Association of Clinical Features With Incidental Findings From Exome Sequencing in 3,223 African Americans

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1 Association of Clinical Features With Incidental Findings From Exome Sequencing in 3,223 African Americans The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Natarajan, Pradeep Association of Clinical Features With Incidental Findings From Exome Sequencing in 3,223 African Americans. Master's thesis, Harvard Medical School. Citable link Terms of Use This article was downloaded from Harvard University s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at nrs.harvard.edu/urn-3:hul.instrepos:dash.current.terms-ofuse#laa

2 Thesis Committee: Dr. Sekar Kathiresan (Chair), Pradeep Natarajan Dr. Heidi L. Rehm, and Dr. Daniel G. MacArthur \ ASSOCIATON OF CLINICAL FEATURES WITH INCIDENTAL FINDINGS FROM EXOME SEQUENCING IN 3,223 AFRICAN AMERICANS Abstract Importance. The American College of Medical Genetics and Genomics recommends informing individuals who carry mutations in a set of Mendelian disease genes that might require clinical action regardless of genetic testing indication. However, whether such mutations lead to an increased risk for diseases in individuals not referred for clinical genetic testing has not been evaluated. Objective. To evaluate whether those in the unselected general population who carry potentially actionable mutations are more likely to manifest the associated diseases than those without such mutations. Design, Setting, and Participants. Cross-sectional observational study of participants enrolled in the Jackson Heart Study (Jackson, MS) between 2000 and 2004 who underwent whole exome sequencing (n = 3223). All participants were on African descent. Exposures. Mutations across a set of 56 recommended clinically actionable genes ascertained by whole exome sequencing. ii

3 Main Outcomes and Measures. Evidence for pathogenicity for all mutations identified in 56 genes was determined by bioinformatic analyses and extensive literature review. Anticipated clinical findings for each of the 56 genes were extracted from study surveys, echocardiography, electrocardiography, and lipid panels across all participants without knowledge of mutation carrier status. The main outcome was the difference in expected clinical findings in those with mutations compared to those without. Results. 3,223 African Americans had a total of 4,429 mutations across the 56 genes. Bioinformatic filters yielded 462 candidate pathogenic variants in 1945 participants (60%). Subsequent manual review of the evidence yielded 30 pathogenic variants in 44 (1.3 %) participants and 12 likely pathogenic variants in 23 (0.7 %). Participants with pathogenic or likely pathogenic variants were more likely to display suggested clinical features (19.6 %) compared with expected (7.1 %; one-sided P = 0.002) by a factor of 2.75 (95% CI, 1.37 to 4.92). In secondary analyses, the excess of observed clinical features was apparent in cardiovascular and cancer genes by 2.67-fold (95% CI, 1.07 to 5.51) and 2.89-fold (95% CI, 0.78 to 7.39), respectively. Conclusions and Relevance. Unselected African Americans in the general population with pathogenic or likely pathogenic variants have an increased risk of displaying features associated with clinical disease. iii

4 Table of Contents Abstract Figure Captions Table Captions Acknowledgements ii v vi vii Introduction 1 Methods 3 Subjects 3 Exome Sequencing and Variant Calling 3 Variant Annotation and Classification 4 Phenotypes 6 Statistical Analyses 6 Results 8 Discussion 13 References 17 Supplemental Materials 20 Supplemental Table 1 20 Supplemental Table 2 23 iv

5 Figure Captions Figure 1. Site allelic frequency spectrum of ACMG-recommended 56 genes in JHS. Each point represents a 5E-03 minor allele frequency bin and the proportion of all variants within this bin. The grey line represents the cumulative proportion. Figure 2. Overlap of ClinVar classifications of candidate pathogenic mutations. Reported classifications of the 337 of 462 variants identified for classification are plotted to describe distinct classifications between groups for each variant; 125 variants were not previously classified in ClinVar. P = pathogenic; LP = likely pathogenic; VUS = variant of unknown significance; LB = likely benign; B = benign. Figure 3. Observed proportion of subjects with pathogenic or likely pathogenic variants and suggestive clinical features compared to expected proportion. Standardized incidence ratios (SIR) and 95% confidence intervals for each gene category is presented to describe the relative increased prevalence of suggested clinical features in carriers of pathogenic or likely pathogenic variants. Standardized incidence ratio is calculated as the ratio of observed case rate to the expected case rate based on the prevalent rate of suggested clinical features for each gene. The expected proportion for genotype positive patients is derived from the background combined rate of observed suggestive clinical features in genotype negative participants. Given the absence of surgical anesthesia data, carriers of CACNA1S and RYR1 were not included in analysis. v

