Methods to improve genetic understanding of cardiometabolic traits through multiple traits and diverse population studies

Lead Research Organisation: University of Cambridge
Department Name: MRC Biostatistics Unit

Abstract

There has been great success in identifying hundreds of genetic variants associated with a large spectrum of diseases and traits, but very few of these variants have an understood role in how they impact the trait. Moreover, a detected variant does not necessarily contribute to effects in the trait, since it may instead have a high correlation with the variant that causes the effect. There is substantial interest in understanding the underlying biology of genetic variants that have an impact on disease or disease-relevant measurements (e.g. cholesterol levels), since there is evidence that this could lead to better disease treatment and prevention. I am particularly interested in improving our knowledge of cardiometabolic diseases due to their high impact on society, as well as globally. Cardiovascular disease (CVD) caused almost one third of deaths worldwide in 2013 and accounted for 45% of all deaths in European countries in 2016, while cardiometabolic disorders are expected to have a greater burden than infectious diseases (e.g. HIV/AIDS) in developing countries.

Recent technological advances have made it possible to obtain hundreds of measurements related to metabolism and there is evidence that understanding the genetic influences on human metabolism could improve our understanding of cardiometabolic diseases, as well as inform strategies for modifying existing drugs to treat additional diseases. However, the genetic analysis of many traits is often tackled by one-by-one analyses of individual traits without considering any correlations between them. Instead I will develop a method that identifies associations between many traits with many genetic variants. There is a broad applicability of this method to any large set of traits so there is high potential for impact on diseases and traits beyond those that I will analyse in this fellowship. I will also develop methods that combine information from multiple traits to create sets of genetic variants that will contain the true causal variants with a certain probability. Joint analyses of multiple traits have been shown to result in more refined sets of potential causal variants, but such methods do not yet exist when there are overlapping individuals between the studies, a common situation; this is a gap in methods that I intend to fill. These methods will be applied to several unique datasets, such as hundreds of metabolomics measurements and cardiometabolic, anthropometric and blood-related measurements from both European and African ancestry populations.

Gains in the probability to detect associations between genetic variants and traits, as well as the construction of finer resolution sets of potential causal variants, are often likely when information from different ancestries are considered together. However, most methods for jointly analysing diverse ancestries encounter difficulties in the balance between combining the information across the populations to detect associated variants and losing population-specific effects. Instead, I will develop an adaptive analysis approach that is expected to achieve this balance and will also jointly consider multiple traits. At the moment, no methods exist to construct sets of potential causal variants for multiple traits and multiple ethnicities; considering multiple traits is known to give improvements, as does multiple ethnicities, but the two have not yet been combined. This is another void in the methodological toolbox that I plan to fill.

All methods will be freely available on-line in user-friendly software and I will also produce an on-line reference database of relationships that are found between the many metabolomics measurements. These are expected to be of wide-spread use to a wide spectrum of researchers from methodological to disease-specific.

Technical Summary

Many trait-associated genetic variants have been identified, but the underlying biological mechanisms behind the genetic effects are unknown and the lists of potential causal variants behind these effects need refinement. The molecular underpinnings of cardiometabolic diseases may be better understood by examining how metabolism is affected by genetic variation via the association analysis of biochemical measures, including metabolites. To improve detection power and fine-mapping, I will develop multi-trait methods, as well as trans-ethnic multi-trait methods, for which methods currently do not exist.

To meta-analyse across diverse ancestries, I will construct an adaptable approach to trans-ethnic meta-analysis that balances between combining effects over cohorts without the loss of ethnicity-specific signals. This involves the use of a measure for the degree of genetic architecture overlap at each SNP to partition the SNPs according to heterogeneity and then adapting two approaches: a high-powered trans-ethnic meta-regression approach to detect associations in the presence of allelic heterogeneity due to ancestry and a powerful meta-analysis method in a Bayesian framework.

Few methods are applicable to hundreds of traits, as from metabolomics assays. For such data, I will develop an analysis approach that merges factor analysis and multiple regression. For multi-trait fine-mapping, I propose a Bayesian approach that takes advantage of the relatedness between traits by assigning a higher prior probability to joint models with shared variants between the traits. I will also adapt this approach across diverse ethnicities to capitalise on the differences in linkage disequilibrium between them. Further improvements will be sought by integrating external biological data.

