A previous analysis of 200,000 exome-sequenced UK Biobank participants using weighted burden analysis of rare, damaging variants failed to identify any genes associated with risk of affective disorder requiring specialist treatment. Exome-sequence data has now been made available for the remaining 270,000 participants and a two-stage process was applied in order to test for association in this second sample using only genes showing suggestive evidence for association in the first sample.

Cases were defined as participants who reported having seen a psychiatrist for ‘nerves, anxiety, tension or depression’. Exhaustive testing of the first sample was carried out using rare variant analyses informed by 45 different predictors of impact of nonsynonymous variants. The 100 genes showing the strongest evidence for association were then analysed in the second sample using the same predictor as had been most statistically significant in the first sample.

The results for the 100 nominated genes conformed closely with the null hypothesis, with none approaching statistical significance after correction for multiple testing.

Risk of common affective disorder, even if severe enough to warrant specialist referral, is not sufficiently impacted by effects of rare variants in a small enough number of genes that effects can be detected even with large sample sizes. Actionable results might be obtained with a more extreme phenotype but very significant resources would be required to achieve adequate power. This research has been conducted using the UK Biobank Resource.

Implementation of genome-scale sequencing in clinical care has significant challenges: the technology is highly dimensional with many kinds of potential results, results interpretation and delivery require expertise and coordination across multiple medical specialties, clinical utility may be uncertain, and there may be broader familial or societal implications beyond the individual participant. Transdisciplinary consortia and collaborative team science are well poised to address these challenges. However, understanding the complex web of organizational, institutional, physical, environmental, technologic, and other political and societal factors that influence the effectiveness of consortia is understudied. We describe our experience working in the Clinical Sequencing Evidence-Generating Research (CSER) consortium, a multi-institutional translational genomics consortium.

A key aspect of the CSER consortium was the juxtaposition of site-specific measures with the need to identify consensus measures related to clinical utility and to create a core set of harmonized measures. During this harmonization process, we sought to minimize participant burden, accommodate project-specific choices, and use validated measures that allow data sharing.

Identifying platforms to ensure swift communication between teams and management of materials and data were essential to our harmonization efforts. Funding agencies can help consortia by clarifying key study design elements across projects during the proposal preparation phase and by providing a framework for data sharing data across participating projects.

In summary, time and resources must be devoted to developing and implementing collaborative practices as preparatory work at the beginning of project timelines to improve the effectiveness of research consortia.