Personalized genomic disease risk of volunteers

Personalized genomic disease risk of volunteers Manuel L. Gonzalez-Garay'', Amy L. McGuireb, Stacey Pereirab, and C. Thomas Casket' 'Center for Molecular Imaging, Division of Genomia and Bioinformatics, The Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center, Houston, TX 77030; and ',Center for Medical Ethics and Health Policy, Department of Medicine and Medical Ethics, and 'Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 Contributed by C. Thomas Caskey, August 27, 2013 (sent for review July 11, 2013) Next-generation sequencing (NGS) Is commonly used for researching the causes of genetic disorders. However, its useful- ness in clinical practice for medical diagnosis is in early de- velopment. In this report, we demonstrate the value of NGS for genetic risk assessment and evaluate the limitations and barriers for the adoption of this technology into medical practice. We performed whole exome sequencing (WES) on 81 volunteers, and for each volunteer, we requested personal medical histories, constructed a three-generation pedigree, and required their participation in a comprehensive educational program. We lim- ited our clinical reporting to disease risks based on only rare damaging mutations and known pathogenic variations in genes previously reported to be associated with human disorders. We identified 271 recessive risk alleles (214 genes), 126 dominant risk alleles (101 genes), and 3 X-recessive risk alleles (3 genes). We linked personal disease histories with causative disease genes in 18 volunteers. Furthermore, by incorporating family histories into our genetic analyses, we identified an additional five heritable diseases. Traditional genetic counseling and disease education were provided in verbal and written reports to all volunteers. Our report demonstrates that when genome results are carefully interpreted and integrated with an individual's medical records and pedigree data, NGS is a valuable diagnostic tool for genetic disease risk. molecular medicine I disease prediction I whole exome sequencing S equencing the whole genome of patients with genetic dis- orders has become reality since the sequencing of the first individual human in 2007 (1). Further advances in massively parallel DNA sequencing are reducing the price of sequencing an entire genome or exome. The quality and speed of sequencing and analyzing a personal genome are improving at an unprece- dented pace, making possible the introduction of next-generation sequencing (NGS) into the clinic on a research basis (2-7). Advancements in NOS have stimulated international research initiatives to identify genetic links to rare disorders in children, with an average diagnostic success of 20-25% and the discovery of new disease-gene associations (8-12). The rapidly increasing number of aging adults in our society will place unprecedented demands on the health care system. To provide adults with a healthy longevity we need to develop a system to identify genetic risk and apply early intervention on pathology progression. In this report, we decided to sequence the whole exomes of a healthy adult cohort of 81 volunteers and evaluate the value of applying NOS in combination with medical history and pedigree data. In this report we plan to address three main questions. (i) What genetic discoveries need to be provided to the volunteers? (ii) What is the practical value of delivering this information to volunteers? (iii) What are the challenges and barriers to the adoption of this powerful technology into medical practice? The individual genetic reports yield helpful medical risk in- formation, suggesting that population sequencing of asymptom- atic adults may prove to be valuable and useful. We provided to the participants, under our institutional review board, genetic risk findings from the analyses and genetic counseling to discuss their results. Results Categories of Variants to Report to Patients. Variants obtained from our workflow (described in Fig, 1) were reported using three categories. Our first variant category consists of variants identified in an individual where the alleles are found in Human Genome Mutation Database (HGMD) (13, 14) and labeled disease-causing mutations (DM). These alleles also were re- quired to be rare (<1% allele frequency in 6,500 exomes from the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (15) and the 1,000 Genomes Project Genomes (16, 17)] and predicted to be damaging to protein function by two of three predictions algorithms [Polyphen 2.0 (18), Sift (19-24), and MutationTaster (25)] using Database of Human Non-synonymous SNVs and their functional predictions and annotations (dbNSFP) (26) as described in Fig. 2. The genome sequence data of each volunteer were reviewed and interpreted, taking into account personal medical history, a three-generation pedigree with family history of diseases, and bioinformatics analysis. The medical history of each volunteer in this cohort was rich with detail because each had a private physician used for annual examinations, and in some cases. disease therapy. Fig. 3 summarizes the results of our pipeline: we recruited 81 non- related volunteers and sequenced their genomic DNA using exome sequencing. We detected 65,582 unique nonsyttonymous coding variants (nscv). Every nscv was interrogated for human inherited disease mutations using the HGMD (13, 14) database from Biobase (DM category consisting of 109,708 variations). We were able to detect 1,036 HGMD (13, 14) DM variations. After using the filters described in Fig. 2, the number was reduced to 275 pathogenic variants. We identified in our cohort 208 au- tosomal recessive (AR) alleles (169 genes), 64 autosomal domi- nant (AD) alleles (44 genes), and three X-linked recessive (XLR) Significance Replacing traditional methods for genetic testing of inheritable disorders with next-generation sequencing (NGS) will reduce the cost of genetic testing and increase the information avail- able for the patients. NGS will become an invaluable resource for the patient and physicians, especially if the sequencing in- formation is stored properly and reanalyzed as bioinformatics tools and annotations improve. NGS is still at the early stages of development and it is full of false-positive and -negative results and requires infrastructure and specialized personnel to properly analyze the results. This paper will explain our expe- rience with an adult population, our bioinformatics analysis, and our clinical decisions to assure that our genetic diagnostics were accurate to detect carrier status and serious medical conditions in our volunteers. Author contributions: m I.G.-0. and CT.C. designed research; PAL.G.4 . A LM.. 5.P.. and CT.C. performed research; PA.L.G.4. analysed data; and M.LGA...A.L.M. S.P. and CT.C. mote the paper. The authors declare no conflict of Interest. Freely available online through the PNAS open access option. 'To whom correspondence may be addressed. E-mail: manuell.GonzalezGarayeluth,unc. edu or tcaskeyelbotedu. This article contains supporting information online at vninv.pnas.orgiloalcupisuppildoi:10. 1073/Dna 13159341 IONIXTv0Plementet www.yroas.orgrcgikloill0.10734mas.1315936110 PNAS Earty Edition I 1 of 6 EFTA01140242 IINININOISNONInC• NovoMen moNnint la enlocenot MIN Went+ meow,: SAPAtesb/Picard SAM Me .Remove duplicate •Rnallbrate aligrenents GAN •local rtalignmnits a c" dance SNIVIndel taller GAIN% Bantian SNM Welt %Ns uwEll ANNMAR annotated sciostindels a I Moons Cereal° deournamoon Hg. 1. Workflow for processing NGS data. Raw sequencing data are aligned against the reference sequence using Novoalign software from NovoCraft. SAM files are preprocessed using SAMtoots and Picard to create BAM files and remove duplicates. The Genome Analysis Tcolkit (GATK) is then used to recalibrate the alignments, perform local realignments, and identify SNPs and indels. Finally, SnpEff and ANNOVAR are used to annotate variants. alleles (3 genes). These data resulted in an average of 3.5 disease allele reports per volunteer. The approach for a second category of variants consisted of creating a personalized list of candidate genes from Online Mendelian Inheritance in Man (OMIM) (27, 28) known to be associated with the disorders reported in the medical literature. We detected 131 alleles (131 genes) using this approach. Each one of these variants provided a potential causation for the