References

Laboratory Practice

Select publications that evaluate technical aspects of WGS, including analytical validity, quality metrics and best practices
A randomized controlled trial of the analytic and diagnostic performance of singleton and trio, rapid genome and exome sequencing in ill infants.
Kingsmore SF, Cakici JA, Clark MM, Gaughran M, Feddock M, Batalov S, Bainbridge MN, Carroll J, Caylor SA, Clarke C, Ding Y, ellsworth K, Farnaes L, Hildreth A, Hobbs C, James K, King CI, Lenberg J, NahasS, Prince L, Reyes I, Salz L, Sanford E, Schols P, Sweeney N, Tokita M, Veeraraghavan N, Watkins K, Wigby K, Wong T, Chowdhury S, Wright MS, Dimmock D, RCIGM Investigators.  A J Hum Gen, 2019;105:1-15.

NSIGHT2 is a prospective, randomized-controlled trial comparing rapid whole genome sequencing (WGS) and rapid whole exome sequencing (WES) to infants <4 months of age within 96 hours of NICU/PICU admission or onset of features. Singleton analysis was performed for all samples with secondary trio reanalysis performed as reflex to negative results. Gravely ill infants underwent trio, ultra-rapid WGS. The combined diagnostic yield in this study was 23%. Yield was not significantly different between rapid WGS (20%) and rapid WES (19%); however, it was higher (24%) in ultra-rapid WGS. Median time to diagnosis was similar between rWGS and rWES (11.0 d vs. 11.2 d). urWGS and rWGS combined identified two times more pathogenic and likely pathogenic variants than WES. The authors conclude that the analytical performance of rWGS is superior to rWES supporting the use of rWGS as a first-tier test in the NICU/PICU setting.

A Rigorous Interlaboratory Examination of the Need to Confirm Next-Generation Sequencing-Detected Variants with an Orthogonal Method in Clinical Genetic Testing.
Lincoln SE, Truty R, Lin CF, Zook JM, Paul J, Ramey VH, Salit M, Rehm HL, Nussbaum RL, Lebo MS.  J Mol Diagn. 2019 Mar;21(2):318-329. doi: 10.1016/j.jmoldx.2018.10.009.

 

Good laboratory practice for clinical next-generation sequencing informatics pipelines. 
Gargis AS, Kalman L, Bick DP, da Silva C, Dimmock DP, Funke BH, Gowrisankar S, Hedge MR, Kulkarni S, Mason CE, Nagarajan R, Voelkerding KV, Worthey EA, Aziz N, Barnes J, Bennett SF, Bisht H, Church DM, Dimitrova Z, Gargis SR, Hafez N, Hambuch T, Hyland FCL, Luna RA, MacCannell D, Mann T, McCluskey MR, McDaniel TK, Ganova-Raeva LM, Rehm HL, Reid J, Campo DS, Resnick RB, Ridge PG, Salit ML, Skums P, Wong LJC, Zehnbauer BA, Lubin IM.  Nat Biotechnol. 2015;33(7):689–693. doi:10.1038/nbt.3237.

 

Rapid Paediatric Sequencing (RaPS): comprehensive real-life workflow for rapid diagnosis of critically ill children.
Mestek-Boukhibar l, Clement E, Jones WD, Drury S, Ocaka L, Gagunashvili A, Le Quesne Stabej P, Bacchelli C, Jani N, Rahman S, Jenkins L, Hurst JA, Bitner-Glindzicz M, Peters M, Beales PL, Williams HJ. J Med Genet. 2018; 55:721–728.

 

A survey of current practices for genomic sequencing test interpretation and reporting processes in US laboratories. 
O’Daniel JM, McLaughlin HM, Amendola LM, Bale SJ, Berg JB, Bick D, Bowling KM, Chao EC, Chung WK, Conlin LK, Cooper GM, Das S, Deignan JL, Dorschner MO, Evans JP, Ghazani AA, Goddard KA, Gornick M, Farwell Hagman KD, Hambuch T, Hedge M, Hindorff LA, Holm IA, Jarvik GP, Knight Johnson A, Mighion L, Morra M, Plon SE, Punj S, Richards CS, Santani A, Shirts BH, Spinner NB, Tang S, Weck KE, Wolf SM, Yang Y, Rehm HL. Genet Med. 2016;19(5):575–582. doi:10.1038/gim.2016.152.