6 SIR = standardized incidence ratio; CI = confidence interval; ACMG = American College of Medical Genetics and Genomic vi

7 Table Captions Table 1. Pre-specified clinical features ascertained among sequenced Jackson Heart Study participants. Each of the ACMG-recommended 56 genes are listed with the correlated suggested clinical feature case definitions within the Jackson Heart Study. LV = left ventricle; RV = right ventricle; RA = right atrum; EKG = electrocardiogram; LDL-C = low-density lipoprotein cholesterol vii

8 Acknowledgements I would like to thank the members of my thesis committee, specifically Drs. Sekar Kathiresan, Heidi L. Rehm, Daniel G. MacArthur for their mentorship and guidance. Additionally, notable collaborators for this project include Drs. Alexander G. Bick, Nina B. Gold, Gina M. Peloso, Heather McLaughlin, Peter Kraft, J.G. Seidman, Christine E. Seidman, Robert C. Green. I would also like to thank the Jackson Heart Study staff, led by Dr. Jim Wilson, and the participants of the Jackson Heart Study. Lastly, I wish to acknowledge the John S. LaDue Memorial Fellowship in Cardiology and the Massachusetts General Hospital Division of Cardiology for their support. viii

9 Introduction The advent of next-generation sequencing is revolutionizing the study of human genetic variation and its contribution to human biology and disease. Additionally, such technologies have increased the diagnostic yield of clinical genetic testing in individuals with clinical Mendelian disorders. 1,2 However, surveys of the entire exome have the potential to discover variants in sequenced individuals that may predispose to disease and the implications are unknown. Such incidental findings may have bearing on clinical care and surveillance for a proband and family. 3,4 With the recent announcement of the United States Personalized Medicine Initiative, at least one million additional individuals may have exome- or genome-wide data generated to understand human health and disease. Now, more than ever, it is critical to understand genotype-predicted disease risk as large-scale sequencing efforts continue to exponentially grow. The American College of Medical Genetics and Genomics (ACMG) recently recommended reporting pathogenic variants in 56 genes secondarily discovered by genomic sequencing. 5 However, the data to support these recommendations come from analyses of probands and families with severe phenotypes referred for genetic testing. 6-9 Since genetic and environmental factors contribute to the penetrance of a pathogenic variant, ethnicity is likely to be an important predictor of penetrance which is often inadequately addressed due to restricted sample sizes. 10 Furthermore, recent human history has had a substantial impact on protein-coding DNA sequence variation leading to a relative depletion of disruptive variants in Mendelian disease genes among African versus European descended individuals. 11 As research cohorts and hospital-based biobanks initiate and expand sequencing 1

10 efforts, determining the application of the ACMG recommendations across diverse ethnicities has become increasingly prudent. While approximately 25 % of patients who undergo diagnostic exome sequencing obtain a molecular diagnosis, approximately 3 % of individuals are also left with a secondary diagnosis of unclear significance. 1,2 Within the NHLBI Exome Sequencing Project, participants of European and African ancestry from the general population underwent whole exome sequencing and were assessed for potentially clinically actionable genomic findings. 12, % of the 4,430 European ancestry participants and 1.2 % of the 2,203 African ancestry participants were noted to have potentially clinically actionable variants. However, follow-up phenotyping has largely not been performed in these studies. Understanding penetrance is ideally determined with longitudinal follow-up of a large cohort of individuals in the community who have undergone genetic sequencing in the absence of an affected. Furthermore, penetrance studies require the analyses of diverse populations, as allelic background is likely to influence the phenotypic consequences of incidental genetic findings. Here, we use a community-based highly phenotyped cohort of 3,273 African Americans from the Jackson Heart Study who have undergone whole exome sequencing to understand the implications of incidentally discovered genetic findings in African Americans. 2

