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Cited by 231Genomics and Rare DiseasesConnective tissue disorders researchMetabolism and Genetic Disorders

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Lessons learned from additional research analyses of unsolved clinical exome cases

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Eldomery, Mohammad K., Coban-Akdemir, Zeynep, Harel, Tamar, Rosenfeld, Jill A., Gambin, Tomasz, Stray-Pedersen, Asbjørg, Küry, Sébastien, Mercier, Sandra, Lessel, Davor, Denecke, Jonas, Wiszniewski, Wojciech, Penney, Samantha, Liu, Pengfei, Bi, Weimin, Lalani, Seema R., Schaaf, Christian P., Wangler, Michael F., Bacino, Carlos A., Lewis, Richard Alan, Potocki, Lorraine, Graham, Brett H., Belmont, John W., Scaglia, Fernando, Orange, Jordan S., Jhangiani, Shalini N., Chiang, Theodore, Doddapaneni, Harsha, Hu, Jianhong, Muzny, Donna M., Xia, Fan, Beaudet, Arthur L., Boerwinkle, Eric, Eng, Christine M., Plon, Sharon E., Sutton, V. Reid, Gibbs, Richard A., Posey, Jennifer E., Yang, Yaping, Lupski, James R.·2017·Genome Medicine
DOI 10.1186/s13073-017-0412-6PMC PMC5361813
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Abstract

BACKGROUND: Given the rarity of most single-gene Mendelian disorders, concerted efforts of data exchange between clinical and scientific communities are critical to optimize molecular diagnosis and novel disease gene discovery. METHODS: We designed and implemented protocols for the study of cases for which a plausible molecular diagnosis was not achieved in a clinical genomics diagnostic laboratory (i.e. unsolved clinical exomes). Such cases were recruited to a research laboratory for further analyses, in order to potentially: (1) accelerate novel disease gene discovery; (2) increase the molecular diagnostic yield of whole exome sequencing (WES); and (3) gain insight into the genetic mechanisms of disease. Pilot project data included 74 families, consisting mostly of parent-offspring trios. Analyses performed on a research basis employed both WES from additional family members and complementary bioinformatics approaches and protocols. RESULTS: Analysis of all possible modes of Mendelian inheritance, focusing on both single nucleotide variants (SNV) and copy number variant (CNV) alleles, yielded a likely contributory variant in 36% (27/74) of cases. If one includes candidate genes with variants identified within a single family, a potential contributory variant was identified in a total of ~51% (38/74) of cases enrolled in this pilot study. The molecular diagnosis was achieved in 30/63 trios (47.6%). Besides this, the analysis workflow yielded evidence for pathogenic variants in disease-associated genes in 4/6 singleton cases (66.6%), 1/1 multiplex family involving three affected siblings, and 3/4 (75%) quartet families. Both the analytical pipeline and the collaborative efforts between the diagnostic and research laboratories provided insights that allowed recent disease gene discoveries (PURA, TANGO2, EMC1, GNB5, ATAD3A, and MIPEP) and increased the number of novel genes, defined in this study as genes identified in more than one family (DHX30 and EBF3). CONCLUSION: An efficient genomics pipeline in which clinical sequencing in a diagnostic laboratory is followed by the detailed reanalysis of unsolved cases in a research environment, supplemented with WES data from additional family members, and subject to adjuvant bioinformatics analyses including relaxed variant filtering parameters in informatics pipelines, can enhance the molecular diagnostic yield and provide mechanistic insights into Mendelian disorders. Implementing these approaches requires collaborative clinical molecular diagnostic and research efforts.