volunteer's disorders. Each one of the variations obtained from this approach passed our stringent pipeline. This approach added on average another 2.0 disease alleles per volunteer report. The third approach used a family history to create a person- alized list of candidate genes from OMIM (27, 28). and as be- fore, we compared our list of candidate genes with the disorders reported in the family history. Before reporting an allele to the volunteer, we reviewed the original publications that support the pathogenicity of all of the alleles (HGMD) and/or the evidence associating the gene with the disorder (OMIM). At this time, all three abovementioned categories of investigation were reported in full recognition; some would be found to be non-disuse-producing alleles as databases improve and functional assays complement informatics predictions. We have updated clinical reports as these data emerged and counseled the patients on the options for reducing or eliminating the disease risk. Disease Genes Identified in the Cohort. Table SI summarizes our disease associations. Matching personal medical records to per- sonal genome reports was informative. We elected to report findings as disease-gene associations instead of reporting findings as diagnostic because we did not included in our study traditional "surrogate markers" (analytes, proteins, and imaging) for the confirmation of a disease diagnosis. We considered potentially causative findings to be those mutations that are predicted to be damaging in addition to being reported in either HGMD (13, 14) or OMIM (27, 28) databases. These mutations are considered to be "need to know" and are reported to volunteers. There was identification of associations for vascular disease and/or hyper- cholesterolemia in five individuals related to LDL receptor (LDLR) alleles. LDLR mutations are causative of early onset autosomal dominant coronary artery disease (CAD) and manifest hypercholesterolemia (29, 30). Three individuals were taking statins related to their hypercholesterolemia. Two individuals were not under care but had history of personal hypercholester- olemia and in one case a son with hypercholesterolemia. There were four volunteers detected with risk genes for di- abetes mellitus (31-34). Two of the individuals were under therapy for diabetes 2, whereas two additional volunteers had elevated fasting blood sugars and were being followed by their physicians for further analytes measurements. There were two individuals with morbid obesity (body mass index of 32 and 37 kghto who carried an MC4R allele associated with pediatric obesity and rare heterozygotic adults (35, 36). Two ophthalmo- logic disease/gene associations were identified. The childhood brittle corneal syndrome type 1 occurred in a volunteer who had undergone successful corneal transplant and carried a putative compound heterozygosity in ZNF469 (37). One volunteer was under care for macular dystrophy and carried an ABCA4 allele (38). One sterile male volunteer was found to have an insertion in gene USP26 (known to be responsible for infertility in men) (39). Associations for melanoma and breast cancer were identi- fied. The two patients with melanoma carried different gene allele associations: GRIN2A and BAG4 (40-42). Two volunteers diagnosed with breast cancer had different allele associations in BRCA2 (43, 44). Single cases of early onset prostate (LRP2) (45) and follicular thyroid cancer (TPR) cancer were identified (46, 47). A volunteer with nonsyndromic deafness was found to have risk alleles in two genes associated with autosomal dominant (AD) deafness and had a three-generation positive family history of deafness (48). In each case, the volunteer was instructed to inform their Physician and was requested to confirm the ge- nomic allele identification in a Clinical Laboratory Improve- ment Amendments (CLIA)-certified laboratory, even when each reported allele had been sequenced twice in independent studies. The finding provided information for personal and family risk counseling not possible before gene association. Incorporation of Three•Generation Pedigrees into the Genetic Analyses. The three-generation pedigree medical information was analyzed to identify those volunteer families who warranted additional ge- netic study. Table S2 lists those genetic disorders identified by pedigree/familial medical history. In each case, the volunteer was counseled for the family risk and encouraged to contact at risk family members who may benefit from focused genetic studies. Three of the families have reported that they have had their fa- milial genetic diagnosis resolved at this time paraganglioma (49), Prader-Willi syndrome (50, 51), and ankylosing spondylitis (AS) (52)1. One additional family is under study rourette syndrome (534 Additional familial disease risks were identified by history for atrial fibrillation (AR), bicuspid aortic valve (BAV), dyslexia (AR), Fatny's (XLR), gall stones (AD), and myotonic dystrophy (anticipation AD). Success with this approach was productive but not universally accepted because disease/gene resolution requires interaction with interested and motivated family members. WWICall Rant NM 1 dOSNP 132 Sam Ms CGI var.annosadon Fie ,0. Gam retSess brown Gene OK%) mpg fl LANNCNAR (na-toding vatianta) 4 Stank:IKIND Db foe (Mum, Cause* Mutations Aker Out variants MM >r 1% n Should have been convect damaging for menu 2/3 ptecutions tools .11 . frolyphen-2. Sift and hivtatronTaster1 Sternalfrequencyfilter < 3% YPOVarlards Won Fig. 2. Pipeline to generate variants reports. Every variant in the variant call format file is annotated using spnEff and ANNOVAR; nonsynonymous cod- ing variants are annotated using the commercial version of the HGMD da- tabase. (Left) Our selection of variants by the creation of a personalized candidate gene list using medical history and family history for each vol- unteer. Mutations with a minor allele frequency of >1% are removed using frequencies from the NHLSI exome sequencing project (ESP), 1,000 Genomes Project. Variants that are consider benign by two of three predictions tools are removed (using dbNSFP). Finally, we remove variants that are present in our cohort more than three times. 2 of 6 I www.pnas.orgfcgikloW10.10734mas.1315934110 Gonzalez-Garay et al. EFTA01140243 81 volunteers Using HGMD (109,708 annotated variants) 65,582 NSCV NSC-snps Exon Sequencing 1,036 NSC-sts from HGMD A 275 NSC-snps from HGMD after filtering 160 NSC•snps from OMIM Medical and family History Interpretation Medical History B 23 disease-gene associations Family History B 4 resolved 1 In progress ck.1 Negative History 206 HGMD Autosomal recessive (169 Genes) 63 MAIM (No.HGMD)Autosemal recessive (63 Genes) 3 HGMD X linked recessive (3 Genes) 6 OMIM (No-HGMD) X linked recessive (6 Genes) 64 HGMD Autosomal Dominant (44 Genes) 62 °Mill (tio.HGIND)AulosoM31 Dominant (62 Genes) Fig. 3. Summary of result. The flowchart provides the number of variants from each step of the pipeline described in Fig. 2. Table S3 provides a sampling of the recessive risk alleles. They constitute the majority of the observed alleles. Of the 160 off- spring of the 81 volunteers, no children were affected with these disorders. MI volunteers indicated their families were complete, and thus, no spousal genetic studies were recommended, but information was proposed to be provided to reproductive age descendants. Many of the genes identified are pan of prenatal carrier screens and/or newborn state-sponsored screening pro- grams [phenylketonuria, maple syrup urine disease, cystic fibro- sis, Niemann-Pick disease, Gaucher disease, factor V Leiden thrombophilia, medium-chain acyl-CoA dehydrogenase (MCAD) deficiency]. Undoubtedly, NGS will expand the number of non- unreported disease alleles and scope of genes studied for couples in the pregnancy setting. The Beyond Batten Disease Foundation of Austin, TX (54), has this goal. Table S4 shows that a category of high concern was the identification of XLR disease risk alleles among our female vol- unteers. One volunteer had an affected son (isolated case) with Fabry disease that was diagnosed before our study. There were four disease alleles identified, each listed in HGMD (13, 14). There was no family history of these disorders found in the three- generation pedigree of each. MI were counseled to have their test confirmed and daughters studied in a CLIA-certified laboratory given the