 

An open resource for accurately benchmarking small variant and reference calls.
Zook JM, McDaniel J, Olson ND, Wagner J, Parikh H, Heaton H, Irvine SA, Trigg L, Truty R, McLean CY, De La Vega FM, Xiao C, Sherry S, Salit M. Nat Biotechnol. 2019;37(5):561-566.

 

Genomic medicine for undiagnosed diseases
Wise AL, Manolio TA, Mesah GA, Peterson JF, Roden DM, Tamburro C, Williams MS, Green ED.. Lancet, 2019; 394: 533-40.

 

Best practices for benchmarking germline small-variant calls in human genomes.
Krusche P, Trigg L, Boutros PC, Mason CE, De La Vega FM, Moore BL, Gonzalez-Porta M, Eberle MA, Tezak Z, Lababidi S, Truty R, Asimenos G, Funke B, Fleharty M, Chapman BA, Salit M, Zook JM, Global Alliance for Genomics and Health Benchmarking Team.  Nat Biotechnol. 2019 May;37(5):555-560. doi: 10.1038/s41587-019-0054-x.

Clinical Utility

Select publications evaluating changes in medical management as a result of WGS-derived test results
Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected diseases.
Clark MM, Stark Z, Farnaes L, Tan TY, White SM, Dimmock D, Kingsmore SF. NPJ Genom Med. 2018 Jul 9;3:16. doi: 10.1038/s41525-018-0053-8.

This meta-analysis examined 37 studies comprising 20,068 children from January 2011 to August 2017. Overall, the diagnostic utility was 8.3 times greater with WES/WGS (36%/41%) compared to CMA (10%). 25.7% of diagnostic variants identified by WGS were not apparent by WES. WGS detected diagnostic variants beyond the scope of WES, including intronic SNVs, SNVs in noncoding RNA, small CNVs, and mitochondrial DNA mutations, as well as exonic SNVs under-covered by WES.

The NSIGHT1-randomized controlled trial: rapid whole-genome sequencing for accelerated etiologic diagnosis in critically ill infants.
Petrikin JE, Cakici JA, Clark MM, Willig LK, Sweeney NM, Farrow EG, Saunders CJ, Thiffault I, Miller NA, Zellmer L, Herd SM, Holmes AM, Batalov S, Veeraraghavan N, Smith LD, Dimmock DP, Leeder JS, Kingsmore SF. NPJ Genom Med. 2018 Feb 9;3:6. doi: 10.1038/s41525-018-0045-8.

Rapid WGS plus standard clinical testing yielded higher genetic diagnosis rate and shorter time to diagnosis compared to standard clinical testing alone. In this partially blinded, randomized controlled trial, 65 infants (<4 months of age) with highly variable phenotypes had rapid WGS to determine diagnosis at 28 days post enrollment. Median time to diagnosis with WGS was 13 days vs. 107 days with standard clinical testing and median day of life at diagnosis was 25 days with WGS vs. 130 days with standard genetic testing. This study was terminated early due to equipoise and the growing inclusion of NGS in standard clinical testing strategies.

Whole genome sequencing expands diagnostic utility and improves clinical management in pediatric medicine
Stavropoulos DJ, Merico D, Jobling R, Bowdin S, Monfared N, Thiruvahindrapuram B, Nalpathamkalam T, Pellecchia G, Yuen RKC, Szego MJ, Hayeems RZ, Shaul RZ, Brudno M, Girdea M, Frey B, Alipanahi B, Ahmed S, Babul-Hirji R, Badilla Porras R, Carter MT, Chad L, Chaudhry A, Chitayat D, Jougheh Doust S, Cytrynbaum C, Dupuis L, Ejaz R, Fishman L, Guerin A, Hashemi B, Helal M, Hewson S, Inbar-Feigenberg M, Kannu P, Karp N, Kim RH, Kronick J, Liston E, MacDonald H, Mercimek-Mahmutoglu S, Mendoza-Londono R, Nasr E, Nimmo G, Parkinson N, Quercia N, Raiman J, Roifman M, Schulze A, Shugar A, Shuman C, Sinajon P, Siriwardena K, Weksberg R, Yoon G, Carew C, Erickson R, Leach RA, Klein R, Ray PN, Meyn MS, Scherer SW, Cohn RD, Marshall CR. NPJ Genomic Medicine, 2016; 1.

