Skip to content

Clinical applications for the Helix Exome+ Assay

Helix’s proprietary Exome+® assay combines a highly uniform panel-grade* exome with over 300,000 highly informative non-coding regions and is designed specifically to enable both current and future clinical and research applications, with many such examples described below. For example, the Exome+ assay provides ≥ 99.5% base by base coverage at ≥ 20x for the ~ 600 genes most relevant to clinical applications (hereditary cancer, cardiovascular disease, pharmacogenomics, and carrier screening). The same Exome+ assay also supports imputation of tens of millions of common genotypes for clinical polygenic risk score applications, with technical equivalence to low pass whole genome sequencing and microarrays1 . The Helix Exome+ assay is run exclusively at the Helix’s CLIA and CAP accredited laboratory facility in San Diego, CA (CLIA #05D2117342, CAP #9382893). The Helix Laboratory is a highly automated facility with the ability to process millions of Exome+ assays annually. This paper provides a quick data reference for common Exome+ clinical applications. For custom or more detailed queries, please contact the Helix team directly. The data itself is of high quality, as demonstrated in Table 11.

Table 1: Summary performance metrics for Helix exome+ assay data outputs.

Single Nucleotide Variants99.9% > 99.99%
Copy Number Variants100% for ≥ 2 exons100%
CYP2D6 star alleles 99.8%100%

The applications of Helix Exome+ assay allow for high quality panel grade tests for medically actionable conditions like hereditary cancer, cardiovascular, or carrier screening conditions. Our partners can also design their own panels based on the condition of interest. This assay also supports analysis of difficult-to-sequence genes such as CYP2D6 for pharmacogenomics and robust data for developing and implementing polygenic risk scores in multiple-ethnicities. We provide coverage checks and share information on the quality of the data to help you choose the best test for your patients.

The following are examples of performance data for some of our panels. Please reach out to the Helix team if you have questions about specific genes or regions.

Medically Actionable Conditions

The American College of Medical Genetics and Genomics (ACMG) has identified a set of 59 genes that are medically actionable for conditions such as certain cancers, specific cardiovascular diseases, and metabolic disorders including malignant hyperthermia susceptibility2 . Genetic testing of these genes can identify predisposition to cancer, metabolic, or cardiovascular disease. Helix’s Exome+ assay offers a 99.911% variant call rate (the percentage of bases with a high-quality variant call across about 4,000 production datasets) across these 59 medically actionable genes, with gene-level call rates described in Table 5. Additionally, CNV detection is 100% for events spanning ≥ 2 exons.

Table 5: Helix Exome+ assay performance metrics for 59 medically actionable genes.

Variant Call Rate Per Gene

ACTC199.98%KCNQ192.36%PCSK999.59% SMAD4100.00%
APOB99.99%LMNA 99.98%PMS2*95.53%TGFBR193.74%
ATP7B100.00%MEN1 99.43%PRKAG299.36%TGFBR299.99%
BRCA1100.00%MSH22 99.36% RB199.46%TNNI3100.00%
COL3A1100.00%MYBPC399.82%RYR299.93%TPM1 99.99%
DSG299.86% MYH799.99%SDHAF2100.00% TSC2100.00%
DSP100.00%MYL299.38%SDHB100.00% VHL100.00%

* PMS2 exons 11-15 are not included due to the inability to detect gene recombination events, which impact the relevance of PMS2 variants in these exons.

CDC Tier 1

The CDC Office of Public Health Genomics (OPHG) has defined Tier 1 (T1) genomics applications as those with significant potential for making positive improvements in public health according to evidence-based guidelines and recommendations3. The conditions chosen as CDCT1 have proven clinical utility and validity. These include Familial Hypercholesterolemia (FH), Hereditary Breast and Ovarian Cancer Syndrome (HBOC), and Lynch Syndrome. The prevalence of these conditions in the general population is estimated to be ~ 1 to 2%4 . Genetic testing for these disease areas in healthy individuals can identify those that are at high risk for disease despite the lack of obvious symptoms and indicate that these individuals are candidates for increased preventive treatment.

