Personalized genomic disease risk of volunteers

Personalized genomic disease risk of volunteers Manuel L. Gonzalez -Garay'', Amy L. McGuire b, 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 Sequencing 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 132Sam 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! Institutes of Health Lnoiegnesect diseases pro- gram: Insights Into rare diseases Genet Med 14(1)51-59. 11. Gehl WA lifft 0 (2011) The NIH undiagnosed diseases program: Lessons learned. /AMA 305(I8):1904 -I905. 12. Koenekoop RK. et al; Finding of Rare Disease Genes (FORGE) Canada Consortium (2012) Mutations in NMNAT1 MAO Leber congenital amaurosis and identify a new disease pathway for retinal degeneration. Nat Genet 44(9):1035-1039. 13. Stetson PD. et al. (2012) The Human Gene Mutation Database (IMMO) and Its ex- ploitation in the fields of personalized genomlcs and molecular evolution. Curr Pro- tocol erolnlorm 39:1.13.1-1.1320. Genzakz-Gairay et al. PNAS Early Edition I 5 of 6 EFTA01140246 14. Stenson PD, et al. (2009) The Human Gene Mutation Database: 2008 update. Genome Med 1(1)13. IS. Anonymous NHLBI exome sequencing project (ESP)exane variant server. Available at http:Nevsgswashington.edteEVSL Accessed September 19, 2013. 16. Oarke L Zheng-Bradley X. et at 12012) The 1800 Genomes Project: Data management and canmunity access. Nat Methods 9(5)459-462. 17. Abecasb GR. et al; 1000 Genomes Protect Consortium (2010) A map of human ge- nome variation f ran poptiation-scale sequencing. Nature 4670319):1061-1073. ILL Adzhubei La, et al. (2010) A method and server for predicting damaging missense mutations. Nat Methods 7(41:248-249. 19. Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding nonsynonymous variants on protein function using the SIFT algorithm. Nat Probst 40)1073-1081. 20. Slm NL. Kumar P. et al (2012) SIFT web server: Predicting effects of amino acid sub- stitutions on proteins. Nucleic Acids Re, 40(Web Saver issuckYV4S2-W457. 21. Hu 1. Ng PC 8012) Predicting the effects of frameshdling lads. Genuine BIN 1342)119. 22. Ng PC Henatoff S (2001) Predicting deleterious amino acid substitutions. Gnome Re, 11(5)1163-874. 23. Ng PC Henikoff S 0003) 5SF: Predicting amino acid changes that affect protein function. Nucleic Acids Re, 31(13):3812-3814. 24. Ng PC. Henikoff 5 (2006) Predicting the effects of amino acid substitutions on protein function. Anne Rev Genomics Num Genet 7:61-80. 25. Schwarz 109, Rodelsperger C Schuelke NI, Seelow LI (2010) MutationTaster evaluates thseasecausMg potential of sequence alterations. Nat Methods 7181:575-576. 26. Liu X. Nan X. Boer-winkle E (2011) dbNSFP: a lightweight database of human non- synonymous SNPs and their functional predictions. Man Mutat 32(8)490499. 27. Anonymous Online Mendelian Inheritance in man 0M61. Available at httpllornimorg Accessed September 19,2013. 21. Anonymous NCBI OMIM Online Mendelian Inheritance in Man. Available at httpli www.ncbLnlanih.govlornim. Accessed September 19. 2013. 29. Huijgen K Kindt I, Defesche 1C, Kastelein II (2012) Cardiovascular risk in relation to functionality of sequence variants in the gene coding for the low-density koprcrtein receptor: A study among 29.365 iedwolva tested for 64 specific low-density lipo- protein-receptor sequence variants. Cur Heart 133(181:2325-2330. 30. Boekhoktt 5M. et al. (2012) ASSOciateell of LDt cholesterol, non.HDL cholesterol and aPoliPoprotein B levels with risk of cardiovascular events among patients treated with statins: A meta-analysis. JAIAA 307(12k1302-1309. 