Medical Policy

 

Subject: Genetic Testing of an Individual’s Genome for Inherited Diseases
Document #: GENE.00043 Publish Date:    08/29/2018
Status: Reviewed Last Review Date:    07/26/2018

Description/Scope

This document addresses the framework for consideration of genetic testing for any disease with an established genetic basis.

Notes:

Please refer to the documents indicated below for information regarding genetic testing for the following conditions (this is not an all-inclusive list of related genetic testing documents):

Genetic testing involves the analysis of an individual’s deoxyribonucleic acid (DNA), ribonucleic acid (RNA), chromosomes, genes, or gene products (such as enzymes and other proteins) to identify inherited or somatic (noninherited) genetic variations associated with health or disease.  The use of genetic testing information is being explored as a means to:

Genetic testing may be conducted by several methods.  Molecular genetic tests analyze single genes or short lengths of DNA to identify variations or mutations that lead to a genetic disorder. Chromosomal genetic tests examine whole chromosomes or long lengths of DNA to identify large genetic changes, such as an extra copy of a chromosome, that cause a genetic condition (karyotype).  Biochemical genetic tests or GEP measure the activity level or amount of specific proteins, metabolites or enzymes which may be indicative of changes to the DNA that result in a genetic disorder.

Genetic tests can be considered in four general categories: predictive, diagnostic, prognostic and therapeutic. Refer to the following chart and the Background/Overview section. 

 

Asymptomatic

Symptomatic

Predictive

Screening for ris
Presymptomatic: disease certain to develop

N/A

Predispositional: disease may develop
For example: BRCA for breast or ovarian
cancer

N/A

Diagnostic

For example: cystic fibrosis, fragile X

Confirm suspected diagnosis
For example: achondroplasia

Prognostic

Understand likelihood of disease or
condition occurrence or course (penetrance and heterogeneity)
For example: BRCA, cystic fibrosis

Understand course of disease or condition For example: familial adenomatous polyposis (FAP)

Therapeutic, including but not limited to pharmacotherapeutic

Guide preventive treatment
For example: BRCA or FAP

Impact treatment planning
For example: cytochrome p450, BRCA or FAP

In addition to the general four classifications addressed above for testing the individual’s genome, genetic tests can also be done for two other genomic categories:

Pregnancy-related genetic testing (preconception, prenatal, pre-implantation, in vitro fertilization) is carried out prior to or during pregnancy for various indications, including but not limited to guiding reproductive decisions and as part of assistive reproductive procedures.  This includes carrier testing which is used to identify individuals who possess one copy of a gene mutation that, when present in two copies, result in a specific genetic disorder.  Having only one copy of the gene mutation does not place the individual being tested at increased risk of developing the disease, but will increase the risk of the individual having an affected child who will develop the disease and may necessitate pregnancy related genetic testing.  Genetic testing for pregnancy-related conditions is more specifically addressed in other documents.

Somatic cell genetic testing involves the testing of tissue (most often cancerous tissue) for mutations that are not inherited.  This testing is generally done for diagnostic purposes or to assist in the selection of a cancer treatment.  Genetic testing for somatic cell mutations is addressed more specifically in other documents.

Position Statement

Medically Necessary:

Genetic testing of the individual’s genome for inherited diseases is considered medically necessary when all the following criteria (#I and #II) are met:

  1. The individual for whom the test is requested:
    1. Is asymptomatic but is judged to be at significant risk, as determined by the likelihood of future disease and burden of suffering, for a genetic disease (for example, based on family history); or
    2. Is currently symptomatic with suspicion of a known genetic disease; and
  2. All of the following criteria apply:
    1. A specific mutation, or set of mutations, has been established in the scientific literature to be reliably associated with the disease; and
    2. A biochemical or other test is identified but the results are indeterminate, or the genetic disorder cannot be identified through biochemical or other testing; and
    3. The genetic disorder is associated with a potentially significant disability or has a lethal natural history; and
    4. Results of the genetic test, whether affirmative or negative, will impact the clinical management (predictive, diagnostic, prognostic or therapeutic) of the individual.  For example, genetic test results will guide treatment decisions, surveillance recommendations or preventive strategies; and
    5. The findings of the genetic test will likely result in an anticipated improvement in net health outcomes; that is, the expected health benefits of the interventions outweigh any harmful effects (medical or psychological) of the intervention; and
    6. Genetic counseling, which encompasses all of the following components, has been performed:
      1. Interpretation of family and medical histories to assess the probability of disease occurrence or recurrence; and
      2. Education about inheritance, genetic testing, disease management, prevention and resources; and
      3. Counseling to promote informed choices and adaptation to the risk or presence of a genetic condition; and
      4. Counseling for the psychological aspects of genetic testing.

Investigational and Not Medically Necessary:

Genetic testing of the individual’s genome for inherited diseases in individuals not meeting the above criteria is considered investigational and not medically necessary, including, but not limited to, genetic testing for melanoma (hereditary), amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig's disease) and ataxia telangiectasia.

Genetic testing of the individual’s genome for inherited diseases using panels of genes (with or without next generation sequencing), including but not limited to whole genome and whole exome sequencing, is considered investigational and not medically necessary unless all components of the panel have been determined to be medically necessary based on the criteria above.  However, individual components of a panel may be considered medically necessary when criteria above are met.

The use of the Corus CAD test is considered investigational and not medically necessary.

Rationale

Because of the rapidly evolving field of genetic testing, each genetic test must be carefully evaluated to determine whether or not the identified genetic mutation reliably identifies a genetic disorder, and that the results of the genetic test, whether affirmative or negative, will impact the predictive, diagnostic, prognostic or therapeutic management of the individual (for example, guide treatment decisions, surveillance recommendations or preventive strategies).  The results of genetic testing are also expected to improve net health outcomes, (that is, the anticipated health benefits of the interventions outweigh any harmful effects (medical or psychological) of the intervention).  Genetic testing may be employed to investigate malignant and non-malignant diseases.

Predictive Genetic Testing
One of the limitations of predictive genetic testing is the challenge in interpreting positive test results.  Some individuals who test positive for a disease-associated mutation may never develop the disease.  In order to be useful in the clinical setting, the results of predictive genetic testing should have a high positive predictive value and evidence should demonstrate that such results improve either disease prevention or management as compared with care without genetic testing.

Adult-Onset Diabetes Mellitus
There has been a growing interest in the use of predictive genetic testing for adult-onset diabetes mellitus.  In a prospective cohort study, Talmud and colleagues (2010) assessed the performance of a panel of common single nucleotide polymorphisms (genotypes) associated with susceptibility to type 2 diabetes as well as two established phenotype-based risk models (the Cambridge type 2 diabetes risk score and the Framingham offspring study type 2 diabetes risk score) in estimating the absolute risk of type 2 diabetes.  Enrollment consisted of 5535 initially healthy individuals (33% women, mean age 49 years) of whom 302 developed new onset type 2 diabetes over 10 years.  The researchers concluded that the addition of genetic information to the phenotype-based risk models does not substantially improve the accuracy of risk estimation for the future development of type 2 diabetes. 

The incremental clinical utility of predictive genetic testing for adult-onset diabetes mellitus, as compared with standard care, has not yet been demonstrated.  Even if genetic testing results indicate the individual to be at increased risk for the development of diabetes mellitus, it has not yet been confirmed that changing clinical management based on the findings of the genetic test would improve outcomes, as compared with assessing the family history of diabetes mellitus and encouraging the individual to maintain a healthy weight, maintain an exercise routine and make healthy dietary choices.  It also remains to be proven how many of the individuals who are believed to be at increased risk for the development of diabetes mellitus based on predictive genetic testing actually go on to develop the disease.

