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Pharmacogenomic Incidental Findings in 308 Families: the NIH Undiagnosed Diseases Program Experience

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Pharmacogenomic Incidental Findings in 308 Families: the NIH Undiagnosed Diseases Program Experience © American College of Medical Genetics and Genomics ORIGINAL RESEARCH ARTICLE Pharmacogenomic incidental findings in 308 families: The NIH Undiagnosed Diseases Program experience Elizabeth M.J. Lee, BA1,2, Karen Xu1,2, Emma Mosbrook1,2, Amanda Links, BS1,2, Jessica Guzman, BA1,2, David R. Adams, MD, PhD1,2, Elise Flynn, BA1,2, Elise Valkanas, BA1,2, Camillo Toro, MD1,2, Cynthia J. Tifft, MD, PhD1,2, Cornelius F. Boerkoel, MD, PhD1,2, William A. Gahl, MD, PhD1,2 and Murat Sincan, MD1,2 Purpose: Using single-nucleotide polymorphism (SNP) chip and were ­identified in the exome sequence data. Nine participants had exome sequence data from individuals participating in the National ­incidental pharmacogenetic variants associated with altered efficacy Institutes of Health (NIH) Undiagnosed Diseases Program (UDP), of a prescribed medication. we evaluated the number and therapeutic informativeness of inci- Conclusions: Despite the small size of the NIH UDP patient cohort, dental pharmacogenetic variants. we identified pharmacogenetic incidental findings potentially use- Methods: Pharmacogenomics Knowledgebase (PharmGKB) anno- ful for guiding therapy. Consequently, groups conducting clinical tated sequence variants were identified in 1,101 individuals. Medi- genomic studies might consider reporting of pharmacogenetic inci- cation records of participants were used to identify individuals pre- dental findings. scribed medications with a genetic variant that might alter efficacy. Genet Med advance online publication 2 June 2016 Results: A total of 395 sequence variants, including 19 PharmGKB Key Words: next generation sequencing; NIH undiagnosed 1A and 1B variants, were identified in SNP chip sequence data, ­diseases program; pharmacogenomics; precision medicine; and 388 variants, including 21 PharmGKB 1A and 1B variants, ­secondary ­findings Incidental or secondary genetic findings are variants with med- for pharmacogenetic incidental findings based on variant–drug ical or social implications discovered during genetic testing associations listed in the Pharmacogenomics Knowledgebase for an unrelated indication.1 Recent discussions and a report (PharmGKB). The typical number of such pharmacogenetic by the American College of Medical Genetics and Genomics incidental findings has not been widely studied, particularly (ACMG) Working Group on Incidental Findings in Clinical when family members other than the proband are included Exome and Genome Sequencing have focused on disease-asso- in diagnostic studies. Consequently, more data are needed to ciated genes but not genetic determinants of drug metabolism.2 assess the possible impact and need for resources. Given that genomic variation influences human responsiveness To delineate the impact of identifying pharmacogenetic inci- to many drugs and contributes to phenotypes ranging from dental findings, we analyzed SNP chip data from 1,101 indi- life-­threatening adverse drug reactions to lack of therapeutic viduals derived from 308 families and research exome sequence efficacy,3 the return of pharmacogenetic incidental findings has data from 645 individuals derived from 158 families. For the potentially significant medical benefit.4 868 pharmacogenetic loci listed in the PharmGKB, we identi- The Clinical Pharmacogenetics Implementation Consortium fied 949 independent pharmacogenetic findings using the SNP (CPIC), which develops guidelines for incorporating phar- chip and exome sequence data and found that each individual macogenomics findings into clinical practice, has identified had at least one. For nine individuals, these constituted inci- variant–drug associations of high concern for clinicians and dental findings relevant to a medication that they were using. provides drug-dosing guidelines based on patient genotype. These data refine strategies for reporting of pharmacogenetic Therefore, we hypothesized that designing and implementing incidental findings. a process to identify pharmacological incidental findings in the genomic data generated by the National Institutes of Health MATERIALS AND METHODS (NIH) Undiagnosed Diseases Program5 (UDP) provide infor- Subject cohort mation to the medical community regarding the quantity and Family members gave informed consent or assent under pro- quality of pharmacogenetic incidental findings. tocol 76-HG-0238, “Diagnosis and Treatment of Patients with The NIH UDP routinely conducts single-nucleotide poly- Inborn Errors of Metabolism and Other Genetic Disorders,” morphism (SNP) chip and exome sequencing analyses on pro- approved by the National Human Genome Research Institute bands and their