high disease risk (50% for men). Three men in our study had alleles predicted from the OMIM (27, 28) disease database to be causative for cutis laxus, Duchenne muscular dystrophy, con- genital nystagmus, and hemophilia A, illustrating the challenge of predicting damaging mutations bioinformatically. None had the disorders. Counseling and family study were individualized for each disease risk. Volunteers were made aware of database errors in the reports. Tables S5-510 provide a third category that is very problem- atic, the AD group. The allele identification is as previously described, but counseling is more difficult because of variation in severity and time onset. For this age group of volunteers, the interest was high because disease prevention was frequently expressed as a goal in the face-to-face counseling meetings. A poststudy survey also reflected this objective. We focused in this paper on the three major causes of death in the United States: cancer, cardiovascular disease, and neurodegenerative disease. In our analysis of each volunteer, we reviewed the genomic and family data. Table S5 lists the breast cancer risk results. There were 12 volunteers found to have breast cancer risk alleles of genes BRCAI, BRCAZ PALB2, R4D5IC, and RADS& Two volunteers with BRCA2 risk alleles were diagnosed with breast cancer. One man carried a premature chain termination mutation and has a first-degree relative with breast cancer (50s). A third volunteer had a frame shift mutation (high-risk allele) but not found to have breast cancer. All alleles were predicted to be damaging. Eight volunteers had first-degree relatives with breast cancer, whereas four had a negative family history of disease. All were advised to seek confirmation via a CLIA-cenified laboratory. One patient with an HGMD (13, 14) allele was confirmed but predicted to be "neutral" by a commercial laboratory. All were counseled regarding the need for regular mammograms and gynecological examinations and were requested to inform their physician of this research risk allele identification. Table S6 displays the colon cancer alleles. There was no disease incidence of colon cancer in this group with the exception of one volunteer with a positive dysplastic polyp biopsy. Five volunteers had a positive family history of colon cancer. Five volunteers had no family history of disease. All were advised to obtain confir- matory CLIA-certified laboratory diagnosis and advise their phy- sician of the research allele identification. Of the 10 volunteers, many had undergone colonoscopy as pan of their health care. Table S7 includes all of the remaining type of cancers. Two volunteers diagnosed with melanomas were found to have dif- ferent disease gene risk alleles. We identified 10 volunteers with prostate risk alleles. One volunteer reported a diagnosis of prostate cancer at age 55 while the other nine volunteers reported no familial history of the disease. Genetic counseling for cancer risk required the greatest counseling time. The con- cepts of the two-hit hypothesis (55) and "somatic mutations" (56) were difficult to grasp for the volunteers, even when we discussed the subject in great detail during the education session. All volunteers were provided information regarding standard of practice approaches for early detection of the respective cancer. Table S8 lists all of the affected volunteers with cardiomyop- athies (57). Five volunteers had a medical history of cardiac dysrhythmia with identified risk alleles. One younger (50s) vol- unteer had first-degree relatives requiring pacemakers and car- ried two risk alleles. Three volunteers had either stent placements or bypass procedures related to CAD. Each was in their 70s. Table S9 lists the 11 volunteers who had no apparent disease but had a positive family history of tachycardia, sudden death, and CAD and carried risk alleles. We provide this experience to broaden alertness to both genetic causation and risk of disease Gonzalez-Garry et al. PNOS tarty Edition I 3 of 6 EFTA01140244 for adult-onset cardiovascular disease (58). Of the alleles listed in Tables SE and S9, 13 alleles were found in HGMD (13, 14). We advised volunteers to inform their physicians of these results for their long-term clinical care. In Table SI0, we listed the results for adult-onset neurodegen- erative diseases. Our findings were limited but of high interest to the cohort. It was frequently asked by volunteers if they had Alz- heimer's risk. We summarize our findings for Alzheimer's and Parkinson risk alleles (59, 60). The genes included APOE, APP, PSENI, MAPT, El F461, GBA, GIGYF2, LRRIC.2, PARIC2, PM20DI, and SNCA. There were nine volunteers with HGMD (13, 14) listed risk alleles. Of these, two had a positive family history of Parkinson disease and one with Alzheimer's disease. One of the PARK2 alleles occurred in a volunteer who provided a history of three second- degree relatives in a sibship affected with disease. The reminder had no family history of either disease. There were 25 alleles predicted to be damaging. One is a frameshift allele. None of these volunteers had a family history of disease. Discussion Exome Sequendng Is Limited. The full spectrum of disease muta- tion identification is not satisfied by exome sequencing alone because large deletions, copy number variations (CNVs), and triplet repeats are not reliably identified at this time. Further- more, exon capture relies on probe design. For example, the discovery of the MAGEL2 mutation in our Prader-Willi patient was made using whole genome sequencing (WGS) from com- plete genomics and missed by exome capture because of high GC content (51). The accuracy of coding allele identifications was. however, quite high and thus of great utility as a genome screening approach. CGI (61) sequencing produced higher cov- erage than exome sequencing data for CNV, large deletions, and regulatory elements will have utility as we analyze previously labeled "junk" DNA for disease causation (62). There is also the issue of our limited knowledge of disease alleles within the databases. One of our biggest challenges for the interpretation of human genomes is the lack of gene annotations and the errors in databases. Our knowledge base for human disorders is small. There are only —100,000 pathogenic variants in the HGMD (13, 14) database and a fraction of them have errors. If we do not use annotated variants but instead gene annotations as our source of information, we can calculate the fraction of knowledge that we can use at this time. For example, the number of genes associ- ated with human disorders reported by HGMD (13, 14), OMIM (27, 28), UniProtICB (63), Gene Atlas (64), etc. is 4,622. From the 4,622 genes, only 1,955 genes have high-quality data because they are part of the GeneTest (65) database. GeneTest (65) is a database originally created by the National Center for Bio- technology Information to track all of the laboratories worldwide that offer a genetic test for a gene. With this information, we know that the fraction of genes that we can use for the in- terpretation of a human genome of a successful high-quality whole exome or whole genome dataset is -7-18% when using the high confidence set of 1,955 genes or a set of 4,622 genes. Despite these limitations, this report documents the utility for disease associations and risk. During the last few years, the field of NOS has developed a large number of tools that make it easier to handle the analysis of reads, variant calling, functional prediction, and annotation (66). There are also large publicly available datasets of healthy individuals that can be used as controls that can be used to remove technology specific errors or filter out common poly- morphisms. As we begin to use whole genome sequencing at an increasing depth, we are discovering more variants, so these public datasets are becoming increasingly important for quality control and filtering of variants in smaller projects. One of the main limitations is the lack of access to public and private ge- nome and exome variants. There are thousands of datasets, but the majority are inaccessible to the scientific community. We recognize the existence of the 1,000 Genomes project, the NHLBI Exome Sequencing Project (ESP), Exome variant server, and the 69 sets of whole genomes from CGI (15-17, 67). How- ever, we need larger datasets from very carefully phenotyped patients to assist in the interpretation