31. Waeber G. et al. (2000) The gene MAPKINPI. encoding islet.bran-I, is a candidate for type 2 diabetes. Nat Genet 24(3)291-295. 32. Mosta L el al. (2011) Genetic variability of the fructosamme 3-kinase gene in diabetic patients. CM Chem Lab Med 41(5):803-808. 33. da Silva Xavier G, et al. (2011) Per-arntsim (PM) domaM-containing protein kinase is downregulated In human Islets in type 2 diabetes rid regulates gluCagOn secretion. Diabetobgia 54(4)219-827. 34. MacDonald PE, Rottman P (2011) Per-amt.sim (PAS) domain kinase (PAW as a reg. uLatOr of glucagon secretion. Diabetologia 54(4):719-721. 35. Oltahilly S (2009) Human genetics ilurninates the paths to metabolic disease. Nature 462(7271)307-314. 36. van did Berg L et al. 12011) Melanocordn-4 receptor gene mutations In a Dutch cohort of obese children. Obesity (Silver Spring) 19(3)400-611. 37. Al-Owain M. A1.Doseri MS. Sunker A. Shuaib T. Alkuraya FS (2012) Identification of a novel ZNF469 mutation in a large family wit's Ehlen.Danlos phenotype. Gene S11(2k497-430. 38. Fritsch. LG, et al, (2012) A subgroup of age-related macular degeneration Is emaci- ated with mono-allelic sequence variants in the ABCAO gene. Invest Ophthalmol Vin Sal 53(4):2112-2118. 39. %hang 1, et al. (2012) IPOtyrnerphism of Usp26 correlates with Idiopathic male In- fertaityl. Ihonghua Nan Ke Xue 18(2)10S-10B. 40. Wel X. et al.; MSC Comparative Sequencing Program (2011) Ellen* sequencing identifies GRIN2A as frequently mutated in melanoma. Nat Genet d3(5)A42-446. 91. Howell PM, Jr. Li X, Riker AI, )G Y (2010) MicroRNA in melanoma. °droner J 10(2k 83-92. 92. Xi V, et al. (2008) Global comparative gene expression analysis of melanoma patient samples. derived <es lines and corresponding turner xenografts. Canter Genomics Proteomks 50):1-35. Q. Nelson HO, Huffman LH, Fu R, Harris EL; U.S. Preventive Services Task Force (2005) Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibiky: Systematic evidence review for the V.S. Preventive Services Task Force. Ann intern Med 143(5):362-379. 44. Anonymous National Cancer Institute BRCA1 and BRCAZ. Available at httplAwm. cancer.govkancertopiatfactsheet/RiskttIRCA. Accessed September 19,2013. 45. Holt SIC. et al. (2008) ASSO0atiOn of megalin genetic polymorphism with prostate cancer risk and prognosis. CM Cancer ReS 14(12):3823-3831. 96. Frank.Raue K, et al. (2013) Prevalence and clinical spectrum of nonsecretoni medul- lary thyroid carcinoma In a series of 839 patients with sporadic medullary thyrOld carcinoma. Thyroid 23(3):294-300. 97. Mak HH, et aL (2007)Oncogenic activation of the Met receptor tyrosine kinase fusion protein, Ter-Met. Involves exclusion from the endocytic degradative pathway. On- cogene 26(51k7213-7221. M. Ruel Let al. (2008) Impairment of SLC17A8 encoding vesicular glutamate transporter. 3, VGLUT3, underlies nOnSyndrOmk deafness DFNA2S and inner hair cell dysfunction in null mice. Am .1 Hum Genet 83(2):278-292. 49. van Hulstelp LT, Dekkers OM, Mn Fl. Smlt 1W, Calmat EP 0012) Risk of malignant paraganglioma 1n 9211B-mutation and 50410mtnatiOn canals A systematic review and meta-analysis./ Med Genet 49(12):768-776. 50. Pang Y, Tsal TF, Bressler J. Beaudet AL 11998) Imprinting in Angelman and Prader- Willi syndromes. Cuss Opin Genet On B(3):334-342. SI. Schaaf CP, et al. (2013) Truncating mutations of MAGEL2 cause autism and erader- Willi syndrome (PWS) or PWS.like phenotypes. Nat Genet. In press. 52. Rashid T, (bringer A (2011) Gut-mediated and MLA-827-assoriated arthritis: An em- phasis on ankylosing spondylitis and CrohNs disease with a proposal for the use of new treatment. DiSCOY hied 12(64):187-194. 