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. Author manuscript; available in PMC: 2021 Apr 20.
Published in final edited form as: Am J Med Genet A. 2020 Mar 19;182(6):1400–1406. doi: 10.1002/ajmg.a.61558

Limitations of exome sequencing in detecting rare and undiagnosed diseases

Kendall J Burdick 1,#, Joy D Cogan 2, Lynette C Rives 2, Amy K Robertson 2, Mary E Koziura 2, Elly Brokamp 2, Laura Duncan 2, Vickie Hannig 2, Jean Pfotenhauer 2, Rena Vanzo 4, Michael S Paul 4, Anna Bican 2, Thomas Morgan 2, Jessica Duis 2, John H Newman 3, Rizwan Hamid 2, John A Phillips III 2,#; Undiagnosed Diseases Network
PMCID: PMC8057342  NIHMSID: NIHMS1690586  PMID: 32190976

Abstract

While exome sequencing (ES) is commonly the final diagnostic step in clinical genetics, it may miss diagnoses. To clarify the limitations of ES, we investigated the diagnostic yield of genetic tests beyond ES in our Undiagnosed Diseases Network (UDN) participants. We reviewed the yield of additional genetic testing including genome sequencing (GS), copy number variant (CNV), noncoding variant (NCV), repeat expansion (RE), or methylation testing in UDN cases with nondiagnostic ES results. Overall, 36/54 (67%) of total diagnoses were based on clinical findings and coding variants found by ES and 3/54 (6%) were based on clinical findings only. The remaining 15/54 (28%) required testing beyond ES. Of these, 7/15 (47%) had NCV, 6/15 (40%) CNV, and 2/15 (13%) had a RE or a DNA methylation disorder. Thus 18/54 (33%) of diagnoses were not solved exclusively by ES. Several methods were needed to detect and/or confirm the functional effects of the variants missed by ES, and in some cases by GS. These results indicate that tests to detect elusive variants should be considered after nondiagnostic preliminary steps. Further studies are needed to determine the cost-effectiveness of tests beyond ES that provide diagnoses and insights to possible treatment.

Keywords: copy number variants, exome sequencing, genome sequencing, noncoding variants, Undiagnosed Diseases Network

1 |. INTRODUCTION

While some patients are easily diagnosed, others are more difficult and embark on a diagnostic odyssey to find the answer and solution to their malaise (Rosenthal, Biesecker, & Biesecker, 2001). The Undiagnosed Diseases Network (UDN) aims to diagnose patients with rare and complex symptoms that remain without etiology despite extensive testing. All UDN participants receive an in-depth review of their medical history, and many also receive multidisciplinary evaluations, genetic testing, and in some cases, model organism research studies.

Prior to UDN admission, participants’ clinical genetic evaluations typically include a chromosomal microarray, gene panel sequencing, copy number variant (CNV) testing, and often exome sequencing (ES). Since the cost of ES has decreased to a moderately affordable price (with insurance), it is an increasingly more common test for many individuals suspected of having a rare condition or an unusual presentation (Nolan & Carlson, 2016; Stark et al., 2017).

While ES is considered to have strong diagnostic utility, it provides a genetic diagnosis in only about 31% of cases (range 24–68%, note the higher rates are seen in consanguineous cohorts; Clark et al., 2018; Farwell et al., 2015; Lee et al., 2014; Retterer et al., 2016; Yang et al., 2013; Yang et al., 2014). ES is suggested by some as a first line test (Srivastava et al., 2019); however, it is often thought of as the last resort by many other clinicians. Since many variations including CNV and noncoding variants (NCV) may not be detected, the patient may not receive a diagnosis even if one is genetically identifiable. For example, Niguidula et al. found that 63% of cases receive a negative or uncertain ES result. For 38% of those patients who received a negative and 86% an uncertain ES result, further genetic testing was not done, and the diagnostic odyssey was abandoned (Niguidula et al., 2018). Additional studies have found reanalysis of ES after a negative or uncertain result to increase ES diagnostic yield 10–15% in early reports (Gibson et al., 2018; Wenger, Guturu, Bernstein, & Bejerano, 2017) to 22% or even 51% in later reports that use more robust methods as well as publications on subsequently discovered diseases (Eldomery et al., 2017; Liu et al., 2019). Thus, not only may ES fail to provide answers for patients, but also the patient is often told that further testing may not be offered or yield high results.