of the variants in our patients. The million genome project of the US Department of Veterans Affairs (68) has the potential to provide such data, as well as private health plans considering adaptation of genome sequencing. Genetic Discoveries Provided to Volunteers. There are several approaches to disclose the results to volunteers. Groups like Patel et al. use the statistics and epidemiology approach in reporting the polygenic risk assessment using common SNPs that have been previous associated with genetic disorders from ge- nome-wide association studies (69). The PGP-10 project uses an automated tool or Genome Environment Trait Evidence (GET- Evidence) system, with is a system that is collaboratively edited (70). For this project, we decided to focus on reporting only high- quality variants that are rare in the population and considered damaging by two of three commonly used predictions algorithms. In addition, the variant has to be either reported in HGMD under category DM or the gene has to have been previous associated with a genetic disorder (OMIM). The group of vol- unteers consisted of adults with complete medical and family history so we personalized the reports as described in Fig. 2 to specifically try to identify molecular explanations for the mal- adies reported in their medical or family history. This approach generated reports that were easy to explain and accepted by the patients during the genetic counseling session. Medical Histories and Family Pedigrees Complement Sequencing Resift. The utility of genome data was significantly enhanced when integrating standard medical care features of personal and family disease diagnosis. The significant number of 23 disease associations in all likelihood represents a bias of our volunteers to seek answers to their personal disease history. This observa- tion may hold a key to how we obtain maximal use of genome sequencing--sequence the disease index cases. Our experience would suggest a high value for that utilization. This approach has been clearly documented to be successful for pediatric genetic disorders but not exploited for adult-onset disease. The practical value of this study is summarized in Tables SI and S2 and fell into two general categories: (i) new knowledge of the genetic risk and heritability for themselves and family; and (ii) options for therapy (CAD) or imaging (cancer) for personal and extended family care. By using the medical and family history, we were able to clarify the genetic risk in 6 of the 81 cases. One of the cases yielded a new discovery of a gene associated with Prader- Willi syndrome. which is described in another paper (51). Prenatal vs. Adult Genetic Screening. The technology and this report beg the question of whether we are prepared to offer adult disease risk screening. Currently, prenatal and newborn screening for a selected set of frequently occurring disease alleles (not genome sequencing) is a standard of practice. There are questions that deserve medical and ethical review before adult screening becomes a standard of practice. First, for reproductive and new- born diagnosis, typically only actionable childhood diseases are explored, which respects the future autonomy of the child and preserves her right to an open future (71, 72). Because adult screening decisions would be made by an autonomous individual for her own health decisions, broader conceptions of utility, in- cluding personal utility, need to be considered (73). It is a clear and simple decision to provide patients with actionable genetic information from a WES study; on the other hand, it is challenging and it raises a difficult ethical question to decide what to do with incidental genetic findings that are not actionable and could lead to physiological distress to the patient (e.g. APO-E for Alzheimer dictate). Despite this ethical dilemma our group of volunteers elected to receive information even if the genetic information might not be actionable. Only 3% of the volunteers were uncertain about receiving nonactionable information (SI Pausnuly Survey). 4 of 6 I www.pnas.orglegildoi/10.10734wias.13I5934110 Gotualez-Garay et al. EFTA01140245 Volunteer Response to Clinical Reports. From our poststudy survey, we found that 72% of the responders reported speaking with their physician about their results. This raises important ques- tions about whether nongeneticists are adequately prepared to counsel patients based on WES results and whether such follow- up will lead to iatrogenic harm or unjustified use of health care resources (74). Twenty-five percent reported changing their behaviors because of the results, which is surprising given that previous reports found no significant behavior change resulting from adult risk screening in a direct-to-consumer setting (75). Despite that all of the participants were clearly informed that their results originated from two independent sequencing experi- ments and that we advised them to have their results clinically validated in a CLIA-certified laboratory, 78% reported that they did not have the results confirmed. This low percentage of confirmatory results from the volunteers raises the question of whether it is sufficient to counsel research participants to have results clinically confirmed or if investigators should be required to confirm results before disclosure. It was apparent for some volunteers that they were seeking information related to familial diseases. Resolution of these questions required family member interest and motivation be- cause, in all cases, we had sequenced the nonrisk family mem- ber. We followed up each case with a referral to a qualified genetics program with diagnostic capacity for the suspected genetic disease. Our efforts to analyze cancer, cardiovascular, neurodegener- ative, and obesity/diabetes risk were successful but needed con- siderable education/counseling to avoid confusion over risk vs. diagnosis. Second, there are standard of care options for those with risk alleles for cancer, cardiovascular disease, and diabetes for disease modification or early diagnosis. 'Thus, sequencing serves as a new screening risk detection approach toward the objective of improved health. It is expected that genomic studies will increase surveillance studies (e.g., colonoscopy. gynecologic examinations, mammograms, cardiovascular markers and scan- ning studies) but has the possibility of more precisely identifying the patients who may benefit from rlititsce prevention surveillance. The area of adult-onset neurologic disorders is an increasing concern worldwide as our population ages, thus exposing disease incidence not seen earlier. The genetic disease discoveries are limited. Confirmatory diagnostics such as image analysis and biomarkers/surrogate markers are just emerging, and prevention therapeutic options are nonexistent. Although one might ques- tion the utility of screening for these disorders at this time, the experience with Huntington disease (76) screening taught valu- able lessons on how to proceed with studying and counseling families at risk. Furthermore, there are new therapeutic trials in disease prevention for Alzheimer's (58) and Parkinson disease based on the genetic cause of disease. These clinical trials use genetic diagnosis to select participants, which is also a successful approach in cancer drug development (77-79). Barriers to the Adoption of Genetic Screening via Sequendng. Al- though the above comments would present the case for the value of adult genetic screening via whole genome sequencing, there are major issues to be addressed. In our opinion, the least is sequencing 1. Lew S. et al. (2007) The diploid genome sequence of an individual human. PLoS Riot 3(10):4254. 2. Bamshad Mi, et aL (2011) Excaie sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet 12(1 1):74S-7SS. 3. Tabor 14K, Berkman BE. Hull 5C. aamShad Ml (2011) GenanKs really gets personal: How exome and whole genome sequencing challenge the ethical framework of hu- man genetics research. Am Med Genet A 1SSA(12):2916-2924. 4. Lander ES R011)Genomesequeuingannhersary. The accelerator. Scknce 331(6020): 1024. S. Lander ES 0011) Initial impact of the sequencing of the human genome. Nature 470(7333):187-197. 6. Biesedser LC, Burke W, Kahane I, Non SE, limn ern R (2012) Next.generation se. quencing in the clinic Are we ready? Nat Rev Genet 13(11)1318424. 