Cardiovascular Disease
There has also been a growing interest in the use of predictive genetic testing for classifying the risk of cardiovascular disease.  Paynter and colleagues (2009) used a genetic variation at chromosome 9p21.3 to evaluate cardiovascular risk prediction in 22,129 white, female health professionals who were observed for a median of 10 years.  Consideration was also given to conventional risk factors-such as family history of early cardiovascular disease, cholesterol, smoking, blood pressure and C-reactive protein levels-with risk prediction using conventional risk factors alone.  The researchers concluded that adding genetic information to conventional risk factors did not improve the accuracy of classifying cardiovascular risk.

Palomaki and colleagues (2010) investigated the association between chromosome 9p21 single-nucleotide polymorphisms (SNPs) and heart disease.  The objective of the study was to perform a targeted systematic review of published literature for effect size, heterogeneity, publication bias, and strength of evidence and to consider whether testing for 9p21 SNPs would provide clinical utility.  Of the 22 articles analyzed, researchers were able to identify 47 distinct data sets on chromosome 9p21 SNPs and heart disease.  These included a total of 95,837 controls and 35,872 cases.  Individuals with two 9p21 at-risk alleles had a 25% increased risk of heart disease compared with individuals with only one at-risk allele.  The researchers concluded that there is a statistically significant association between 9p21 SNPs and heart disease which varies by age at disease onset, but the magnitude of the association was small.

Corus CAD®
Corus CAD (CardioDx Inc., Palo Alto, CA) is a peripheral blood test which integrates the expression levels of 23 genes known to play a role in the development of or response to atherosclerosis.  Data on the expression of the 23 genes as well as the individual’s age and sex are combined in a mathematical algorithm to generate a gene expression score (GES) which ranges from 1-40.  Higher GES scores are associated with an increased likelihood of CAD.  Corus CAD is intended to be used in stable, nondiabetic individuals suspected of having CAD because they are symptomatic, have a high risk of CAD based on history or have had a recent positive or inconclusive test. 

Elashoff and colleagues (2011) describe the initial validation study of the Corus CAD test.  The researchers used a series of microarray and real-time polymerase chain reaction (RT-PCR) data sets to develop a blood-based gene expression algorithm for assessing obstructive CAD in non-diabetic subjects.  The components of the algorithm were the expression levels of 23 genes, sex and age.  The study was sponsored by CardioDx., Inc.

In another report by CardioDx, Rosenberg and colleagues (2010) evaluated the results of a multicenter trial to validate Corus CAD for diagnosis of obstructive CAD in nondiabetic individuals.  The PREDICT (Personalized Risk Evaluation and Diagnosis in the Coronary Tree) trial was comprised of a total of 526 nondiabetic participants.  Of the 526 participants, 192 positive for CAD and 334 negative for CAD were used to validate the GES algorithm.  CAD was defined as at least 50% stenosis in at least one major coronary artery.  To assess performance, receiver operating characteristics (ROC) area under the curve (AUCs) were calculated for the GES algorithm in addition to two clinical risk assessment models (the Diamond-Forrester risk score and an expanded clinical factor model which was developed by the researchers).  Both clinical risk assessment models included age, sex, and chest pain type.  However, the expanded clinical model also included race, systolic blood pressure, the presence of hypertension and dyslipidemia and the use of medications (statins, aspirin, antiplatelet medications, and ACE inhibitors).  The ROC AUC for the GES only was 0.70 ± 0.02 (P<0.001).  The ROC AUC for the use of the GES along with the Diamond-Forrester score was 0.72, a statistically significant improvement compared to the use of the Diamond-Forrester score alone (0.66; P=0.003).  The ROC AUC for the use of the GES as well as the expanded clinical model was similar to that of the expanded clinical model alone (0.745 versus 0.732; P=0.089 for the difference).  An analysis of reclassification found that the net reclassification improvement was 20% (P<0.001) for the GES compared to the Diamond-Forrester score, and 16% (P<0.001) for the expanded clinical model in addition to GES versus the expanded clinical model alone.  The authors concluded that Corus CAD may be useful for assessing obstructive CAD in nondiabetic individuals not known to have CAD (Clinical trial #NCT00500617).

Elashoff and colleagues (2012) proposed to identify factors contributing to variability in Corus CAD.  Over a 2-year period, the researchers evaluated the technical aspects (total variability, intrabatch variability, laboratory process variability, and interbatch variability) of the test using 895 control samples.  In order to assess intrabatch variability, five batches of 132 whole blood controls were processed; interbatch variability was measured using 895 whole blood control samples.  In a regression model including all 11 laboratory variables, the researchers found that assay plate lot and cDNA kit lot contributed the most to variability (p=0.045; 0.009 respectively).  Reagent lots for RNA extraction, cDNA synthesis, and qRT-PCR contributed the most to interbatch variance (52.3%), followed by operators and machines (18.9% and 9.2% respectively).  At the conclusion of the analysis, the remaining 19.6% variance was unexplained.

In another study, McPherson and colleagues (2013) evaluated the impact of gene expression testing on disease management by a group of cardiology specialists.  Participants (n=171) presenting with stable chest pain and related symptoms without a history of CAD were referred to six cardiologists for evaluation.  In the prospective cohort of 88 participants, the cardiologist's diagnostic strategy was evaluated before and after GES testing.  A total of 83 individuals were evaluable for study analysis, which included 57 (69%) women, mean age 53 ± 11 years, and mean GES 12.5 ± 9.  Presenting symptoms were classified as typical angina, atypical angina, and noncardiac chest pain in 33%, 60%, and 7% of the participants (n=27, 50, and 6), respectively.  This study demonstrated that individuals with low gene expression scores (≤ 15) were more likely to have a decrease in the intensity of diagnostic testing.  Individuals with elevated GES were more likely to undergo additional testing for the evaluation of obstructive CAD.  Limitations of this study include its small sample size and the evaluation of short term (6 months) outcomes (Clinical trial #NCT01251302).

In a prospective, multicenter, double blind trial study, Thomas and colleagues (2013) evaluated gene expression as a method to assess obstructive CAD (COMPASS Study).  The study group was made up of individuals with angina or angina equivalent symptoms who were referred for diagnostic MPI stress testing.  Blood samples for gene expression were obtained prior to MPI and, based on MPI results, participants were referred for either invasive coronary angiography or CT angiography.  Participants were followed for 6 months with the study end point being a major adverse cardiac event.  The results of the angiography were compared to the GES and MPI results.  The researchers found that GES was significantly correlated with maximum percent stenosis (≥ 50).  Sensitivity, specificity, and negative predictive value were reported at 89%, 52%, and 96%, respectively.  The authors concluded that the GES was more predictive of obstructive CAD compared to MPI and other clinical factors.  The authors acknowledged that the study included potentially lower disease prevalence in the subjects due to the inclusion/exclusion criteria, and the lack of comparison of GES scores to other noninvasive imaging modalities (Clinical trial #NCT01117506).  