An alternate option is to use GS to analyze for both the coding and noncoding (NC) genome. Since the majority of research has been focused on the coding genome, Spielmann and Mundlos focused on explaining why the NC genome is also important to human genetics (Henrichsen, Chaignat, & Reymond, 2009; Klopocki & Mundlos, 2011; Lupiáñez et al., 2015; Reymond, Henrichsen, Harewood, & Merla, 2007; Spielmann & Mundlos, 2016). They concluded that NC DNA plays an integral part in gene regulation and protein folding, and that NCVs in regulatory regions may result in gain/loss of gene function and expression (Henrichsen et al., 2009; Klopocki & Mundlos, 2011; Lupiáñez et al., 2015; Reymond et al., 2007; Spielmann & Mundlos, 2016). In addition, a growing number of disorders are found to result from CNV (Shaikh, 2017) or other pathogenic variants in the NC region (e.g., Wilson Disease) (Chen et al., 2018), emphasizing the need for a genetic test that is able to analyze these regions (Gross et al., 2019). In these cases, GS would provide the appropriate coverage of both the coding and NC genome. For example, Turner et al. used GS to identify gene disruptive CNVs missed by ES that are linked to simplex autism (Turner et al., 2016). GS has greater breadth of coverage as well as detection of variants likely to affect protein function than ES and thus may be better equipped to identify and characterize NCV, CNV, inversions, and translocations (Belkadi et al., 2015; Clark et al., 2018; Kingsmore et al., 2019; Lelieveld, Spielmann, Mundlos, Veltman, & Gilissen, 2015; Meienberg, Bruggmann, Oexle, & Matyas, 2016; Turner et al., 2016).

When comparing ES and GS, Belkadi et al. found GS is better than ES in identifying single-nucleotide variants and small insertions/deletions. GS also had more uniform distributions of coverage depth, genotype quality, and a lower rate of false-positive variants. ES was not reliable for the detection of CNVs, because almost all extended beyond the targeted regions (of note that generalization of this study is limited by the small, comparative cohort of ES and GS; Belkadi et al., 2015). Importantly, studies also indicate that GS is able to detect 15–17% more potential pathogenic variants than ES, both within the ES region and beyond (Belkadi et al., 2015; Lionel et al., 2018; Meienberg et al., 2016; Taylor et al., 2015; Turner et al., 2016). Therefore, cohorts of patients whose diagnostic odyssey are terminated after a negative or uncertain ES likely include many who have an undetected genetic disease.

Our study investigated the diagnostic yield of genetic tests beyond ES in UDN participants who have received diagnoses. The yield of additional genetic testing including GS, CNV, NCV, repeat expansions (RE), or methylation testing for cases with negative or uncertain ES results was quantified and explored.

2 |. MATERIALS AND METHODS

The Vanderbilt UDN clinical site is funded by the NIH. The participants were consented to the study (IRB protocol NHGRI 15-HG0130) before any study procedures were performed. The dbGaP accession for the UDN is phs001232.

Each accepted subject receives a detailed review of their medical records, as well as an in-depth diagnostic investigation that generally has a genetic focus. Each UDN evaluation at Vanderbilt included: (a) review of medical records, (b) OMIM and SimulConsult (Chestnut Hill, MA) searches using key features, (c) physical examinations and clinical/research consultations, and usually (d) ES or GS analyses of participants and selected relatives using PhenoDB and Fabric Genomics Opal pipelines, BioVU (https://victr.vanderbilt.edu/pub/biovu) phenotype searches, structural biology modeling, and co-segregation studies.