A prospective study evaluated the diagnostic yield of WGS compared to standard clinical testing on 100 consecutive children referred for chromosomal microarray. The authors concluded that as a first-tier test, WGS reduces the number of genetic investigations and potentially the time to diagnosis, ultimately acting as a more cost-effective approach. WGS diagnosis rate was 34% compared to 13% for the total from standard clinical testing (CMA plus targeted gene sequencing (P value = 0.0009). CNV resolution is greater for WGS than CMA, typically detecting >1,500 unbalanced changes that cannot be found using CMA. Split read mapping can further reveal complex overlapping CNVs missed by CMA.

Clinical whole genome sequencing as a first-tier test at a resource-limited dysmorphology clinic in Mexico.
Scocchia A, Wigby KM, Masser-Frye D, Del Campo M, Galarreta CI, Thorpe E, McEachern J, Robinson K, Gross A, ICSL Interpretation and Reporting Team, Ajay SS, Rajan V, Perry DL, Belmont JW, Bentley DR, Jones MC, Taft R. NPJ Genom Med. 2019;4:5.

In a resource limited setting, clinical WGS was provided at no-cost to 60 children with a mean age of 7.6 years who met testing criteria. Indications included 77% with suspected pattern of malformation and 23% with primary neurological presentation. The overall diagnostic yield was 68%. 41/60 had a genomic finding consistent with the phenotype. This included 76% of those referred for suspected malformations and 43% referred for primary neurological presentation. (p=0.0455). Post-test counseling was modified for both patient with and without a molecular diagnosis. The absence of diagnosis helpful in 6 of 19 cases without molecular diagnosis.

Rapid whole genome sequencing has clinical utility in the PICU
Sanford EF, Clark MM, Farnaes L, Williams MR, Perry JC, Inguli EG, Sweeney NM, Doshi A, Gold JJ, Briggs B, Bainbridge MN, Feddock M, Watkins K, Chowdhury S, Nahas SA, Dimmock DP, Kingsmore SF, Coufal NG. Rapid whole genome sequencing has clinical utility in children in the PICU. PediatrCritCare Med. 2019 Jun 19. doi: 10.1097/PCC.0000000000002056.
Rapid whole-genome sequencing decreases infant morbidity and cost of hospitalization.
Farnaes L, Hildreth A, Sweeney NM, Clark MM, Chowdhury S, Nahas S, Cakici, JA, Benson W, Kaplan RH, Kronick R, Bainbridge MN, Friedman J, Gold JJ, Ding Y, Veeraraghavan N, Dimmock D, Kingsmore SF.  NPJ Genom Med. 2018 Apr 4;3:10. doi: 10.1038/s41525- 018-0049-4.

Economic Utility

Select publications that evaluate the cost effectiveness of clinical WGS
Estimating the burden and economic impact of pediatric genetic disease
Gonzaludo N, Belmont JW, Gainullin VG, Taft RJ. Genet Med. 2019.  doi: 10.1038/s41436-019-0458-5. Epub ahead of print.

To estimate the economic burden of genetic disease in pediatric patients, the authors analyzed the 2012 KID database, a national all-payer database for children in US that includes 10% of uncomplicated births and 80% of complicated in-hospital births. Genetic disease-linked discharges were associated with higher healthcare utilization, including additional procedures (up to 4 more), longer length of stay (2-18 days) and higher total costs per discharge ($12,000-$77,000). Discharges with multiple genetic disease-linked diagnosis codes yield higher cost per discharge with an incremental increase of $13,999 per code (up to 7th code). Overall genetic disease-linked discharges account for a proportionately larger amount of the “national bill” vs. non genetic disease-linked.

Rapid whole-genome sequencing decreases infant morbidity and cost of hospitalization.
Farnaes L, Hildreth A, Sweeney NM, Clark MM, Chowdhury S, Nahas S, Cakici, JA, Benson W, Kaplan RH, Kronick R, Bainbridge MN, Friedman J, Gold JJ, Ding Y, Veeraraghavan N, Dimmock D, Kingsmore SF.  NPJ Genom Med. 2018 Apr 4;3:10. doi: 10.1038/s41525- 018-0049-4.

