Chris Simon Haley

Background While genome-wide association studies (GWAS) hold great promise for unravelling disease pathophysiology, the translation of disease-associated genetic loci into clinically actionable information remains a challenge. Mendelian randomisation (MR), using expressed proteins as exposures and disease as an outcome, stands as a powerful analytical approach for leveraging GWAS data to identify potential drug-targets—at scale—in a data-driven manner. Cardiovascular disease (CVD) is a major health burden worldwide, and therefore is an important outcome for which to establish and prioritise potential therapeutic targets. Methods In this study, we utilised generalised summary-data-based MR (GSMR) with novel mass-spectrometry-based isoform-specific protein groups measured from peripheral-blood mononuclear cell (PBMC) obtained from Generation Scotland and antibody-based plasma protein measures from UK Biobank as exposures, and two CVD and three CVD-related risk-factors from UK Biobank as outcomes. Further, we used colocalisation to assess support for a shared causal variant between the proteins and the disease outcomes providing further evidence supporting a causal link. Results We evaluate expression of 5,114 isoform-specific protein groups in PBMCs from 862 individuals. GSMR analysis, using this data, found 16 putative causal proteins across three of the CVD/CVD-related risk-factors with seven supported by colocalisation analysis. Within the plasma GSMR analysis, 761 putative causal proteins were identified, of which 145 were supported by colocalisation. In addition, we go on to examine enrichment amongst the results and find enrichment of pathways which relate to cholesterol metabolism and platelet function. There was an overlap of three proteins between significant GSMR results in PBMCs and plasma, with two proteins (COMT and SWAP70) identifying opposite directions of effect of the relevant outcome, and one identifying a concordant direction of effect (HLA-DRA). Discussion This study identifies a number of proteins and pathways that may be involved in CVD pathogenesis. It also demonstrates the importance of the location of protein measurement and the methods by which it is quantified. Our research contributes to ongoing efforts to bridge the gap between genotype and phenotype.

A commercial breeding programme in Atlantic salmon utilises a four-year generation interval with four parallel breeding populations. In this study, we develop a computer simulation of a salmon breeding programme and explore the impact of gene flow between the parallel year groups on the accuracy of genomic prediction within and between breeding lines. We simulated four parallel lines for 10 discrete generations with random selection and different mixing rates between parallel year groups. The genetic distance between fish (as a measure of diversity) and the accuracy of estimated genomic breeding values were used as criteria of comparison. With no mixing the genetic distance increased between populations, the genetic variation within populations decreased and there was no increase in accuracy when combining data across populations. Even a low percentage of mixing decreased the genetic distance between populations and increased the genetic variation within populations. The higher the percentage of mixing the faster the lines became more similar. The accuracy of prediction climbed as the percentage of mixing increased. The increase in accuracy from the combined evaluation approach compared to the within evaluation approach was greater with an increased percentage of mixing. In conclusion, if there is no gene flow between populations the lines drift apart and there is no value in combining information across populations for genomic breeding value prediction. Only a low amount of mixing between lines brings the lines closer together and facilitates the use of information across lines to improve breeding value prediction. Optimising gene flow between lines should be an integral part of salmon breeding programme design.

Background: Cross-species comparison of transcriptomes is important for elucidating evolutionary molecular mechanisms underpinning phenotypic variation between and within species, yet to date it has been essentially limited to model organisms with relatively small sample sizes. Results: Here, we systematically analyze and compare 10,830 and 4866 publicly available RNA-seq samples in humans and cattle, respectively, representing 20 common tissues. Focusing on 17,315 orthologous genes, we demonstrate that mean/median gene expression, inter-individual variation of expression, expression quantitative trait loci, and gene co-expression networks are generally conserved between humans and cattle. By examining large-scale genome-wide association studies for 46 human traits (average n = 327,973) and 45 cattle traits (average n = 24,635), we reveal that the heritability of complex traits in both species is significantly more enriched in transcriptionally conserved than diverged genes across tissues. Conclusions: In summary, our study provides a comprehensive comparison of transcriptomes between humans and cattle, which might help decipher the genetic and evolutionary basis of complex traits in both species.