7. Hennekam Rc, Biese<ker LG (2012) Next-generation sequencing demands next-gen- eration phenotypIng. Men Muth 33(5)1384-886. technology and cost. Bioinformatics focused on the practical ex- traction of medical relevant/actionable data are a challenge. We relied heavily on HGMD alleles for "need to know" information to patients. This approach is flawed in three ways: (i) databases contain errors; (ii) highly validated disease databases are scattered, private, and limited; and (iii) the future will provide more disease risk alleles by sequencing than by patient reports in the literature. Our current limitation for interpretation of a genome is not the quality of the data of the coverage of the genome but our disease knowledge database. R. Cotton's Human Variome Project (62) together with Beijing Genome Institute are proposing to create a highly validated disease allele database. New technological advances such as structure-based pre- diction of protein-protein interactions on a genome wide scale (80), 3D structure of protein active and contact sites (SI), high- throughput functional assays of damaging alleles (81-83), and new approaches that combine analytes, metabolomics and ge- netic information from a single individual (84) are just a few examples of the new technologies that will help us to generate better interpretation of genomic data. The delivery of the genome risk information will need to be carried out by a new cadre of physicians and counselors skilled in medicine, genetics, and education/counseling. These experts will need to integrate into medical care as well as has been done for newborn screening, prenatal diagnosis, and newborn genetic disease diagnosis. The approach of adult screening is in its early phase but from our data appears very promising. We conclude that the genomic study of adults deserves intensified effort to determine if "need to know" genome information has the utility for improved quality of health for our aging population. Materials and Methods The oversight of this research was under two institutional review boards: (i) HSC-IMM-08-0641 (University of Texas Health Science Center at Houston) and (ii) H-30710 (Baylor College of Medicine). Cohort Description. The cohort consists of members and spouses in the Houston Chapter of the Young President Organization (YPO) (85). Theentire description of the cohort can be found in SI Materials and Methods. MS Sequencing. Standard NGS was performed using illumine HighSeq; an extended explanation can be found in Materials and Methods. Sequencing Analysis. Fig. 1 illustrates OUf pipeline, and fig. 2 describes our pipeline to detect known pathogenic variations. Additional details can be found in Sf Materials and Methods. Counseing. Genome counseling was conducted by a board-certified internist and medical geneticist by both individual meetings and two written sum- maries over a period of 12 mo. Additional information can be found in SI Materials and Methods. ACKNOWLEDGMENTS. This work was supported by the Cullen Foundation for Higher Education and the Governing Board of the Greater Houston Community Foundation. The funding organizations made the awards to the University of Texas Health Science Center at Houston and Baylor College of Medicine. C.T.C. was the principal investigator of both grants. 8. Anonymous Finding of rare disease genes in Canada (forge Canada). Available at http/Avenv.genomebccaipartfolia/projects/health.projecb/finding.of.raredisease. genevincanada.forge-canada/. Accessed September 19,2013. 9. Gehl WA, et al. (2012) The National Institutes of Health 8a-diagnosed diseases pro- gram: Insights into rare diseases. Genet Med ta(tkm-59. 10. Gant WA et al. 12012) The !Catena! 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Jarvis BM. Donnelly-Roberts D (2012) High throughput functional assays for P2X receptors. Cliff Protocol Phannaca lumChapter 9:Unit 9.15. 83. Trivedi 5, Liu /, Liu R. Bostwick R (2010) Advances in functional assays for high. thrOughput greening of ion thannelstargets Expert Opal Ono) Gismo 5(I 0)1995-I C06. 89. Suhre K. et at; CARDloGRAM (2011) Human metabolic individuality in biomedical and pharmaceutical research. Nature 477(7362):54-60. 85. AnCelyMOW Membership criteria YPO. Available at httinivnwrypo.orgdoin.ypor. Accessed September 19,2013. GM 6 I www.priaS.OrgfCgildOi/10.1073/13ries.1315939110 Gonzalez-Garay et al. EFTA01140247 Supporting Information Gonzalez-Garay et al. 10.1073/pnas.1315934110 SI Materials and Methods Cohort Description. cohort consists of members and spouses in the Houston Chapter of the Young Presidents Organization (YPO). Criteria for membership into the YPO includes corporate and community leadership (1). This cohort is well educated and of higher socioeconomic status. All 450 YPO members were invited to attend an 8-h educational program incorporating technology, human genetics, anticipated outcomes, ethical con- siderations, discussion groups, and technology demonstrations and printed materials. Of the 150 attendees, 81 volunteered to participate in this study: 46 men and 35 women, with an average age of 54 y. All 81 elected under the terms of the University of Texas Health Science Center at Houston's institutional review board to receive "need to know" genomic disease risk results. Each volunteer provided a detailed medical and drug use history reviewed by our physician-researcher (C.T.C.). A three-genera- tion medical pedigree was acquired on each volunteer. One volunteer could provide no family history. Whole exome sequencing (WES) Sequendng. Genomic DNA was extracted using a UNA kit (Promega wizard genomic DNA puri- fication kit) following Promega's instructions (2). The cohort was sequenced twice: the first whole exome sequencing experiment (2011) was performed using Illumina's HiSeq and the Genome Analyzer Hz system (3) after enrichment with Nimblegen V2 kit (44 Mb) (4) (outsourced to the national center for genome re- sources). Our second WES experiment (2013) was performed us- ing Illumines newest machines HiSEq. 2500 (3) after enrichment with Agilent SureSelect target enrichment V5+UTRs (targeting coding regions plus UTRs) (5) (outsourced to Axeq Technologies). Genome sequencing of a small subset (24 subjects) for validation purposes was carried out by Complete Genomics Inc. (CGI) (6). Sequendng Analysis. Our analysis pipeline consists of Novoalign (7), Samtools (8), Picard (9), and The Genome Analysis Toolkit (GATK) (10), followed by variant annotation (11-14) using multiple databases from the University of California Santa Cruz (UCSC) Genome bioinformatics site (15). Fig. 1 illustrates our pipeline. Fig. 2 describes our pipeline to detect known patho- genic variations. We detected known variants associated with human diseases using the Human Genome Mutation Database (HGMD) database from Biobase (16, 17) and genes known to be associated with human disorders from Online Mendelian In- heritance in Man (OMIM) (18, 19) and GeneTests (20). Func- tional effects of each nonsynonymous coding variant were evaluated using three different functional prediction algorithms [Polyphen 2.0 (21), Sift (n-r), and MutationTaster (28)] using the Database of Human Non-synonymous SNVs and their func- tional predictions and annotations (dbNSFP) (29). Filtration of common polymorphisms was accomplished using frequencies from the National Heart. Lung, and Blood Institute (NHLBI) exome sequencing project (ESP) (30), 1,000 Genomes (31, 32), and in- ternally by removing any variant that appeared more than three times in our cohort. In addition, a group of candidate genes was obtained from OMIM (18, 19) for each volunteer after a careful analysis of the family and personal health history of each volunteer. Variations in those OMIM (18, 19) candidate genes were identified and submitted to the same frequency and functional effects filter as described before. Variant Validation. Every variant identified in our pipeline was evaluated for quality control, and the variant's read alignments in the BAM file [Binary version of a SAM (Sequencing Alignment Map) file] file were visualized using Integrative Genomics Viewer (IGV) (33). The purpose of this step was to try to remove the remaining false positives. Each genetic variant was validated using the following steps: (i) retrieve reads over variant sites for each individual; (ii) make SamTools (8) genotype calls (an alternate calling algorithm); (iii) retrieve quality scores for all reads; (iv) keep track of the directional depth and