Herman and colleagues (2014) conducted a study (IMPACT-PCP) which assessed the impact of gene expression testing on clinical decision-making in individuals presenting to a primary care setting with symptoms of suspected CAD.  The study was comprised of 261 consecutive stable, nonacute, nondiabetic participants presenting with typical and atypical symptoms of CAD.  Of the 251 eligible study participants, 140 (56%) were women.  Providers initially determined the subjects’ pretest probability for CAD based on risk factors, assessment of clinical symptoms and results of any prior testing.  All of the participants underwent GES testing, with clinicians documenting their planned diagnostic strategy both prior to and after GES.  The primary objective was to assess whether the utilization of GES altered patient management.  After 30 days, a change in the diagnostic plan before and after GES testing was noted in 145 (58%) of the participants.  A total of 93 (37%) of the participants had decreased intensity of testing versus the 52 (21%) which experienced an increase in the intensity of testing.  In particular, among the 127 low score Corus CAD individuals (51% of study participants), 60% (76/127) experienced decreased testing, and only 2% (3/127) experienced increased testing.  The authors concluded that including the GES into the diagnostic workup demonstrated clinical utility above and beyond conventional clinical factors by optimizing the individual’s diagnostic evaluation.  Limitations of the study include but are not necessarily limited to a potential for bias due to manufacturer sponsorship of the study, the inclusion of individuals at low risk for CAD, short term follow-up and modest sample size (Clinical trial #NCT01594411).

The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group found insufficient evidence to recommend the use of genomic profiling to assess cardiovascular risk and “discourages clinical use unless further evidence supports improved clinical outcomes” (EGAPP, 2010).

In summary, Corus CAD has been proposed as a means to help physicians identify individuals who are unlikely to have obstructive CAD and who may avoid invasive diagnostic testing.  However, at the time of this review, additional studies are needed which demonstrate the clinical utility (improved clinical outcomes) as a result of the information provided by Corus CAD.

Other Conditions
Researchers are exploring the use of commercial genetic testing for assessing disease risk for various other conditions in currently asymptomatic but perhaps higher risk individuals.  As yet, no clinical studies have definitively confirmed the incremental clinical utility of such testing, thus it is unknown whether making clinical management changes based on such testing alters outcome as compared with standard care of such individuals before as well as once such disease is diagnosed. 

Diagnostic Genetic Testing
Diagnosis of a genetic disorder in asymptomatic individuals is closely related to predictive testing; in symptomatic individuals, diagnosis may typically be made using biochemical testing.  However, in some situations, genetic testing may be the method used to identify, confirm or rule out a condition in conjunction with clinical signs and symptoms. 

Amyotrophic lateral sclerosis (ALS)
There has been a surge of interest in the genetic mutation responsible for ALS.  ALS is a progressive neurodegenerative disease which involves both upper and lower motor neurons.  The mean age of onset is 56 years in individuals with no known family history of the disease and 46 years in individuals with more than one affected family member (familial ALS or FALS).  The average duration of the disease is about 3 years, but it can vary significantly.  Death usually occurs as the result of respiratory failure. 

ALS can be inherited in an autosomal recessive, autosomal dominant or X-linked manner.  Several guidelines have been published addressing the clinical diagnosis of ALS which is typically based on clinical features, electrodiagnostic testing, and exclusion of other health conditions with related symptoms.  Researchers have identified several possible causes of ALS including, but not limited to mutations in the gene that produces the superoxide dismutase (SOD1) enzyme.  While genetic tests are available to identify mutations in the SOD1 gene, it is not clear how the results of diagnostic genetic testing would improve the care or outcome of the affected individual (Andersen, 2012; Brooks, 2000; Burgunder, 2011). 

Melanoma
BRAF molecular diagnostic testing for melanoma is addressed in a separate document.

Spinal Muscular Atrophy (SMA)
SMA is largely an inherited autosomal recessive disease caused by mutations in chromosome 5q that leads to a deficiency in SMN1-related proteins.  In rare instances (2-3% of SMA), SMA can occur de novo, where a mutation occurs in an individual during egg or sperm production, rather than inheriting a defective copy of the gene from each parent.  This deficiency results in degeneration of motor neurons causing muscle atrophy, particularly in the limbs and the muscles that control the mouth, throat and respiration.  There are four types of SMA, types I, II, III, and IV which are defined based on the severity of muscle weakness and the age of symptom onset.  SMA type I (Werdnig-Hoffmann disease) is the most severe.  SMA type I-affected infants represent approximately 60% of SMA diagnoses and present with the disease by 6 months of age.  These infants are profoundly hypotonic and often succumb to complications of the disease by their second year of life.  SMA type II affected children (intermediate form) present with symptoms prior to 18 months of age and develop the ability to sit unaided but not the ability to stand or walk.  Individuals affected by SMA type III (Kugelberg-Welander disease) are also generally diagnosed by 18 months but are able to stand and walk.  SMA type III affected individuals may live into their thirties and beyond.  SMA IV, the least severe, typically presents in the second or third decade of life, but is otherwise similar to type III.

SMN2, a closely related gene to SMN1 that also produces functional SMN, can compensate for SMN1 deficiency and modify the SMA phenotype. Therefore, although the role of SMN protein in motor neurons is not completely understood and the amount for normal functioning undefined, the phenotype of spinal muscular atrophy (type I, II, III, or IV) is largely related to the number of SMN2 gene copies present.  The number of copies of SMN2 in individuals diagnosed with SMA has been found to negatively correlate with disease severity.  For instance, infants diagnosed with SMA type I, are likely to have two copies or less of SMN2 and individuals with SMA type III and IV are likely to have three copies or more (Mailman, 2002).

A number of other motor neuron diseases exist, also termed SMA, that are caused by mutations in genes other than the SMN1 gene.  These are referred to as non-5q- SMA diseases, meaning that the genes causing these forms of SMA are not located in the SMN region of chromosome 5.

Whole Genome Sequencing
Whole genome sequencing (WGS), also known as full genome sequencing (FGS), complete genome sequencing, or entire genome sequencing, is a laboratory procedure which seeks to determine an individual's entire DNA sequence, specifying the order of every base pair within the genome at a single time.  WGS allows researchers to study the 98% of the genome that does not generally contain protein-coding genes.  In the clinical setting, this process frequently involves obtaining a DNA sample from the individual (typically from blood, saliva or bone marrow) and sequencing an individual's entire chromosomal and mitochondrial DNA.  Because of the large volume of genomic data involved in this process, the genomic information is processed by and stored on microprocessors and computers. 

Researchers continue to explore the relationship between mutations in the genomic material and the development or presence of disease.  The clinical role of WGS has yet to be established.  Research is still being done to determine if WGS can be used to accurately identify the presence of a disease, predict the development of a particular disease in asymptomatic individuals as well as how an individual might respond to pharmacological therapy.  It has been theorized that WGS might eventually improve clinical outcomes by preventing the development of disease.

Hereditary retinal dystrophies or inherited retinal diseases (IRDs)
Hereditary retinal dystrophies or inherited retinal diseases (IRDs) are progressive disorders which typically result in blindness at a young age. There are approximately 220 mutations associated with IRDs including the RPE65 mutation (Russell, 2017).  Missense mutations in the retinal pigment epithelium-specific protein 65 kDa (RPE65) result in a more severe form of the retinal degenerative disease that is associated with photoreceptor dysfunction and degeneration (Cideciyan, 2013).  A lack of functional RPE65 results in rod photoreceptor cells that are unable to respond to light, contributing to decreased ability to see in low light conditions and diminished visual fields.  As retinal degeneration progresses and affects cones, the disorder leads to blindness (FDA, 2017).

IRDs attributed to biallelic RPE65 mutations are associated with several forms of disease, including an early-onset form of disease, Leber congenital amaurosis type 2, and a later-onset form, retinitis pigmentosa type 20, as well as various forms of Early-Onset Retinal Dystrophy (EORD).  In the United States there are an estimated 1000 to 3000 individuals with biallelic RPE65 mutation-associated retinal dystrophy (FDA, 2017).  On December 19, 2017, the FDA approved Voretigene neparvovec-rzyl (Luxturna™), a gene replacement therapy, for the treatment of individuals with confirmed biallelic RPE65 mutation-associated retinal dystrophy.  Prior to the development of gene therapy, there were no pharmacologic treatments available to treat IRDs.  For additional information on voretigene neparvovec-rzyl gene therapy for the treatment of treat retinal dystrophies caused by biallelic RPE65 gene mutations, see MED.00120 Voretigene neparvovec-rzyl (Luxturna™).

