Genomic diagnostics within a medically underserved population: efficacy and implications

Article metrics

Abstract

Purpose

We integrated whole-exome sequencing (WES) and chromosomal microarray analysis (CMA) into a clinical workflow to serve an endogamous, uninsured, agrarian community.

Methods

Seventy-nine probands (newborn to 49.8 years) who presented between 1998 and 2015 remained undiagnosed after biochemical and molecular investigations. We generated WES data for probands and family members and vetted variants through rephenotyping, segregation analyses, and population studies.

Results

The most common presentation was neurological disease (64%). Seven (9%) probands were diagnosed by CMA. Family WES data were informative for 37 (51%) of the 72 remaining individuals, yielding a specific genetic diagnosis (n = 32) or revealing a novel molecular etiology (n = 5). For five (7%) additional subjects, negative WES decreased the likelihood of genetic disease. Compared to trio analysis, “family” WES (average seven exomes per proband) reduced filtered candidate variants from 22 ± 6 to 5 ± 3 per proband. Nineteen (51%) alleles were de novo and 17 (46%) inherited; the latter added to a population-based diagnostic panel. We found actionable secondary variants in 21 (4.2%) of 502 subjects, all of whom opted to be informed.

Conclusion

CMA and family-based WES streamline and economize diagnosis of rare genetic disorders, accelerate novel gene discovery, and create new opportunities for community-based screening and prevention in underserved populations.

Introduction

Whole-exome sequencing (WES) and chromosomal microarray analysis (CMA) have revolutionized investigation of rare genetic disorders and intellectual disability,1, 2, 3, 4, 5, 6 but important diagnostic and service gaps remain. The pretest probability of a genetic lesion is high for individuals who move through contemporary diagnostic algorithms to arrive at CMA or WES,7, 8, 9 yet many remain undiagnosed at the culmination of the process.2, 3, 9, 10 Moreover, the cost and complexity of these methods limit access for people who are poor, uninsured, or otherwise medically underserved.11

The Clinic for Special Children (CSC) is a medical home for children who derive from endogamous Old Order Amish and Mennonite (Plain) populations of Pennsylvania and surrounding states,12, 13 integrating clinical care with an in-house laboratory for Clinical Laboratory Improvement Amendments–certified targeted testing and research-based CMA analysis. Approximately 90% of CSC patients are medically underserved, as defined by their geographic, social, and economic circumstances (http://www.hrsa.gov/). Many live below the federal poverty threshold (http://www.census.gov/) in state-designated Health Professional Shortage Areas (http://www.health.pa.gov/) and the majority are uninsured, which is the strongest predictor of health disparity in the United States.14

Uninsured Americans are most commonly served by community health centers,15 only 12% of which offer the most basic forms of genetic testing.16 Such services remain particularly sparse in rural settings.16, 17 Urban areas have meanwhile witnessed the rise of ambitious genomic centers funded by academic18 and industry stakeholders19enthused by the promise of precision medicine.20 However, these large-scale genomics initiatives are not necessarily intended to democratize genomic testing16 or confront barriers to its broader implementation.11, 21

To bridge the gap between technical resources and medical need, CSC and the Regeneron Genetics Center (RGC) forged a collaboration to make WES freely accessible to uninsured members of the Plain community (Supplementary Figure S1 online). The arrangement provided benefit to all major stakeholders: uninsured patients received high-quality genomic testing at no cost, CSC received genomic data and operating support, and RGC streamlined their investigation of clinically relevant disease genes and pathways. Through this partnership, we have been able to optimize the yield of genomic testing, explore its broader social and economic value in community practice, and advance precision medicine while simultaneously redressing health care disparities unique to the genomic era.

Materials and methods

We identified 79 probands (36 female, mean age 6.9 ± 9.4 years, range newborn to 49.8 years) who presented to CSC for evaluation between September 1998 and 2015, had clinical signs of an underlying genetic disorder, and remained without a diagnosis following focused biochemical and genetic investigations spanning an average of 3.3 ± 3.2 (range 0.1 to 16.9) years. All but three probands descended from Old Order Amish and Mennonite founder populations.12, 22

The Lancaster General Hospital Institutional Review Board approved the study. Subjects (or their parents) had pretest counseling to explain goals, process, timing, and limitations of CMA and WES before consenting in writing to participate. Subjects could choose whether or not to receive medically actionable secondary findings that fit American College of Medical Genetics and Genomics (ACMG) guidelines,23 including pathogenic variants known to segregate with high frequency in Plain populations (e.g., APOB c.10580 G > A; p.Arg3527Gln).24

