Lynne J. Hocking

Summary: There is an urgent need for more effective and better-tolerated treatments for mental illness. Achieving this requires responsible collaboration between academia, healthcare professionals, industry and people with lived experience. However, regulatory and compliance frameworks can make early-stage, non-promotional collaboration difficult, particularly when discussions are exploratory and multidisciplinary. Drawing on experience from the UK Research and Innovation Mental Health Platform’s attempted collaboration with an international pharmaceutical company, this article describes how engagement was delayed and constrained by uncertainty over regulatory interpretation, contractual requirements and differing compliance processes across jurisdictions. These barriers risk excluding people with lived experience, narrowing scientific dialogue and reducing opportunities to align innovation with patient priorities. Robust regulation remains essential to protect patients, maintain public trust and prevent inappropriate influence. However, frameworks should better distinguish promotional activity from transparent, pre-competitive scientific exchange enabling proportionate, inclusive and responsible early dialogue between academia, healthcare, industry and patient communities.

NHS genetics centres in Scotland sought to investigate the Genomics England 100,000 Genomes Project diagnostic utility to evaluate genome sequencing for in rare, inherited conditions. Four regional services recruited 999 individuals from 394 families in 200 rare phenotype categories, with negative historic genetic testing. Genome sequencing was performed at Edinburgh Genomics, and phenotype and sequence data were transferred to Genomics England for variant calling, gene-based filtering and variant prioritisation. NHS Scotland genetics laboratories performed interpretation, validation and reporting. New diagnoses were made in 23% cases – 19% in genes implicated in disease at the time of variant prioritisation, and 4% from later review of additional genes. Diagnostic yield varied considerably between phenotype categories and was minimal in cases with prior exome testing. Genome sequencing with gene panel filtering and reporting achieved improved diagnostic yield over previous historic testing but not over now routine trio-exome sequence tests. Re-interpretation of genomic data with updated gene panels modestly improved diagnostic yield at minimal cost. However, to justify the additional costs of genome vs exome sequencing, efficient methods for analysis of structural variation will be required and / or cost of genome analysis and storage will need to decrease.

Co-creation describes a process of bringing customer and creator together to produce items of mutual value, allowing the design aspect of production to sit between customer and creator. A major step-change enabling such a process is the ability to link consumer design inspiration with pattern creation software linked to manufacturing equipment. One aspect of supporting such a process includes fashioning tractable methods to collect and manipulate "design inspiration" that a customer can input into and that are amenable to the application of computer-based production. Here we describe research in progress exploring the use of "data-driven designs" that challenge the existing visual bias of textile design, using the Scottish context as an example; and offer up a process by which these explorations can be transformed towards a customer-creator model: transforming bioacoustics data recorded from the soundscape into woven, knitted, printed or embroidered textiles.

The Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based population cohort with DNA, biological samples, socio-demographic, psychological and clinical data from approximately 24,000 adult volunteers across Scotland. Although data collection was cross-sectional, GS:SFHS became a prospective cohort due to of the ability to link to routine Electronic Health Record (EHR) data. Over 20,000 participants were selected for genotyping using a large genome-wide array. GS:SFHS was analysed using genome-wide association studies (GWAS) to test the effects of a large spectrum of variants, imputed using the Haplotype Research Consortium (HRC) dataset, on medically relevant traits measured directly or obtained from EHRs. The HRC dataset is the largest available haplotype reference panel for imputation of variants in populations of European ancestry and allows investigation of variants with low minor allele frequencies within the entire GS:SFHS genotyped cohort. Genome-wide associations were run on 20,032 individuals using both genotyped and HRC imputed data. We present results for a range of well-studied quantitative traits obtained from clinic visits and for serum urate measures obtained from data linkage to EHRs collected by the Scottish National Health Service. Results replicated known associations and additionally reveal novel findings, mainly with rare variants, validating the use of the HRC imputation panel. For example, we identified two new associations with fasting glucose at variants near to Y_RNA and WDR4 and four new associations with heart rate at SNPs within CSMD1 and ASPH, upstream of HTR1F and between PROKR2 and GPCPD1. All were driven by rare variants (minor allele frequencies in the range of 0.08–1%). Proof of principle for use of EHRs was verification of the highly significant association of urate levels with the well-established urate transporter SLC2A9. GS:SFHS provides genetic data on over 20,000 participants alongside a range of phenotypes as well as linkage to National Health Service laboratory and clinical records. We have shown that the combination of deeper genotype imputation and extended phenotype availability make GS:SFHS an attractive resource to carry out association studies to gain insight into the genetic architecture of complex traits.

Background: So far, more than 170 loci have been associated with circulating lipid levels through genome-wide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels. Methods: We used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ∼60 000 individuals in the discovery stage and ∼90 000 samples in the replication stage. Results: Our study resulted in the identification of five new associations with circulating lipid levels at four loci. All four loci are within genes that can be linked biologically to lipid metabolism. One of the variants, rs116843064, is a damaging missense variant within the ANGPTL4 gene. Conclusions: This study illustrates that GWAS with high-scale imputation may still help us unravel the biological mechanism behind circulating lipid levels.

Background Several regions of the genome have shown to be associated with COPD in genome-wide association studies of common variants. Objective To determine rare and potentially functional single nucleotide polymorphisms (SNPs) associated with the risk of COPD and severity of airflow limitation. Methods 3226 current or former smokers of European ancestry with lung function measures indicative of Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 COPD or worse were genotyped using an exome array. An analysis of risk of COPD was carried out using ever smoking controls (n=4784). Associations with %predicted FEV₁ were tested in cases. We followed-up signals of interest (p−5) in independent samples from a subset of the UK Biobank population and also undertook a more powerful discovery study by meta-analysing the exome array data and UK Biobank data for variants represented on both arrays. Results Among the associated variants were two in regions previously unreported for COPD; a low frequency non-synonymous SNP in MOCS3 (rs7269297, p_discovery=3.08×10⁻⁶, p_replication=0.019) and a rare SNP in IFIT3, which emerged in the meta-analysis (rs140549288, p_meta=8.56×10⁻⁶). In the meta-analysis of % predicted FEV₁ in cases, the strongest association was shown for a splice variant in a previously unreported region, SERPINA12 (rs140198372, p_meta=5.72×10⁻⁶). We also confirmed previously reported associations with COPD risk at MMP12, HHIP, GPR126 and CHRNA5. No associations in novel regions reached a stringent exome-wide significance threshold (p−7). Conclusions This study identified several associations with the risk of COPD and severity of airflow limitation, including novel regions MOCS3, IFIT3 and SERPINA12, which warrant further study.

Impairment of cognitive function is a common feature of many neurodevelopmental disorders. Systems genetics analysis in the brain uncovered a convergent gene network for both cognition and neurodevelopmental disorders. As the network does not recapitulate known pathways, this finding represents a new basis for understanding factors influencing normal and disordered cognition. Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease–associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.

Nature Communications 6, Article number: 7756 (2015); Published 4 August 2015; Updated 17 December 2015 In the Results section and in the legend of Table 1 of this Article, the company deCODE genetics, Inc. is incorrectly referred to as ‘Diabetes Epidemiology: collaborative analysis of Diagnostic criteria in Europe’.