Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disorders

BACKGROUND

Heritable bleeding and platelet disorders (BPD) are heterogeneous and frequently have an unknown genetic basis. The BRIDGE-BPD study aims to discover new causal genes for BPD by high throughput sequencing using cluster analyses based on improved and standardised deep, multi-system phenotyping of cases.

METHODS

We report a new approach in which the clinical and laboratory characteristics of BPD cases are annotated with adapted Human Phenotype Ontology (HPO) terms. Cluster analyses are then used to characterise groups of cases with similar HPO terms and variants in the same genes.

RESULTS

We show that 60% of index cases with heritable BPD enrolled at 10 European or US centres were annotated with HPO terms indicating abnormalities in organ systems other than blood or blood-forming tissues, particularly the nervous system. Cases within pedigrees clustered closely together on the bases of their HPO-coded phenotypes, as did cases sharing several clinically suspected syndromic disorders. Cases subsequently found to harbour variants in ACTN1 also clustered closely, even though diagnosis of this recently described disorder was not possible using only the clinical and laboratory data available to the enrolling clinician.

CONCLUSIONS

These findings validate our novel HPO-based phenotype clustering methodology for known BPD, thus providing a new discovery tool for BPD of unknown genetic basis. This approach will also be relevant for other rare diseases with significant genetic heterogeneity.

Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to play an important role in genetic susceptibility to common disease. To address this we undertook a large direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed ~19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated ~50% of all common CNVs larger than 500bp. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell-lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease, IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis, and type 1 diabetes, and TSPAN8 for type 2 diabetes, though in each case the locus had previously been identified in SNP-based studies, reflecting our observation that the majority of common CNVs which are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs which can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.

Sequencing-based genotyping and association analysis of the MICA and MICB genes in type 1 diabetes

OBJECTIVE

The nonclassical major histocompatibility complex (MHC) class I chain-related molecules (MICs), encoded within the MHC, function in immunity. The transmembrane polymorphism in MICA (MICA-STR) has been reported to be associated with type 1 diabetes. In this study, we directly sequenced both of the highly polymorphic MIC genes (MICA and MICB) in order to establish whether they are associated with type 1 diabetes independently of the known type 1 diabetes MHC class II genes HLA-DRB1 and HLA-DQB1.

RESEARCH DESIGN AND METHODS

We developed a sequencing-based typing method and genotyped MICA and MICB in 818 families (2,944 individuals) with type 1 diabetes from the U.K. and U.S. (constructing the genotype from single nucleotide polymorphisms in exons 2-4 of MICA and 2-5 of MICB) and additionally genotyped the MICA-STR in 2,023 type 1 diabetic case subjects and 1,748 control subjects from the U.K. We analyzed the association of the MICA and MICB alleles and genotypes with type 1 diabetes using regression methods.

RESULTS

We identified known MICA and MICB alleles and discovered four new MICB alleles. Based on this large-scale and detailed genotype data, we found no evidence for association of MICA and MICB with type 1 diabetes independently of the MHC class II genes (MICA P = 0.08, MICA-STR P = 0.76, MICB P = 0.03, after conditioning on HLA-DRB1 and HLA-DQB1).

CONCLUSIONS

Common MICA and MICB genetic variations including the MICA-STR are not associated, in a primary way, with susceptibility to type 1 diabetes.