Significant variation between SNP-based HLA imputations in diverse populations: the last mile is the hardest.

Four single nucleotide polymorphism (SNP)-based human leukocyte antigen (HLA) imputation methods (e-HLA, HIBAG, HLA*IMP:02 and MAGPrediction) were trained using 1000 Genomes SNP and HLA genotypes and assessed for their ability to accurately impute molecular HLA-A, -B, -C and -DRB1 genotypes in the Human Genome Diversity Project cell panel. Imputation concordance was high (>89%) across all methods for both HLA-A and HLA-C, but HLA-B and HLA-DRB1 proved generally difficult to impute. Overall, <27.8% of subjects were correctly imputed for all HLA loci by any method. Concordance across all loci was not enhanced via the application of confidence thresholds; reliance on confidence scores across methods only led to noticeable improvement (+3.2%) for HLA-DRB1. As the HLA complex is highly relevant to the study of human health and disease, a standardized assessment of SNP-based HLA imputation methods is crucial for advancing genomic research. Considerable room remains for the improvement of HLA-B and especially HLA-DRB1 imputation methods, and no imputation method is as accurate as molecular genotyping. The application of large, ancestrally diverse HLA and SNP reference data sets and multiple imputation methods has the potential to make SNP-based HLA imputation methods a tractable option for determining HLA genotypes.

Genomewide association study of peanut allergy reproduces association with amino acid polymorphisms in HLA-DRB1.

BACKGROUND: Genetic variants for IgE-mediated peanut allergy are yet to be fully characterized and to date only one genomewide association study (GWAS) has been published. OBJECTIVE: To identify genetic variants associated with challenge-proven peanut allergy. METHODS: We carried out a GWAS comparing 73 infants with challenge-proven IgE-mediated peanut allergy against 148 non-allergic infants (all ~ 1 year old). We tested a total of 3.8 million single nucleotide polymorphisms, as well as imputed HLA alleles and amino acids. Replication was assessed by de novo genotyping in a panel of additional 117 cases and 380 controls, and in silico testing in two independent GWAS cohorts. RESULTS: We identified 21 independent associations at P ≤ 5 × 10-5 but were unable to replicate these. The most significant HLA association was the previously reported amino acid variant located at position 71, within the peptide-binding groove of HLA-DRB1 (P = 2 × 10-4 ). Our study therefore reproduced previous findings for the association between peanut allergy and HLA-DRB1 in this Australian population. CONCLUSIONS AND CLINICAL RELEVANCE: Genetic determinants for challenge-proven peanut allergy include alleles at the HLA-DRB1 locus.

Strong genetic evidence for a selective influence of GABAA receptors on a component of the bipolar disorder phenotype.

Despite compelling evidence for a major genetic contribution to risk of bipolar mood disorder, conclusive evidence implicating specific genes or pathophysiological systems has proved elusive. In part this is likely to be related to the unknown validity of current phenotype definitions and consequent aetiological heterogeneity of samples. In the recent Wellcome Trust Case Control Consortium genome-wide association analysis of bipolar disorder (1868 cases, 2938 controls) one of the most strongly associated polymorphisms lay within the gene encoding the GABA(A) receptor beta1 subunit, GABRB1. Aiming to increase biological homogeneity, we sought the diagnostic subset that showed the strongest signal at this polymorphism and used this to test for independent evidence of association with other members of the GABA(A) receptor gene family. The index signal was significantly enriched in the 279 cases meeting Research Diagnostic Criteria for schizoaffective disorder, bipolar type (P=3.8 x 10(-6)). Independently, these cases showed strong evidence that variation in GABA(A) receptor genes influences risk for this phenotype (independent system-wide P=6.6 x 10(-5)) with association signals also at GABRA4, GABRB3, GABRA5 and GABRR3. [corrected] Our findings have the potential to inform understanding of presentation, pathogenesis and nosology of bipolar disorders. Our method of phenotype refinement may be useful in studies of other complex psychiatric and non-psychiatric disorders.

Genetic utility of broadly defined bipolar schizoaffective disorder as a diagnostic concept.

BACKGROUND: Psychiatric phenotypes are currently defined according to sets of descriptive criteria. Although many of these phenotypes are heritable, it would be useful to know whether any of the various diagnostic categories in current use identify cases that are particularly helpful for biological-genetic research. AIMS: To use genome-wide genetic association data to explore the relative genetic utility of seven different descriptive operational diagnostic categories relevant to bipolar illness within a large UK case-control bipolar disorder sample. METHOD: We analysed our previously published Wellcome Trust Case Control Consortium (WTCCC) bipolar disorder genome-wide association data-set, comprising 1868 individuals with bipolar disorder and 2938 controls genotyped for 276 122 single nucleotide polymorphisms (SNPs) that met stringent criteria for genotype quality. For each SNP we performed a test of association (bipolar disorder group v. control group) and used the number of associated independent SNPs statistically significant at P<0.00001 as a metric for the overall genetic signal in the sample. We next compared this metric with that obtained using each of seven diagnostic subsets of the group with bipolar disorder: Research Diagnostic Criteria (RDC): bipolar I disorder; manic disorder; bipolar II disorder; schizoaffective disorder, bipolar type; DSM-IV: bipolar I disorder; bipolar II disorder; schizoaffective disorder, bipolar type. RESULTS: The RDC schizoaffective disorder, bipolar type (v. controls) stood out from the other diagnostic subsets as having a significant excess of independent association signals (P<0.003) compared with that expected in samples of the same size selected randomly from the total bipolar disorder group data-set. The strongest association in this subset of participants with bipolar disorder was at rs4818065 (P = 2.42 x 10(-7)). Biological systems implicated included gamma amniobutyric acid (GABA)(A) receptors. Genes having at least one associated polymorphism at P<10(-4) included B3GALTS, A2BP1, GABRB1, AUTS2, BSN, PTPRG, GIRK2 and CDH12. CONCLUSIONS: Our findings show that individuals with broadly defined bipolar schizoaffective features have either a particularly strong genetic contribution or that, as a group, are genetically more homogeneous than the other phenotypes tested. The results point to the importance of using diagnostic approaches that recognise this group of individuals. Our approach can be applied to similar data-sets for other psychiatric and non-psychiatric phenotypes.