Informatics Challenges in Newborn Screening

1. Informatics Challenges in Newborn Screening Kim Hart Nicole Ruiz-Schultz Utah Newborn Screening Program

3. Importance Disorders • Detect disorders in apparently healthy infants • Initiate treatment prior to onset of symptoms • Significant mortality and morbidity when not diagnosed early • Once symptoms appear, they are often irreversible, lead to severe developmental problems or death • Not consistently identified clinically in the neonatal period • Often identified in families with no family history Statute 26-10-6 Rule 438-15

4. NBS Disorders screened for in Utah (41) Amino Acid Disorders • Arginase Deficiency • Argininosuccinic Acidemia • Biopterin Defect • Citrullinemia, Type I • Citrullinemia, Type II • GAMT Deficiency • Homocystinuria • Maple Syrup Urine Disease • Phenylketonuria (PKU) • Prolinemia • Pyruvate Carboxylase Deficiency • Tyrosinemia Endocrine Disorders • Congenital Adrenal Hyperplasia • Congenital Hypothyroidism Organic Acid Disorders • 2-Methylbutyryl CoA Dehydrogenase Deficiency (2MBG Deficiency) • 2M3HBA Deficiency • 3-Hydroxy-3-Methylglutaryl-CoA Lyase Deficiency (HMG Deficiency) • 3MCC Deficiency • 3MGA • BKT • GA-1 • IGB • Isovaleric Acidemia • Malonic Acidemia • MCD • MMA • Propionic Acidemia Fatty Acid Oxidation Disorders • CACT Deficiency • CPT1 Deficiency • CPT2 Deficiency • Glutaric Acidemia, Type 2 • LCHAD Deficiency • MCAD Deficiency • Primary Carnitine Deficiency • SCAD Deficiency • SCHAD Deficiency • TFP Deficiency • VLCAD Deficiency Hemoglobin Disorders • Alpha Thalassemia Disorders • Hemoglobin C, D and E Disorders • Hemoglobin Trait (C, D, E or Unidentified) • Sickle Cell Disease • Sickle Cell Trait Immunodeficiency Disorders • SCID Neuromuscular Disorders • SMA Other Disorders • Biotinidase Deficiency • Critical Congenital Heart Disease (CCHD) Screening • Cystic Fibrosis • Early Hearing Detection and Intervention (EHDI) • Galactosemia

5. What kind of testing methods are used in NBS? • In all cases, there are robust biochemical markers to detect a disorder. • Initial abnormal marker often reflexed to a second tier test and sometimes a third tier test. • For clinical action confirmatory testing is required. • Increasingly, gene sequencing methods are used. • Next-generation sequencing is one of these methods

6. Infrastructure Challenges in NBS Program • Computing power for data analyses • Storage of genomic data • Reporting of results to primary care providers and parents • Transfer of raw genomic data to specialists or third party lab for additional analyses (with patient/parent consent)

7. What is Bioinformatics? Bioinformatics is a subdiscipline of biology and computer science concerned with the acquisition, storage, analysis, and dissemination of biological data, most often DNA and amino acid sequences. Public Health Informatics • Surveillance • Prevention • Health promotion Healthcare Informatics • Clinical decision support • Clinical documentation • Provider order entry systems Bioinformatics • Interpretation of biological data • Sequence analysis to identify mutation associated with disease • Identify pathogen responsible for outbreak https://www.genome.gov/genetics-glossary/Bioinformatics

8. Bioinformatics Use Cases • Ancestry determination • Drug target discovery • Discovery of gene/disease associations • DNA sequencing analysis to determine changes in sequence which are associated with a disease

9. Universal (gene agnostic) second-tier sequencing based test Implementing a next generation DNA sequencing method • Applicable to all current and future NBS disorders • Gene-specific

10. Overview of NGS pipeline

11. NGS analysis pipeline – High-level overview Dolled-Filhart MP, Lee M, Ou-Yang C-W, Haraksingh RR, Lin JC-H. Computational and bioinformatics frameworks for next-generation whole exome and genome sequencing. ScientificWorldJournal. 2013 Jan;2013:730210.