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Fig. 1 Analysis of WES data. a SNVs were filtered and prioritized according to specific criteria, including mode of inheritance, mutation type, variant frequency, conservation, and predictions of pathogenicity. b Candidate genes were further prioritized by data mining, taking into consideration gene function, expression, and networks. In addition, other cohorts were interrogated for additional families with variants in the same candidate gene. MutationMapper (http://www.cbioportal.org/mutation_mapper.jsp), ARIC Atherosclerosis Risk in Communities Study, AR-Hom autosomal recessive-homozygous, BHCMG Baylor-Hopkins Center for Mendelian Genomics, BG Baylor Genetics laboratories, CNV copy number variation, Comp compound, db database, ExAC Exome Aggregation Consortium, Het heterozygous, HGMD Human Gene Mutation Database, MAF minor allele frequency; SNV single nucleotide variant, XLR-Hem X-linked recessive-hemizygous
Fig. 1 Analysis of WES data. a SNVs were filtered and prioritized according to specific criteria, including mode of inheritance, mutation type, variant frequency, conservation, and predictions of pathogenicity. b Candidate genes were further prioritized by data mining, taking into consideration gene function, expression, and networks. In addition, other cohorts were interrogated for additional families with variants in the same candidate gene. MutationMapper (http://www.cbioportal.org/mutation_mapper.jsp), ARIC Atherosclerosis Risk in Communities Study, AR-Hom autosomal recessive-homozygous, BHCMG Baylor-Hopkins Center for Mendelian Genomics, BG Baylor Genetics laboratories, CNV copy number variation, Comp compound, db database, ExAC Exome Aggregation Consortium, Het heterozygous, HGMD Human Gene Mutation Database, MAF minor allele frequency; SNV single nucleotide variant, XLR-Hem X-linked recessive-hemizygousCC BY · PubMed Central
Fig. 2 Overview of the study design and results. a Clinical WES cases that lacked a definitive molecular diagnosis (left) were eligible for recruitment into a research environment. In a pilot study of 74 families (right), we identified strong candidate genes for 51% (38/74) of cases. Identified variants were categorized into six major classes based on mode of inheritance and known or novel gene. b According to stringent criteria, a potential contributory variant was achieved in 27 of 74 (36%) cases. Of these, 12 were independently solved by the clinical exome laboratory upon reanalysis of WES data and updated literature review. When taking into account strong candidate genes identified in only a single family to date, a potential molecular diagnostic rate of 51% was achieved
Fig. 2 Overview of the study design and results. a Clinical WES cases that lacked a definitive molecular diagnosis (left) were eligible for recruitment into a research environment. In a pilot study of 74 families (right), we identified strong candidate genes for 51% (38/74) of cases. Identified variants were categorized into six major classes based on mode of inheritance and known or novel gene. b According to stringent criteria, a potential contributory variant was achieved in 27 of 74 (36%) cases. Of these, 12 were independently solved by the clinical exome laboratory upon reanalysis of WES data and updated literature review. When taking into account strong candidate genes identified in only a single family to date, a potential molecular diagnostic rate of 51% was achievedCC BY · PubMed Central
Fig. 3 Location of DHX30 and GNB5 variants, dinucleotide variants culled from WES data and UPD. a Variants identified in DHX30 and GNB5 are located in specific protein domains. b Sanger confirmation of a de novo dinucleotide variant in SYN3. c The B-allele frequency extracted from WES data in the patient with the homozygous SLC1A4 variant showed a single region of AOH in the genome (chromosome 2), suggestive of uniparental disomy (UPD) of chromosome 2. d Segregation analysis of the SLC1A4 homozygous variant did not conform to Mendelian expectations
Fig. 3 Location of DHX30 and GNB5 variants, dinucleotide variants culled from WES data and UPD. a Variants identified in DHX30 and GNB5 are located in specific protein domains. b Sanger confirmation of a de novo dinucleotide variant in SYN3. c The B-allele frequency extracted from WES data in the patient with the homozygous SLC1A4 variant showed a single region of AOH in the genome (chromosome 2), suggestive of uniparental disomy (UPD) of chromosome 2. d Segregation analysis of the SLC1A4 homozygous variant did not conform to Mendelian expectationsCC BY · PubMed Central

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Authors
Eldomery, M. K., Coban-Akdemir, Z., Harel, T., Rosenfeld, J. A., Gambin, T., Stray-Pedersen, A., Küry, S., Mercier, S., Lessel, D., Denecke, J., Wiszniewski, W., Penney, S., Liu, P., Bi, W., Lalani, S. R., Schaaf, C. P., Wangler, M. F., Bacino, C. A., Lewis, R. A., Potocki, L., Graham, B. H., Belmont, J. W., Scaglia, F., Orange, J. S., Jhangiani, S. N., Chiang, T., Doddapaneni, H., Hu, J., Muzny, D. M., Xia, F., Beaudet, A. L., Boerwinkle, E., Eng, C. M., Plon, S. E., Sutton, V. R., Gibbs, R. A., Posey, J. E., Yang, Y., & Lupski, J. R.
Affiliations
  • Baylor College of Medicine (US)
  • Warsaw University of Technology (PL)
  • Centre Hospitalier Universitaire de Nantes (FR)
  • University Medical Center Hamburg-Eppendorf (DE)
Corresponding author
Lupski, James R.
ORCID
Venue
Genome Medicine
Publisher
Springer Science and Business Media LLC
Published
2017-03-21
Volume
9
Issue
1
Pages
26-26
ISSN
1756-994X (ISSN-L)
DOI
10.1186/s13073-017-0412-6
PMC
PMC5361813
OpenAlex
W2598900401
Cited by
230
References
111
Citations by year
2026: 7 · 2025: 6 · 2024: 22 · 2023: 22 · 2022: 30 · 2021: 27 · 2020: 33 · 2019: 43 · 2018: 30 · 2017: 10
FWCI
21.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion, cc-by-nc-nd)
  • Purdue University Indianapolis (Indiana University) (submittedVersion, cc-by-nc-nd)
Funders
  • National Human Genome Research Institute (U54HG006542)
  • National Institutes of Health (T32 GM07526, NIH T32 GM07526)
  • Genetics Resource Association of Texas
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Connective tissue disorders research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
Keywords
monogenic disorders, disease gene discovery, molecular diagnosis, exome sequencing, parent-offspring trios, copy number variation, single nucleotide variants, clinical research collaboration, multiplex families, PURA, TANGO2, GNB5, ATAD3A, MIPEP, DHX30, EBF3
MeSH terms
ATPases Associated with Diverse Cellular Activities, Adenosine Triphosphatases, DNA-Binding Proteins, Female, Humans, Male, Membrane Proteins, Metalloendopeptidases, Pilot Projects, Transcription Factors, Sequence Analysis, DNA, Computational Biology, Polymorphism, Single Nucleotide, Genomics, Mitochondrial Proteins, Genetic Diseases, Inborn, GTP-Binding Protein beta Subunits, DNA Copy Number Variations, Exome

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