11 Methods Subjects The Jackson Heart Study (JHS), based in Jackson, MS, is a community-based, observational study designed to understand the social and biological mediators of the high prevalence of cardiovascular disease among African Americans. JHS began enrolling in 1998 and has completed three distinct clinical assessments to date Exam 1 ( ), Exam 2 ( ), and Exam 3 ( ). The details of the cohort, including sampling, recruitment, and examinations have been previously described DNA samples were collected from consenting participants at Exams 1 and 2. As a part of the NHLBI Exome Sequencing Project, 3,273 participants from JHS underwent whole exome sequencing. 17 Exome Sequencing and Variant Calling Exome sequencing was performed at three sequencing center (the Broad Institute [n = 2,317], University of Washington [n = 481], and Baylor University [n = 475]). Laboratory methods for library construction, exome capture, sequencing, and mapping for the NHLBI Exome Sequencing Project have been previously described and are briefly summarized here. 18 Upon receipt of samples, initial quality control included DNA concentration assessment by PicoGreen, confirmation of high-molecular weight DNA, and sex determination. Prepared libraries were captured for exome enrichment using: Agilent SureSelect Human All Exon v2 (~38 Mb; Agilent, Santa Clara), CCDS 2008 (~26 Mb), Roche/Nimblegen SeqCap EZ Human Exome Library v1.0 (~32 Mb; Roche Nimblegen, Madison WI), or Roche/Nimblegen SeqCap 3

12 EZ Human Exome Library v2.0 (~34 Mb; Roche Nimblegen, Madison WI). After cluster amplification, enriched libraries were sequenced on an Illumina GAIIx or HiSeq Raw enriched short-reads were aligned to the human reference genome (hg19) using BWA followed by de-duplication, insertion-deletion realignment, and base quality recalibration to generate individual BAM files. 19 Variant calling was performed with the Genome Analysis Toolkit s (GATK) v3 HaplotypeCaller. 20,21 Variant quality score recalibration from GATK filtered out low quality variants. Samples were checked for total number of variants, observed number of singletons and doubletons, Ti/Tv ratio, Het/Hom ratio, missingness, contamination statistics, and non-reference concordance with available genotype data from the Illumina HumanExome BeadChip v A final variant called format (VCF) file was generated and subsetted for the exons and 50bp flanking introns of the ACMG 56 genes. 5 Variant Annotation and Classification Transcripts for analysis have been determined by Partners Laboratory for Molecular Medicine (LMM), a CLIA-certified molecular diagnostic laboratory, and were typically the longest transcripts (Table S1). Variants were annotated using multiple sources, including the Human Gene Mutation Database (HGMD) Professional, ANNOVAR, Alamut, Variant Effect Predictor, ClinVar, and the UCSC Genome Browser database Variant classification is based on a multistep algorithm developed by LMM and is consistent with the recent ACMG guidelines. 28,29 First, variants with minor allele frequencies greater than 1% in 1000 Genomes, Exome Sequencing Project, or Exome Aggregation 4

13 Consortium were filtered. Variants previously reported in an index case as a disease mutation, DM or DM?, by HGMD, or pathogenic or likely pathogenic in ClinVar, or novel loss-offunction, nonsense, splice site, and frame shift variants were then classified. Variants were classified as pathogenic variants (PVs) if (1) they were protein truncating variants (nonsense, frameshift or splice-site) in a gene where loss-of-function is a wellestablished disease mechanism and the variant was expected to result in nonsense-mediated decay or (2) literature review identified significant segregation with disease ( 10 meioses) and the amino acid was conserved in at least mammals and birds or (3) literature review identified moderate segregation with disease (5-9 meioses) and the impact of the variant was supported by strong functional data. Variants were classified as likely pathogenic variants (LPVs) if: (1) literature review showed moderate segregation (5-9 meioses) with disease, the amino acid was conserved in all mammals and birds but functional data was either limited or absent, or (2) literature review identified minimal familial segregation (<5 meioses), but the amino acid was both conserved in all mammals and supported by strong functional data; or (3) they were protein truncating variants (nonsense, frameshift, or splice site) in a gene where loss-of-function variants have been observed but are not yet a well-established disease mechanism and the variant was expected to result in nonsense-mediated decay. Variants were classified as Benign if the frequency of the variant was above 0.3% for variants associated with dominantly inherited diseases. All other variants were classified as uncertain significance (VUS). For secondary analyses, we grouped the ACMG genes into cancer-related (APC, BRCA1, BRCA2, MEN1, MLH1, MSH2, MSH6, MUTYH, NF2, PMS2, PTEN, RB1, RET, SDHD, 5