Familial Hypercholesterolemia

Familial Hypercholesterolemia (FH) is one of the most common forms of hereditary cardiovascular conditions with a prevalence of 1:200-1:250 in the United States5,6. FH confers an increased risk of developing heart disease in individuals that may appear otherwise completely healthy. Studies have shown that individuals with FH have a higher chance of developing coronary artery disease than individuals without a diagnosis of FH. Proactive treatment and management can significantly reduce the risk of developing the disease7 . The four genes tested for cases of FH are APOB, LDLR, LDLRAP1, and PCSK9. Helix’s Exome+ assay offers high performance across an FH panel, as shown in Table 2. Variant call rates and CNV call rates represent the percentage of bases or exons across the gene assigned a clinical-quality genotype or copy number value across about 4,000 Helix Exome+ assay datasets generated in production.

Table 2: Helix Exome+ assay performance metrics for FH

GeneVariant Call Rate*CNV Call Rate**
APOB99.99% 100%
LDLR99.98% 100%
PCSK999.59% 100%

Hereditary Breast and Ovarian Cancer Syndrome

BRCA1 and BRCA2 variants can result in Hereditary Breast and Ovarian Cancer Syndrome (HBOC). This syndrome is present in both men and women. Individuals with this condition can have a high risk of developing cancers such as breast (~38 to 87%), ovarian (16 to 63%), melanoma, and pancreatic cancers8. In addition to melanoma and pancreatic cancers, men also are at an increased risk of developing male breast cancer and prostate cancer (up to 20%). Pathogenic (disease-causing) variants in additional genes such as ATM, CDH1, CHEK2, PALB2, PTEN, STK11, and TP53 can also convey increased risk for breast and/or ovarian cancer. Helix’s Exome+ assay coverage for these genes is described in Table 3.

Table 3: Helix Exome+ assay performance metrics for genes related to breast and ovarian cancer, including but not limited to HBOC.

GeneVariant Call RateCNV Call Rate
BRCA1 100%100%
CDH199.97% 100%
STK11 99.44%100%

*CNV not offered for PTEN exon 9. Pathogenic/likely-pathogenic variants have not been found isolated to PTEN exon 99.

Lynch Syndrome

Lynch Syndrome, also known as hereditary nonpolyposis colorectal cancer (HNPCC), confers increased risk to many different cancer types, particularly to colon, endometrial, and gastric cancers. Regular screening and intervention, such as colonoscopies, can significantly reduce the risk of developing colon cancer in individuals with Lynch Syndrome10. Helix’s Exome+ assay coverage for the genes underlying this disease are described in Table 4.

Table 4: Helix Exome+ assay performance metrics for Lynch Syndrome genes.

GeneVariant Call RateCNV Call Rate
MLH1 99.62%100%
MSH6 100%100%

*PMS2 exons 11-15 are not included due to the inability to detect gene recombination events, which impact the relevance of PMS2 variants in these exons.


Pharmacogenomics (PGx) is supported by the Helix Exome+ assay. Star alleles and copy number are reported for CYP2C9, CYP2C19, CYP2D6, and CYP4F2, based on the comprehensive menu of star alleles listed in PharmVar (Table 6). For CYP2D6, Helix has built a proprietary probabilistic caller with ~100% accuracy to confidently identify complex alleles and copy number status.

Table 6: Output delivered by the PGx Pipeline. For each gene, all star alleles in PharmVar are reported#. Additionally, whole-gene and exon-level copy number including both deletions and duplications are reported. Novel alleles (putative loss of function SNPs or indels) are also included.