Specific to testing, GS was conducted when repeat ES studies were nondiagnostic. Furthermore, when GS was nondiagnostic, methylation, karyotype, RE, or other appropriate studies were conducted. For patients who had ES previously performed at different clinical/commercial labs, most labs used Agilent Clinical Research Exome kits or biotin labeled VCRome 2.1 in-solution exome probes, Illumina HiSeq or Illumina HISeq 2000 platforms, with coverages that ranged from 10 to 135×. In contrast, all UDN ES and our GS were performed at two UDN labs which used standard Illumina reagents or biotin labeled VCRome 2.1 in-solution exome probes, Illumina HiSeq or Illumina HiSeq X platforms, fluorescent methods and quantitative PCR and bioanalysis QC methods with an average coverage of 40 or 100X. Analytic methods of GS detection of CNV were being developed. All patients who were not solved exclusively by ES were included in the preliminary calculation of diagnostic yield for genetic tests beyond ES. The National UDN research program is ongoing but the diagnostic rate was 35% of which 11% were made by clinical review, 11% by directed clinical testing, 4% by nonsequencing genome-wide diagnostic assay, and 74% by ES or GS in the first 382 UDN subjects whose evaluations were completed (Splinter et al., 2018).

2.1 |. Demographics

All patients were study participants at the Vanderbilt UDN clinical site. Our cohort spans a period from September 2015 to May 2019, and many participants are at different stages of their evaluation. The 54 participants who had received diagnoses by May 2019 are the subject of this analysis. Additionally, the majority of our participants were referred to the UDN from external facilities.

3 |. RESULTS

Overall, there were 54 diagnoses by May 2019 at the Vanderbilt UDN Clinical Site. Of those 54, 36 (67%) diagnoses were based on coding variants found in Steps a–d, including ES (see Section 2), and 3/54 (6%) clinically by Steps a–c without ES/GS. Interestingly, the remaining 15/54 (28%) required testing beyond ES. Of these, 7/15 (47%) had a NCV, 6/15 (40%) a CNV, and 2/15 (13%) a RE or a DNA methylation disorder (Table 1).

TABLE 1.

Summary of patients not solved exclusively by ES: Patient summaries include clinical features, UDN diagnoses & OMIM clinical synopsis numbers. Variants specify type, gene and ClinVar classification. Proof details type of variant, detection method and functional verification (cDNA analysis, CNV detection method, repeat expansion) and basis of clinical diagnosis