Diagnostic Utility

Relevant publications demonstrating the diagnostic potential of clinical WGS in selected patient populations
A randomized controlled trial of the analytic and diagnostic performance of singleton and trio, rapid genome and exome sequencing in ill infants.
Kingsmore SF, Cakici JA, Clark MM, Gaughran M, Feddock M, Batalov S, Bainbridge MN, Carroll J, Caylor SA, Clarke C, Ding Y, ellsworth K, Farnaes L, Hildreth A, Hobbs C, James K, King CI, Lenberg J, NahasS, Prince L, Reyes I, Salz L, Sanford E, Schols P, Sweeney N, Tokita M, Veeraraghavan N, Watkins K, Wigby K, Wong T, Chowdhury S, Wright MS, Dimmock D, RCIGM Investigators.  A J Hum Gen, 2019;105:1-15.

NSIGHT2 is a prospective, randomized-controlled trial comparing rapid whole genome sequencing (WGS) and rapid whole exome sequencing (WES) to infants <4 months of age within 96 hours of NICU/PICU admission or onset of features. Singleton analysis was performed for all samples with secondary trio reanalysis performed as reflex to negative results. Gravely ill infants underwent trio, ultra-rapid WGS. The combined diagnostic yield in this study was 23%. Yield was not significantly different between rapid WGS (20%) and rapid WES (19%); however, it was higher (24%) in ultra-rapid WGS. Median time to diagnosis was similar between rWGS and rWES (11.0 d vs. 11.2 d). urWGS and rWGS combined identified two times more pathogenic and likely pathogenic variants than WES. The authors conclude that the analytical performance of rWGS is superior to rWES supporting the use of rWGS as a first-tier test in the NICU/PICU setting.

Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test
Lionel AC, Costain G, Monfared N, Walker S, Reuter MS, Hosseini M, Thiruvahindrapuram B, Merico D, Jobling R, Nalpathamkalam T, Pellecchia G, Sung WWL, Wang Z, Bikangaga P, Boelman C, Carter MT, Cordeiro D, Cytrynbaum C, Dell SD, Dhir P, Dowling JJ, Heon E, Hewson S, Hiraki L, Inbar-Feigenberg M, Klatt R, Kronick J, Laxer RM, Licht C, MacDonald H, Mercimek-Andrews S, Mendoza-Londono R, Piscione T, Schneider R, Schulze A, Silverman E, Siriwardena K, Snead OC, Sondheimer N, Sutherland J, Vincent A, Wasserman JD, Weksberg R, Shuman C, Carew C, Szego MJ, Hayeems RZ, Basran R, Stavropoulos DJ, Ray PN, Bowdin S, Meyn MS, Cohn RD, Scherer SW, Marshall CR. Genet Med. 2017; Aug 3. doi: 10.1038/gim.2017.119.

In this prospective comparison of WGS to standard clinical testing (including NGS panels) in 103 pediatric patients with diverse phenotypes, the authors concluded that WGS is superior given its higher diagnostic yield. WGS confirmed significantly more diagnoses than conventional testing (41% vs 24%; P =0.01). All copy number variants reported by chromosomal microarray were detected by WGS and WGS offered more complete coverage of disease associated genes compared to WES.

The NSIGHT1-randomized controlled trial: rapid whole-genome sequencing for accelerated etiologic diagnosis in critically ill infants.
Petrikin JE, Cakici JA, Clark MM, Willig LK, Sweeney NM, Farrow EG, Saunders CJ, Thiffault I, Miller NA, Zellmer L, Herd SM, Holmes AM, Batalov S, Veeraraghavan N, Smith LD, Dimmock DP, Leeder JS, Kingsmore SF. NPJ Genom Med. 2018 Feb 9;3:6. doi: 10.1038/s41525-018-0045-8.

Rapid WGS plus standard clinical testing yielded higher genetic diagnosis rate and shorter time to diagnosis compared to standard clinical testing alone. In this partially blinded, randomized controlled trial, 65 infants (<4 months of age) with highly variable phenotypes had rapid WGS to determine diagnosis at 28 days post enrollment. Median time to diagnosis with WGS was 13 days vs. 107 days with standard clinical testing and median day of life at diagnosis was 25 days with WGS vs. 130 days with standard genetic testing. This study was terminated early due to equipoise and the growing inclusion of NGS in standard clinical testing strategies.