The 2016 American Academy of Ophthalmology (AAO) recommends genetic testing be ordered at the initial visit for individuals with a suspected inherited retinal degenerative disease.  The causative mutation can be identified in up to 60-80% of affected individuals, which can guide treatment decisions.  The scope of genetic testing recommended varies, multi-gene testing may be necessary when there are multiple causative genes, while single gene analysis might be more appropriate for certain conditions.  For diseases such as Leber congenital amaurosis, which is caused by multiple different genes, it can be more efficient to order a single test which has been designed to specifically evaluate for all of the known causative genes (Stone, 2012).  A listing of the genes causing retinal disease can be accessed here: https://sph.uth.edu/retnet/.

Whole Exome Sequencing
It is estimated that most disease-causing mutations (around 85%) of clinically important sequence variants occur within the regions of the genome that encode proteins.  While similar to WGS, whole-exome sequencing (WES) reads only the parts of the human genome that encode proteins, leaving the other regions of the genome unread (Choi, 2009).  Since most of the errors that occur in DNA sequences that then lead to genetic disorders are located in the exons, sequencing of the exome is being explored as a more efficient method of analyzing an individual's DNA to discover the genetic cause of diseases or disabilities.  It has been theorized that sequencing of the human exome can be used to identify genetic variants in individuals to diagnose diseases without the high cost associated with WGS.

The American College of Medical Genetics and Genomics (ACMG, 2012) published a position statement addressing points to consider in the clinical application of genomic sequencing.  The policy statement:

Was developed primarily as an educational resource for clinical and laboratory geneticists to help them provide quality clinical and laboratory genetic services.  Adherence to these Points to Consider is voluntary and, in determining the relevance of and weight to be given to any specific point, the clinical and laboratory geneticist should apply his or her own professional judgment to the specific circumstances presented by the individual patient or specimen.

The document contains indications for whole genome and WES as both screening and diagnostic tools.  The ACMG states that clinical diagnostic testing using whole genome or WES is indicated for the following phenotypically affected individuals:

  1. The 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 patient presents with a likely genetic disorder but specific genetic tests available for that phenotype have failed to arrive at a diagnosis.
  4. A fetus 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.
    1. Prenatal diagnosis by genomic (i.e., next-generation whole exome- or whole genome-) sequencing has significant limitations.  The current technology does not support short turn-around times which are often expected in the prenatal setting.  There are high false positive, false negative, and variants of unknown clinical significance rates.  These can be expected to be significantly higher than seen when array CGH is used in prenatal diagnosis (2012).

The ACMG document does not include references to peer reviewed literature in support of the recommendations made, or describe the process by which the recommendations were developed. 

The American College of Obstetricians and Gynecologists’ Committee on Genetics in collaboration with the Society for Maternal–Fetal Medicine published a Committee Opinion Summary which states “the routine use of whole-genome or whole-exome sequencing for prenatal diagnosis is not recommended outside of the context of clinical trials until sufficient peer-reviewed data and validation studies are published (Committee on Genetics, 2016).

WES and WGS present ethical questions about informing individuals about incidental findings that have clinical significance.  Ongoing discussions continue to explore whether or not, and how to inform individuals about medically relevant mutations in genes unrelated to the diagnostic question (i.e., mutations of unknown significance, non-paternity and sex chromosome abnormalities).  This type of information may not only affect the individual being tested, but may also implicate family members.  Also, in light of the small sample sizes and the limited number of studies exploring treatment outcomes, there may be safety considerations if a treatment decision is based on WES or WGS findings.

In 2016, the ACMG updated its recommendations for analyzing and reporting incidental or secondary findings from genome and exome sequencing in the clinical context.  The Working Group continues to recommended that clinical diagnostic laboratories conducting exome or genome sequencing report known pathogenic or expected pathogenic variants in a total of 59 medically actionable genes, even when unrelated to the primary medical reason for testing.  The conditions included for reporting were those that the Working Group and external reviewers considered most likely to be verifiable by other diagnostic methods and amenable to medical intervention.  A complete list of these specified conditions can be found in the ACMG document.  It should be noted that the ACMG clarified that the term “secondary” findings is preferred to the term “incidental” findings because “ these genes are intentionally being analyzed, as opposed to genetic variants found incidentally or accidentally”.  Conditions that were part of routine newborn screening were not addressed because they have their own assessment criteria and are applied in a specific public health framework.  The Working Group recommendations also do not address preconception sequencing, prenatal sequencing, newborn sequencing, or sequencing of healthy children and adults (Kalia, 2016).

A potential major indication of WES is the establishment of a molecular diagnosis in individuals with a phenotype that is suspicious for a genetic disorder or for individuals with known genetic disorders that have a large degree of genetic heterogeneity involving substantial gene complexity.  Such individuals may be left without a clinical diagnosis of their disorder, despite a lengthy diagnostic work-up involving a variety of traditional molecular and other types of conventional diagnostic tests.  For some of these individuals, WES, after initial conventional testing has failed to make the diagnosis, may return a likely pathogenic variant.

While some of the potential advantages of WES include the fact that it can be carried out more quickly than traditional genetic testing and it may be less expensive than some other tests (for example, WGS), it is not without limitations.  WES typically covers only 85-95% of the exome and has no, or limited coverage of other areas of the genome.  Areas of concern with this technology include: (1) gaps in the identification of exons prior to sequencing; (2) the need to narrow the large initial number of variants to manageable numbers without losing the likely candidate mutation; (3) difficulty identifying the potential causative variant when large numbers of variants of unknown significance are generated for each individual. It is more difficult to detect chromosomal changes, duplications, large deletions, rearrangements, epigenetic changes or nucleotide repeats from WES data compared with other genomic technologies (ACMG, 2012; Teer, 2010[a]; Teer, 2010[b]).

At this time, there are limitations to WES that prohibit its use in routine clinical care.  The limited experience with WES on a population level leads to gaps in understanding and interpreting ancillary information and variants of uncertain significance.  As a result, the risk/benefit ratio of WES testing is poorly defined.  Because the peer-reviewed literature on WES for clinical purposes consists primarily of case reports and small case series, the clinical applications of WES has yet to be established (Bilguvar, 2010; Choi, 2009; Clayton-Smith, 2011, Saitsu, 2011; Vissers, 2011).

Cytogenomic Microarray Analysis
Cytogenomic microarray analysis collectively describes two different laboratory techniques: array comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) arrays.  While both of these techniques detect copy number variants (CNVs), they identify different types of genetic variation.  aCGH allows the detection of gains and losses in DNA copy number across the entire genome without prior knowledge of specific chromosomal abnormalities.  SNP arrays allow genotyping based on allele frequency.  SNP arrays have additional oligonucleotide probes which analyze thousands of SNPs throughout the genome in order to identify deletions and duplications.  The use of cytogenomic microarray analysis is specifically addressed more fully in other documents, including but not limited to GENE.00021 Chromosomal Microarray Analysis (CMA) for Developmental Delay, Autism Spectrum Disorder, Intellectual Disability (Intellectual Developmental Disorder) and Congenital Anomalies.