Clinicians phenotyped each proband following a structured and standardized assessment guided by PhenoTips (https://phenotips.org)25 and using Human Phenotype Ontology (HPO) terms. The likelihood of a monogenic disorder was based primarily on conventional clinical indices such as abnormal brain size or morphology, developmental delay or regression, the presence of craniofacial/skeletal dysmorphisms, or characteristic end-organ pathology (e.g., hearing loss, vision impairment, or epilepsy) in the absence of environmental antecedents.7 The apparent inheritance of an autosomal recessive, dominant or X-linked phenotypes supported a genetic etiology in only 14 (17.7%) of 79 cases; 65 remaining probands presented with a unique clinical phenotype in the context of an uninformative family history.

Prior to CMA and WES, most probands with developmental delay or neurological disease had additional analyte testing that could include, but was not limited to, plasma amino acids, acylcarnitines, lactate, ammonia, transferrin glycoforms, homocysteine, urine organic acids, purines and pyrimidines, creatine and guanidinoacetate, lysosomal storage markers, and cerebrospinal fluid glucose and neurotransmitters.26 The specific constellation of analyte tests for each proband was shaped by the clinical presentation, its attendant differential diagnosis, and the newborn screening history. In general, we took a parsimonious approach to metabolic analyte testing based upon its relatively low diagnostic yield (1–5%) in this clinical context.26, 27, 28, 29

Several subjects with Rett- or fragile X–like phenotypes had targeted MECP2 and FMR1 testing, respectively, prior to CMA and WES. Finally, the phenotype of each proband was crossed against an existing panel of more than 200 known population-specific alleles detected by CSC laboratory using high-resolution melt analysis or Sanger sequencing.12 Vetting the process in this way (Figure 1a), we ensured a high pretest probability of genetic illness while limiting representation of known recessive “founder alleles” among probands who advanced to WES; institutional knowledge allowed us to enrich for phenotypes caused by de novo, X-linked, compound heterozygous, and copy-number variants (CNVs).2, 3, 7

Figure 1
figure1

Molecular diagnostic algorithm (a) The standard diagnostic algorithm for each proband included population-specific allele detection, high-density chromosomal microarray (CMA), family whole-exome sequencing (WES), and posttest follow-up to refine phenotype, recruit additional relatives, and confirm segregation. (b) The pretest probability of Mendelian disease was high for 79 probands, 7 of whom had a pathogenic molecular karyotype by CMA. Seventy-two probands proceeded to WES, which allowed us to identify a definitive molecular diagnosis (n = 32; 44%) or find compelling evidence for a novel genetic mechanism (n = 5; 7%). In 5 (7%) additional subjects, “negative” WES reduced the likelihood of monogenic disease. Thirty cases remain “open” for iterative reanalysis. HPO, Human Phenotype Ontology.

Sixty-eight (86%) probands had a 2.6-million marker high-density CMA (CytoScan HD Array, Affymetrix) to detect pathogenic CNVs to a resolution of between 25 kb (losses) and 50 kb (gains) using results from Affymetrix Chromosome Analysis Suite software (ChAS 3.1) filtered against CNV data from more than 350 individuals of Amish and Mennonite descent. We investigated any deletion (regardless of size) that encompassed at least one exon of an OMIM gene and impacted at least three separate NspI fragments.

For probands with an uninformative high-density CMA, we proceeded to WES in collaboration with RGC. Briefly, 1 μg of high-quality genomic DNA was exome-captured using the NimbleGen VCRome SeqCap 2.1 reagent. Libraries were sequenced on the Illumina HiSeq 2500 platform using v4 chemistry, achieving coverage of >85% of bases at 20x or greater. Raw sequence reads were mapped and aligned to the GRCh37/hg19 human genome reference assembly using BWA/GATK bioinformatics algorithms (https://software.broadinstitute.org/). Called variants were assessed by standard metrics (read depth ≥10, genotype quality ≥30, allelic balance ≥20%), annotated for potential functional effects (e.g., synonymous, missense, frameshift, nonsense), and subsequently filtered by observed minor allele frequency ≤1% within public (1000 Genomes, ExAC, and NHLBI ESP6500), RGC internal, and CSC population-specific allele frequency databases.