require at least two variant reads in the 5' and 3' orientation for a variant to be considered true; and (v) filter out variants if the SamTools (8) genotype call disagrees with the GATK (10) call or if the quality scores or directional depth values do not exceed minimum values. Establishing Criteria for Highly Reliable Variant Calling from Exome Sequencing. Our first objective was to define the methods needed to identify a set of "highly reliable- variants from the Illumine sequencing and apply these methods to variant calling on all of our samples. To meet our definition of a highly reliable variant, each variant had to be detected under two independent or- thogonal sequencing technologies and been considered as high quality. Because there is not a common definition of what a high- quality variant is, we decided to take advantage of the confidence category scores provided from complete genomics; variants with a score of VQHIGH are consider high quality (masterVarbeta files version 2.0) and develop an equivalent value in our illumine sequencing data. To accomplish our first objective, a dataset of variants was generated from a set of 24 samples that we se- quenced using Illumine (3) and an orthogonal sequencing tech- nology (CGI) (6). CGI has their own proprietary workflow from alignment to data annotation (34), Fig. 1 describes our analysis workflow for exome sequencing data. Fig. S2A shows the in- tersection between the nonsynonymous coding variants (NSCVs) detected by CGI (6) and Illumine (3) exome sequencing. We extracted variants from CGI with a score of VOHIGH and that were also detected in the corresponding illumina's vcf file (Fig. S2/3). This subset of highly reliable variants represents an aver- age of 72% of the variants detected by CGI. By using our da- taset, we were able to systematically test for conditions and software setting in our pipeline that generate the majority of the highly reliable variants and reduce the probability of selecting variants not present in our dataset. We reached the conclusions that by using two variant callers tools, GATK UnifiedGenotyper and mplileup/bcftools (samtools), and selecting an overlapping set of variants, we obtained variants of the highest quality. In addition, a postcalling filter enforces that each variant has to have a mapping quality >30, a base quality >20, and a coverage >10, with at least a 3:7 ratio of variant to reference (Het) and the presence of the variant in reads from both orientations. By using these postcalling filters, we eliminated the majority of false- positive calls (FP). Counseling. Genome counseling was conducted by a board-cer- tified internist and a medical geneticist by both individual meetings and two written summaries over a period of 12 mo. The summary reports were prepared and jointly endorsed by a bio- informatician and a physician. Additional counseling was con- ducted by phone calls and appointments with their physician as requested by the volunteers. Counseling of Results. Both causative and problematic alleles were reported verbally and in two written reports over an 18-mo period. conzeiez-oarro et al.kwm.pnas.orgicgikontentishorti1315934110 1 el 8 EFTA01140248 The first comprehensive report was updated —1 y after (i) larger control databases downgraded some problematic alleles with more than a 1% frequency; (ii) private consultation with disease experts; and (iii) validation with original publications and small disease center databases. Several new disease—gene associations were discovered for the reported familial diseases found by pedigree and personal medical histories. Volunteers were informed that these were research results and instructed to consult with their personal physician so that they could have the results validated in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Volunteers whose family members warranted genetic study were referred to the Baylor College of Medicine genetics program as a medical referral because this function was outside the institutional review board scope and Baylor College of Medicine offered both clinical genetic and CLIA Laboratory expertise. Our study preceded the publication of the incidental findings guidelines in clinical WES and whole genome se- quencing (WGS) of the American College of Medical Genetics and Genomics (ACMG) (35). However, we have reviewed their list of 57 genes and 24 actionable conditions, and we found that we included all their genes in our analysis. Poststudy Survey We conducted an online survey to assess volunteers' experiences of participating in this project under a Baylor College of Medi- cine instituational review board. The survey consisted of 82 items and focused on how the volunteers felt about taking part in the research project, as well as their perspectives on genetic in- formation in health care and genomic research in general. Study participants were told the survey was completely voluntary and that they could skip any question they preferred not to answer and could end their participation at any time. All 81 study volunteers were invited via e-mail to participate in the anonymous online survey within 12 mo after receiving their individual genome reports. Forty-two participants responded to the online survey (response rate, 51.9%; 38 responses were complete). Of those who responded, 59% were men, 41% were women, and 95% had biological children. Ninety-seven percent described their race as white, and 5% chose "other- (participants could choose all that applied); 5% also identified themselves as Hispanic or Latino. All participants had earned a college degree, and 63% had completed at least some graduate work. All par- ticipants reported having had a routine medical check-up within the last 2 y, and when asked how they would rate their health, 58% reported excellent, 29% reported very good, 11% reported good, and 3% reported fair. Poststudy survey results. This study had as its objective to deliver helpful medical genetic information. The mandatory education program informed volunteers that unexpected risks were to be expected. Our institutional review board required volunteers to have the options of declining this information. None chose that option. 1. Anonymous Membership criteria YPO. Available at http:Itwvnv.yp0.0r940In-ypot Accessed September 19, 2013. 2. Anornmote Wizard. Available at httpd%wnv.pornega.comIresources/probacelsrtedinical- manualgONAtardlenom,r4na.purfficatiankrt.prototoV. Accessed September 19. 2013. 3. Anonymous Illumine. Available at httpdVenw.illumina.com. Accessed September 19, 2013. 4. Anonymous NemtleGet Rome. Available at htipAyntwnirnOlelenCOnOrMiuttMeetefre2/ vgandex.html.Aaessed September 19, 2013. 5. Aglent Te<Mologies Aglimt SureSelect array. Available at httpawnvgenantitS.2114M, comientaorroSequencingSureSelect.Human.All-ExonScat740002&tabickAGPf6 1206. Accessed September 19, 2013. 6. Anonymous Complete Genomlcs mc. Available at httpAmwr.conpletegenomks,com. Accessed September 19, 2013. 7. Novccraft.com (2012) Available at httplAwm.novocraft.com. Accessed September 19. 2013. S. SAMtools. Available at http://samtools.sourceforge.ned. Accessed September 19, 2013. 9. Picard. Available at httpl/pkard.sourceforge.nett Accessed September 19, 2013. The results of the anonymous online survey showed that, overall, participants were motivated to take part in the project to receive their genetic results and learn about their personal risk of disease. Seventy-nine percent of respondents reported that the opportunity to receive their personal genetic results was the most important factor in their decision to take part in the project, whereas another 10% cited a personal interest in genetics in general. When asked to choose which factor was most important in their decision to receive their personal genetic results, most respondents (52%) reported that their interest in finding out their personal risk for diseases was the most important factor; other important factors included the desire to get information about risk of health conditions for their children (17%), the desire to learn more about the medical conditions in their family (10%), and curiosity about their genetic makeup (10%). Ninety-seven percent of respondents agreed or strongly agreed that they were glad that they decided to participate in this study and receive their personal results, leaving only 3% undecided. Most respondents (72%) spoke with their primary care provider about their results, and 50% reported that they spoke with other medical professionals, including cardiologists, oncologists, and obstetricians/gynecologists, among others; 22% reported that they had their twice-confirmed research results confirmed in a CLIA-cenified laboratory. Twenty-five percent of respondents reported that the test results motivated them to make changes to their health care (i.e., undergoing tests, seeing a specialist, taking vitamins or herbal supplements), exercise, medications, or insurance (Table S11). Respondents generally felt that researchers should offer per- sonalized results to research participants: 54% felt that researchers are obligated to offer results. 