14 SDHAF2, SDHB, SDHC, STK11, TP53, TSC1, TSC2, VHL, and WT1) and cardiovascular-related (ACTC1, GLA, LMNA, MYBPC3, MYH7, MYL2, MYL3, PRKAG2, TNNT2, TNNI3, TPM1, DSC2, DSG2, DSP, PKP2, TMEM43, KCNH2, KCNQ1, SCN5A, RYR2, ACTA2, COL3A1, FBN1, MYLK, MYH11, SMAD3, TGFBR1, TGFBR2, APOB, LDLR, and PCSK9). Two genes conferring susceptibility to malignant hyperthermia (CACNA1S, and RYR1) were not considered in analyses because no data on response to surgical anesthesia were available. Phenotypes Phenotype data on JHS participants were extracted from the JHS Vanguard phenotype data set without knowledge of genotypes. Suggested clinical features (SCFs) for diseases corresponding to the ACMG-recommended genes were extracted. 5 All phenotypes, including age, were ascertained at Exam 1 ( ). Prevalent cancer diagnoses were self-reported at Exam 1. We further categorized cardiovascular genes based on common SCFs (Table 1). Statistical Analyses Our overall hypothesis is that African Americans who carry a PV or LPV in one of the ACMG-recommended genes are more likely to manifest the associated diseases than those without such variants. The statistical approach is summarized here. We compared the observed number of SCFs in those with LPVs/PVs compared to subjects without LPVs/PVs. The expected number of SCFs for those with LPVs/PVs was calculated as n! π!, where n! is the number of PV carriers in class i and π! is the proportion of non-carriers with a SCF in class i. To estimate statistical significance, we first randomly sampled a Poisson distribution with the above parameters 100,000 times. Then we determined the one-side p-value for the number of times the 6

15 observed count was less than the simulated expected count. Standardized incidence ratios (SIR) were calculated as the ratio of observed SCF count to the expected count. 30 All statistical analyses were performed using R (Version 3.0.2). Table 1. Pre-specified clinical features ascertained among sequenced Jackson Heart Study participants Genes APC, BRCA1, BRCA2, MEN1, MLH1, MSH2, MSH6, MUTYH, NF2, PMS2, PTEN, RB1, RET, SDHD, SDHAF2, SDHB, SDHC, STK11, TP53, TSC1, TSC2, VHL, WT1 COL3A1, FBN1, TGFBR1, TGFBR2, SMAD3, ACTA2, MYLK, MYH11 MYBPC3, MYH7, TNNT2, TNNI3, TPM1, MYL3, ACTC1, PRKAG2, GLA, MYL2, LMNA RYR2 PKP2, DSP, DSC2, TMEM43, DSG2 Clinical Features Previous diagnosis of cancer Echocardiography with aortic root aneurysm (aortic root diameter > 3.7 cm) Echocardiography with posterior LV posterior wall thickness or septal width > 12 mm, or echocardiography with LV diastolic diameter > 6 cm Heart rate > 100 bpm Echocardiography with RV dilatation KCNQ1, KCNH2, SCN5A EKG with prolonged QT interval (QT > 450 mm in women, QT > 460 mm in men), or prior aborted sudden cardiac death LDLR, APOB, PCSK9 Elevation of LDL-C > 330 mg/dl (8.5 mmol/l) RYR1, CACNA1S No phenotype data available 7

