unaffected relatives. Project team Team members belong to the Functional Genomics Lab at BSRC Al. Fleming and are: Dr Antigone Dimas, Postdoctoral Research Fellow, BSRC Al. Fleming Dr Klelia Salpea, Affiliated Postdoctoral Research Fellow, BSRC Al. Fleming 2

3 Dr Stavros Glentis, Postdoctoral Research Fellow, BSRC Al. Fleming Dr Alexandros Dimopoulos, Postdoctoral Research Fellow, BSRC Al. Fleming This project was conducted in close collaboration with Professor George Chrousos (Chairman, Center for Adolescent Medicine and UNESCO Chair for Adolescent Health Care, First Department of Pediatrics, Athens University Medical School, Aghia Sophia Children's Hospital), Professor Efthimios Deligeoroglou (Head, Division of Pediatric-Adolescent Gynecology and Reconstructive Surgery, Second Department of Obstetrics and Gynecology, Athens University Medical School, Aretaieion Hospital) and Dr Flora Bacopoulou (Research Associate, Center for Adolescent Medicine and UNESCO Chair Adolescent Health Care, First Department of Pediatrics, Athens University Medical School, Aghia Sophia Children's Hospital). From the Genome Quebec side, we collaborated closely with Dr Ragoussis (McGill University and Genome Quebec Innovation Center). Methods Sample collection The initial sample of MRKH families contained data and biological material (blood) from over 30 families with an MRKH-affected member. Samples however had not been collected explicitly for the purposes of WES, or for the purposes of this study. Given that the results of WES depend largely on the careful and directed selection of samples and on precise phenotyping (description of symptoms in case and relatives, family history), we decided to dedicate additional time and effort to maximise sample collection and description. We therefore compiled a detailed questionnaire and re-contacted families to obtain a more complete family history (including phenotype information for second degree relatives), but also to investigate whether further, highly informative samples could be obtained (mostly from distant family members, e.g. aunts/uncles who were willing to give blood and fill in our questionnaire). As a result we obtained extensive phenotypic and family history data for over 25 families, tailored to the purposes of this study and we also collected five additional, highly informative samples. These samples include biological material (blood and tissue) obtained from an individual who underwent reconstructive surgery. Matched samples of this type are difficult to obtain, but result in powerful comparisons of germline (blood) vs. somatic (tissue) DNA. Sample selection for exome sequencing Following collection of biological material and descriptive information, we were faced with the critical step of WES study design. Given the increased sample size that we were able to process i.e. total of 44 (resulting from the use of the illumina platform, but also from a successful grant submitted [to Fondation Santé] by Dr Dimas prior to the Latsis Foundation results), we had the opportunity to build on our proposed WES strategy. Following extensive literature review, but also discussion medical doctors and groups specialising on WES, we decided to pursue both familybased WES, but also WES of unrelated cases, including cases with diverse ancestry. Our study design was such, that the amount of information generated through WES would be maximised. The key points of our strategy include: - Inclusion in study of multiple unrelated cases, of diverse geographic background. Our WES sample contains individuals from all over Greece (including the Peloponnese, the Ionian islands, North-eastern Greece, Thessaloniki, the Cyclades, Central Greece, Evoia), a case and her unaffected 3