53. Deng H, Gao IC, lankovic 1 (2012) The genetics of Tourette syndrome. Nat Rev Neural 80)203-213. 54. Anonymous Beyond Batten Disease Foundation. Available at httrabeyonSatten. orgy. Accessed September 19,2013. 55. Knudson AG (1996) Hereditary cancer: Two hits revisited. Cancer ReS Cen Onttif 122(3):135-140. 56. Milt-Zaino, S. et al; Breast Cancer Working Group of the International Cancer Genome Consortium (2012) The life history of 21 breast cancers. CeN 149(5)394-1007. 57. Alcalai R, Seidman /G, Seidman CE (2008) Genetic bash of hypertrophic cardiony apathy from bench to the clinics. / Carthovasc EintrOphydol 1901:104-110. 58. Rader 01. Cohen 1. Hobbs NH (2003) Monogenk hypercholesterolemla New insights in pathogenesis and treatment. Gin Invert 111(12)179S-1801. 59. Martin I. Dawson VL Dawson TM 12011) Recent advances In the genetics of Parkin- son's disease. Anna Rev Genomics Mum Genet 12:301-325. 60. Selkoe D1 (2012) Preventing Alzheirner's disease. Science 337(6100:1488-1492. 61. Anonymous Complete Genomics Inc. Available at Mbyfernwr.ownpletegenomks. can. Accessed September 19,2013. 62. Anonymous Human varlome project. Available at httplFwenv)umanvarlomeprOjea. Org. Accessed September 19. 2013. 63. Anonymous UniProtKB. Available at http://wnw.uniprotorgtuniprot. Accessed September 19.2013. 64. Anonymous Gene atlas. Available at hnp:Nmws.geneatias.orgIgenelmain.jsp. At- temod September 19, 2013. 65. Anonymous Ger** TeStIlla Registry (GeneTesis). Available at http/Ave.w.geneteStS. org. Accessed September 19,2011 66. Anonymous stganswers. Available at httDINSeganswert con. Accessed September 19. 2013. 67. Anonymous 69 genornes data. Ausilable at httpininwtcornpletegenomicscorn/public- datae69-Genoinest Accessed September 19. 2013. 68. Anonymous The million veteran program. Available at http:Nvnwr.va.gmstopcsipresteir pressrekrze.chraid-2090. Accessed September 19,2013. 69. Patel C.), et at. 12013) Whole genome sequencing In support of wellness and health maintenance. Gramme Med 5(6):58. 70. Ball MP, et at. 0012) A public resource facilitating clinical use of genomes. floc Nate Aced 56 LISA 109(30)11920-11927. 71. American Academy of Pediatrics Committee on Bioethics (2001) Ethical issues with genetic testing In pediatrics. Pediatrics W7(61:1451-1455. 72. Oasis OS (1997) Genetic dilemmas and the child's right to an open future. Hastings Cent Rep 27(2):7-15. 73. Wolf SM, Lawrenz W. et at (2008) Managing Incidental findings In human subjects research: Analysis and recommendations./ Law Med Ethic 36(2)219-24B. 79. McGuire At. Burke W (ZOOM An unwelcome side effect of direCt4O-COMumer per- sonal genome testing: Raiding the medical commons. /AMA 300(22):2669-2671. 75. Blois CS, Scheele N), Topol 61 (2011) Effect of direct-to-consumer gencenewide pro- filing to assess disease risk. N Enloe I Med 364(6):524-534. 76. Wexler NS (2012) Huntington's disease: Advocacy driving science. Annu Rev Med 63: 1-22. 77. Caskey CT (2007) The drug develeprnent crisis: Efficiency and safety. AMIN Rev Med 5a,1-16 78. Casket, CT (2010) Using genetic diagnosis to determine Individual therapeutic utility. Annu Rev Med 61:1-15. 79. Miller G (2012) Alzheimer's research. Stopping Alzheimer's before it starts. Science 337(6096):790-792. 80. Mang GC et al. (2012) Structure-based prediction of protein-protein interactions on a genome.wicte scale. Nature 490(7421):556-560. 81. Edwards AM. BounVa C Kerr DJ, Wilhon TM (2009) Open access chemical and algal probes to support drug discovery. Nat Chem Rio! 50):436-490. 82. Maroon( MT. 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, ant