16 Results Of the 3,273 JHS participants who underwent exome sequencing at three different sequencing centers, 3,223 participants (98.5%) passed sample quality control, which included filtering outliers for total number of variants, observed number of singletons and doubletons, Ti/Tv ratio, and Het/Hom ratio, and samples with a large proportion of locus missingness, contamination statistics, and non-reference concordance. There was no significant difference in the sample filter rate among samples sequenced at the different sequencing centers (P > 0.10). 62.4% (n = 2,012) of the analyzed participants were female. The mean age for all subjects was 55.6 ± 12.8 years. Figure 1. Site allelic frequency spectrum of ACMG-recommended 56 genes in JHS. 8

17 We discovered 4,429 variants in the protein-coding regions and splice-sites of the ACMG-recommended 56 genes from exome sequencing across the 3,223 JHS participants. A median of 49.5 such variants per gene (interquartile range: 17.0, 109.5) across the cohort was observed and this was correlated with canonical transcript length (Spearman R 2 = 0.20; P = 5.3E- 04). The site allelic spectrum demonstrates that the majority of the observed variation is very rare (Figure 1). For example, 37.9 % (n = 1,679) variants were each only seen in one subject. Among the 4,429 variants, 30 were in-frame insertions-deletion, 61 loss-of-function (26 nonsense, 22 frame-shift, and 13 splice-site), 1,832 synonymous, and 2,506 missense variants. 550 variants in the ACMG genes that were previously classified as pathogenic or likely pathogenic in ClinVar, or disease-causing mutation (DM) or likely disease-causing mutation (DM?) in HGMD, or were novel frameshift, nonsense, or canonical splice-site variants were extracted. Of these, 462 had a minor allele frequency < 0.01 in or were absent from 1000 Genomes and Exome Sequencing Project African ancestry participants, and all Exome Aggregation Consortium subjects. These variants were considered candidate pathogenic variants and further variant classification was performed as described in the Methods. 1,945 subjects (60.3%) had at least one candidate variant, requiring additional review. 416 of the 462 variants (90.0%) were classified as DM or DM? in HGMD, but 96 (20.8%) were classified as pathogenic or likely pathogenic by at least one submitter in ClinVar (Figure 2). 378 subjects (11.7 %) have a variant that has been classified as pathogenic or likely pathogenic at least once in ClinVar but 541 subjects (16.8%) have a variant that was not previously submitted to ClinVar at the time of analysis. 9

18 A review of evidence was performed for the 462 candidate variants and resulting in 30 pathogenic variants and 12 likely pathogenic variants across 26 of the 56 ACMG genes (Table S2). Of the 3,223 participants, 44 (1.3%) had a pathogenic variant and 23 (0.7%) had a likely pathogenic variant. One participant had two distinct pathogenic KCNH2 variants. Among variants observed in ClinVar, variants were highly likely to be classified as at least likely pathogenic if at least one submitter classified the variant as at least likely pathogenic in ClinVar (odds ratio = 79.0; P = 1.6E-13) with a sensitivity of 96.0 % and specificity of 76.9 %. Notably, one-third of participants (n = 1,097) harbor a variant of uncertain significance; of these, 201 individuals harbor at least two variants of uncertain significance. Figure 2. Overlap of ClinVar classifications of candidate pathogenic mutations. Given the absence of data on response to surgical anesthesia, CACNA1S or RYR1 pathogenic or likely pathogenic variants were excluded from subsequent analyses; this left 56 10

19 participants with candidate variants in other genes for further follow-up. Subjects with pathogenic or likely pathogenic variants in these genes had a higher proportion of observed suggested clinical features (11 / 56, or 19.6 %) compared with expected rate (4.0 / 56, or 7.1 %; one-sided P = ) (Figure 3). Thus, positive subjects had a 2.75-fold (95% SIR CI = 1.37, 4.92) excess risk. The effect estimates were consistent in secondary analyses of only cardiovascular disease genes (SIR = 2.67; 95% CI = 1.07, 5.51; one-sided P = 0.010) and cancer genes (SIR = 2.89, 95% CI = 0.78, 7.39; one-sided P = 0.046). Figure 3. Observed proportion of subjects with pathogenic or likely pathogenic variants and suggestive clinical features compared to expected proportion 4 of the 20 carriers of pathogenic or likely pathogenic variants reported a history of cancer at Exam 1 but cancer type was not extracted during that visit. An individual with a BRCA2 frameshift mutation (p.thr219fs) was diagnosed with cancer at 60 years. Carriers of MLH1 p.arg687trp and MUTYH p.gly396asp had cancer diagnoses at 36 and 61 years, respectively. And a carrier of TP53 p.arg273his, who was enrolled at the age of 93 years, reported a diagnosis of cancer at 89 years. None of the 12 carriers of variants in ARVD genes or the carrier of a dilated cardiomyopathy variant demonstrated expected abnormalities by echocardiography. However, one of the three carriers of mutations in hypertrophic 11