The annotation process incorporated in silico predictions of functional effect (e.g., LRT, Polyphen2, SIFT, CADD, MutationTaster) and conservation scores based on multispecies alignment (GERP, PhyloP, PhastCons). Primary analyses were performed using RGC’s trio-based pipeline and further vetted through segregation analyses among available affected and unaffected family members. In the large majority of cases, we succeeded in generating WES data for all members (affected and unaffected) of the proband’s nuclear family and, when indicated, more distantly related individuals germane to the analysis. Classification of pathogenicity for candidate exome variants was based upon ACMG guidelines.30 Informative case results were restricted to “pathogenic” and “likely pathogenic” variants as judged by these criteria, whereas variants of unknown significance were deemed “open” cases. Prior to reporting, all copy-number and allelic variants were validated in CSC’s Clinical Laboratory Improvement Amendments–certified molecular laboratory.12, 13

We studied NIN constructs in human embryonic kidney epithelial cells (HEK-293T; ATCC) using a STAT phosphorylation assay (See Supplementary Methods).31

Results

Study population and testing indications

The most common indications for genomic testing (Figure 1a) were central nervous system disease (64%), auditory or visual impairment (7%), neuromuscular weakness (6%), growth delay (5%), hepatopathy (4%), and skeletal dysplasia (4%). Among 52 probands with neurological disease, 85% had developmental delay characterized by diverse and overlapping phenotypes such as global developmental delay/intellectual disability (73%), motor disability with or without hypotonia (60%), executive dysfunction (44%), epilepsy (44%), autism (27%), extrapyramidal movement disorders (17%), and affective illness (15%). Nearly half of children who presented with developmental disability had abnormal brain size and/or morphology (microcephaly, 23%; macrocephaly, 12%; and/or cortical malformation, 13%). Prior to high-density CMA and WES, 61% of probands had between one and six (average two) uninformative targeted molecular tests and several were subjects of unsuccessful low-density autozygosity mapping.32

Molecular diagnoses

A pathogenic abnormality was identified by high-density CMA in 7 (9%) of 79 cases, including split-hand/split foot malformation with long bone deficiency-3 (MIM 246560), latent hereditary neuropathy with liability to pressure palsies (PMP22 deletion; MIM 162500), novel pathogenic CNVs in syndromic developmental delay accompanied by congenital heart disease, and atypical presentations of Angelman (MIM 105830) and Turner syndrome (Table 1, Figure 1b). One three-year-old boy (Proband 4) who presented with the classic cortical dysplasia-focal epilepsy syndrome (CDFES, MIM 610042) inherited one copy of the common Amish CNTNAP2 variant (c.3709delG) through the maternal line and a second pathogenic 37,556 bp deletion of CNTNAP2 (c.403_550del) from his Mennonite father.

Table 1 Pathogenic copy number variants identified by molecular karyotype

Seventy-two probands advanced to “family” WES for phenotypes unique to the individual (n = 62), found among more than one sibling (n = 6), or segregating within a larger pedigree (n = 4) (Table 2). Family WES data were informative for 37 (51%) of 72 remaining individuals, yielding a definitive genetic diagnosis (n = 32, 44%) or suggesting a novel molecular etiology (n = 5, 7%) (Figure 2b). The diagnostic yield of WES was highest (71%) for the 14 probands who shared a phenotype with one or more related individuals in an apparently recessive, dominant, or X-linked segregation pattern. For 5 (7%) additional subjects with an ambiguous clinical phenotype (e.g., varicella encephalitis, transient hypercholanemia, transient glycogen hepatopathy, borderline QTc prolongation, extensive dental caries), negative WES results markedly reduced the likelihood of a genetic disease mechanism. We performed an average of 7 (range 3–17) exomes per proband (502 exomes for the cohort). When compared to trio analysis (proband and parents only), this inclusive strategy narrowed filtered candidate variants more than fourfold, from 22 ± 6 to 5 ± 3 alleles per proband (Figure 2a).