22% felt that researchers are obli- gated to offer results only if the researcher is a physician, and the remaining 24% did not think researchers were obligated to offer results. Respondents were pleased with the methods by which they were given their results in this study, with 95% agreeing or strongly agreeing that they were glad the researchers sent them a person- alized results report, and 100% agreeing or strongly agreeing that they found the in-person consultation about their results very helpful. When asked, 94% said they would also want an electronic record of their entire genome if it were available. When asked about genetic testing in health care, 83% reported that they felt that genetic testing should be a regular part of health care and 97% agreed or strongly agreed that they felt comfortable using these results to make decisions about their health. Nev- ertheless, respondents were evenly split when asked if they thought these results should be part of their medical record. 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Accessed September 19, 2013. 35. Green RC, Berg 1S, et al. (2013) ACMG recommendations for reporting of incidental findings In clinical exome and genome sequencing. Genet Med 15(7): 565-574. 3 • 5 Frequency 0 • 3 )4PG 0FTR GCw23 .3a3TF2 LOUR LRP2 WWI pea irA_...*J Ei4Ql TTN 2 14.0005 kOCA1 MICA3 ARCM trczarA CB SCA0M P.CAN STF42 }WTI }WOO I.. CAM Ts ACVRLI pan *mpg Aims Komi I Fig. SI. Grouping genes by occurrence. frequency of genes with nonsynonymous coding mutations in our cohort. This graphic provides a summary of the number of times alleles were observed for an individual gene. In each of these cases, the allele was either part of HGMD or OMIM, rare, and carried a high polyphen2 score. An example of a gene with frequent risk alleles include Titin, the largest genes in our genome and recently reported to be causative of dilated cardiomyopathy. A second example of a smaller gene wi h a large number of variations is MR, where the disease database is deep, and it is known to be one of the most common autosomal recessive diseases in whites. This graphic supports that we did not select polymorphic genes but unique mutations in each volunteer. Non-syn-coding saps s LI MI alumina 11.171 • III High Quaky Sn ps 06%) 11,054 t 8571100%j NowfyycMInasaps 8.137 • 147 H•gh Quality Saps detected also by Alumina 172%1 Average of 24 samples CGI variants only Rig. S2. Variants detected using Complete Genomics Inc (CGO and Illumine. (Left) Comparison of nonsynonymous coding SNPs (NSCS) obtained from Com- plete Genomics (red) and Illumine (green). Twenty-four human samples were sequenced using both technologies, and NSCS were compared in each sample. The average results were calculated and graphed as a venn diagram. The intersection represents the set of NSCS detected by both technologies. On average, 73% of the NSCS detected by CGI were also detected by Illumine, while 82% of the NSCS detected by Illumine were also detected by CGI. (Right) Using the same samples we calculated that 96% of all the CGI NSCS are considered "High Quality" according to the CGI proprietary quality matrix. An average of 72% of all the Nsa detected by CGI was also detected by Illumine (blue). Since two orthogonal sequence technologies detected the same set of NSCS, this group of variants most likely represents a set of real variants which we refer to as Mighty reliable NSCS." The set of "Highly reliable NSCS" were used to establish quality criteria in our Illumina's variant detection pipeline. GOnZeleZ-Gerity et al. www.pnas.orgicgikontentrshorV1315934110 3 of 8 EFTA01140250 Table Si. Case Disease associations with alleles Disease Risk gene Allele HGMD OMIM gene ID 3937 Hypercholesterolaemia LOLA p.P526H CM 100938 606945 3890 Hypercholesterolaemia LOLA O7261 CM920469 606945 3910 Hypercholesterolaemia LOLA O7261 CM920469 606945 3900 Hypercholesterolaemia LOLA p.V8271 CM920471 606945 3915 Hypercholesterolaemia LOLA p.V8271 CM920471 606945 3923 Obesity MC4R p.1251L CM030483 155541 3923 Diabetes mellitus, type II MAPK8IP1 p.D386E NA 604641 3973 Obesity MC4R p.C326R CM070992 155541 3937 Diabetes mellitus type 2 (MODY) FN3K p.H146R NA 608425 3937 Diabetes mellitus type 2 (MODY) PASK p.P12S6L NA 607505 3923 Macular degeneration, age related ABC*: p.G863A CM970003 601691 3898 Brittle cornea syndrome type 1 ZNF469 pD2902Y NA 612078 (BCS1) keratoconus 3889 Male infertility USP26 p.T123 Q124insT NA 300309 3942 Melanoma BAG4 p.W103X NA 603884 3959 Melanoma GRIN2A p.N1076K NA 138253 3896 Breast or ovarian cancer BRCA2 p.1505T CM010167 600185 3959 Breast or ovarian cancer BRCA2 p.S384F CM065036 600185 3897 Breast or ovarian cancer BRCA2 p.T2515I CM994287 600185 3950 Follicular thyroid cancer (age 41) TPR p.R105C NA 189940 3960 Prostate cancer LRP2 P.N479H NA 600073 3960 Prostate cancer LRP2 P.G4417D NA 600073 3934 Nonsyndromic deafness MYH14 p.M1611 NA 608568 3934 Nonsyndromic deafness SLC17A8 p.R75C NA 607557 NA, not available. %IN Table Si. Familial diseases and assedatIons Case Disorder prer' Association Gene Volunteer relatedness Volunteer Affected relative 3949 3947 3930 3930 3928 Praeder Willie Paraganglioma Ankylosing spondylitis Tourettes Parkinson MAGEL2 SDHB HLA-827 TBD LRRK2 2° 1° 1° 1°13) 1° IP IP —, negative; IP, research in progress. Gonzalez-Casey et al. www.pnas.olgkgVcontent/short/13I5934I10 4 of 8 EFTA01140251 Table S3. Recessive disorders Cases Disease Risk gene Allele HGMD OMIM 3958 Niemann-Pick type C2 disease NPC2 p.N111K CM081368 601015 3896, 3900, 3915, 3895 Antitrypsin al deficiency SERPINA1 p.R247C, p.E366K (3) CM910298, CM830003 107400 3894 Glycogen storage disease 0 GYS2 p.Q183X CM023388 138571 3889 Glycogen storage disease la G6PC p.R83C CM930261 613742 3901 Glycogen storage disease 3 AGL p.R477H CM 104343 610860 3945 Glycogen storage disease 4 GBEI p.Y329S CM960705 607839 3898 Glycogen storage disease 6 PYGL p.D634H CM078418 613741 3941, 3952 Glycogen storage disease 9B PHKB p.Q650K CM031327 172490 3915, 3919, 3943, 3954 Fanconi anemia FANCA p.T126R, p.S858R (3) CM043494, CM992317 607139 3936, 3934 Familial Mediterranean fever MEFV p.E148Q, p.P369S, p.R408Q CM981240, CM990837, CM990838 608107 395, 439, 243, 953 Cystic fibrosis CFTR p.D1152H, p.S1235R, CM950256, CM930133 602421 3933 Sandhoff disease HEXB p.A543T CM970723 606873 3940 Fuchs endothelial dystrophy ZEB1 p.Q824P CM 100242 189909 3908 Factor V deficiency FS p.P18165 CM095204 612309 3952 Hepatic lipase deficiency LIPC p.T405M CM910258 151670 3962 Krabbe disease GALC p.T112A CM960678 606890 3954 Macular corneal dystrophy, type 2 CHST6 p.Q331H CM055930 605294 3891, 3947, 3959, 3924, Usher syndrome Id CDH23 p.A366, p.01806E, p.R1060W CM050545, CM105104, CM021537 605516 3895, 3897 3900, 3910 Phenylketonuria PAH p.A3005, p.R53H CM920555, CM981427 612349 3933, 3946 MCAD (medium-chain acyl-coA dehydrogenase deficiency) ACADM p.K329E (2) CM900001 607008 3914 Adrenal hyperplasia HSD3B2 p.R249X CM950655 613890 3926 17-a-hydroxylase/17,20-Iyase deficiency CYP17A1 p.R449C HM0669 609300 Table 54. X-linked recessive Case Disorder Risk gene Allele Sex HGMD OMIM 3891 ATRX syndrome 3930 Fabry disease 3901 Mucopolysaccharidosis II ATRX GLA IDS p.N18605 p.A143T p.D252N Female Female Female CM950125 CM972773 CM960865 300032 300644 300823 Table SS. Breast cancer risk Case Disease Risk gene Allele Family history Sex Age (y) HGMD OMIM gene ID 3959 Breast cancer BRCA2 p.5384F Affected (44) Female 44 CM065036 600185 3896 Breast cancer BRCA2 p.15057 Affected Female 49 CM010167 600185 3955 Breast cancer BRCA2 p.E1625fs Negative Female 42 CD011121 600185 3962 Breast cancer PALB2 p.V1103M First second, third degree (2) Female 51 CM 118272 610355 (49-60s) 3936 Breast cancer BACA? p.Y856H First degree (sister 40s) Male 62 