Summary/diagnosis & OMIM# Variant Proof
Twenty years F with chronic renal disease & pulmonary hypertension/osler hemorrhagic telangiectasia syndrome 187300 & systemic lupus erythematous NCV: c.816+6T>C ENG
ClinVar 453308 pathogenic		 NCV detected by GS disrupted splicing (cDNA)
Five years F with intractable seizures, CNS anomalies & failure to thrive/congenital disorder glycosylation 1k 608540 NCV: c.1187+3A>G & p. Phe292Leu-Ser293Pro ALG1
ClinVar 224118 & 522809 both likely pathogenic		 NCV detected by GS disrupted splicing (cDNA)
Eighteen years F with progressive muscle weakness since 8/limb-girdle muscular dystrophy (LGMD), Type 2Y 617072 NCV: c.554–4G>A (cryptic acceptor) &p. Ala477Val TOR1AIP1
ClinVar 257701 conflicting & 522870 likely pathogenic		 NCV detected by GS disrupted splicing (cDNA)
Twelve years F with congenital neuropathy & varus feet/Charcot-Marie-tooth disease, axonal, Type 2s 616155 NCV: c.1235+894C>A & p.Leu577Pro IGHMBP2
ClinVar 522869 pathogenic & 522868 uncertain		 NCV detected by local analysis of GS disrupted splicing (cDNA)
Twenty-five years F with autonomic dysfunction, rhabdomyolysis & single FKRP variant/muscular dystrophy-dystroglycanopathy (limb-girdle), Type C, 5607155 NCV: −272G>A (transcription factor site in 5’UTR) & p.Leu276Ile FKRP
ClinVar 522865 pathogenic & 4221 pathogenic		 NCV detected by local analysis of GS caused allele dropout (cDNA)
Four months M with microcephaly, global delay, failure to thrive, seizures & white matter loss/Roifman syndrome 616651 NCV: n.48G>A & n.13C>T RNU4ATAC
ClinVar 218085 likely pathogenic & 218083 pathogenic		 Noncoding RNA gene detected by a repeat ES done by the UDN
Thirty-three years F with pulmonic stenosis, VSD, progressive stridor & weakness/Alagille syndrome 118450 NCV: c.2113+1G>T JAG1
ClinVar 584458 pathogenic		 NCV detected by a repeat ES done by the UDN
Three years F with delayed development, seizures, white matter atrophy & hypotonia/infantile hypotonia with psychomotor retardation & characteristic facies Type 3 616900 NCV: c.1860+1G>A TBCK
CNV: Exon 23 del
ClinVar 636243 pathogenic & 638602 pathogenic		 NCV & CNV detected by GS: CNV confirmed by del/dup analysis and NCV disrupted splicing (cDNA)
Five years M with delayed development, atrial septal defect, hypotonia & skin pigmentation variation/diploid/triploid mosaicism CNV: Diploid/triploid mosaicism CNV detected by GS & karyotype
Six years M with seizures, tremors & developmental delay/epileptic encephalopathy, early infantile Type 4 612164 CNV: ~1.66 kb exon 5 del STXBP1
ClinVar submitted pathogenic pending acceptance		 CNV detected by GS
Sixty years F with carotid body tumor, vagal paraganglioma & labile BP/paraganglioma syndrome Type 1 168000 CNV: 2 kb del exon 4 SDHD
ClinVar submitted pathogenic pending acceptance		 CNV suspected by GS read drop off was proven by breakpoint & co-segregation analyses
Four years M with delayed development, hypotonia, absent speech, seizures & deep-set eyes/infantile hypotonia with psychomotor retardation & characteristic facies Type 3 616900 CNV: Homozygous exon 23 del TBCK
ClinVar 638602 likely pathogenic		 CNV detected by a repeat ES done by the UDN
Eleven years F with developmental delay, syringomelia, low set ears, micrognathia, absent speech & seizures/Wieacker Wolff syndrome 314580 CNV: De novo exon 1 del ZC4H2
ClinVar 598761 pathogenic		 CNV not reported on ES, GS, or standard CMA; detected on Lineagen FirstStepDx PLUS high-resolution CMA (Hensel et al., 2017)
Forty-four years M with onset at 29 of progressive dementia, apraxia, tremor, brain atrophy & choreoathetosis/frontotemporal dementia &/or amyotrophic lateral sclerosis Type 1 105550 RE: > 44 RE in C9orf72
ClinVar 31151 pathogenic		 RE not reported on GS, required RE analysis
Nine years F with short stature, precocious puberty, delayed development, hypotonia, progressive obesity, joint laxity & clinodactyly/Temple syndrome 616222 Methylation defect at imprinted cluster at 14q32 pathogenic DNA methylation
Thirty-six years F with congenital myopathy but non-specific muscle biopsy, hypotonia & progressive respiratory failure/myopathy, congenital, with fiber-type disproportion 255310 Nondiagnostic SELENON variants suggested a disorder that was clinically confirmed Clinical
Fifty years M with novel IgG4-related condition published report doi.org/10.1002/mgg3.686 The diagnosis was made by fivefold elevations in plasma IgG4 & abundant IgG4 staining plasma cells with fibrosis in the submandibular biopsy Clinical
Forty years F with onset sensorineural hearing loss at 32, diplopia at 33, respiratory failure, weakness, dysphagia & demyelinating neuropathy at 34 years/Brown-Vialetto-Van Laere syndrome Type 1 211530 Riboflavin (B2) levels & challenge test response & clinical response to B2 Clinical
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Abbreviations: CNV, copy number variant; ES, exome sequencing; GS, genome sequencing; NCV, noncoding variant; UDN, Undiagnosed Diseases Network.