Clinical whole genome sequencing as a first-tier test at a resource-limited dysmorphology clinic in Mexico.
Scocchia A, Wigby KM, Masser-Frye D, Del Campo M, Galarreta CI, Thorpe E, McEachern J, Robinson K, Gross A, ICSL Interpretation and Reporting Team, Ajay SS, Rajan V, Perry DL, Belmont JW, Bentley DR, Jones MC, Taft R. NPJ Genom Med. 2019;4:5.

In a resource limited setting, clinical WGS was provided at no-cost to 60 children with a mean age of 7.6 years who met testing criteria. Indications included 77% with suspected pattern of malformation and 23% with primary neurological presentation. The overall diagnostic yield was 68%. 41/60 had a genomic finding consistent with the phenotype. This included 76% of those referred for suspected malformations and 43% referred for primary neurological presentation. (p=0.0455). Post-test counseling was modified for both patient with and without a molecular diagnosis. The absence of diagnosis helpful in 6 of 19 cases without molecular diagnosis.

Whole genome sequencing expands diagnostic utility and improves clinical management in pediatric medicine
Stavropoulos DJ, Merico D, Jobling R, Bowdin S, Monfared N, Thiruvahindrapuram B, Nalpathamkalam T, Pellecchia G, Yuen RKC, Szego MJ, Hayeems RZ, Shaul RZ, Brudno M, Girdea M, Frey B, Alipanahi B, Ahmed S, Babul-Hirji R, Badilla Porras R, Carter MT, Chad L, Chaudhry A, Chitayat D, Jougheh Doust S, Cytrynbaum C, Dupuis L, Ejaz R, Fishman L, Guerin A, Hashemi B, Helal M, Hewson S, Inbar-Feigenberg M, Kannu P, Karp N, Kim RH, Kronick J, Liston E, MacDonald H, Mercimek-Mahmutoglu S, Mendoza-Londono R, Nasr E, Nimmo G, Parkinson N, Quercia N, Raiman J, Roifman M, Schulze A, Shugar A, Shuman C, Sinajon P, Siriwardena K, Weksberg R, Yoon G, Carew C, Erickson R, Leach RA, Klein R, Ray PN, Meyn MS, Scherer SW, Cohn RD, Marshall CR. NPJ Genomic Medicine, 2016; 1.

A prospective study evaluated the diagnostic yield of WGS compared to standard clinical testing on 100 consecutive children referred for chromosomal microarray. The authors concluded that as a first-tier test, WGS reduces the number of genetic investigations and potentially the time to diagnosis, ultimately acting as a more cost-effective approach. WGS diagnosis rate was 34% compared to 13% for the total from standard clinical testing (CMA plus targeted gene sequencing (P value = 0.0009). CNV resolution is greater for WGS than CMA, typically detecting >1,500 unbalanced changes that cannot be found using CMA. Split read mapping can further reveal complex overlapping CNVs missed by CMA.

From cytogenetics to cytogenomics: whole-genome sequencing as a first-line test comprehensively captures the diverse spectrum of disease-causing genetic variation underlying intellectual disability
Anna Lindstrand, Jesper Eisfeldt, Maria Pettersson, Claudia M. B. Carvalho, Malin Kvarnung, Giedre Grigelioniene, Britt-Marie Anderlid, Olof Bjerin, Peter Gustavsson, Anna Hammarsjö, Patrik Georgii-Hemming, Erik Iwarsson, Maria Johansson-Soller, Kristina Lagerstedt-Robinson, Agne Lieden, Måns Magnusson, Marcel Martin, Helena Malmgren, Magnus Nordenskjöld, Ameli Norling, Ellika Sahlin, Henrik Stranneheim, Emma Tham, Josephine Wincent, Sofia Ygberg, Anna Wedell, Valtteri Wirta, Ann Nordgren, Johanna Lundin and Daniel Nilsson. Genome Med. 2019; 11: 68. Published online 2019 Nov 7. doi: 10.1186/s13073-019-0675-1

Using short-read whole genome sequencing (WGS), the authors evaluated three cohorts to determine in which WGS would be an ideal first-tier diagnostic test.  The cohorts included: a cohort with validated copy number variants (CNVs) (cohort 1, n=68), individuals referred for monogenic multi-gene panels (cohort 2, n=156), and 100 prospective, consecutive cases referred to chromosomal microarray (CMA). CMA was performed using a custom oligonucleotide microarray with a median probe spacing of approximately 18kb and WGS performed using PCR-free, paired-end sequencing at 30X. Overall 27% of individuals harbored clinically relevant genetic variants by WGS compared to 12% by CMA. The authors concluded that study showed the power of WGS as a first-tier diagnostic test to detect a variety of CNVs and SNVs as well as single tandem repeats, regions of heterozygosity and chromosomal rearrangements.