I will assess method performance via simulation studies and apply the methods to unique datasets to which the outcomes will assist in understanding the aetiology of cardiometabolic diseases.

Planned Impact

The proposed research will contribute to improvements in the quality of life and health and the generation of innovative output. Applications to unique cardiometabolic datasets will increase knowledge of these traits and the diseases that they influence, such as cardiovascular disease. In the longer term, the results may be used in developing treatments for cardiometabolic diseases. For example, the efficacious statin drugs for lowering low-density lipoprotein (LDL) cholesterol target a gene (HMGCR) that contains variants associated with LDL cholesterol [Kathiresan et al. 2008. Nat Gen 40:189-97].

Metabolites act as intermediate phenotypes for diseases that are associated with disruptions in metabolic processes and could functionally link genetic variation to disease predisposing factors and then to complex disease, the clinical end-point. Examples of metabolic traits that are known risk factors for CVD include blood triglyceride, cholesterol and bilirubin levels [Suhre et al., 2012, Nat Rev 13: 759-769]. In addition, many variants associated with metabolites are also associated with response to drug treatment. Variants in SLCO1B1 are associated with risk of statin-induced myopathy and metabolomics GWAS revealed that these variants are also associated with a series of fatty acids. In turn, measurements of these fatty acids in biochemical assays may assist in the redesign of the appropriate drugs [Suhre et al., 2012]. This emphasises the importance of investigating all metabolites and not only known disease risk factors, as this may lead to the discovery of new biological processes or pathways that may be involved or disrupted in disease aetiology. The on-line reference database that I will construct has potential for high impact as a tool to explore metabolite relationships.

The factor analysis approach for the dimension reduction of many correlated traits may also be applied to many diseases to explore the underlying factors that link them. The proposed fine-mapping (FM) approaches are anticipated to lead to smaller sets of SNPs for follow-up, which will imply lower cost and less lab time in following up variants.

Besides trans-ethnic fine-mapping to identify potential causal variants in loci that are shared between ethnicities, identification of associations and fine-mapping within the individual populations is of interest. For instance, in African-Americans, as well as Nigerian Yorubans, APOL1 was identified as a risk locus for chronic kidney disease, which is an independent risk factor for CAD development [Adebamowo et al. 2017. Public Health Genomics. 20:9-16]. However, the association between APOL1 and CAD is not well-understood as one study has shown an association between APOL1 nephropathy variants and lower levels of subclinical atherosclerosis in diabetic African-Americans [Freedman et al. 2015], while another study showed the opposite direction of effect [Mukamal et al. 2016. Arter Thromb Vasc Biol. 36:398-403]. Metabolic traits may assist in better understanding the genetic effect of APOL1 on CAD and I will investigate this in the Ugandan cohort.

The corresponding software for all methods will be freely available on-line in a form that is easily accessible by the scientific community, regardless of statistical and/or computing expertise. The wide applicability of the methods to numerous diseases/traits enhances the potential for further research outcomes beyond the analyses that my research associate and I will carry out. Release of the source code will facilitate innovative output through further methodological developments.

Finally, my research associate will develop experience in the analysis of large-scale unique datasets and use of intensive computing. Experience and knowledge in metabolic and cardiovascular disease will also be expanded for my RA and myself.
 
Description Assessing the genetic impact of metabolic syndrome in Sub-Sahara Africa
Amount £19,970 (GBP)
Funding ID G101892 
Organisation Alborada Trust 
Sector Charity/Non Profit
Country United Kingdom
Start 02/2021 
End 12/2022
 
Description Environment-adjusted genetic analysis methods for cardiometabolic traits in African populations
Amount £500,913 (GBP)
Funding ID MR/W02098X/1 
Organisation Medical Research Council (MRC) 
Sector Public
Country United Kingdom
Start 04/2022 
End 04/2025
 
Title Supplementary Data for multi-group fine-mapping of GLGC lipids traits in five population groups 
Description This Supplementary Data contains four Supplementary Data Tables that gives details on our fine-mapping results for the Global Lipids Genetics Consortium (GLGC). This includes region definitions, 99% credible set sizes, prioritised variants, and comparisons of our fine-mapping results with those of GLGC. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
Impact These data provide lists of high-confidence causal variants for four lipids traits in five population groups (African-like, East Asian-like, European-like, Hispanic-like, South Asian-like) from the Global Lipids Genetics Consortium (GLGC). Such results may be used in downstream functional validation experiments. 
URL https://springernature.figshare.com/articles/dataset/Supplementary_Data_for_multi-group_fine-mapping...
 