Background/Overview

The term “genetic testing” encompasses various techniques used to analyze an individual’s DNA, RNA, chromosome, genes or gene products (such as enzymes) to detect gene variations associated with health and or specific diseases.  Genetic tests may be divided into four broad categories: predictive, diagnostic, prognostic and therapeutic. 

Predictive genetic testing (also known as susceptibility testing) is generally carried out in asymptomatic individuals who are considered to be at high risk for developing a disease due to a strong family medical history of the disorder, or other factors.  Predictive genetic test results replace the individual's prior risks based on family history or population data with risks based on genotype (NIH, 2005).  Predictive genetic testing can be further divided into two categories: presymptomatic and predispositional.  Presymptomatic predictive genetic testing confirms or denies the development of the disease in those at risk as the condition’s gene mutation is highly penetrant and there is little or no variable expression.  Predispositional predictive genetic tests provide information about an individual’s risk of developing a specific disorder in the future.  Predispositional predictive genetic testing is generally carried out for incompletely penetrant conditions and the results are not indicative of the inevitable occurrence of a condition or disease nor are they a guarantee that a disease will not develop in the future. 

Diagnostic genetic testing is used to identify or rule out a genetic or chromosomal disorder in individuals suspected of having a condition or disease.  Depending on the clinical circumstances, diagnostic genetic testing may be performed on symptomatic or asymptomatic individuals.  When performed in asymptomatic individuals, diagnostic genetic testing is generally performed as a screening test.  Genetic screening differs from genetic testing in that it targets general populations rather than individuals.  This document does not address genetic population screening.  When performed in symptomatic individuals, diagnostic genetic testing is generally performed to rule out or confirm the existence of a genetic condition that may be identifiable by biochemical or other diagnostic tests.  This confirmatory evidence should then assist with therapeutic interventions. 

Prognostic genetic testing is used to assess the risk of progression and course in an asymptomatic individual not yet diagnosed with a disease, and as a means to forecast whether an individual diagnosed with a disease will have a serious or benign course (prognostic).

Therapeutic genetic testing (including but not limited to pharmacotherapeutics) involves the identification of a genetic variant that affects the way an individual responds to a therapeutic intervention.  This application is often seen in the area of pharmacogenetic testing where genetic testing results are used to inform treatment decisions with regards to how an individual is expected to respond to drug therapy.  Genetic testing for pharmacotherapeutics is more specifically addressed in other documents.

Whole Genome Sequencing
Whole genome sequencing (WGS), also known as full genome sequencing (FGS), complete genome sequencing, or entire genome sequencing, is a laboratory procedure which seeks to determine an individual's entire DNA sequence, specifying the order of every base pair within the genome at a single time.  The role of WGS in the clinical setting has yet to be established. 

Whole Exome Sequencing
While similar to WGS, WES reads only the parts of the human genome that encode proteins.  Since most of the errors that occur in DNA sequences that then lead to genetic disorders are located in the exons, sequencing of the exome is being explored as a more efficient method of analyzing an individual's DNA to discover the genetic cause of diseases or disabilities.  Researchers are exploring various applications of WES including but not limited to determining if sequencing of the human exome can be used to identify genetic variants in individuals in order to diagnose diseases in individuals without the high cost associated with WGS.

Genetic Counseling
According to the National Society of Genetic Counselors (NSGC), genetic counseling is the process of assisting individuals to understand and adapt to the medical, psychological and familial ramifications of a genetic disease.  This process typically includes the guidance of a specially trained professional who:

  1. Integrates the interpretation of family and medical histories to assess the probability of disease occurrence or recurrence; and
  2. Provides education about inheritance, genetic testing, disease management, prevention and resources; and
  3. Provides counseling to promote informed choices and adaptation to the risk or presence of a genetic condition; and
  4. Provides counseling for the psychological aspects of genetic testing (NSGC, 2006).
Definitions

Amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig's disease): A progressive neurodegenerative disorder that affects nerve cells in the spinal cord and brain which eventually results in paralysis and death. 

Ataxia telangiectasia: A rare, progressive, neurodegenerative childhood disease that affects the brain and other body systems.

Clinical utility: Measures the ability of the test to improve clinical outcomes.

DNA: (deoxyribonucleic acid): A type of molecule that contains the code for genetic information.

Exome: All the exons in a genome.

Exon: The portion of the genome that predominantly encodes protein.

Genome: An organism's entire set of DNA.

Genotype: The genetic structure (constitution) of an organism or cell. 

Mutation: Permanent, structural change in the DNA.

Penetrant: The likelihood that a person carrying a particular variation of a gene will also have an associated trait.

Phenotype: The observable physical or biochemical characteristics of an organism, as determined by both genetic makeup and environmental influences.

Positive predictive value: Percentage of individuals with positive test results who are accurately diagnosed.

Single-nucleotide polymorphisms (SNPs): DNA sequence variations that occur when a single nucleotide in the genome sequence is altered. 

Coding

The following codes for treatments and procedures applicable to this document are included below for informational purposes. Inclusion or exclusion of a procedure, diagnosis or device code(s) does not constitute or imply member coverage or provider reimbursement policy. Please refer to the member's contract benefits in effect at the time of service to determine coverage or non-coverage of these services as it applies to an individual member.

When services may be Medically Necessary when criteria are met:

CPT

 

81200

ASPA (aspartoacylase) (eg, Canavan disease) gene analysis, common variants (eg, E285A, Y231X)

81209

BLM (Bloom syndrome, RecQ helicase-like) (eg, Bloom syndrome) gene analysis, 2281del6ins7 variant

81221

CFTR (cystic fibrosis transmembrane conductance regulator) (eg, cystic fibrosis) gene analysis; known familial variants

81222

CFTR (cystic fibrosis transmembrane conductance regulator) (eg, cystic fibrosis) gene analysis; duplication/deletion variants

81223

CFTR (cystic fibrosis transmembrane conductance regulator) (eg, cystic fibrosis) gene analysis; full gene sequence

81224

CFTR (cystic fibrosis transmembrane conductance regulator) (eg, cystic fibrosis) gene analysis; intron 8 poly-T analysis (eg, male infertility)

81241

F5 (coagulation Factor V) (eg, hereditary hypercoagulability) gene analysis, Leiden variant

81242

FANCC (Fanconi anemia, complementation group C) (eg, Fanconi anemia, type C) gene analysis, common variant (eg, IVS4+4A>T)

81251

GBA (glucosidase, beta, acid) (eg, Gaucher disease) gene analysis, common variants (eg, N370S, 84GG, L444P, IVS2+1G>A)

81252

GJB2 (gap junction protein, beta 2, 26kDa, connexin 26) (eg, nonsyndromic hearing loss) gene analysis; full gene sequence

81253

GJB2 (gap junction protein, beta 2, 26kDa, connexin 26) (eg, nonsyndromic hearing loss) gene analysis; known familial variants

81254

GJB2 (gap junction protein, beta 6, 30kDa, connexin 30) (eg, nonsyndromic hearing loss) gene analysis, common variants (eg, 309kb [del(GJB6-D13S1830)] and 232kb [del(GJB6-D13S1854)])

81255

HEXA (hexosaminidase A [alpha polypeptide]) (eg, Tay-Sachs disease) gene analysis, common variants (eg, 1278insTATC, 1421+1G>C, G269S)

81256

HFE (hemochromatosis) (eg, hereditary hemochromatosis) gene analysis, common variants (eg, C282Y, H63D)

81257

HBA1/HBA2 (alpha globin 1 and alpha globin 2) (eg, alpha thalassemia, Hb Bart hydrops fetalis syndrome, HbH disease), gene analysis; common deletions or variant (eg, Southeast Asian, Thai, Filipino, Mediterranean, alpha3.7, alpha4.2, alpha20.5, and Constant Spring)