Table 2 Exome data verifying (n=32) or suggesting (n=5) a Mendelian disorder
Figure 2
figure2

Whole-exome sequencing (WES) results. (a) We generated an average of 7 (range 3-17) exomes per proband (white circles). Compared to trio analysis, this strategy reduced the average number of filtered candidate variants from 22 ± 6 (gray) to 5 ± 3 (red). (b) Overall results of diagnostic evaluation for 79 subjects. (c), (d) Proband 24 and her younger brother had cleft palate, clubfeet, early onset scoliosis, short stature, and skeletal dysplasia. (c) Anterior-posterior radiograph shows a severe scoliotic angle (46.1 degrees, yellow dotted line) and abnormal morphology of the proximal femurs (yellow arrows). Family WES data showed siblings to be homozygous for two pathogenic variants: one for Charcot-Marie-Tooth type 4 C (SH3TC2 [c.2860 C > T; p.Arg954Ter]; CMT4C, MIM 601596) and another for diastrophic dysplasia (SLC26A2 [c.835 C > T; p.Arg279Trp]; DTD, MIM 222600,)—in linkage disequilibrium on a 953-kb chromosome 5 haplotype. A digenic mechanism explained the unusually severe course of scoliosis, a manifestation of both DTD and CMT4C, and unmasked a demyelinating sensorimotor neuropathy confirmed by nerve conduction velocity testing. (e) We generated WES data for 38 members of a three-generation Mennonite pedigree segregating nonlesional epilepsy, expecting to find a single dominant risk allele. Instead, we identified three different pathogenic epilepsy variants in two epilepsy-associated genes: a de novo deletion in SCN1B (Proband 37 (yellow): c.305_313delAGGATCTGT; p.Q102PdelDLS), a missense variant of SCN1B (Proband 35: c.350 G > A; p.G117D) segregating in a dominant fashion (MIM 604233; blue), and a dominantly segregating frameshift deletion (red) in NPRL3 (Proband 36: c.349_349delG; p.E117Kfs*5; MIM 617118). Color-filled symbols represent affected variant carriers, while open symbols represent unaffected variant carriers. Both dominant variants were incompletely penetrant, further complicating the clinical picture. (f) In an Amish sibship, the eldest child (Proband 16, red circle) presented with intellectual disability, hyperactivity, inattention, and epilepsy. Her three younger siblings had a similar behavioral phenotype but without epilepsy. The mother had classical phenylketonuria, and records revealed teratogenic (red dotted line) maternal phenylalanine levels (white circles) during all four pregnancies. Family WES analysis identified a pathogenic de novo missense variant of SYNGAP1 (c.1526 C > A; p.A509D) in the eldest proband, explaining her unique manifestation of epilepsy (MIM 612621), which does not commonly result from maternal hyperphenylalaninemia (diamonds). Note: light red, yellow, and green shading indicate the teratogenic potential of hyperphenylalaninemia as high, intermediate, or low, respectively, during serial phases of pregnancy.

We identified cases of two Mendelian syndromes segregating in the same proband to produce a complex phenotype, consistent with recent reports of multilocus genomic variation.33 Proband 20 had de novo pathogenic variants in two genes (SHANK3 and TCF20) underlying a presentation of autism spectrum disorder, intellectual disability, and bipolar illness. A sibling pair (Proband 24) with skeletal dysplasia, scoliosis, and clubfoot shared homozygous pathogenic variants in two genes—SLC26A2 (diastrophic dysplasia, MIM 222600) and SH3TC2 (Charcot-Marie-Tooth type 4 C, MIM 601596)—segregating on the same haplotype (Figure 2c). Although diastrophic dysplasia dominated the clinical presentation, nerve conduction velocities subsequently revealed a motor neuropathy characteristic of CMT4C.

Novel, as yet provisional, gene-disease associations listed in Table 2 (Probands 40–44) include four autosomal recessive (CHD1, JKAMP, NIN, NUP188) and one de novo dominant (BMP2) phenotypes. Each allele in Table 2 represents the only compelling variant(s) to pass all filtering criteria and segregate appropriately within the family. However, each is classified as “uncertain significance” according to ACMG criteria,30 largely because such criteria do not accommodate novel gene discoveries or phenotypes that diverge from published reports (Table 2, Figure 3). Pathogenicity of BMP2 was first suspected by matching the “Amish” phenotype to unrelated non-Plain probands (https://genematcher.org) and corroborated by rephenotyping of all affected subjects (Figure 1b).