Title Supplementary Data for multi-group fine-mapping of GLGC lipids traits in five population groups 
Description This Supplementary Data contains four Supplementary Data Tables that gives details on our fine-mapping results for the Global Lipids Genetics Consortium (GLGC). This includes region definitions, 99% credible set sizes, prioritised variants, and comparisons of our fine-mapping results with those of GLGC. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://springernature.figshare.com/articles/dataset/Supplementary_Data_for_multi-group_fine-mapping...
 
Description A Personalised Medicine Approach to Diagnosis and Treatment In Inherited Cardiovascular Diseases Utilising -Omic Technologies 
Organisation University College London
Country United Kingdom 
Sector Academic/University 
PI Contribution Prof Perry Elliott's team, with others, had developed a tool for sudden cardiac death risk prediction which has been incorporated into European Guidelines. I am contributing by developing an alternative prediction tool that accounts for genetic data, and is in a Bayesian variable selection framework. We also have plans to re-purpose my recent multiple outcome fine-mapping methods for a prediction tool involving multiple inherited cardiovascular diseases.
Collaborator Contribution Prof Perry Elliott's team, and others, are collecting large data resources, preparing the data for analysis, and giving guidance on appropriate covariates to consider when building the model for the prediction tool.
Impact in early stages
Start Year 2020
 
Description Multi-trait fine-mapping (of many traits) in a large UK study of blood cell traits 
Organisation University of Cambridge
Department Cardiovascular Epidemiology Unit
Country United Kingdom 
Sector Academic/University 
PI Contribution Initial plans for this project were to apply a multi-trait fine-mapping method (flashfm, developed by my team) to identify likely causal variants in regions that have genetic associations with 33 blood cell traits from ~50,000 UK blood donors (INTERVAL study), as shared by Adam Butterworth. This collaboration has also expanded to include Will Astle from BSU, who has previously led on genetic analyses of this data and is an expert on blood cell traits. We have expanded this to include an additional 66 blood cell traits (from sysmex blood analyser) from INTERVAL, and are making use of my team's dimension reduction approach to efficiently analyse this large number of traits and reveal shared genetic variants between traits.
Collaborator Contribution Adam Butterworth has shared the INTERVAL data with my team. Adam Butterworth and Will Astle have given their insights on the relationships between the different blood cell traits and on the shared high-confidence genetic variants that we have identified for various traits.
Impact This is in progress with a manuscript to be submitted soon. This collaboration is multi-disciplinary, involving methodology expertise from my team and blood cell trait expertise from Adam Butterworth and Will Astle.
Start Year 2019
 
Description Multi-trait fine-mapping for cardiometabolic traits in Uganda 
Organisation University of Cambridge
Department Department of Medicine
Country United Kingdom 
Sector Academic/University 
PI Contribution I developed flashfm (flexible and shared information fine-mapping), a new approach to multi-trait fine-mapping that only needs summary statistics and is efficient for more than two traits. My research associate, Nicolas Hernandez, has applied this to 33 cardiometabolic traits from a Uganda study (largest genetic asociation study from a single African population and led by Dr. Manj Sandhu).
Collaborator Contribution Manj Sandhu (now at Imperial College London) has shared data from the Uganda study with my team. Members of his team pre-processed and imputed the data. Interpretation of results was done together between us and our teams.
Impact The project is complete and is now published in Nature Communications. In fine-mapping signals from 33 cardiometabolic traits in a Ugandan cohort, flashfm improved resolution over that of single-trait fine-mapping; flashfm resulted in an average SNP group size reduction of 29% in 34% of the regions that had signals for at least two traits, compared to single-trait fine-mapping.The results from this analysis will contribute to a better understanding of cardiometabolic genetics in an African population. My research associate, Nicolas Hernandez, presented a poster on this work at the 2021 European Society of Human Genetics annual meeting.
Start Year 2019
 
