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Articles by N. Craddock
Total Records ( 2 ) for N. Craddock
  N. Craddock
 

We have arrived at our current descriptive classifications, with their many and varied array of categories, through the committee processes of DSM and ICD. To date, expert opinion, rather than solid science, has been the driver for change and this helps to explain the bewildering number of diagnostic categories and the fact that many patients meet criteria for several categories. Over the coming years, advances in neuroscience will offer the opportunity to base classification on robust evidence with diagnostic entities mapping more closely onto the workings of the brain. There are major shortcomings to the current classifications but all changes come at a cost to their users. We should be fully aware of the shortcomings and be thinking about the future. However, major changes to classification should await the emergence of robust empirical data and proven clinical utility. This will be the best way to benefit patients.

  M. L Hamshere , E. K Green , I. R Jones , L Jones , V Moskvina , G Kirov , D Grozeva , I Nikolov , D Vukcevic , S Caesar , K Gordon Smith , C Fraser , E Russell , G Breen , D St Clair , D. A Collier , A. H Young , I. N Ferrier , A Farmer , P McGuffin , Holmans Wellcome Trust Case Control Consortium , M. J Owen , M. C O'Donovan and N. Craddock
 

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 = 2.42x10–7). 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.

 
 
 
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