Figure 3
figure3

Novel disease gene discovery. (a), (b) We documented progressive, high-frequency sensorineural hearing loss in Proband 42 and three of her siblings, who shared homozygous nonsense mutations of NIN (c. 4666 C > T; p.Gln1556Ter). Segregation of wild type ( + ) and c.4666 C > T (M) are shown. (b) Characteristic audiogram data for two subjects (circle, square), showing selective insensitivity to high (4000 Hz, red symbols) versus low (250 Hz, gray symbols) frequencies (dotted line represents normal hearing level). Missense alterations of NIN were previously associated with microcephalic primordial dwarfism (MIM 210600) and spondyloepimetaphyseal dysplasia (MIM 603546), neither of which was observed in our subjects. (c) Ninein inhibits JAK2/STAT signaling through its C-terminus, displayed here by a decrease in STAT1 and STAT3 phosphorylation by overexpression of WT and C-terminal Ninein amino acids 1179-1931 in HEK-293T cells. Ninein p.Gln1556Ter was 2.8-fold and 1.5-fold less effective in inhibiting STAT1 and STAT3 phosphorylation, respectively (N = 3), suggesting constitutive upregulation of JAK2/STAT signaling. Inhibition of JAK2/STAT3 signaling attenuates noise-induced hearing loss in mice. (d) Nin−/− mice (yellow) have isolated high-frequency sensorineural hearing loss and elevated auditory brainstem response (ABR) thresholds at 18 and 24 kHz (Figure 3d from RIKEN BioResource Center: http://www.mousephenotype.org/phenoview/?gid=8293&qeid=IMPC_ABR_010_001).

Our analyses were nondiagnostic in 30 (38%) cases, 18 of which were characterized by a short list of candidate alleles (in mostly uncharacterized genes) that could not be narrowed to a specific variant. For a number of such cases, bioinformatic analyses in conjunction with expression and literature investigations implicated a single candidate allele as pathogenic, but an association could not be firmly established without further evidence, such as in vitro functional data, animal models, or additional patients (Figure 1b).

Allele spectrum

We identified 37 pathogenic or likely pathogenic exome variants among 32 probands represented in Table 2 (Supplementary Table S1). Twenty-seven (84%) of these individuals presented with primary neurological disease, most commonly symptomatic epilepsy (n = 7), intellectual disability (n = 7), or syndromic global developmental delay (n = 6). Half the variants were missense changes, 27% were insertions or deletions leading to frameshift variants, 13% were nonsense, and 13% affected canonical splice sites. Inheritance was de novo dominant in 16 (50%) cases, autosomal recessive in 12 (38%; 9 homozygous, 3 compound heterozygous) cases, and X-linked recessive in 1 case. Dominant inheritance was observed for two probands within large multigenerational families segregating nonlesional generalized epilepsy; in one such pedigree, seizures were attributable to three variants in two different genes: SCN1B (MIM 604233) and NPRL3 (MIM 617118)(Figure 2e). We identified one putative case of germ-line mosaicism in which two siblings with Rubinstein–Taybi syndrome (MIM 180849) carried a variant of CREBBP that was not present in peripheral blood DNA of either biological parent.

Secondary findings

Among 502 subjects included for WES analysis, 490 (98%) elected to receive secondary ACMG findings. Twenty-one (4.2%) subjects harbored one of four known or likely pathogenic variants in three genes: BRCA2 (c.5073dupA and c.7378_7379delAA), APOB (c.10580 G > A), and DSC2 (c.1580_1583delTCAA); all opted to receive these results.

Discussion

Those who stand to benefit most from genetic testing often have complex medical needs and experience their health care as expensive, fragmented, and confusing. As a corollary, referrals for WES are commonly rejected by insurance carriers2, 21 and authorized samples are sometimes linked to incomplete or unreliable clinical data.1, 3 Such prosaic problems reinforce healthcare disparities and also reduce diagnostic efficacy. In one study of 814 consecutive probands, WES had a diagnostic yield of 26%, but provided potential diagnoses for 228 (28%) additional probands. For the latter, promising variants were assigned “uncertain significance” pending further segregation (50%), phenotyping (25%), or CNV analyses (25%).

Integration of molecular methods into medical practice not only engenders better clinical outcomes but also improves laboratory performance.13 Nonprofit-industry collaboration allowed us to apply this principle to deep sequencing by incorporating a sophisticated genomic testing pipeline, optimized for performance, into the clinical workflow (Supplementary Figure S1). Our overall WES diagnostic yield across diverse phenotypes was 44–51%, approaching the theoretical yield (~50%) proposed for larger outbred cohorts2 and the observed (45–49%) yield among carefully selected children with neurological disease.34 Embedding this service in a community-based practice with clinical laboratory capability12 allowed us to fully interrogate the genomic data, rephenotype patients as needed, validate WES variants on-site, directly report clinically actionable results, and apply new molecular findings to population-based health initiatives.13

