Supplementation of a clinical trial by historical control data: is the prospect of dynamic borrowing an illusion?

There are strong arguments, ethical, logistical and financial, for supplementing the evidence from a new clinical trial using data from previous trials with similar control treatments. There is a consensus that historical information should be down-weighted or discounted relative to information from the new trial, but the determination of the appropriate degree of discounting is a major difficulty. The degree of discounting can be represented by a bias parameter with specified variance, but a comparison between the historical and new data gives only a poor estimate of this variance. Hence, if no strong assumption is made concerning its value (i.e. if 'dynamic borrowing' is practiced), there may be little or no gain from using the historical data, in either frequentist terms (type I error rate and power) or Bayesian terms (posterior distribution of the treatment effect). It is therefore best to compare the consequences of a range of assumptions. This paper presents a clear, simple graphical tool for doing so on the basis of the mean square error, and illustrates its use with historical data from clinical trials in amyotrophic lateral sclerosis. This approach makes it clear that different assumptions can lead to very different conclusions. External information can sometimes provide strong additional guidance, but different stakeholders may still make very different judgements concerning the appropriate degree of discounting. Copyright © 2016 John Wiley & Sons, Ltd.

Genome-wide association study of recurrent major depressive disorder in two European case-control cohorts.

Major depressive disorder (MDD) is a highly prevalent disorder with substantial heritability. Heritability has been shown to be substantial and higher in the variant of MDD characterized by recurrent episodes of depression. Genetic studies have thus far failed to identify clear and consistent evidence of genetic risk factors for MDD. We conducted a genome-wide association study (GWAS) in two independent datasets. The first GWAS was performed on 1022 recurrent MDD patients and 1000 controls genotyped on the Illumina 550 platform. The second was conducted on 492 recurrent MDD patients and 1052 controls selected from a population-based collection, genotyped on the Affymetrix 5.0 platform. Neither GWAS identified any SNP that achieved GWAS significance. We obtained imputed genotypes at the Illumina loci for the individuals genotyped on the Affymetrix platform, and performed a meta-analysis of the two GWASs for this common set of approximately half a million SNPs. The meta-analysis did not yield genome-wide significant results either. The results from our study suggest that SNPs with substantial odds ratio are unlikely to exist for MDD, at least in our datasets and among the relatively common SNPs genotyped or tagged by the half-million-loci arrays. Meta-analysis of larger datasets is warranted to identify SNPs with smaller effects or with rarer allele frequencies that contribute to the risk of MDD.

Diallel analysis for aluminium tolerance in tropical soybeans [Glycine max (L.) Merrill].

The soybean is a major crop in the agricultural systems of the Brazilian Cerrados (Savannahs), whose soils are acidic, devoid of nutrients and need to be amended before they are cultivated. However, below the ploughed layer there is a scarcity of nutrients and toxic aluminium (Al). These limit root growth, subsequently causing nutritional imbalance and drought stress. Our aim in the investigation described here was to identify genetic differences in the aluminium tolerance of soybeans by a 9 × 9 diallel cross among contrasting varieties grown in high-Al areas and in hydroponics. Combining ability analysis indicated predominantly additive gene effects, and the additive-dominance model explained most of the genetic differences in this germ plasm for mineral element absorption and root growth under aluminium stress. The relationship between the two factors suggest that conjugation hydroponics and field evaluations in breeding programmes would further improve soybeans with respect to yield stability under tropical cultivation conditions.

The use of ecogeographical data in the exploitation of variation from gene banks.

As the variation of species is known to be influenced both by ecological and geographical factors, data on the origin of a sample from a given species could be used to infer some of its genetic characteristics. This concept was examined in the context of gene banks, where the assembled diversity usually represents a large range of environments and geographic locations. Results suggest that, although ecological variables in the site of origin can be useful in predicting genetic characteristics in the samples, the use of such data is neither simple nor precise. On the other hand simple geographic data, irrespective of their ecological content, were found to offer an effective method of stratifying and sampling variation in germ plasm collections.