Periodic reanalysis of whole-genome sequencing data enhances the diagnostic advantage over standard clinical genetic testing. 
Costain G, Jobling R, Walker S, Reuter MS, Snell M, Bowdin S, Cohn RD, Dupuis L, Hewson S, Mercimek-Andrews S, Shuman C, Sondheimer N, Weksberg R, Yoon G, Meyn MS, Stavropoulos DJ, Scherer SW, Mendoza-Londono R, Marshall CR. Eur J Hum Genet. 2018;26(5):740–744. doi:10.1038/s41431-018-0114-6.
Interpretation of genomic sequencing results in healthy and ill newborns: Results from the BabySeq Project
Ceyhan-Birsoy O, Murry JB, Machini K, Lebo MS, Yu TW, Fayer S, Genetti CA, Schwartz TS, Agrawal PB, Parad RB, Holm IA, McGuire AL, Green RB, Rehm HL, Beggs AH, The BabySeq Project Team.. Am J Hum Genet. 2019; 104(1): 76–93. doi: 10.1016/j.ajhg.2018.11.016

Reviews

Commentaries and scoping literature reviews that discuss rare disease, genomic medicine and general NGS considerations
Estimating cumulative point prevalence of rare disease: analysis of the Orphanet database.
Nguengang Wakap S, Lambert DM, Orly A, Rodwell C, Gueydan C, Lanneau V, Murphy D, Le Cam Y, Rath A.. Europ J Hum Genet. 2019;https://doi.org/10.1038/s41431-019-0508-0

Nguengang Wakap et al. analyzed epidemiological data in the Orphanet database to determine a cumulative point prevalence of rare disease. Analysis included information in database as of October 1, 2018. Based on all cases in the database, the authors estimate that RD affects at least 3.5-5.9% of the global population and approximately 72% of RD are reported to have a genetic etiology. This publication limits its calculations to only unique clinical disorders, therefore, removing the possibility of duplicate counting and overestimation.

Genome sequencing and implications for rare disorders.
Posey JE. Orphanet Journal of Rare Diseases. 2019; 14:153. https://doi.org/10.1186/s13023-019-1127-0
Genomic medicine for undiagnosed diseases
Wise AL, Manolio TA, Mesah GA, Peterson JF, Roden DM, Tamburro C, Williams MS, Green ED.. Lancet, 2019; 394: 533-40.
Case for genome sequencing in infants and children with rare, and undiagnosed genetic disease.
Bick D, Jones, M, Taylor SL, Taft RJ, Belmont J. J Med Genet 2019;0:1–9. doi:10.1136/jmedgenet-2019-106111.

Societal Guidelines

Current guidelines from key medical societies
Indications for WES/WGS1

ACMG published recommendations for when to consider WES/WGS in a phenotypically affected individual. These include:

  1. Phenotype or family history data strongly implicate a genetic etiology, but the phenotype does not correspond with a specific disorder for which a genetic test targeting a specific gene is available on a clinical basis.
  2. A patient presents with a defined genetic disorder that demonstrates a high degree of genetic heterogeneity, making WES or WGS analysis of multiple genes simultaneously a more practical approach.
  3. A likely genetic disorder but specific genetic tests available for that phenotype have failed to arrive at a diagnosis.
  4. When a fetus presents with a likely genetic disorder in which specific genetic tests, including targeted sequencing tests, available for that phenotype have failed to arrive at a diagnosis.
Tools and Methods for Variant Classification2,3

ACMG and others note that a careful review of the evidence for and against pathogenicity are important in variant classification. Use of sequence, population, and disease databases can be useful when classifying variants, however, clinical laboratories should be aware of potential limitations. In addition, laboratories are encouraged to contribute to variant databases and to form collaborations with clinicians.