To test the broader applicability of this strategy, we took steps to attenuate inflated yield (i.e., >70%) when WES is used as a first-tier diagnostic test for multiplex, consanguineous families.35 Within our Plain patients, “founder” alleles enriched by genetic drift manifest as more than 150 autosomal recessive and 25 autosomal dominant disorders.12, 22 By recognizing and testing for these variants, we provide molecular diagnoses for more than 40% of probands after one office encounter, obviating their need for CMA or WES (Figure 1a).13 To further limit representation of homozygous recessive genotypes within the cohort, we selected study subjects who had unique clinical phenotypes, in many cases with uninformative homozygosity mapping results. These pre-WES procedures largely abrogated the impact of founder variants, as half the pathogenic alleles we discovered were de novo (Table 2), approximating what one expects to find in an outbred cohort.1, 2

In complex clinical contexts, WES data clarify the relative contribution of genetic versus environmental factors and can deliver unexpected results. In some cases, WES data reveal digenic or multigenic interactions (e.g., Table 2, Probands 20 and 24) and in others, are informative only when combined with CMA results (e.g., Table 1, Proband 4). Five probands underscore the phenotypic overlap that often exists between genetic (e.g., neonatal rigidity and multifocal seizure syndrome; MIM 614498)32 and nongenetic (e.g., congenital viral encephalitis) afflictions. In such cases, “negative” WES data reduce the likelihood of a genetic disease mechanism and can critically inform clinical management, whereas “positive” WES data can challenge tacit assumptions about environmental pathogenesis, as we found in one sibship (Proband 16, Figure 2f) affected by both maternal phenylketonuria and SYNGAP1 haploinsufficiency (MIM 612621).

Full genetic ascertainment can reveal surprising complexity at the root of seemingly simple diagnostic problems. Such was the case for a nonlesional epilepsy phenotype segregating through a 38-member Mennonite pedigree (Figure 2e), in which we expected to find a single dominant risk allele. Instead, we identified three different pathogenic epilepsy variants in two epilepsy-associated genes (SCN1B and NPRL3). The relatively low observed penetrance (40–45% as compared to an expected value of ~70%) is noteworthy, but true penetrance may prove higher if currently asymptomatic individuals develop new seizure onsets over time or systemic electroencephalography (not done) reveals epileptiform cortical signatures in otherwise asymptomatic individuals. Such cases provide a potentially informative platform for discovering loci that modify disease expression, providing a fruitful area for future study.

A financial calculation invariably weighs on the use of new technologies and, without better value accounting, constrains the use of WES in clinical practice. In a recent study of 2,000 probands,3 WES was performed at the discretion of the referring physician unless denied by an insurance carrier. Pre-authorization for WES is required by more than 80% of US insurance carriers, who may ultimately fail to reimburse as many as 50% of completed studies.21 As with other measures of health care, this “reimbursement wall” stands as a principal determinant of disparate access to genetic testing.

This study was enabled by a nonprofit–industry collaboration that posed opportunities as well as challenges (Supplementary Figure S1). The final decision to enter into partnership was reached after careful negotiations to insure CSC’s clinical and operational autonomy, shared ownership of data, stringent protection of patient privacy, and unanimous acceptance by the CSC’s nonprofit Board of Directors, most of whom are leaders within Old Order communities. Adult members of the Plain community tend to be entrepreneurial and exceptionally pragmatic, and generally embrace creative forms of collaboration that allow their people to flourish.13 The overall success of the partnership has engendered strong ongoing community support for collaboration, which should enable us prospectively to perform WES on each proband for whom it is indicated.

Growing evidence supports the economy of this approach. Within the US healthcare system, standard evaluation of a child with neurodevelopmental disability costs an average of US$19,000 (range $9,000 to $35,000)9, 13, 36 for testing, not including professional fees or other indirect institutional expenses.9, 36 This approach, which does not encompass WES,7, provides a genetic diagnosis in about one third of cases. By comparison, first-tier WES for children with neurodevelopmental disorders yields a molecular diagnostic rate of 40–60%5, 9, 34 for an average $1,920 (range $1,170 to $3,150) per exome trio (based on 34 reporting labs at http://www.scienceexchange.com). Using this information to calculate a simple metric of value (i.e., favorable outcomes per dollar spent),37 we assign a theoretical genomic evaluation cost of $4,000 per study subject (to comprise costs of targeted allele detection, CMA, and 0–4 additional exomes per proband; Figure 1a) to return actionable information in at least 50% of cases. This strategy yields one molecular diagnosis per $8,000 dollars spent, compared to one diagnosis per $60,000 via the standard approach.