81258

HBA1/HBA2 (alpha globin 1 and alpha globin 2) (eg, alpha thalassemia, Hb Bart hydrops fetalis syndrome, HbH disease), gene analysis; known familial variant

81259

HBA1/HBA2 (alpha globin 1 and alpha globin 2) (eg, alpha thalassemia, Hb Bart hydrops fetalis syndrome, HbH disease), gene analysis; full gene sequence

81260

IKBKAP (inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase complex-associated protein) (eg, familial dysautonomia) gene analysis, common variants (eg, 2507+6T>C, R696P)

81269

HBA1/HBA2 (alpha globin 1 and alpha globin 2) (eg, alpha thalassemia, Hb Bart hydrops fetalis syndrome, HbH disease), gene analysis; duplication/deletion variants

81290

MCOLN1 (mucolipin 1) (eg, Mucolipidosis, type IV) gene analysis, common variants (eg, IVS3-2A>G, del6.4kb)

81330

SMPD1(sphingomyelin phosphodiesterase 1, acid lysosomal) (eg, Niemann-Pick disease, Type A) gene analysis, common variants (eg, R496L, L302P, fsP330)

81361

HBB (hemoglobin, subunit beta) (eg, sickle cell anemia, beta thalassemia, hemoglobinopathy); common variant(s) (eg, HbS, HbC, HbE)

81362

HBB (hemoglobin, subunit beta) (eg, sickle cell anemia, beta thalassemia, hemoglobinopathy); known familial variant(s)

81363

HBB (hemoglobin, subunit beta) (eg, sickle cell anemia, beta thalassemia, hemoglobinopathy); duplication/deletion variant(s)

81364

HBB (hemoglobin, subunit beta) (eg, sickle cell anemia, beta thalassemia, hemoglobinopathy); full gene sequence

81412

Ashkenazi Jewish associated disorders (eg, Bloom syndrome, Canavan disease, cystic fibrosis, familial dysautonomia, Fanconi anemia group C, Gaucher disease, Tay-Sachs disease), genomic sequence analysis panel, must include sequencing of at least 9 genes, including ASPA, BLM, CFTR, FANCC, GBA, HEXA, IKBKAP, MCOLN1, and SMPD1

81434

Hereditary retinal disorders (eg, retinitis pigmentosa, Leber congenital amaurosis, cone-rod dystrophy), genomic sequence analysis panel, must include sequencing of at least 15 genes, including ABCA4, CNGA1, CRB1, EYS, PDE6A, PDE6B, PRPF31, PRPH2, RDH12, RHO, RP1, RP2, RPE65, RPGR, and USH2A

81479

Unlisted molecular pathology procedure

81599

Unlisted multianalyte assay with algorithmic analysis

 

 

HCPCS

 

S3841

Genetic testing for retinoblastoma

S3842

Genetic testing for von Hippel-Lindau disease

S3844

DNA analysis of the connexin 26 gene (GJB2) for susceptibility to congenital, profound deafness

S3845

Genetic testing for alpha-thalassemia

S3846

Genetic testing for hemoglobin E beta-thalassemia

S3849

Genetic testing for Niemann-Pick diseases

S3853

Genetic testing for myotonic muscular dystrophy

 

 

ICD-10 Diagnosis

 

 

All other diagnoses not listed below as investigational and not medically necessary

When services may also be Medically Necessary when criteria are met:

CPT

 

81400

Molecular pathology procedure, Level 1 (eg, identification of single germline variant [eg, SNP] by techniques such as restriction enzyme digestion or melt curve analysis) [when specified as the following]:

  • SMN1 (survival of motor neuron 1, telomeric) (eg, spinal muscular atrophy), exon 7 deletion

81403

Molecular pathology procedure, Level 4 (eg, analysis of single exon by DNA sequence analysis, analysis of >10 amplicons using multiplex PCR in 2 or more independent reactions, mutation scanning or duplication/deletion variants of 2-5 exons) [when specified as the following]:

  • SMN1 (survival of motor neuron 1, telomeric) (eg, spinal muscular atrophy), known familial sequence variant(s)

81405

Molecular pathology procedure, Level 6 (eg, analysis of 6-10 exons by DNA sequence analysis, mutation scanning or duplication/deletion variants of 11-25 exons, regionally targeted cytogenomic array analysis) [when specified as the following]:

  • SMN1 (survival of motor neuron 1, telomeric) (eg, spinal muscular atrophy), full gene sequence

81406

Molecular pathology procedure, Level 7 (eg, analysis of 11-25 exons by DNA sequence analysis, mutation scanning or duplication/deletion variants of 26-50 exons, cytogenomic array analysis for neoplasia)  [when specified as the following]:

  • HEXA (hexosaminidase A, alpha polypeptide) (eg, Tay-Sachs disease), full gene sequence
  • RPE65 (retinal pigment epithelium-specific protein 65kDa) (eg, retinitis pigmentosa, Leber congenital amaurosis), full gene sequence

 

 

ICD-10 Diagnosis

 

E75.02

Tay-Sachs disease

G12.0-G12.1

Infantile spinal muscular atrophy, type I, other inherited spinal muscular atrophy

H35.50-H35.54

Hereditary retinal dystrophy

When services are Investigational and Not Medically Necessary:
For the procedure codes listed above when criteria are not met or for the diagnoses listed below, or for the codes listed below:

CPT

 

81403

Molecular pathology procedure, Level 4 (eg, analysis of single exon by DNA sequence analysis, analysis of >10 amplicons using multiplex PCR in 2 or more independent reactions, mutation scanning or duplication/deletion variants of 2-5 exons)  [when specified as the following]:

  • ANG (angiogenin, ribonuclease, RNase A family, 5) (eg, amyotrophic lateral sclerosis), full gene sequence

81404

Molecular pathology procedure, Level 5 (eg, analysis of 2-5 exons by DNA sequence analysis, mutation scanning or duplication/deletion variants of 6-10 exons, or characterization of a dynamic mutation disorder/triplet repeat by Southern blot analysis)  [when specified as the following]:

  • CDKN2A (cyclin-dependent kinase inhibitor 2A) (eg, CDKN2A-related cutaneous malignant melanoma, familial atypical mole-malignant melanoma syndrome), full gene sequence
  • SOD1 (superoxide dismutase 1, soluble) (eg, amyotrophic lateral sclerosis), full gene sequence

81405

Molecular pathology procedure, Level 6 (eg, analysis of 6-10 exons by DNA sequence analysis, mutation scanning or duplication/deletion variants of 11-25 exons, regionally targeted cytogenomic array analysis)  [when specified as the following]:

  • TARDBP (TAR DNA binding protein) (eg, amyotrophic lateral sclerosis), full gene sequence

81406

Molecular pathology procedure, Level 7 (eg, analysis of 11-25 exons by DNA sequence analysis, mutation scanning or duplication/deletion variants of 26-50 exons, cytogenomic array analysis for neoplasia)  [when specified as the following]:

  • FUS (fused in sarcoma) (eg, amyotrophic lateral sclerosis), full gene sequence;
  • OPTN (optineurin) (eg, amyotrophic lateral sclerosis), full gene sequence

81408

Molecular pathology procedure, Level 9 (eg, analysis of >50 exons in a single gene by DNA sequence analysis)  [when specified as the following]:

  • ATM (ataxia telangiectasia mutated) (eg, ataxia telangiectasia), full gene sequence

 

 

ICD-10 Diagnosis

 

C43.0-C43.9

Malignant melanoma of skin  [when test is specified for hereditary melanoma]

Note: BRAF molecular diagnostic testing for melanoma is addressed in a separate document.