Description Multi-trait fine-mapping for glycaemic traits in five ancestral populations 
Organisation University of Exeter
Department Medical School
Country United Kingdom 
Sector Academic/University 
PI Contribution I have developed a multi-trait fine-mapping method (flashfm) to identify likely causal variants in regions that have genetic associations with multiple quantitiave traits. I have given guidance on the application of this method and had shared an early version of the software with Inês Barroso's team in Exeter who have applied it to their glycaemic traits data (MAGIC - the Meta-Analyses of Glucose and Insulin-related traits Consortium) in Exeter.
Collaborator Contribution Inês Barroso's team have applied my method to their glycaemic trait data that they have acquired through the Meta-Analyses of Glucose and Insulin-related traits Consortium. We have discussed extending flashfm to incorporate functional annotation information, which they have implemented and demonstrated that this gives further improvements in prioritisation of likely causal variants.
Impact Initially, flashfm was applied to glycaemic traits as a summer project to a MSc student, Jana Soenksen, at Exeter. She had written her report, with some guidance from me, and continued as a PhD student there. A component of her dissertation is based on this work, following up the findings in the lab, and we will also submit a paper on these findings. Jana has presented this work at the American Diabetes Association annual meetings and at the American Society of Human Genetics annual meetings. This collaboration is multi-disciplinary, involving methodology expertise from my team and metabolic disease expertise from the team of Ines Barroso in Exeter.
Start Year 2020
 
Description Multi-trait fine-mapping in 125,000 individuals of African ancestry 
Organisation MRC/UVRI Uganda Research Unit on AIDS
Country Uganda 
Sector Public 
PI Contribution I gave a tutorial on my recently developed multi-trait fine-mapping method, flashfm, to the research group of Segun Fatumo and Tinashe Chikowore. My research group and I have also given guidance to three of his post-doctoral fellows on its application and interpretation of its results, as well as optimal visualisations of the results. One of my group members also ran analyses and summarised results.
Collaborator Contribution Their research group members have acquired and processed the African ancestry data, run meta-analyses and multi-trait analysis approaches. They have also given insights on interpretation of results.
Impact This collaboration is multi-disciplinary, involving methodology expertise from my team and cardiometabolic disease expertise from the teams of Segun Fatumo and Tinashe Chikowore. The manuscript resulting from this collaboration was published in Nature Communications, 2023 (Multi-trait discovery and fine-mapping of lipid loci in 125,000 individuals of African ancestry). This resulted in a mean 18% reduction in credible set size and revealed potential causal variants not detected by single-trait fine-mapping. This paper was extremely popular on the social media platform X, with more than 200 likes and 80 re-posts, but unfortunately this is not reflected in the Altmetric score (https://twitter.com/SFatumo/status/1699025066556604908).
Start Year 2020
 
Title MFM: Multinomial Fine-Mapping 
Description MFM is an R package for simultaneous fine-mapping of genetic associations for several diseases, in a Bayesian framework that borrows information between the diseases. 
Type Of Technology Software 
Year Produced 2019 
Open Source License? Yes  
Impact In simulation studies MFM was found to have greater accuracy than single disease analyses, when there are shared causal variants, and negligible loss of precision otherwise. MFM was applied to data from six autoimmune diseases (type 1 diabetes, multiple sclerosis, autoimmune thyroid disease, rheumatoid arthritis, juvenile idiopathic arthritis, celiac disease) and revealed causal variants undetected in individual disease analysis, including in IL2RA where we confirm functional effects of multiple causal variants using allele-specific expression in sorted CD4+ T cells from genotype-selected individuals. MFM has the potential to increase fine-mapping resolution in related diseases enabling the identification of associated cellular and molecular phenotypes. 
URL https://www.biorxiv.org/content/10.1101/553560v1
 
Title MFMextra: Specific analyses for MFM package 
Description This R package provides the simulation functions used to assess the joint fine-mapping methods of MFM. In particular, it provides the tools to generate phenotype and genotype data for two diseases with shared controls. 
Type Of Technology Software 
Year Produced 2019 
Open Source License? Yes  
Impact It was used to evaluate MFM and show that MFM has more accuracy than single disease analyses. It has potential for an impact on method development as it could be used to assess other methods for the anlaysis of disease with shared controls. 
URL https://www.biorxiv.org/content/10.1101/553560v1
 