4 |. DISCUSSION

Overall, we diagnosed 54 patients. As our research program is ongoing, and a final diagnostic yield cannot yet be determined. Of all our UDN diagnoses, 18/54 (33%) were not solved exclusively by ES. A variety of methods were needed to detect and/or confirm the effects of the CNV, NCV, RE, or methylation variants that were missed by ES, and in some cases by GS. These results indicate that reanalysis of ES data and the use of other tests to detect elusive variants should be considered after Steps a–d (see Section 2) are not diagnostic. They also indicate that additional genetic tests can considerably improve the diagnostic rate above that of ES.

Based on the tendency to halt genetic testing after a negative or inconclusive ES, we believe that many of our participants who were in fact diagnosed via testing beyond ES, would not have been diagnosed. We propose that these diagnoses resulted from the Vanderbilt UDN methodology of increased communication and focused testing. Many steps may be made in clinical genetics and genetic counseling that can improve the diagnostic rate of all patients presumed to have a genetic disease.

There are multiple areas of growth for the methods of investigating rare and complex genetic phenotypes. Primarily, there should be better communication between those who have seen the patient, those who are ordering testing, and those who are interpreting the genetic tests. Research teams may begin to do this by first clarifying their diagnostic steps, such as shown in methods above. Better communication between the clinical and ES/GS analysis teams can improve the phenotypic information that is used by the latter to prioritize detected variants. Possible suggestions include providing a discrete list of unique and prioritized features using Human Phenotype Ontology terminology and genes of clinical interest, so that the analysis team can better detect candidate and possible interacting genes (Köhler et al., 2018). Also, the variants detected can provide the clinical team with clues to additional phenotypes as well as disorders to consider in their evaluations. This iterative interaction between the clinical and ES/GS analysis teams could enhance the probability of identifying the correct diagnosis through optimally matching the phenotypic and genotypic findings. Based on phenotype, possible genes and molecular pathways may be hypothesized as disease related, to create a focused list of candidate variants and to possibly select RE or methylation studies. Time spent clarifying the phenotypes focuses the clinical interpretation, which can in turn make the ES/GS analyses and research investigations more efficient and effective.

In addition to increased communication, programs such as OMIM and SimulConsult can be used to analyze combinations of signs and symptoms to identify and prioritize differential diagnoses. Use of these databases can also help identify missed phenotypic details that can strengthen or eliminate candidate disorders. Also, since these programs are periodically updated, they can provide currently available, relevant and useful insights. Information that can be made even more current by web searches on specific disorders and variants that may not yet have been curated into databases.

Beyond the use of outside sources, the technology that we use to detect genetic variations must improve for the yield of diagnoses to increase. We detected a number of CNVs, but likely missed others due to the limitations of CNV detection. Additionally, since the longer reads of GS may detect more small deletions, further studies (i.e., PCR analysis and MLPA) are needed to confirm the findings. In the absence of nanopore-based methodologies sequencing (Carvalho et al., 2019), GS would also miss imprinting disorders that require methylation testing, which suggests that their considerations be added as a prudent additional step to ensure that known and yet-to-be discovered epigenetic disorders are not missed. Despite the sensitivity and GS coverage, there is no single genetic test that can be solely relied upon for clinical diagnoses of complex genetic disorders. There also needs to be further research on and development of clinical databases and lab tools used for clinical decision-making and therapeutic strategies.

Further studies are needed to determine the cost effectiveness of such tests that are needed beyond ES to provide possible diagnoses and treatments. As the price of GS and ES approach equality, cost justification to do ES over GS is decreasing. GS has several benefits over ES, including interrogation of the NC genome and better CNV coverage which is imperative if sequencing becomes a front-line test. However, further investigation, as well as a larger, more detailed and comprehensive genetic database, is required for a deeper understanding of ES, GS, and other focused genetic testing results.