G11.3

Cerebellar ataxia with defective DNA repair (ataxia telangiectasia)

G12.21

Amyotrophic lateral sclerosis

When services are also Investigational and Not Medically Necessary:
For the following procedure codes; or when the code describes a procedure indicated in the Position Statement section as investigational and not medically necessary.

CPT

 

81410

Aortic dysfunction or dilation (eg, Marfan syndrome, Loeys Dietz syndrome, Ehler Danlos syndrome type IV, arterial tortuosity syndrome); genomic sequence analysis panel, must include sequencing of at least 9 genes, including FBN1, TGFBR1, TGFBR2, COL3A1, MYH11, ACTA2, SLC2A10, SMAD3, and MYLK

81411

Aortic dysfunction or dilation (eg, Marfan syndrome, Loeys Dietz syndrome, Ehler Danlos syndrome type IV, arterial tortuosity syndrome); duplication/deletion analysis panel, must include analyses for TGFBR1, TGFBR2, MYH11, and COL3A1

81415

Exome (eg, unexplained constitutional or heritable disorder or syndrome); sequence analysis

81416

Exome (eg, unexplained constitutional or heritable disorder or syndrome); sequence analysis, each comparator exome (eg, parents, siblings)

81417

Exome (eg, unexplained constitutional or heritable disorder or syndrome); re-evaluation of previously obtained exome sequence (eg, updated knowledge or unrelated condition/syndrome)

81425

Genome (eg, unexplained constitutional or heritable disorder or syndrome); sequence analysis

81426

Genome (eg, unexplained constitutional or heritable disorder or syndrome); sequence analysis, each comparator exome (eg, parents, siblings)

81427

Genome (eg, unexplained constitutional or heritable disorder or syndrome); re-evaluation of previously obtained genome sequence (eg, updated knowledge or unrelated condition/syndrome)

81430

Hearing loss (eg, nonsyndromic hearing loss, Usher syndrome, Pendred syndrome); genomic sequence analysis panel, must include sequencing of at least 60 genes, including CDH23, CLRN1, GJB2, GPR98, MTRNR1, MYO7A, MYO15A, PCDH15, OTOF, SLC26A4, TMC1, TMPRSS3, USH1C, USH1G, USH2A, and WFS1

81431

Hearing loss (eg, nonsyndromic hearing loss, Usher syndrome, Pendred syndrome); duplication/deletion analysis panel, must include copy number analyses for STRC and DFNB1 deletions in GJB2 and GJB6 genes

81440

Nuclear encoded mitochondrial genes (eg, neurologic or myopathic phenotypes), genomic sequence panel, must include analysis of at least 100 genes, including BCS1L, C10orf2, COQ2, COX10, DGUOK, MPV17, OPA1, PDSS2, POLG, POLG2, RRM2B, SCO1, SCO2, SLC25A4, SUCLA2, SUCLG1, TAZ, TK2, and TYMP

81442

Noonan spectrum disorders (eg, Noonan syndrome, cardio-facio-cutaneous syndrome, Costello syndrome, LEOPARD syndrome, Noonan-like syndrome), genomic sequence analysis panel, must include sequencing of at least 12 genes, including BRAF, CBL, HRAS, KRAS, MAP2K1, MAP2K2, NRAS, PTPN11, RAF1, RIT1, SHOC2, and SOS1

81460

Whole mitochondrial genome (eg, Leigh syndrome, mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes [MELAS], myoclonic epilepsy with ragged-red fibers [MERFF], neuropathy, ataxia, and retinitis pigmentosa [NARP], Leber hereditary optic neuropathy [LHON]), genomic sequence, must include sequence analysis of entire mitochondrial genome with heteroplasmy detection

81465

Whole mitochondrial genome large deletion analysis panel (eg, Kearns-Sayre syndrome, chronic progressive external ophthalmoplegia), including heteroplasmy detection if performed

81470

X-linked intellectual disability (XLID) (eg, syndromic and non-syndromic XLID); genomic sequence analysis panel, must include sequencing of at least 60 genes, including ARX, ATRX, CDKL5, FGD1, FMR1, HUWE1, IL1RAPL, KDM5C, L1CAM, MECP2, MED12, MID1, OCRL, RPS6KA3, and SLC16A2

81471

X-linked intellectual disability (XLID) (eg, syndromic and non-syndromic XLID); duplication/deletion gene analysis, must include analysis of at least 60 genes, including ARX, ATRX, CDKL5, FGD1, FMR1, HUWE1, IL1RAPL, KDM5C, L1CAM, MECP2, MED12, MID1, OCRL, RPS6KA3, and SLC16A2

81479

Unlisted molecular pathology procedure [when specified as diagnostic genetic testing using panels of genes (with or without next generation sequencing) not elsewhere specified]

81493

Coronary artery disease, mRNA, gene expression profiling by real-time RT-PCR of 23 genes, utilizing whole peripheral blood, algorithm reported as a risk score
Corus CAD, CardioDx, Inc.

81506

Endocrinology (type 2 diabetes), biochemical assays of seven analytes (glucose, HbA1c, insulin, hs-CRP, adiponectin, ferritin, interleukin 2-receptor alpha), utilizing serum or plasma, algorithm reporting a risk score
[PreDx Diabetes Risk Score™, Tethys Clinical Laboratory]

81599

Unlisted multianalyte assay with algorithmic analysis [when specified as  other MAAA test not meeting medical necessity criteria]

0012U

Germline disorders, gene rearrangement detection by whole genome next-generation sequencing, DNA, whole blood, report of specific gene rearrangement(s)
MatePair Targeted Rearrangements, Congenital, Mayo Clinic

 

 

HCPCS

 

S3800

Genetic testing for amyotrophic lateral sclerosis (ALS)

 

 

ICD-10 Diagnosis

 

 

All diagnoses

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  51. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet. 2007; 39(6):770-775.
  52. Talmud PJ, Hingorani AD, Cooper JA, et al. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010; 340:b4838. 
  53. Teer JK, Bonnycastle LL, Chines PS, et al. Systematic comparison of three genomic enrichment methods for massively parallel DNA sequencing. Genome Res. 2010(a) 20(10):1420-1431.
  54. Teer JK, Mullikin JC. Exome sequencing: the sweet spot before whole genomes. Hum Mol Genet. 2010(b) 19(R2):R145-151.
  55. Thomas GS, Voros S, McPherson JA, et al. A blood-based gene expression test for obstructive coronary artery disease tested in symptomatic nondiabetic patients referred for myocardial perfusion imaging the COMPASS study. Circ Cardiovasc Genet. 2013; 6(2):154-162.
  56. Thorleifsson G, Magnusson KP, Sulem P, et al. Common sequence variants in the LOXL1 gene confer susceptibility to exfoliation glaucoma. Science. 2007; 317(5843):1397-1400.
  57. Torkamani A, Topol EJ, Schork NJ. Pathway analysis of seven common diseases assessed by genome-wide association. Genomics. 2008; 92(5):265-272.
  58. Vaxillaire M, Veslot J, Dina C, et al; DESIR Study Group. Impact of common type 2 diabetes risk polymorphisms in the DESIR prospective study. Diabetes. 2008; 57(1):244-254.
  59. Vissers LE, Fano V, Martinelli D, et al. Whole-exome sequencing detects somatic mutations of IDH1 in metaphyseal chondromatosis with D-2-hydroxyglutaric aciduria (MC-HGA). Am J Med Genet A. 2011; 155A(11):2609-2616.
  60. Weedon MN. The importance of TCF7L2. Diabet Med. 2007; 24(10):1062-1066.
  61. Weinstein LB. Selected genetic disorders affecting Ashkenazi Jewish families. Fam Community Health. 2007; 30(1):50-62.
  62. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007; 447(7145):661-678.
  63. Wright AF. Long-term effects of retinal gene therapy in childhood blindness. N Engl J Med. 2015; 372(20):1954-1955.
  64. Yang Y, Muzny DM, Xia F, et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA. 2014; 312(18):1870-1879.
  65. Yang X, Zabriskie NA, Hau VS, et al. Genetic association of LOXL1 gene variants and exfoliation glaucoma in a Utah cohort. Cell Cycle. 2008; 7(4):521-524.