Title flashfm-ivis - interactive visualisation of fine-mapping results 
Description flashfm-ivis provides a suite of interactive visualisation plots to view potential causal genetic variants that underlie associations that are shared or distinct between multiple quantitative traits and compares results between single- and multi-trait fine-mapping. Unique features include network diagrams that show joint effects between variants for each trait and regional association plots that inte-grate fine-mapping results, all with user-controlled zoom features for an interactive exploration of potential causal variants across traits. An attractive feature of this tool is that its web implementation does not require any programming experience - users just upload their files and everything is point and click. It is also available as a downloadable R package for those who prefer to use it directly on their machine. A paper on this has been published in Bioinformatics (https://doi.org/10.1093/bioinformatics/btac453) 
Type Of Technology Webtool/Application 
Year Produced 2022 
Open Source License? Yes  
Impact In our analysis of 125,000 individuals of African genetic ancestry, we used this software to help in interpretations of results and to generate publication-ready figures - this work was published in Nature Communications (https://doi.org/10.1038/s41467-023-41271-0) 
URL http://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/
 
Title flashfm: flexible and shared information fine-mapping 
Description Flashfm (flexible and shared information fine-mapping) is a package to simultaneously fine-map genetic associations (select potential causal variants) for multiple quantitative traits that are measured in the same study(s) by sharing information between the traits. It is flexible to the inclusion of related individuals in the sample and to missing trait measurements. There are no other existing methods that are able to fine-map assocaitions from multiple quantitative traits, allowing for different causal variants between the triats. 
Type Of Technology Software 
Year Produced 2021 
Open Source License? Yes  
Impact In the main paper introducing this method, published in Nature Communications (https://doi.org/10.1038/s41467-021-26364-y), flashfm is applied to 33 cardiometabolic traits from a Ugandan cohort. Simulations demonstrate that flashfm gives higher resolution than single-trait fine-mapping, and we also find that this is true in our real data analysis. In a region that is harbours genes APOE and TOMM40, there are four lipid traits with association signals and the use of flashfm reduced two of the potential sets of causal variants by 50% and 25% of that obtained from single-trait fine-mapping. The impact of this is fewer SNPs that would need to be followed up as potential causal variants for cardiometabolic traits. Flashfm has also been applied to glycaemic traits (MAGIC study) by researchers in Exeter (details in collaboration section) and to cardiometabolic traits in several African cohorts by researchers from Uganda (details in collaboration section). All of these applications will lead to further contributions in the understanding of cardiometabolic disease in both European and African ancestry populations. 2024 updates: (i) Flashfm is in use by at least seven research groups from four countries and was a featured case study in the MRC Biostatistics Unit's (BSU) progress review for the QQR. (ii) Flashfm was used to identify credible causal variants for clozapine (antipsychotic drug) metabolism. It helped to refine lists of likely causal variants, compared to single-trait fine-mapping, and to identify variants that were putatively causal for multiple phenotypes. (described in Appendix 1 of Pardinas et al. (2023). "Pharmacokinetics and pharmacogenomics of clozapine in an ancestrally diverse sample: a longitudinal analysis and genome-wide association study using UK clinical monitoring data". The Lancet Psychiatry https://doi.org/10.1016/S2215-0366(23)00002-0) (iii) Flashfm is in use by my collaborators in Exeter and we have extended it to integrate functional annotation, which further improves resolution in their application to glycaemic traits (see collaboration section). 
URL https://jennasimit.github.io/flashfm/
 
Title flashfmZoom - joint fine-mapping and exploration of GWAS results in the UK Biobank 
Description flashfmZoom is an all-in-one tool for analysis and interactive visualisation of potential causal genetic variants that underlie associations with quantitative traits from the UK Biobank. It offers a user-friendly interface and guides users in the selection of pleiotropic regions among subsets of 134 quantitative traits, such as cardiometabolic, hematologic, and respiratory traits. Users may then run single-trait fine-mapping, allowing for multiple causal variants, and leverage information between the traits using multi-trait fine-mapping to improve resolution. A series of interactive plots and downloadable tables are generated within flashfmZoom to identify potential causal variants that are shared or distinct between the traits; it also lists relevant literature for the traits and/or variants. 
Type Of Technology Webtool/Application 
Year Produced 2023 
Open Source License? Yes  
Impact Too early to say, as we are about to submit the preprint and manuscript for publication. However, we have already identified some interesting combinations of traits and results. This emphasises that besides exploring traits that are well-known to be related, flashfmZoom encourages interactive exploration for the joint analysis of traits that may not often be considered together. This may reveal common aetiological pathways between traits related to different disorders. 
URL https://github.com/fz-cambridge/flashfmZoom
 