5 |. CONCLUSION

The diagnostic odyssey for patients with potential genetic disorders can be addressed by improved communication and planning between clinical, ES/GS analysis and research teams. As a result, focused sequencing may be more likely to identify pathogenic variants that mesh with the clinical finding and identify the genetic basis of the disorder without the need of numerous, costly additional tests. We suggest that when specific gene testing panels do not provide a clear answer, GS should be increasingly considered before ES, when available, because GS has increased coverage and diagnostic yield compared to ES. By combining clinical phenotyping and ES/GS analysis, improved diagnostic rates can be achieved. As a result, more diagnoses can be made which can provide, in turn, information on natural history, recurrence risks, treatments, and access to clinical trials. Further studies are needed to determine the cost effectiveness of such tests that are needed beyond ES and the benefits of these diagnoses and the insights that they can provide to possible treatment.

ACKNOWLEDGMENTS

Research reported in this article was supported by the NIH Common Fund, through the Office of Strategic Coordination/Office of the NIH Director under Award Number UO1HG007674. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Authors RV and MP have an association with Lineagen, Inc. that provides commercial clinical genetic diagnostic testing. Other authors do not have a commercial association that might pose, create, or create the appearance of a conflict of interest with the information presented in this article.

CONSORTIA

Members of the Undiagnosed Diseases Network include, Maria T. Acosta, David R. Adams, Aaron Aday, Mercedes E. Alejandro, Patrick Allard, Euan A. Ashley, Mahshid S. Azamian, Carlos A. Bacino, Guney Bademci, Eva Baker, Ashok Balasubramanyam, Dustin Baldridge, Deborah Barbouth, Gabriel F. Batzli, Alan H. Beggs, Hugo J. Bellen, Jonathan A. Bernstein, Gerard T. Berry, Anna Bican, David P. Bick, Camille L. Birch, Stephanie Bivona, Carsten Bonnenmann, Devon Bonner, Braden E. Boone, Bret L. Bostwick, Lauren C. Briere, Elly Brokamp, Donna M. Brown, Matthew Brush, Elizabeth A. Burke, Lindsay C. Burrage, Manish J. Butte, Olveen Carrasquillo, Ta Chen Peter Chang, Hsiao-Tuan Chao, Gary D. Clark, Terra R. Coakley, Laurel A. Cobban, Joy D. Cogan, F. Sessions Cole, Heather A. Colley, Cynthia M. Cooper, Heidi Cope, William J. Craigen, Precilla D’Souza, Surendra Dasari, Mariska Davids, Jean M. Davidson, Jyoti G. Dayal, Esteban C. Dell’Angelica, Shweta U. Dhar, Naghmeh Dorrani, Daniel C. Dorset, Emilie D. Douine, David D. Draper, Annika M. Dries, Laura Duncan, David J. Eckstein, Lisa T. Emrick, Christine M. Eng, Gregory M. Enns, Cecilia Esteves, Tyra Estwick, Liliana Fernandez, Carlos Ferreira, Elizabeth L. Fieg, Paul G. Fisher, Brent L. Fogel, Irman Forghani, Noah D. Friedman, William A. Gahl, Rena A. Godfrey, Alica M. Goldman, David B. Goldstein, Jean-Philippe F. Gourdine, Alana Grajewski, Catherine A. Groden, Andrea