CM042673 113705 3936 Breast cancer BRCA2 p.K2729N First degree (sister 40s) Male 62 CM021957 600185 3963 Breast cancer BRCA2 p.R2034C First degree (60s) Male 48 CM994286 600185 3897 Breast cancer BRCA2 p.T25151 First degree (80) Female 51 CM994287 600185 3934 Breast cancer RADS1C pT287A First degree (uterine) Female 50 NA 602774 3939 Breast cancer RADSO p.R1069X First degree breast (60s)hecond colon (60s) Male 56 NA 604040 3912 Breast cancer RADS1C p.A126T Negative Male 77 CM1010201 602774 3923 Breast cancer RADS1C pT287A Negative Male 60 CM1010198 602774 3956 Breast cancer RADS1C pT287A Negative Male 59 CM1010198 602774 NA, not available. Gonzalez-Gas ay et al. www.pnas.orgkgkontent/shortfl3I5934I10 S of 8 EFTA01140252 Table S6. Case Colon cancer risk Disease Risk gene Allele Family history Sex Age (y) HGMD OMIM gene ID 3896 Colon cancer MLHI p.K618A First degree Female 49 CM973729, CM950808 120436 3891 Colon cancer MLH3 p.E1451K First degree (70s) Female 62 CM013011 604395 3897 Colon cancer APC p.A2690T First and second degree cancer Female 51 CM045404 611731 3904 Colon cancer MSH2 p.G315V Second degree Male 49 CM 995220 609309 3897 Colon cancer MSH2 p.G12D Negative Female 51 CM 950813 609309 3962 Colon cancer APC p.52621C Negative Female 51 CM921028 611731 3955 Colon cancer APC p.R2505C? Negative Female 42 NA 611731 3933 Colon cancer MUTYH p.63820 Negative Female 69 CM020287 604933 NA, not available. Table 57. Other cancer risk Case Disease Risk gene Allele Family history Sex Age (y) HGMD OMIM gene ID 3959 Melanoma GRINIA p.N1076K Affected Female 44 NA 138253 3942 Melanoma BAG4 p.W103X Affected Male 70 NA 603884 3950 Follicular thyroid cancer TPR p.R105C Affected Male 48 NA 189940 3960 Prostate cancer LRP2 p.N479H Affected Male 65 NA 600073 3946 Prostate cancer LRP2 p.M46011 Negative Female 59 NA 600073 3957 Prostate cancer LRP2 p.N17975 First degree Male 44 NA 600073 (father) 3957 Prostate cancer DLC1 p.089N First degree Male 44 NA 604258 (father) 3932 Prostate cancer CHEKI p.E64K Negative Male 47 CM030414 604373 3935 Prostate cancer ELACI p.R781H Negative Female 70 CM010221 605367 3902 Prostate cancer MSR1 p.H441R Negative Female 46 CM023581 153622 3900 Prostate cancer MSR1 p.R293X Negative Male 45 CM023579 153622 3954 Prostate cancer RNASEL p.E265X Negative Male 72 CM020300 180435 3954 Prostate cancer RNASEL p.6595 Negative Male 72 CM031342 180435 3963 Retinoblastoma RBI p.R656W Negative Male 48 CM030511 614041 3896 Pituitary cancer ACVRL1 p.A482V Negative Female 46 CM994582 601284 3896 Pituitary cancer ACVRL1 p.A482V Negative Female 46 CM994582 601284 3930 Esophageal cancer WWOX p.G 1785 Negative Female 52 NA 605131 3973 Esophageal cancer WWOX p.R120W Negative Male 71 CM016224 605131 3916 Esophageal cancer WWOX p.R120W Negative Male 70 CM016224 605131 3941 Gastric cancer MET p.A347T Negative Male 46 NA 164860 NA, not available. Gonzalez-Gas ay et al. www.pnas.orgkgifccintent/shOrtfl3I5934I10 6 of 8 EFTA01140253 Table 58. Cardiomyopathy-affected volunteers Case Disease Risk gene Allele Clinical Age (y) HGMD OMIM gene ID 3925 Dilated cardiomyopathy MYH6 p.A1443D Atrial fibrillation 65 CM107536 160710 3926 Cardiomyopathy arrhythmogenic right ventricular DSG2 p.V158G Arrhythmia 65 CM070921 125671 3935 Dilated cardiomyopathy MYH6 p.R1398Q Cardiac dysrhythmia 70 NA 160710 3935 Cardiomyopathy, dilated, 1EE MYH6 p.R1398Q Cardiac dysrhythmia 70 NA 160710 3935 Arrhythmogenic right ventricular cardiomyopathy TTN p.P3751R Cardiac dysrhythmia 70 NA 188840 3955 Dilated cardiomyopathy ACTN2 p.Q349L V pacemaker 53 NA 102573 3955 Familial hypertrophic cardiomyopathy 12 CSRP3 p.R100H V pacemaker 53 CM091458 600824 3916 Dilated cardiomyopathy type 1A LAMA2 p.T821M Stent placement 71 NA 156225 3887 Cardiomyopathy, hypertrophic MYBPC3 p.R326Q Stent placement (3) 73 CM020155 600958 3887 Cardiomyopathy familial hypertrophic (CMH) MYLK2 p.V402F Stent placement (3) 73 NA 606566 3953 Brugada syndrome (arrhythmia) KCNE3 p.M65T Two bypass, scent, and familial history of CAD 71 NA 604433 3953 Arrhythmogenic right ventricular cardiomyopathy TTN p.P5237T Two bypass, scent, and familial history of CAD 71 NA 188840 3937 Hypercholesterolaemia LDLR p.P526H Three generations of early MI, elevated LDL, cholesterol, triglycerides, and treated with statins 53 CM 100938 606945 3890 Hypercholesterolaemia LDLR p.T7261 1° early MI 57 CM920469 606945 3910 Hypercholesterolaemia LDLR p.T7261 V aortic occlusion, elevated cholesterol 51 CM920469 606945 3900 Hypercholesterolaemia LDLR p.V8271 1° early MI 45 CM920471 606945 3915 Hypercholesterolaemia LDLR p.V8271 Three generations of elevated cholesterol, treated with statins 70 CM920471 606945 CAD, coronary artery disease; MI, myocardial infarction; NA, not available. Table S9. Cardiomyopathy unaffected but family history Case Disease Risk gene Allele Clinical Age (y) HGMD OMIM gene ID 3943 Arrhythmogenic right ventricular cardiomyopathy TTN p.G1345D Familial history of arrhythmia 44 NA 188840 3896 Dilated cardiomyopathy SYNE1 p.I.3057V Familial history 45 NA 608441 3896 Arrhythmogenic right ventricular dysplasia/cardiomyopathy JUP p.V648I Familial history 45 NA 173325 3944 Hypertrophic cardiomyopathy OBSCN p.K1671N Father 45 NA 608616 3931 Dilated cardiomyopathy MYH6 p.R1398Q Familial history 46 NA 160710 3907 Cardiomyopathy, hypertrophic ACTN2 p.T495M Father 47 CM101366 102573 3950 Cardiomyopathy MYOMI p.G11625 Familial history 48 NA 603508 3919 Romano-Ward syndrome (arrhythmia) SCNSA p.51769N Familial history 51 CM002391 600163 3889 Romano-Ward syndrome (arrhythmia) SCNSA p.51769N Mother 51 CM002391 600163 3917 Cardiomyopathy MYOMI p.R1573Q Familial history + father 51 NA 603508 3960 Dilated cardiomyopathy NEBL p.K60N Son CAD 66 CM106905 605491 3976 Cardiomyopathy MYOM1 p.E704K Older brother 72 NA 603508 3976 Early onset myopathy MYH2 p.V9701 Older brother 72 CM051560 160740 Gonzalez-Gatay et al. www.pnas.orgkgkontentishort./13I5934I10 7 of EFTA01140254 Table 510. Neurodegenerative risk Case Disease Risk gene Allele Family history Age (y) HGMD OMIM 3908 Alzheimer's disease APOE p.C130R Negative 44 CM900020 107741 3916 Alzheimer's disease APOE p.L46P Parkinson 1° (72) 71 CM990167 107741 3954 Alzheimer's disease APP p.R469H Negative 72 NA 104760 3942 Frontotemporal dementia MAPT p.5427F Negative 71 NA 157140 3954 Frontotemporal dementia MAPT p.V224G Negative 72 NA 157140 3895 Parkinson disease ElF4G1 p.G686C Negative 49 CM117028 600495 3916 Parkinson disease ElF4G1 p.R120SH Parkinson 1° (78) 64 CM117009 600495 3951 Parkinson disease ElF4G1 p.51596T Negative 64 NA 600495 3931 Parkinson disease 11 GIGYF2 p.P1222fs Negative 44 NA 612003 3946 Parkinson disease 11 GIGYF2 p.H1171R Negative 59 NA 612003 3957 Parkinson disease 11 GIGYF2 p.M481 Negative 44 NA 612003 3930 Parkinson disease 11 GIGYF2 p.51035C Negative S2 NA 612003 3933 Parkinson disease 11 GIGYF2 p.5103SC Negative 68 NA 612003 3928 Parkinson disease LRRK2 p.A419V Tremor 1° Parkinson 2° 68 CM125746 609007 3903 Parkinson disease LRRK2 p.O972G Negative 54 NA 609007 3919 Parkinson disease LRRK2 p.O972G Negative 51 NA 609007 3889 Parkinson disease LRRK2 p.620195 Negative 51 CM050659 609007 3951 Parkinson disease LRRK2 p.L119P Negative 50 NA 609007 3918 Parkinson disease LRRK2 p.L286V Negative 64 NA 609007 3907 Parkinson disease LRRK2 p.P15425 Alzheimer's 2° 47 NA 609007 3935 Parkinson disease LRRK2 p.P15425 Negative 70 NA 609007 3893 Parkinson disease LRRK2 p.R1514Q Negative 45 CM057190 609007 3943 Parkinson disease LRRK2 p.R1514Q Negative SO CM057190 609007 3949 Parkinsonism, juvenile, autosomal recessive PARK2 p.R275W 2° three siblings S2 CM991007 602544 3924 Parkinsonism, juvenile, autosomal recessive PARK2 p.R334C Negative 54 CM003865 602544 3927 Parkinson PM20D1 p.A332V Negative 73 NA 613164 3886 Parkinson PM20D1 p.P2S1Q Negative 62 NA 613164 Table 511. Percentage of survey respondents reporting having made behavioral changes specifically motivated by their test results Type of behavior change Yes No Changes to diet 4 (10%) 36 (90%) Changes to health care (such as undergoing tests or seeing a specialist) 4 (10%) 36 (90%) Changes to use of vitamins/herbal supplements 4 (10%) 36 (90%) Changes to exercise 3 (8%) 37 (92%) Changes to medications 1 (2%) 39 (98%) Changes to insurance coverage 1 (2%) 39 (98%) Number of respondents making at least one of the above behavior changes 10 (25%) Gonzalez-Gatay et al. www.pnas.orglegikontent/short/13I5934I10 8 of 8 EFTA01140255