Genomic Interpretation and Reporting2,3,4

ACMG recommends that genetic reports should be concise, easy to understand and contain all essential testing elements, supporting evidence for variants and follow up recommendations, if indicated. Careful review of the patient’s phenotype and the method of ascertainment are important in test interpretation.

Secondary Findings2,4-10

The ACMG has published lists of medically actionable genes recommended for return if a pathogenic variant is detected as a secondary finding in clinical genomic sequencing, based on conditions with the potential for intervention and improved outcomes if caught early. They also created a secondary findings working group to maintain the list of medically actionable secondary findings (currently 59 genes). Several other groups recommend that laboratories and clinics offering WGS/WES should have clear policies in place related to disclosure of secondary findings. Informed consent should indicate an option to opt-in or opt-out of receiving secondary findings and include a discussion about ramifications of either decision.

Clinical Data Sharing2,11-13

Clinical genomic data sharing is an important aspect of genomic sequencing that has been addressed by multiple societies, including ACMG, AMP, CCMG, ESHG and NSGC. Data-sharing is essential to reduce the number of variants of uncertain significance and to improve consistency and accuracy of variant interpretation.

Confirmatory Testing3,14-16

There are a range of opinions from ACMG, AMP and CAP about whether confirmation of all pathogenic or likely pathogenic sequence variants is recommended.

Variant Reevaluation and Reanalysis17

Requests for variant reanalysis can come from the patient, healthcare provider or laboratory. Clinical laboratories should have separate policies and protocols for initial variant classification, variant-level reclassification, and case-level reanalysis, which should be periodically reviewed and updated.