GeneStar AllelesGene-level Copy NumberExon-level Copy NumberNovel Alleles
CYP2C19*1-*19, *22-*26, *28-*36
CYP2D6*1-*15, *4N, *17-*65, *68-*75, *81, *83-*114

# CYP2D6 star alleles are based on PharmVar 3.4, excluding *82. CYP2C19 star alleles are based on PharmVar 4.0.3 with the addition of *36 (a whole gene deletion). CYP2C9 and CYP4F2 star alleles are based on PharmVar 4.1.3.

Star alleles for additional PGx genes can be easily determined from the SNPs and indels reported from the Exome+ (Table 7). Coverage of these genes was boosted similarly to genes involved in clinical outcomes to ensure high call rates and reliable results.

Table 7: Performance for a subset of PGx-relevant targets for which SNPs, indels, and CNVs are reportable from the Exome+.

GeneStar AlleleVariantCall Rate
CYP1A2*1F rs762551 100%
*22 rs35599367100%
CYP3A5*3 rs776746100%

Call Rate = Rate at which this site was assigned a high-quality genotype call across about 4,000 samples.

Need for a comprehensive PGx test to avoid misclassifications Helix offers a comprehensive reportable range for CYP2D6 including 106 star alleles. While some of these star alleles are rare and/or lack interpretation, the importance of identifying these remains significant. When these star alleles are excluded from the reportable range, approximately 16.8% individuals are often mis-classified as *1 with high-confidence, indicating that the individual is a normal metabolizer when the classification for this individual, in truth, remains undetermined and interpretation should not be provided with any confidence (Table 8)14 .

Table 8: Impact of using an incomplete CYP2D6 panel. This table presents the outcome of using CYP2D6 tests with an incomplete star allele list as compared to the Helix PGx Pipeline, based on 30,000 individuals. For comparison, a representative panel was used that includes the common alleles: *1, *2, *3, *4, *4N, *5, *6, *9, *10, *17, *29, *35, *36, *41, and duplications.

Consequence of using an incomplete panelSamples affected
Mischaracterization of ultrarapid metabolizers7.7%
Mischaracterization of poor metabolizers4.4%

Importance of full gene analysis including CNVs

Copy number is available for all pharmacogenomics genes reported. This is important, as duplications can quickly result in the transition of metabolizer status. Across a set of 16k individuals, ~ 16% of samples were identified to have whole gene CYP2D6 duplications (with another 5% harboring whole gene deletions), highlighting the importance of analyzing CYP2D6 copy number in conjunction with CYP2D6 star alleles.

Polygenic Risk Scores

In addition to developing panel-grade products, we also have the capability to impute tens of millions of common genotypes in support of polygenic risk score applications. Polygenic Risk Scores (PRS) are emerging as a powerful tool to predict an individual’s risk of disease. PRS can be used to predict disease risk as effectively as variants within single genes15.

Because PRS are based on many genomic loci, often focused in non-coding regions, their coverage requirements are significantly different than those for standard genetic testing of monogenic disease. The Helix Exome+ assay supports analysis of PRS with direct coverage of hundreds of thousands of non-coding variants plus tens of millions of genome-wide imputed variants. These millions of variant calls ensure that PRS of all sizes are supported. Analysis of imputation accuracy shows technical equivalence of the Exome+ to low pass whole genome sequencing (WGS) and microarrays1.


The breadth of the Exome+ assay allows researchers to not only conduct comprehensive common-variant GWAS analysis, but also discover and analyze novel low-frequency and rare variant contributions to phenotypes and disease. The Helix Research Team is experienced with a variety of clinical, pharmacological, and statistical methods, and is available to collaborate with partners with IRB-approved research protocols to: facilitate panel design; perform sample stratification and selection, algorithm selection, and common and rare-variant GWAS; develop, test, and replicate polygenic risk scores; and assist with data integration and normalization. Our high performance and scalable infrastructure enables screening of thousands of traits against hundreds of thousands of samples in less than a day. This research platform was employed to analyze the 50,000 exomes on > 4,000 traits from the UKBiobank, with results described and published on our blog16 in under two weeks.