Background: Single-step genomic best linear unbiased prediction (ssGBLUP) allows the inclusion of information from genotyped and ungenotyped individuals in a single analysis. This avoids the need to genotype all candidates with the potential benefit of reducing overall costs. The aim of this study was to assess the effect of genotyping strategies, the proportion of genotyped candidates and the genotyping criterion to rank candidates to be genotyped, when using ssGBLUP evaluation. A simulation study was carried out assuming selection over several discrete generations where a proportion of the candidates were genotyped and evaluation was done using ssGBLUP. The scenarios compared were: (i) three genotyping strategies defined by their protocol for choosing candidates to be genotyped (RANDOM: candidates were chosen at random; TOP: candidates with the best genotyping criterion were genotyped; and EXTREME: candidates with the best and worse criterion were genotyped); (ii) eight proportions of genotyped candidates ( p ); and (iii) two genotyping criteria to rank candidates to be genotyped (candidates’ own phenotype or estimated breeding values). The criteria of the comparison were the cumulated gain and reliability of the genomic estimated breeding values (GEBV). Results: The genotyping strategy with the greatest cumulated gain was TOP followed by RANDOM, with EXTREME behaving as RANDOM at low p and as TOP with high p . However, the reliability of GEBV was higher with RANDOM than with TOP. This disparity between the trend of the gain and the reliability is due to the TOP scheme genotyping the candidates with the greater chances of being selected. The extra gain obtained with TOP increases when the accuracy of the selection criterion to rank candidates to be genotyped increases. Conclusions: The best strategy to maximise genetic gain when only a proportion of the candidates are to be genotyped is TOP, since it prioritises the genotyping of candidates which are more likely to be selected. However, the strategy with the greatest GEBV reliability does not achieve the largest gain, thus reliability cannot be considered as an absolute and sufficient criterion for determining the scheme which maximises genetic gain.

Variation in obesity-related traits has a genetic basis with heritabilities between 40 and 70%. While the global obesity pandemic is usually associated with environmental changes related to lifestyle and socioeconomic changes, most genetic studies do not include all relevant environmental covariates, so the genetic contribution to variation in obesity-related traits cannot be accurately assessed. Some studies have described interactions between a few individual genes linked to obesity and environmental variables but there is no agreement on their total contribution to differences between individuals. Here we compared self-reported smoking data and a methylation-based proxy to explore the effect of smoking and genome-by-smoking interactions on obesity related traits from a genome-wide perspective to estimate the amount of variance they explain. Our results indicate that exploiting omic measures can improve models for complex traits such as obesity and can be used as a substitute for, or jointly with, environmental records to better understand causes of disease.

STratifying Resilience and Depression Longitudinally (STRADL) is a population-based study built on the Generation Scotland: Scottish Family Health Study (GS:SFHS) resource. The aim of STRADL is to subtype major depressive disorder (MDD) on the basis of its aetiology, using detailed clinical, cognitive, and brain imaging assessments. The GS:SFHS provides an important opportunity to study complex gene-environment interactions, incorporating linkage to existing datasets and inclusion of early-life variables for two longitudinal birth cohorts. Specifically, data collection in STRADL included: socio-economic and lifestyle variables; physical measures; questionnaire data that assesses resilience, early-life adversity, personality, psychological health, and lifetime history of mood disorder; laboratory samples; cognitive tests; and brain magnetic resonance imaging. Some of the questionnaire and cognitive data were first assessed at the GS:SFHS baseline assessment between 2006-2011, thus providing longitudinal measures relevant to the study of depression, psychological resilience, and cognition. In addition, routinely collected historic NHS data and early-life variables are linked to STRADL data, further providing opportunities for longitudinal analysis. Recruitment has been completed and we consented and tested 1,188 participants.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22411-w

Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to create eleven omics ageing clocks, with correlations of 0.45-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 94% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.46/0.45 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.