Guideline References

  1. American College of Medical Genetics and Genomics Board of Directors. Clinical utility of genetic and genomic services: A position statement of the American College of Medical Genetics and Genomics. Genet Med. 2015; 17(6): 505-507.
  1. Hegde M, Bale S, Bayrak-toydemir P, Gison J, Bone Jeng LJ, Joseph L, Laser J, Lubin IM, Miller CE, Ross LF, Rothberg PG, Tanner AK, VItazka P, Mao R. Reporting Incidental Findings in Genomic Scale Clinical Sequencing d A Clinical Laboratory Perspective A Report of the Association for Molecular Pathology. J Mol Diagnostics. 2015;17(2):107-117. doi:10.1016/j.jmoldx.2014.10.004.
  2. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hedge M, Lyon E, Spector E, Voelkerding K, Rehm HL, on behalf of the ACMG Laboratory Quality Assurance Committee. 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(5):405-424.
  3. Matthijs G, Souche E,, Alders M., Corveleyn A, Eck S, Feenstra I, Race V, Sistermans E, Sturm M, Weiss M, Yntema H, Bakker E, Scheffer H, Bauer P. Guidelines for diagnostic next-generation sequencing. Euro J Hum Genet. 2016;24:2–5.
  4. American College of Medical Genetics and Genomics (ACMG). Policy statement: updated recommendations regarding analysis and reporting of secondary findings in clinical genome-scale sequencing. Genet Med. 2015;17(1):68-69.
  5. American College of Medical Genetics and Genomics. Points to consider in the clinical application of Genomic Sequencing. a position statement of the American college of medical genetics and genomics. May 2012. epub.
  6. Boycott K, Hartley T, Adam S, Bernier F, Chong K, Fernandez BA, Friedman JM, Geraghty MT, Hume S, Knoppers BM, Laberge AM, Majewski J, Mendoza-Londono R, Meyn MS, Michaud JL, Nelson TN, Richer J, Sadikovic B, Skidmore DL, Stockley T, Taylor S, van Karnebeek C, Zawati MH, Lauzon J, Armour CM on behalf of the Canadian College of Medical Genetics. The clinical application of genome-wide sequencing for monogenic diseases in Canada: Position Statement of the Canadian College of Medical Geneticists. J Med Genet. 2015;52:431-437.
  7. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, McGuire A, Nussbaum RL, O’Daniel JM, Ormond KE, Rehm HL, Watson MS, Williams MS, Biesecker LG. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med. 2013;15(7):565-574.
  8. Kalia SS, Adelman K, Bale SJ, Chung WK, Eng C, Evans JP, Herman GE, Hufnagel SB, Klein TE, Korf BR, McKelvey KD, Ormond KE, Richards CS, Vlangos CN, Watson M, Martin CL, Miller DT on behalf of the ACMG Secondary Findings Maintenance Working Group. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med. 2017;19(2):249-255.
  9. National Society of Genetic Counselors. Position Statement: Incidental findings in genetic testing. April 2019.
  10. American College of Medical Genetics and Genomics Board of Directors. Laboratory and clinical genomic data sharing is crucial to improving genetic health care: a position statement of the American College of Medical Genetics and Genomics. Genet Med. 2017;19(7):721-722.
  11. Position Statement on Licensed Databases and Plans for the Global Sharing of Variant Data. 2015. Accessed February 2019.
  12. National Society of Genetic Counselors. Clinical data sharing. Position Statement. April 2015.
  13. Aziz N, Zhao Q, Bry L, Driscoll DK, Funke B, Gibson JS, Grody WW, Hedge MR, Hoeltge GA, Leonard DGB, Merker JD, Nagarajan R, Palicki LA, Robetorye RS, Schrijver I, Weck KE, Voelkerding KV. College of American Pathologists’ laboratory standards for next-generation sequencing clinical tests. Arch Pathol Lab Med. 2014;doi;10.5858/arpa.2014-0250-CP.
  14. Rehm HL, Bale SJ, Bayrak-Toydemir P, Berg JS, Brown KK, Deignan JL, Friez MJ, Funke BH, Hedge MR. Lyon E, from the Working Group of the American College of Medical Genetics and Genomics Laboratory Quality Assurance Committee. ACMG clinical laboratory standards for next-generation sequencing. Genet Med. 2013;15:733-47.
  15. Schrijver I, Aziz N, Farkas DH, Furtado M, Ferreira Gonzalez A, Greiner TC, Grody WW, Hambuch T, Kalman L, Kant JA, Klein RD, Leonard DGB, Lubin IM, Mao R, Nagan N, Pratt VM, Sobel ME, Voelkerding KV, Gibson JS. Opportunities and challenges associated with clinical diagnostic genome sequencing: A report of the association for molecular pathology. J Mol Diagn. 2012; 14(6): 525-540.
  16. Deignan JL, Chung WK, Kearney HM, Monaghan KG, Rehder CW, Chao EC, on behalf of the ACMG Laboratory Quality Assurance Committee. Points to consider in the reevaluation and reanalysis of genomic test results: a statement of the American College of Medical Genetics and Genomics (ACMG).  On behalf of the ACMG Laboratory Quality Assurance Committee. Genet Med. 2019;
  17. American College of Medical Genetics and Genomics Board of Directors. The use of ACMG secondary findings recommendations for general population screening: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2019; https://doi.org/10.1038/s41436-019-0502-5.
  18. American College of Medical Genetics and Genomics Board of Directors. Points to consider for informed consent for genome/exome sequencing. Genet Med. 2013; 15(9):748-749.
  19. Bush, LW, Beck AE, Biesecker LG, Evans JP, Hamosh A, Holm IA, Martin CL, Richards CS, Rehm HL. Professional responsibilities regarding the provision, publication and dissemination of patietn phenotypes in the context of clinical genetic and genomic testing: Points to consider – a statement of the Amercian College of Medical Genetics and Genomics (ACMG). Genet Med. 2018.
  20. Gibson W, Stavropoulos J, Sinasac D, McCready E, Mahmutoglu S, Nelson TN. Canadian College of Medical Genetics Laboratory Practice Committee. CCMG statement on germline variant classification. 2017.
  21. Rooney Riggs E, Andersen EF, Cherry AM, Kantarci S, Kearney H, Patel A, Raca G, Ritter DI, South ST, Thorland EC, Pineda-Alvarez D, Aradhya S, Martin CL. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen).Genet Med. 2019: https://www.nature.com/articles/s41436-019-0686-8
  22. van El CG, Cornel MC, Borry P, Hastings RJ, Fellmann F, Hodgson SV, Howard HC, Cambon-Thomsen A, Knoppers BM, Meijers-Heijboer H, Scheffer H, Tranebjaerg L, Dondorp W, de Wert GMWR on behalf of the ESHG Public and Professional Policy Committee. Whole genome sequencing in health care. Recommendations of the European Society of Human Genetics. Euro J Hum Genet. 2013;21:580-584.

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