20 cardiomyopathy genes had echocardiographic findings consistent with expected findings (rate in genotype negative participants: 8.8%); a carrier of MYH7 p.ala797thr, implicated in hypertrophic cardiomyopathy, had left ventricular hypertrophy with an interventricular septal thickness of 13.2 mm and posterior wall thickness of 12.8 mm. Whereas, 5 of 8 carriers of pathogenic/likely pathogenic variants associated with long QT syndrome (KCNQ1 p.arg518*, KCNQ1 p.gly179ser, KCNQ1 p.val205met, and 2 of 3 carriers of SCN5A p.glu1072*) had prolonged QTc intervals (range ms) in the resting state (rate in genotype negative participants: 21.7%). One of two subjects with an incidental finding in a lipid gene (LDLR p.pro685leu) is the only participant who met WHO criteria for definite heterozygous familial hypercholesterolemia and had the highest LDL cholesterol level (357.5 mg/dl; normal mg/dl) of all sequenced subjects. Notably, a participant with a truncating mutation in APOB (p.arg2012*) had a very low concentration of LDL cholesterol (49.4 mg/dl), consistent with familial hypobetalipoproteinemia; as this is typically characterized by diminished cardiovascular risk and thus not clinically actionable, this variant was not included in this study although supports the notion of incidental findings in ACMG genes being penetrant. 12

21 Discussion Penetrance approximations from referred patients and families enriched for severe phenotypes are inherently limited by ascertainment bias. A reverse genetics approach starting with a genotype in an unselected population then analyzing predicted resultant phenotypes is the only approach that can provide unbiased, accurate estimates of variant penetrance. Such accuracy is critical for genetic counseling, especially as the wider accessibility of genome-wide sequencing approaches increase the probability of genetic incidental findings being discovered in people without a pre-existing clinical condition or family history. The frequency of reportable variants observed in our study is similar to other descriptive reports with fewer African Americans. 12,13 Here, we further show that, in aggregate, incidental pathogenic or likely pathogenic variants in ACMG-recommended genes are associated with a 2.75-fold increase in expected features in a community-based research cohort of 3,223 African Americans who have undergone whole exome sequencing. The 56 ACMG genes were selected not only based on predicted heightened penetrance but also the opportunity for tailored clinical management or surveillance if detected. 5 Since we observe heighted expression of suggestive clinical features with likely pathogenic or pathogenic variants in these genes, reporting may facilitate preventive medicine approaches. As the costs of whole exome and genome sequencing continue to decrease and with the recent initiation of nationally supported large-scale genomic studies, including President Obama s recent announcement of the United States Precision Medicine Initiative, the number of individuals worldwide who will have undergone whole exome or genome sequencing will 13

22 increase exponentially. Such initiatives will provide dense longitudinal phenotypes and have the potential to refine penetrance estimates in aggregate, and potentially by gene. However, our study suggests that the return of incidental genomic findings to participants and their providers during such efforts may identify individuals who are significantly more likely to manifest disease due to genetic risk than the general population. Scalability of refined variant classification is required to support such an effort. We demonstrate that publicly available databases for variant classification can be invaluable resources and provide a suitable initial filter but are still plagued by false positives and classification heterogeneity requiring additional curation The substantial efforts required to manually classify a subset of prioritized variants from just 3,223 participants suggest that applying this approach to 1,000,000 individuals is not feasible. Much of the effort requires extensive review of literature text, literature supplements, scientific meeting abstracts, and public and proprietary databases to examine the evidence for segregations, non-segregations, and in vitro functional characterizations. The heterogeneity and lack of structure of data sources even within source categories limits automated extraction, including natural language processing, of relevant criteria for classification. Efforts such as the Clinical Genomics Resource (ClinGen) have recently begun trying to standardize genotype-phenotype data sharing and variant curation. 28 Such efforts effectively crowd-source the labor in a standardized fashion and will help with more scalable implementation of classification and reporting. In addition to scalability, policies regarding the reporting of potentially clinically actionable variants incidentally discovered by sequencing should consider several principles that 14