family members. These data can be analyzed (NHGRI) institutional review board. The SNP chip data 1NIH Undiagnosed Diseases Program, Common Fund, Office of the Director, National Institutes of Health, Bethesda, Maryland, USA; 2National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA. Correspondence: Murat Sincan ([email protected]) Submitted 13 November 2015; accepted 8 March 2016; advance online publication 2 June 2016. doi:10.1038/gim.2016.47 Genetics in medicine | Volume 18 | Number 12 | December 2016 1303 ORIGINAL RESEARCH ARTICLE LEE et al | Reporting pharmacogenomic incidental findings were derived from a cohort of 308 families consisting of 355 SNP chip analysis affected individuals, 278 unaffected siblings, 459 unaffected The extracted DNA was submitted to the NHGRI core lab and parents, and 9 other unaffected family members; this cohort run on the Human OmniExpressExome v1.2 SNP oligo array included 564 females and 537 males. The exome sequence (Illumina, San Diego, CA). Each family was analyzed individu- data were from a subset of this cohort; 158 families contained ally following the UDP’s standard operating procedure. Call 182 affected individuals, 150 unaffected siblings, 313 unaf- rates were >98% before quality control processing and were fected parents, and 326 females and 319 males. The average typically >99.7%. and median ages of the subjects at time of SNP chip analysis were 36.1 (SD 22.4) and 36.0 years, respectively. The aver- Exome sequencing age and median ages at the time of exome analysis were 35.2 Genomic DNA was submitted for exome sequencing using the (SD 22.4) and 36.0 years, respectively. Some subjects were Illumina TruSeq exome capture kit (Illumina), which targets deceased at the time of study; for those subjects, projected roughly 60 million bases consisting of the Consensus Coding age at time of sequencing was used, since it is anticipated that Sequence annotated gene set as well as some structural RNAs. incidental findings will be sought only in living subjects. Self- Captured DNA was sequenced on the Illumina HiSeq platform reported ancestry was white/European (75.3%), black/African until coverage was sufficient to call high-quality genotypes at American (2.5%), American Indian or Alaskan Native (0.4%), 85% or more of targeted bases. Asian (2.1%), multiracial (5.3%), and unknown (14.4%). These families included all those admitted to the NIH Undiagnosed Alignment and genotype calling Diseases Program and who had SNP chip or exome analyses. Beagle software version 4 and the 1000 Genome Project’s The SNP chip genotyping and exome sequencing were per- HapMap data were used to generate a phased and imputed vari- formed on a research basis between 2009 and 2014, not in ant call format (VCF) file from SNP chip data for the parents a Clinical Laboratory Improvement Amendments–certified and offspring.6 The VCF file was then used by AlleleSeq ver- fashion. sion 0.2.3 (ref. 7) to modify the human reference and create a maternal reference and a paternal reference, which are concat- DNA extraction enated to generate a parental reference. Patient short reads from Genomic DNA was extracted from patients’ peripheral whole exome sequencing were aligned to all three reference sequences blood using the Gentra Puregene Blood kit (Qiagen, Valencia, with Novoalign version 2.08.03 and lifted back over to the stan- CA). dard human reference using custom Java code. BAM files were UDP sequencing cohort (1,101 individuals) SNP chip sequencing (1,101 individuals) Exome sequencing (645 individuals) Identify variants with a PharmGKB annotation Identify variants with a PharmGKB annotation (696 variants) (949 variants) High-priority variants (19) High-priority variants (21) (Pharm GKB 1A or 1B category) (PharmGKB 1A or 1B category) Indentification of available medication records (359 individuals) Intersection of prescribed medications with annotated variants (76 individuals) Manual identification of medically relevant findings (9 individuals) Figure 1 Flow chart summarizing the NIH Undiagnosed Diseases Program analysis of and observations for the pharmacogenetic variants listed in PharmGKB. The observations were derived from analysis of SNP chip and exome sequence data. The SNP chip data were derived from a cohort of 308 families consisting of 355 affected individuals, 278 unaffected siblings, 459 unaffected parents, and 9 other unaffected family members; this cohort included 564 females and 537 males. The exome sequence data were from a subset of this cohort: 158 families consisting of 182 affected individuals, 150 unaffected siblings, 313 unaffected parents, 326 females, and 319 males. NIH, National Institutes of Health; PharmGKB, Pharmacogenomics Knowledgebase; SNP, single-nucleotide polymorphism. 1304 Volume 18 | Number 12 | December 2016 | GENETICS in MEDICINE Reporting pharmacogenomic incidental findings
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