4 sister from the Philippines, two cases from Cyprus, and two cases from Albania. - Inclusion of informative relatives either as: parent-child trios, affected vs. unaffected sisters, or distant relatives with a partial pheontype (e.g. paternal aunt with polydactyly). - Inclusion of rare tissue sample to use for comparison with variants detected in blood from the same individual (i.e. to compare somatic vs. germline variants) - Inclusion of two duplicate samples to assess capture, sequencing and analytical replication. The 44 samples selected as part of the enhanced sample collection and brought forward to WES (using funds from both the Latsis Foundation and the Fondation Santé grants) derive from 25 families and comprise: 25 unrelated cases (one case sequenced in duplicate), three mothers, two fathers (making up two complete family trios), one distant aunt (sequenced in duplicate), and nine unaffected sisters (one of whom is a monozygotic twin, discordant for MRKH syndrome, but who bears a partial phenotype including skeletal and renal system defects). For one of the cases, a matching tissue sample was obtained (two pieces of tissue were processed and sequenced). Sample preparation DNA extraction Genomic DNA was extracted from 400 µl of thawed blood samples using the PureLink Genomic DNA Mini Kit (Invitrogen ) and from 25mg of tissue sample using the NucleoSpin Tisuue kit (Macherey-Nagel ). DNA quality and quantity was assessed using the NanoDrop ND-100 spectrophotometer (Thermo Fisher Scientific ) and through agarose gel electrophoresis (Figure 1). Extracted DNA was sent to Genome Quebec for WES. Figure 1. Agarose gel showing high quality genomic DNA (upper band) extracted from 10 samples. Not all samples discussed here are shown, but all samples that were processed displayed a similar profile. Sample 134 is DNA extracted from tissue and is at a lower concentration. 4

5 Exome sequencing All 44 samples passed rigorous QC performed at Genome Quebec. Exon capture was performed using the NimbleGen SeqCap EZ Human Exome Library v3.0, which captures protein-coding and mirna genes and covers a target region of over 64 MB. Paired-end, 100 base pair (bp) sequencing was performed on the illumina HiSeq 2000 platform, using the TrueSeq DNA protocol with 36 samples run at 1/3 of sequencing lane per sample (giving an approximate average coverage of over 100x) and eight samples run at 1/4 of sequencing lane per sample (giving an approximate coverage of 80x). Sequencing data were made available as BAM files (aligned against hg19 using BWA-mem). Data analysis Data were processed and analysed using our in-house analytical pipeline that was developed in the course of 2014 in parallel to sample collection and processing. Aligned reads were sorted using SortSam (Picard tools v1.109) duplicates were flagged through MarkDuplicates (Picard tools v1.109). IndelRealigner (GATK v3.2-2 [1]) was subsequently run for local realignment of mapped, sorted and markedduplicate reads in order to correct for misalignments arising from the presence of indels. Additional recalibration of base quality scores was performed using GATK BaseRecalibrator. Data analysed included 50 bp padding upstream and downstream of the target region. The recalibrated output was fed to GATK HaplotypeCaller for simultaneous SNP and indel calling via local de-novo assembly of haplotypes in an active region. The final output of HaplotypeCaller for each sample was a genomic VCF file (gvcf). All gvcfs generated using the MRKH samples and gvcfs produced from Genomes [2] CEU samples, were used as input for GATK GenotypeGVCFs, which produced a single raw VCF file, containing information for all samples. The next steps involved variant quality score recalibration for both SNPs and indels using GATK VariantRecalibrator. Finally for each sample, a single VCF file was produced, filtering out variants with a VQSLOD score that ranked below a specified threshold (GATK tranche 99.9 %). Each VCF was annotated using VEP (v75) [3] with canonical transcripts flagged, but also using SnpEff (v3.6.a - work in progress [4]). Ensembl genes were used for annotation purposes to ensure compatibility with downstream use of Gemini (v0.11.0a) [5], but we are also running analyses using RefSeq genes (work in progress). For the five unrelated cases from the eight samples that we report on here shared SNPs and indels were obtained using VCF-iseq from VCF tools (v0.1.12). Unforeseen issues During the course of this project we came across minor issues that were successfully resolved. Briefly: - The sample collection and evaluation process lasted longer than planned. This decision was made consciously, with the aim to optimise the phenotypic information that was available for each sample (as this allows for more refined analyses), but also with the objective to obtain a number of critical, highly informative samples. - Given significant progress in sequencing technology (speed, accuracy, cost) we decided to change the sequencing platform from Ion Proton (available at the sequencing facility at BSRC Al. Fleming) to illumina (available at Genome Quebec). This change resulted in more precise data (including indels which to date, are not captured reliably by Ion Proton) and a considerably lower cost. We therefore were able to process 16 samples instead of the eight that we had initially proposed to study. This doubling of samples increases the power of 5