L. Gropman, Melissa Haendel, Rizwan Hamid, Neil A. Hanchard, Frances High, Ingrid A. Holm, Jason Hom, Alden Huang, Yong Huang, Rosario Isasi, Fariha Jamal, Yong-hui Jiang, Jean M. Johnston, Angela L. Jones, Lefkothea Karaviti, Emily G. Kelley, David M. Koeller, Isaac S. Kohane, Jennefer N. Kohler, Deborah Krakow, Donna M. Krasnewich, Susan Korrick, Mary Koziura, Joel B. Krier, Jennifer E. Kyle, Seema R. Lalani, Byron Lam, Brendan C. Lanpher, Ian R. Lanza, C. Christopher Lau, Jozef Lazar, Kimberly LeBlanc, Brendan H. Lee, Hane Lee, Roy Levitt, Shawn E. Levy, Richard A. Lewis, Sharyn A. Lincoln, Pengfei Liu, Xue Zhong Liu, Sandra K. Loo, Joseph Loscalzo, Richard L. Maas, Ellen F. Macnamara, Calum A. MacRae, Valerie V. Maduro, Marta M. Majcherska, May Christine V. Malicdan, Laura A. Mamounas, Teri A. Manolio, Thomas C. Markello, Ronit Marom, Martin G. Martin, Julian A. Martínez-Agosto, Shruti Marwaha, Thomas May, Jacob McCauley, Allyn McConkie-Rosell, Colleen E. McCormack, Alexa T. McCray, Jason D. Merker, Thomas O. Metz, Matthew Might, Eva Morava-Kozicz, Paolo M. Moretti, Marie Morimoto, John J. Mulvihill, David R. Murdock, Avi Nath, Stan F. Nelson, J. Scott Newberry, John H. Newman, Sarah K. Nicholas, Donna Novacic, Devin Oglesbee, James P. Orengo, Stephen Pak, J. Carl Pallais, Christina GS. Palmer, Jeanette C. Papp, Neil H. Parker, Loren DM. Pena, John A. Phillips III, Jennifer E. Posey, John H. Postlethwait, Lorraine Potocki, Barbara N. Pusey, Genecee Renteria, Chloe M. Reuter, Lynette Rives, Amy K. Robertson, Lance H. Rodan, Jill A. Rosenfeld, Robb K. Rowley, Ralph Sacco, Jacinda B. Sampson, Susan L. Samson, Mario Saporta, Judy Schaechter, Timothy Schedl, Kelly Schoch, Daryl A. Scott, Lisa Shakachite, Prashant Sharma, Vandana Shashi, Kathleen Shields, Jimann Shin, Rebecca Signer, Catherine H. Sillari, Edwin K. Silverman, Janet S. Sinsheimer, Kevin S. Smith, Lilianna Solnica-Krezel, Rebecca C. Spillmann, Joan M. Stoler, Nicholas Stong, Jennifer A. Sullivan, David A. Sweetser, Cecelia P. Tamburro, Queenie K.-G. Tan, Mustafa Tekin, Fred Telischi, Willa Thorson, Cynthia J. Tifft, Camilo Toro, Alyssa A. Tran, Tiina K. Urv, Tiphanie P. Vogel, Daryl M. Waggott, Colleen E. Wahl, Nicole M. Walley, Chris A. Walsh, Melissa Walker, Jennifer Wambach, Jijun Wan, Lee-kai Wang, Michael F. Wangler, Patricia A. Ward, Katrina M. Waters, Bobbie-Jo M. Webb Robertson, Daniel Wegner, Monte Westerfield, Matthew T. Wheeler, Anastasia L. Wise, Lynne A. Wolfe, Jeremy D. Woods, Elizabeth A. Worthey, Shinya Yamamoto, John Yang, Amanda J. Yoon, Guoyun Yu, Diane B. Zastrow, Chunli Zhao, Stephan Zuchner

Footnotes

CONFLICT OF INTEREST

None.

DATA AVAILABILITY STATEMENT

For all UDN research, specific variants are submitted to ClinVar (www.ncbi.nlm.nih.gov/clinvar/; Landrum et al., 2013), and raw sequencing files are submitted to dbGAP (www.ncbi.nlm.nih.gov/gap/; Mailman et al., 2007) by the UDN Coordinating Center per UDN protocol, and therefore, are publically available.

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