The implication is clear: for select patients, a diagnostic method that prioritizes CMA and WES can be efficient and cost-effective in a variety of clinical contexts, provided cases are chosen carefully and executed systematically. Embedding this service within community-based practice further improves its value and aligns well with the World Health Organization’s call to implement genetics in underserved settings.17, 38 We returned actionable secondary results to 21 subjects and, by designing rapid molecular tests for 17 (46%) alleles discovered by WES,13 created new opportunities for screening and prevention (Figure 3). We conclude that emerging genomic technologies, judiciously applied, can empower communities to curtail wasteful medical spending and improve population health.

References

  1. 1

    Retterer K, Juusola J, Cho MT et al. Clinical application of whole-exome sequencing across clinical indications. Genet Med 2016;18:696–704.

  2. 2

    Lee H, Deignan JL, Dorrani N et al. Clinical exome sequencing for genetic identification of rare Mendelian disorders. JAMA 2014;312:1880–1887.

  3. 3

    Yang Y, Muzny DM, Xia F et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA 2014;312:1870–1879.

  4. 4

    Yang Y, Muzny DM, Reid JG et al. Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N Engl J Med 2013;369:1502–1511.

  5. 5

    Sharma P, Gupta N, Chowdhury MR et al. Application of chromosomal microarrays in the evaluation of intellectual disability/global developmental delay patients - A study from a tertiary care genetic centre in India. Gene 2016;590:109–119.

  6. 6

    Miller DT, Adam MP, Aradhya S et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am J Hum Genet 2010;86:749–764.

  7. 7

    Moeschler JB, Shevell M, Committee on G . Comprehensive evaluation of the child with intellectual disability or global developmental delays. Pediatrics 2014;134:e903–918.

  8. 8

    Rauch A, Wieczorek D, Graf E et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 2012;380:1674–1682.

  9. 9

    Soden SE, Saunders CJ, Willig LK et al. Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders. Sci Transl Med 2014;6:265ra168.

  10. 10

    Fogel BL, Lee H, Deignan JL et al. Exome sequencing in the clinical diagnosis of sporadic or familial cerebellar ataxia. JAMA Neurol 2014;71:1237–1246.

  11. 11

    Cragun D, Bonner D, Kim J et al. Factors associated with genetic counseling and BRCA testing in a population-based sample of young Black women with breast cancer. Breast Cancer Res Treat 2015;151:169–176.

  12. 12

    Strauss KA, Puffenberger EG . Genetics, medicine, and the Plain people. Annu Rev Genomics Hum Genet 2009;10:513–536.

  13. 13

    Strauss KA, Puffenberger EG, Morton DH . One community’s effort to control genetic disease. Am J Public Health 2012;102:1300–1306.

  14. 14

    Noonan D . No insurance?: that’s a killer. Newsweek 2008;152:20.

  15. 15

    Ku L, Patrick R, Ellen T et al. Strengthening Primary Care to Bend the Cost Curve: The Expansion of Community Health Centers Through Health Reform 2010.

  16. 16

    Hawkins AK, Hayden MR . A grand challenge: providing benefits of clinical genetics to those in need. Genet Med 2011;13:197–200.

  17. 17

    Tekola-Ayele F, Rotimi CN . Translational Genomics in Low- and Middle-Income Countries: Opportunities and Challenges. Public Health Genomics 2015;18:242–247.

  18. 18

    NIH Prepares to Launch Precision Medicine Study. Cancer Discov 2016;6:938.

  19. 19

    Shuldiner AR . An audience with: Alan Shuldiner. Nat Rev Drug Discov 2016;15:378–378.

  20. 20

    Shapiro SD . The promise of precision medicine for health systems. Am J Health Syst Pharm 2016;73:1907–1908.

  21. 21

    Lennerz JK, McLaughlin HM, Baron JM et al. Health care infrastructure for financially sustainable clinical genomics. J Mol Diagn 2016;18:697–706.

  22. 22

    Puffenberger EG . Genetic heritage of the Old Order Mennonites of southeastern Pennsylvania. Am J Med Genet C Semin Med Genet 2003;121C:18–31.