Helix’s specialized research database is regularly updated with newly-sequenced samples, allowing allele frequencies to be calculated and updated on whole or cohort-specific subsets and further subdivided against selected sample metadata, all in just seconds. Because every sample has already been fully sequenced, updates or additions to a study design can be supported immediately, without having to re-sequence participants. The Helix Research Team encourages submissions to scientific conferences and peer-reviewed publications based on Helix Exome+ data, such as this ​upcoming paper​4​ in press at Nature Medicine describing population health genetic screening for CDCT1 conditions, where it was found that 90% of at-risk carriers were missed by standard protocols.


Ancestry can help provide context for the relevance of clinical genetic testing, for example when certain populations have an increased concentration of carriers of specific risk alleles. The Helix Exome+ assay results in ancestry assignments spanning 26 ancestries across eight regions (Table 9). Alternatively, it is possible to receive a lower resolution ancestry result with only six ancestry groups (African, Ashkenazi Jewish, East Asian, European, Indigenous American, and South Asian).

​Table 9​: List of 26 populations, grouped into eight regions, reported by Helix.

EuropeanAshkenazi Jewish, Finnish, Northeast European, Northwest European, Southeast European, Southwest European, Sardinian
East AsianFilipino, Japanese, Korean, Northern Han Chinese, Southeast Asian
AfricanEast Bantu, Nigerian, Senegalese and Gambian, Sierra Leonean and Liberian
Indigenous AmericanIndigenous North American, Indigenous South American
South AsianGujarati, Punjabi and Pathan, South Indian
Middle Eastern & North AfricanBedouin, North African, Persian

Learn More

Analytic performance for our variant calls are described in various other white papers. Of interest, small variant performance is described in our Performance White Paper1​ ​and CNV performance is described in our CNV White Paper2 .​


  1. Performance White Paper:
  3. Grzymski et al., Population genetic screening efficiently identifies carriers of autosomal dominant diseases. Nature Medicine, 26, 1235–1239 (2020).
  4. Akioyamen et al., Estimating the prevalence of heterozygous familial hypercholesterolaemia: a systematic review and meta-analysis. BMJ Open. 2017; 7(9).
  5. Singh and Bittner, Familial hypercholesterolemia -- epidemiology, diagnosis, and screening. Cur Atheroscler Rep. 2015; 17(2)482.
  6. Safarova, M. S., & Kullo, I. J. (2016). My Approach to the Patient With Familial Hypercholesterolemia. ​Mayo Clinic proceedings​, ​91​(6), 770–786. doi:10.1016/j.mayocp.2016.04.013
  7. Petrucelli et al., BRCA1- and BRCA2-Associated Hereditary Breast and Ovarian Cancer. Gene Reviews. 1998 Sep 4
  8. Adam et al., PTEN Harmartoma Tumor Syndrome. Gene Reviews. Updated 2016.
  9. Jarvinen HJ, Mecklin JP, Sistonen P. Screening reduces colorectal cancer rate in families with hereditary nonpolyposis colorectal cancer. Gastroenterology 1995;108:1405–11
  10. Kalia 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. Genetics in Medicine 19(2) Feb 2017.
  11. Monte, Andrew A et al. “CYP2D6 Genotype Phenotype Discordance Due to Drug-Drug Interaction.” ​Clinical pharmacology and therapeutics​ vol. 104,5 (2018): 933-939. doi:10.1002/cpt.1135
  12.​, with v3.4 star alleles found in the excel spreadsheet dated ‘Nov 5, 2018’, downloaded here:
  13. Khera et al., Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 2018 Sep; 50(9):1219-1224.
  14. A comprehensive landscape of CYP2D6 variation across 30,000 individuals:
  15. Cirulli and Washington, Researchers have access to new data on thousands of exomes. Here’s what we found. March 27,2019. ​