6 this study substantially and allows us to apply a more complex study design than originally planned. Results We report here on eight samples (as was the original number proposed) and we are currently processing 36 additional samples that will contribute to the larger MRKH analysis. These eight samples (five MRKH type II cases and three relatives, all of Greek ancestry) were sequenced at 1/3 sequencing lane per sample resulting in a range of million reads per sample, covering a range of billion bases with an average sequencing quality (phred) score of 36. The distrubtion of read mapping is shown in Figure 2. Figure 2. Overview of read mapping for eight samples. For each sample, approximately 99.95% of total reads mapped to the genome sequence. Of these, uniquely mapped reads ranged from 87-88%. Average coverage for all samples was high ( x), but most importantly the distribution of % coverage across the target region was consistent with over 95% of regions being covered at 20x and ~90% of regions being covered at 40x. These statistics are summarised in Figure 3 and form a solid starting point for exome sequencing analysis. As specified in the Methods section, variant calling was performed using 50 CEU individuals from the 1000 Genomes Project. CEU is a population of Northern European descent and is a valid proxy to use for samples from the Greek population, although some variants may display differences in allele frequencies given the geographic distance. For this reason and for comparison purposes, we plan to re-run 6

7 our analyses using the TSI population (individuals from Tuscany, Italy). In parallel, we also aim to examine the degree of allele frequency differences to establish which population is better-suited for analysis of individuals from the Greek population. Figure 3. Distribution of coverage across samples. Average coverage across eight samples was 120x. Importantly, the distribution of coverage across the target region was such, that over 95% of regions were covered at 20x and ~90% of regions were covered at 40x. Given our analytical pipeline we report variants detected with and without the 50 bp padding around the target region. With padding, the region covered increases significantly and SNPs detected in these eight samples, using tranche 99.9, ranged from 83,500 (with padding) to 57,500 (without padding), with respective numbers for indels being 16,000 and 8,800 (Table 1). Table 1. Count of SNPs and indels detected in eight samples. 7

8 Preliminary analysis of the eight individuals has resulted in a list (submitted separately) of prioritised variants that have a higher chance to be functionally linked to the MRKH phenotype based on a number of criteria (e.g. predicted impact, allele frequency, but also scores from combined indices of functional impact, such as the CADD score for SNPs [6]). Prioritised indels were enriched for genes with a known (to date) role in development, cell adhesion, sensory perception and cognition. Prioritised SNPs were enriched for genes involved in axis specification, sexual reproduction, transport and immune response. We are currently processing this list and will complement findings with additional bioinformatics analysis on these eight samples (e.g. run functional annotation using SnpEff, but also use RefSeq genes for annotation purposes), but also analyses for the remaining 36 samples. This will enable a much more comprehensive picture of genetic variants that are likely to be critical for MRKH syndrome and for female reproductive tract development. Given that we have a wealth of samples and very high quality phenotyping and sequencing data, we are in the position to conduct a number of powerful analyses. Ongoing work and very near future objectives include conducting analyses using our larger sample (i.e. 44 individuals) and specifically: a) analysis of the 25 unrelated cases (e.g. study variants shared by all cases), b) family-based analysis (include/exclude variants based on relatedness and phenotype segregation), c) comparison of variants in type I vs type II MRKH syndrome. These data and forthcoming results will give us a good first understanding of the genetic contribution to the MRKH phenotype, of the phenotypic variation observed for MRKH syndrome (type I vs type II), but will also consitute a solid starting point for the elucidation of female reproductive system development, and for genetic counselling for individuals with MRKH syndrome and their families. References 1. McKenna, A., et al., The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res, (9): p Genomes Project, C., et al., An integrated map of genetic variation from 1,092 human genomes. Nature, (7422): p McLaren, W., et al., Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics, (16): p Cingolani, P., et al., A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin), (2): p Paila, U., et al., GEMINI: integrative exploration of genetic variation and genome annotations. PLoS Comput Biol, (7): p. e Kircher, M., et al., A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet, (3): p