  23. 23

    Green RC, Berg JS, Grody WW et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med 2013;15:565–574.

  24. 24

    Shen H, Damcott CM, Rampersaud E et al. Familial defective apolipoprotein B-100 and increased low-density lipoprotein cholesterol and coronary artery calcification in the old order amish. Arch Intern Med 2010;170:1850–1855.

  25. 25

    Girdea M, Dumitriu S, Fiume M et al. PhenoTips: patient phenotyping software for clinical and research use. Hum Mutat 2013;34:1057–1065.

  26. 26

    van Karnebeek CD, Shevell M, Zschocke J, Moeschler JB, Stockler S . The metabolic evaluation of the child with an intellectual developmental disorder: diagnostic algorithm for identification of treatable causes and new digital resource. Mol Genet Metab 2014;111:428–438.

  27. 27

    Battaglia A, Bianchini E, Carey JC . Diagnostic yield of the comprehensive assessment of developmental delay/mental retardation in an institute of child neuropsychiatry. Am J Med Genet 1999;82:60–66.

  28. 28

    Shevell M, Ashwal S, Donley D et al. Practice parameter: evaluation of the child with global developmental delay: report of the Quality Standards Subcommittee of the American Academy of Neurology and The Practice Committee of the Child Neurology Society. Neurology 2003;60:367–380.

  29. 29

    Michelson DJ, Shevell MI, Sherr EH, Moeschler JB, Gropman AL, Ashwal S . Evidence report: Genetic and metabolic testing on children with global developmental delay: report of the Quality Standards Subcommittee of the American Academy of Neurology and the Practice Committee of the Child Neurology Society. Neurology 2011;77:1629–1635.

  30. 30

    Richards S, Aziz N, Bale S et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17:405–424.

  31. 31

    Jay J, Hammer A, Nestor-Kalinoski A, Diakonova M . JAK2 tyrosine kinase phosphorylates and is negatively regulated by centrosomal protein Ninein. Mol Cell Biol 2015;35:111–131.

  32. 32

    Puffenberger EG, Jinks RN, Sougnez C et al. Genetic mapping and exome sequencing identify variants associated with five novel diseases. PLoS One 2012;7:e28936.

  33. 33

    Posey JE, Harel T, Liu P et al. Resolution of disease phenotypes resulting from multilocus genomic variation. N Engl J Med 2017;376:21–31.

  34. 34

    Kuperberg M, Lev D, Blumkin L et al. Utility of whole exome sequencing for genetic diagnosis of previously undiagnosed pediatric neurology patients. J Child Neurol 2016;31:1534–1539.

  35. 35

    Charng WL, Karaca E, Coban Akdemir Z et al. Exome sequencing in mostly consanguineous Arab families with neurologic disease provides a high potential molecular diagnosis rate. BMC Med Genomics 2016;9:42.

  36. 36

    Joshi C, Kolbe DL, Mansilla MA, Mason SO, Smith RJ, Campbell CA . Reducing the cost of the diagnostic odyssey in early onset epileptic encephalopathies. Biomed Res Int 2016;2016:6421039.

  37. 37

    Porter ME . What is value in health care? N Engl J Med 2010;363:2477–2481.

  38. 38

    Kingsmore SF, Lantos JD, Dinwiddie DL et al. Next-generation community genetics for low- and middle-income countries. Genome Med 2012;4:25.

Download references

Acknowledgements

This work was supported in part by charitable contributions from Old Order Amish and Mennonite Communities of Pennsylvania and surrounding states. CMA analysis at CSC and functional studies performed by R.N.J. were supported in part by a grant to Franklin & Marshall College from the Howard Hughes Medical Institute through the Precollege and Undergraduate Science Education Program. The authors thank D. Holmes Morton, Zineb Ammous, Olivia Wenger, and James Deline for contributions to proband phenotyping and sample collection.

Author information

Correspondence to Kevin A Strauss.

Ethics declarations

Competing interests

C.G.-J., A.K.K., C.V.H., K.P., A.B., J.G.R., J.D.O., F.E.D, S.J.M., and A.R.S. are full-time employees of the Regeneron Genetics Center, Regeneron Pharmaceuticals, Inc., and receive stock options as part of their compensation. The other authors declare no conflicts of interest.

Additional information

Supplementary material is linked to the online version of the paper at

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Keywords

  • chromosomal microarray
  • developmental delay
  • exome
  • genomic
  • intellectual disability

Further reading