12. Utah NBS sequencing pipeline • Targeted second-tier sequencing for confirmatory testing • Whole-exome sequencing • A priori restriction to disease-specific genes • Why are we choosing this method? • Cheaper to sequence entire exome versus using sequencing panels for each disorder (economies of scale) Whole-genome sequencing Whole-exome sequencing

13. What are we looking for in NBS DNA sequencing analysis? Cystic fibrosis DNA sequence Reference sequence ...A T C A T C T T T G G T G T T... Alternate sequence ...A T C A T - - - T G G T G T T... Molecular level CFTR gene CFTR protein sequence Reference sequence ...IsoleucinePhenylalanineGlycineValine... ...Isoleucine-GlycineValine... Alternate sequence

14. When will the NGS pipeline run? • Cystic Fibrosis • 1st screen – Abnormal IRT • 2nd screen – Abnormal IRT • analysis of CFTR gene • Pompe Disease • 1st screen – Low/Absent enzyme activity • 2nd screen – Low/Absent enzyme activity • analysis of GAA gene • Hemoglobin disorders • 1st screen – Abnormal HPLC • 2nd screen – Abnormal HPLC • analysis of HBA1, HBA2 genes • MPS I • 1st screen – Low enzyme activity • 2nd screen – Low enzyme activity • analysis of IDUA gene

15. NGS sequencing analysis pipeline *Based on GATK Best Practices guidelines Data pre-processing/QC Sequence Alignment Variant Calling Filter Variants Genome Analysis Toolkit (GATK) Hard filtering recommendations from GATK Seqyclean FastQC BWA Variant Interpretation Variant Annotation • SnpEff • Variant Effect Predictor Pipeline output: VCF file of annotated variants Local variant database • Public variant data • Utah NBS variant data Variant reinterpretation every 6 months Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinforma. NIH Public Access; 2013;43(1110):11.10.1-33.

16. Summary slide

17. NBS Variant Database • MySQL database • 2 main parts • Public variants • ClinVar • Disease/Gene-specific variant databases • Utah NBS population variants • Public variant resources will be regularly checked for updates

18. NBS Variant Database - Public Variants

19. NBS Variant Database - Utah NBS Variants

20. Re-evaluation of variants • Variants of unknown significance (VUS) • How often should our local database be updated? • Plan on updating as often as databases provide new releases • What if the clinical interpretation of a variant changes? • Plan on re-evaluating patient variants every 6 months • Update LIMS to re-query VUSs and generate updated reports

21. Targeted Sequencing Validation Plan • Laboratory methodology validation • Processing known disorder samples through laboratory protocol • Bioinformatics pipeline validation • Genomic variant discovery • Genomic variant interpretation • Analysis of data from known disorder samples • Data simulation of all NBS disorders

23. How are results reported? • Variant information with interpretation • No gene specific variant identified, sharing exome information using HL7 FHIR (with parental consent)

24. Sync for Genes Phase 2 Pilot Site • Sync for Genes Aim • Leverage HL7 FHIR infrastructure for communicating information from clinical genomics labs in a format for universal use across medicine • Utah Newborn Screening Program Aim • Enable electronic transfer of abnormal screening results to healthcare providers to reduce turnaround time for time-sensitive results to ultimately improve patient care

25. Sync for Genes: Data Transfer between Utah NBS and Provider Exome data Healthcare Provider Utah NBS Program Patient consent Patient consent request Request for exome data Abnormal NBS result/Variant report

26. Sync for Genes: Data Transfer between Utah NBS and Provider Third Party Genetic Testing Lab Disease Specialist Healthcare Provider Genetic Testing Lab Healthcare Provider UPHL

27. Summary • As the number and complexity of NBS disorders increases, gene sequencing based technologies are required to accurately detect these disorders • Utilization of technologies requires appropriate infrastructure • Informatics methods can be used to address these needs • Problems are not unique to NBS • Solutions can be applied to other areas of public health

28. Acknowledgements Utah Department of Health Newborn Screening Program • Andy Rohrwasser • Mary Rindler • David Jones Utah Department of Health Infectious Disease • Kelly Oakeson • Erin Young University of Utah Biomedical Informatics • Karen Eilbeck • David Sant • Jordan Little (Summer intern) • Krystal Chung (Rotation student)