23 apply to the risks of screening tests: 1) proportion of population affected, 2) risk conferred from carrying such mutations, 3) efficacy of clinical management after return of results, and 4) fiscal and emotional costs from down-stream testing and management. Our study has provided estimates for the first two considerations but last two highlight understanding the personal and societal value of return of results. Since carrying a pathogenic or likely pathogenic variant does not appear to be 100% deterministic of disease, these considerations are prudent. Management considerations range from blood-work to a surgical procedure, which captures the range of costs and range of anxieties a carrier may experience. If the United States Precision Medicine Initiative is reflective of United States demographics, approximately 130,000 African Americans may undergo sequencing, 2,600 African may have a reportable incidental finding with downstream clinical testing. Our study has key limitations. First, given the scarcity of pathogenic or likely pathogenic variants per gene in an unselected population, we are underpowered to make inferences about the penetrance of specific genes or variants at the current sample size. To overcome such limitations in statistical power, we aggregate clinically actionable to genes into a single statistical unit and consider expected clinical features for each gene. Second, the lack of relative substantial followup in the Jackson Heart Study limits analyses of incidental disease and the reporting of prevalent age-related diseases, such as cancer, but the age of those carrying incidental mutations was not significantly different than those who were not (P = 0.21). Thus, this approach is conservative and the effects of some variants may not fully be appreciated effectively underestimating the true standardized incidence ratio. Third, our analysis is only performed in a cohort of African Americans and given the variation in distribution of variation in clinically actionable genes, 15

24 similar analyses are required in diverse ethnicities Fourth, in addition the use of in silico prediction algorithms, the influence of observed segregations on variant classification might be influenced by publication bias. 29 We anticipate that analyses such as these in unselected populations will continue to help identify false positive classifications. And lastly, for some phenotypes, conventional testing may incompletely capture suggestive clinical features, such as right ventricular dilatation on echocardiography for arrhythmogenic right ventricular dysplasia genes, limiting the ability to detect penetrance. The study limitations demonstrate the challenges in analyses of rare diverse instances across a heterogeneous population yet the observation of increased predilection for associated clinical features despite such obstacles is compelling. While the ACMG guidelines were originally devised for the return of results for clinical sequencing, the number of individuals being genotyped through research and personal genomics platforms continues to grow exponentially as well. In this report, we describe a key opportunity to improve the health of individuals undergoing genomic testing. Refined gene- and variant-level penetrance will ultimately be required and national prospective biobanks will be poised to clarify such figures. 34,35 Concurrently, effective management strategies and cost analyses are required prior to implementation of return of results. 16

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28 20 Supplemental Materials Table S1. ACMG Genes and Transcripts Analyzed Gene Transcript ACTA2 NM_ ACTC1 NM_ APC NM_ APOB NM_ BRCA1 NM_ NM_ NM_ NM_ NM_ BRCA2 NM_ CACNA1S NM_ COL3A1 NM_ DSC2 NM_ NM_ DSG2 NM_ DSP NM_ FBN1 NM_ GLA NM_ KCNH2 NM_ NM_ NM_ KCNQ1 NM_ NM_ LDLR NM_ LMNA NM_ MEN1 NM_ MLH1 NM_ MSH2 NM_ MSH6 NM_ MUTYH NM_ MYBPC3 NM_ MYH11 NM_ NM_ MYH7 NM_ MYL2 NM_ MYL3 NM_ MYLK NM_

29 21 NF2 NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ PCSK9 NM_ PKP2 NM_ PMS2 NM_ PRKAG2 NM_ PTEN NM_ RB1 NM_ RET NM_ NM_ RYR1 NM_ NM_ RYR2 NM_ SCN5A NM_ NM_ NM_ SDHAF2 NM_ SDHB NM_ SDHC NM_ NM_ SDHD NM_ SMAD3 NM_ NM_ STK11 NM_ TGFBR1 NM_ TGFBR2 NM_ NM_ TMEM43 NM_ TNNI3 NM_ TNNT2 NM_ NM_ TP53 NM_ TPM1 NM_ NM_