Government Agency, Medical Society, and Other Authoritative Publications:

  1. American College of Medical Genetics and Genomics. Position Statement. Points to consider in the clinical application of genomic sequencing. 2012.  Approved May 15, 2012. Available at: http://www.acmg.net/StaticContent/PPG/Clinical_Application_of_Genomic_Sequencing.pdf. Accessed on June 23, 2018.
  2. Andersen PM, Abrahams S, Borasio GD, et al. EFNS guidelines on the clinical management of amyotrophic lateral sclerosis (MALS)-revised report of an EFNS task force. Eur J Neurol. 2012; 19(3):360-375.
  3. Arnett DK, Baird AE, Barkley RA, et al. American Heart Association Council on Epidemiology and Prevention; American Heart Association Stroke Council; Functional Genomics and Translational Biology Interdisciplinary Working Group. Relevance of genetics and genomics for prevention and treatment of cardiovascular disease: a scientific statement from the American Heart Association Council on Epidemiology and Prevention, the Stroke Council, and the Functional Genomics and Translational Biology Interdisciplinary Working Group. Circulation. 2007; 115(22):2878-2901.
  4. Blue Cross and Blue Shield Association. Sequencing for Clinical Diagnosis of Patients with Suspected Genetic Disorders. TEC Assessment, 2013; 28(3).
  5. Brooks BR, Miller RG, Swash M, Munsat TL. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord. 2000; 1:293-299.
  6. Burgunder JM, Schols L, Baets J, et al. EFNS guidelines for the molecular diagnosis of neurogenetic disorders: motoneuron, peripheral nerve and muscle disorders. Eur J Neurol. 2011; 18(2):207-217.
  7. Committee on Genetics and the Society for Maternal-Fetal Medicine. Microarrays and Next-Generation Sequencing Technology: The Use of Advanced Genetic Diagnostic Tools in Obstetrics and Gynecology. Obstet Gynecol. 2016; 128(6):e262-e268.
  8. European Society of Human Genetics. Genetic testing in asymptomatic minors: Recommendations of the European Society of Human Genetics. Eur J Hum Genet. 2009; 17(6):720-721.
  9. Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group. Recommendations from the EGAPP Working Group: genomic profiling to assess cardiovascular risk to improve cardiovascular health. Genet Med. 2010; 12(12):839-843.
  10. 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(7):565-574.
  11. Holtzman NA, Watson MS. Promoting safe and effective genetic tests in the United States: work of the Task Force on Genetic Testing. Clin Chem. 1999; 45(5):732-738.
  12. Kalia SS, Adelman K, Bale SJ, et al. 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.
  13. National Society of Genetic Counselors' Definition Task Force, Resta R, Biesecker BB, et al. A new definition of Genetic Counseling: National Society of Genetic Counselors' Task Force report. J Genet Couns. 2006; 5(2):77-83.
  14. National Society of Genetic Counselors. Genetic Counselor Scope of Practice: Available at: https://www.nsgc.org/p/cm/ld/fid=18#scope. Accesed on June 23, 2018.
  15. Stone EM, Aldave AJ, Drack AV, et al. Recommendations for genetic testing of inherited eye diseases: report of the American Academy of Ophthalmology task force on genetic testing. Ophthalmology. 2012; 119(11):2408-2410.
  16. Teutsch SM, Bradley LA, Palomaki GE, et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet Med. 2009; 11(1):3-14.
  17. U. S. Food and Drug Administration (FDA). FDA Advisory Committee Briefing Document: Spark Therapeutics, Inc LUXTURNATM (voretigene neparvovec). October 27, 2017. Available at: https://www.fda.gov/downloads/advisorycommittees/committeesmeetingmaterials/bloodvaccinesandotherbiologics/
    cellulartissueandgenetherapiesadvisorycommittee/ucm579300.pdf
    . Accessed on June 23, 2018.
Websites for Additional Information
  1. Centers for Disease Control and Prevention (CDC). Public Health Genomics (NOPHG). Last updated June 21, 2018. Available at: http://www.cdc.gov/genomics. Accessed on June 23, 2018.
  2. Human Genome Project. Frequently asked questions about genetic testing. Last updated July 5, 2017. Available at: http://www.genome.gov/19516567#al-2. Accessed on June 23, 2018.
  3. National Library of Medicine (NLM). Genetic Conditions: Amyotrophic lateral sclerosis. Reviewed March 2016. Published June 19, 2018. Available at: http://ghr.nlm.nih.gov/condition=amyotrophiclateralsclerosis. Accessed on June 23, 2018.
  4. National Library of Medicine (NLM). Genetic Conditions: Ataxia-telangiectasia. Reviewed January 2013. Published June 19, 2018. Available at: http://ghr.nlm.nih.gov/condition/ataxia-telangiectasia. Accessed on June 23, 2018.
Index

Corus™ CAD
Diagnostic genetic test
Pharmacotherapeutic genetic test
Predictive genetic test
Prognostic genetic test
Therapeutic genetic test
Whole exome sequencing
Whole genome sequencing

The use of specific product names is illustrative only. It is not intended to be a recommendation of one product over another, and is not intended to represent a complete listing of all products available.

Document History

Status

Date

Action

Reviewed

07/26/2018

Medical Policy & Technology Assessment Committee (MPTAC) review. Updated Rationale, Coding, References and History sections.

 

12/27/2017

The document header wording updated from “Current Effective Date” to “Publish Date.” Updated Coding section with 01/01/2018 CPT changes; added codes 81258, 81259, 81269, and 81361-81364 replacing Tier 2 codes for these genes.

Reviewed

08/03/2017

Medical Policy & Technology Assessment Committee (MPTAC) review. Updated Rationale, References and History sections. Updated Coding section with 08/01/2017 CPT PLA code changes.

Revised

05/04/2017

MPTAC review. Updated the medically necessary statements to include criteria for genetic counseling. Updated Background/Overview, Definitions, Coding, References and History sections.

Reviewed

08/04/2016

MPTAC review. Updated formatting in Position Statement. Updated the Rationale, History and References sections.

 

01/01/2016

Updated Coding section with 01/01/2016 CPT changes; removed ICD-9 codes.

Revised

08/06/2015

MPTAC review. Position statement added which indicates the use of the Corus CAD test is considered investigational and not medically necessary. Updated the Rationale, References and History sections.

Revised

05/07/2015

MPTAC review. In bullet 1(a) of the medically necessary position statement, removed the words “late onset or slowly evolving”.  Removed references to deCODE tests from document. Updated the Description/Scope, Rationale, References and History sections.

New

02/05/2015

MPTAC review. Initial document development. This document subsumes two previous documents: GENE.00013 Diagnostic Genetic Testing of a Potentially Affected Individual (Adult or Child) and GENE.00015 Predictive Genetic Testing for Non-Malignant Diseases.