title: Genomic medicine and risk prediction across the disease spectrum.
abstract: Genomic medicine is based on the knowledge that virtually every medical condition, disease susceptibility or response to treatment is caused, regulated or influenced by genes. Genetic testing may therefore add value across the disease spectrum, ranging from single-gene disorders with a Mendelian inheritance pattern to complex multi-factorial diseases. The critical factors for genomic risk prediction are to determine: (1) where the genomic footprint of a particular susceptibility or dysfunction resides within this continuum, and (2) to what extent the genetic determinants are modified by environmental exposures. Regarding the small subset of highly penetrant monogenic disorders, a positive family history and early disease onset are mostly sufficient to determine the appropriateness of genetic testing in the index case and to inform pre-symptomatic diagnosis in at-risk family members. In more prevalent polygenic non-communicable diseases (NCDs), the use of appropriate eligibility criteria is required to ensure a balance between benefit and risk. An additional screening step may therefore be necessary to identify individuals most likely to benefit from genetic testing. This need provided the stimulus for the development of a pathology-supported genetic testing (PSGT) service as a new model for the translational implementation of genomic medicine in clinical practice. PSGT is linked to the establishment of a research database proven to be an invaluable resource for the validation of novel and previously described gene-disease associations replicated in the South African population for a broad range of NCDs associated with increased cardio-metabolic risk. The clinical importance of inquiry concerning family history in determining eligibility for personalized genotyping was supported beyond its current limited role in diagnosing or screening for monogenic subtypes of NCDs. With the recent introduction of advanced microarray-based breast cancer subtyping, genetic testing has extended beyond the genome of the host to also include tumor gene expression profiling for chemotherapy selection. The decreasing cost of next generation sequencing over recent years, together with improvement of both laboratory and computational protocols, enables the mapping of rare genetic disorders and discovery of shared genetic risk factors as novel therapeutic targets across diagnostic boundaries. This article reviews the challenges, successes, increasing inter-disciplinary integration and evolving strategies for extending PSGT towards exome and whole genome sequencing (WGS) within a dynamic framework. Specific points of overlap are highlighted between the application of PSGT and exome or WGS, as the next logical step in genetically uncharacterized patients for whom a particular disease pattern and/or therapeutic failure are not adequately accounted for during the PSGT pre-screen. Discrepancies between different next generation sequencing platforms and low concordance among variant-calling pipelines caution against offering exome or WGS as a stand-alone diagnostic approach. The public reference human genome sequence (hg19) contains minor alleles at more than 1 million loci and variant calling using an advanced major allele reference genome sequence is crucial to ensure data integrity. Understanding that genomic risk prediction is not deterministic but rather probabilistic provides the opportunity for disease prevention and targeted treatment in a way that is unique to each individual patient.
authors.affiliation: Division of Anatomical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town , South Africa .
authors.forename: Maritha J
authors.initials: MJ
authors.lastname: Kotze
authors.forename: Hilmar K
authors.initials: HK
authors.lastname: Lückhoff
authors.forename: Armand V
authors.initials: AV
authors.lastname: Peeters
authors.forename: Karin
authors.initials: K
authors.lastname: Baatjes
authors.forename: Mardelle
authors.initials: M
authors.lastname: Schoeman
authors.forename: Lize
authors.initials: L
authors.lastname: van der Merwe
authors.forename: Kathleen A
authors.initials: KA
authors.lastname: Grant
authors.forename: Leslie R
authors.initials: LR
authors.lastname: Fisher
authors.forename: Nicole
authors.initials: N
authors.lastname: van der Merwe
authors.forename: Jacobus
authors.initials: J
authors.lastname: Pretorius
authors.forename: David P
authors.initials: DP
authors.lastname: van Velden
authors.forename: Ettienne J
authors.initials: EJ
authors.lastname: Myburgh
authors.forename: Fredrieka M
authors.initials: FM
authors.lastname: Pienaar
authors.forename: Susan J
authors.initials: SJ
authors.lastname: van Rensburg
authors.forename: Yandiswa Y
authors.initials: YY
authors.lastname: Yako
authors.forename: Alison V
authors.initials: AV
authors.lastname: September
authors.forename: Kelebogile E
authors.initials: KE
authors.lastname: Moremi
authors.forename: Frans J
authors.initials: FJ
authors.lastname: Cronje
authors.forename: Nicki
authors.initials: N
authors.lastname: Tiffin
authors.forename: Christianne S H
authors.initials: CS
authors.lastname: Bouwens
authors.forename: Juanita
authors.initials: J
authors.lastname: Bezuidenhout
authors.forename: Justus P
authors.initials: JP
authors.lastname: Apffelstaedt
authors.forename: F Stephen
authors.initials: FS
authors.lastname: Hough
authors.forename: Rajiv T
authors.initials: RT
authors.lastname: Erasmus
authors.forename: Johann W
authors.initials: JW
authors.lastname: Schneider
mesh_terms: D030541:Databases, Genetic; D019317:Evidence-Based Medicine; D005192:Family Health; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing; D023281:Genomics / Q000379:methods*; D006801:Humans; D057285:Precision Medicine / Q000941:ethics / Q000379:methods*
keywords: Breast cancer; database; exome; genome; hg19; major allele reference sequence; metabolic syndrome; multiple sclerosis