30 TSC1 TSC2 VHL WT1 NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_ NM_

31 Table S2. Listing of adjudicated pathogenic and likely pathogenic variants among exome sequenced participants of the Jackson Heart Study. Gene Variant RefSeq cdna Amino Acid Carriers Class Transcript Consequence Consequence MUTYH 1: _C/T NM_ c.1187g>a Gly396Asp 2 P MUTYH 1: _TC/T NM_ c.963delg Gly321fs 3 P MUTYH 1: _C/T NM_ c.338g>a Trp113* 1 P CACNA1S 1: _CT/C NM_ c.5369dela Lys1790fs 1 LP CACNA1S 1: _C/T NM_ c.900g>a Trp300* 1 P RYR2 1: _C/T NM_ c.7511c>t Thr2504Met 5 LP APOB 2: _C/T NM_00038 c.10580g>a Arg3527Gln 1 P MLH1 3: _C/T NM_ c.1153c>t Arg385Cys 1 LP MLH1 3: _C/T NM_ c.2059c>t Arg687Trp 1 P SCN5A 3: _C/A NM_ c.3214g>t p.glu1072* 3 P MYL3 3: _G/C NM_ c.170c>g Ala57Gly 1 P DSP 6: _C/T NM_ c.3865c>t Gln1289* 1 P PMS2 7: _T/TA NM_ c.802dupt Tyr268Leufs 2 P KCNH2 7: _C/CG NM_ c.2690_2691insc Lys897fs 1 P KCNH2 7: _T/TGT NM_ c.2689_2690insc Lys897fs 1 P CCG GGAC KCNQ1 11: _G/A NM_ c.535g>a Gly179Ser 1 LP KCNQ1 11: _G/A NM_ c.613g>a Val205Met 1 LP KCNQ1 11: _G/A NM_ c.643g>a Val215Met 1 LP KCNQ1 11: _A/G NM_ c.1085a>g Lys362Arg 1 P KCNQ1 11: _GA/G NM_ c.1258dela Lys420fs 3 P KCNQ1 11: _C/T NM_ c.1552c>t Arg518* 1 P MYBPC3 11: _G/A NM_ c.1504c>t Arg502Trp 1 P PKP2 12: _C/G NM_ c g>c. 1 P PKP2 12: _G/A NM_ c.1237c>t Arg413* 7 LP BRCA2 13: _CTG/C NM_ c.657_658del Thr219fs 1 P BRCA2 13: _CAAT NM_ c.5574_5577del Thr1858fs 2 P T/C BRCA2 13: _CAGT NM_ c.5611_5615del Ser1871fs 1 P AA/C BRCA2 13: _T/A NM_ c.5855t>a Leu1952* 4 P BRCA2 13: _C/T NM_ c.9382c>t Arg3128* 2 P MYH7 14: _C/T NM_ c.2389g>a Ala797Thr 1 P TP53 17: _C/T NM_ c.818g>a Arg273His 1 LP BRCA1 17: _TTTT NM_ c.5177_5180del Arg1726fs 1 P C/T BRCA1 17: _G/A NM_ c.3607c>t Arg1203* 1 P 23

32 LDLR 19: _C/T NM_ c.2054c>t Pro685Leu 1 LP RYR1 19: _G/GC NM_ c.3946dupc Gln1315fs 1 P RYR1 19: _CCT/C NM_ c.5340_5341del Pro1780fs 1 P RYR1 19: _C/T NM_ c.6721c>t Arg2241* 1 P RYR1 19: _G/A NM_ c.7300g>a Gly2434Arg 1 LP RYR1 19: _C/A NM_ c.11763c>a Tyr3921* 2 P RYR1 19: _G/GT NM_ c inst. 2 LP RYR1 19: _G/A NM_ c g>a. 2 P GLA X: _C/T NM_ c.427g>a Ala143Thr 1 LP 24