Title jennasimit/MGflashfm: MGflashfm 
Description This version includes: wrapper functions to run multi-group multi-trait fine-mapping with single-trait fine-mapping and flexible versions that allow for single-trait fine-mapping input from an alternative method wrapper functions to run multi-group fine-mapping with single-trait fine-mapping and flexible versions that allow for single-trait fine-mapping input from an alternative method a single-trait fine-mapping approach that adjusts the maximum number of causal variants new flashfm (multi-trait fine-mapping) wrapper function that runs with a single-trait fine-mapping approach that adjusts the maximum number of causal variants 
Type Of Technology Software 
Year Produced 2023 
Open Source License? Yes  
Impact A paper introducing the statistical methods within this software package has been published in Nature Communications (https://doi.org/10.1038/s41467-023-43159-5). It is already in use by several research groups. In our own analyis of lipids traits from five population groups (Global Lipids Genetic Consortium; GLGC) we showed that MGflashfm allows refinement of lists of likely casual variants by joint analysis of multiple traits and prioritisation of genetic variants that are not present in all population groups (a limitation of current multi-group methods). 
URL https://zenodo.org/record/7974535
 
Description 50th European Mathematical Genetics Meeting 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Jennifer Asimit led the scientific organising committee for the 50th European Mathematical Genetics Meeting hosted by the BSU in April 2022, with 180 delegates from across academia and industry.
Year(s) Of Engagement Activity 2022
 
Description A day in the life blog post - Nicolas Hernandez 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact 'A day in the life' blog post for the BSU website written by Research Associate, Nicolas Hernandez, giving audiences a glimpse into his role at the BSU.
Year(s) Of Engagement Activity 2021
URL https://www.mrc-bsu.cam.ac.uk/blog/a-day-in-the-life-of-a-statistician-nicolas-hernandez/
 
Description Big Biology Day science festival 2023 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Public/other audiences
Results and Impact Participation in Big Biology Day 2023 - presenting bespoke hands-on activity based on statistical research taking place at the BSU.
Year(s) Of Engagement Activity 2023
 
Description Blog for general audience on MGflashfm publication 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact After publication of a new statistical method, MGflashfm, in Nature Communications, I wrote a general (non-scientific) summary on it for a blog on the MRC Biostatistics Unit's News page. To reach a larger audience, I circulated the blog on twitter (re-tweeted from the MRC Biostatistics Unit). The tweet of the publication generated nearly 16,000 impressions and over 700 engagements, including over 100 likes and nearly 40 re-posts; the altmetric score for this paper is currently 25. I have received a few emails asking questions on the use of the software, so i am aware of others using the software, who are outside of my network.
Year(s) Of Engagement Activity 2023
URL https://www.mrc-bsu.cam.ac.uk/blog/researchers-develop-first-multi-trait-multi-group-fine-mapping-me...
 
Description Blog for general audience on flashfm publication 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact After publication of a new method (flashfm) in Nature Communications, I wrote a general (non-scientific) summary on it for a blog on the MRC Biostatistics Unit's News page - it was also featured in the University of Cambridge's School of Clinical Medicine newsletter. To reach a larger audience, I circulated the blog on twitter (re-tweeted from the MRC Biostatistics Unit) and see that my tweet generated1600 impressions and 66 engagements (this does not include engagements from the tweet sent by the Unit).

I have received a few emails asking questions on the use of the software for this method, so i am aware of others using the method, who are outside of my network.
Year(s) Of Engagement Activity 2021
URL https://www.mrc-bsu.cam.ac.uk/blog/new-approach-to-pinpointing-genetic-variants-behind-diseases/
 
Description Blog for general audience on flashfm-ivis publication 
Form Of Engagement Activity Engagement focused website, blog or social media channel
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact After publication of a new web tool (flashfm-ivis) in Bioinformatics, I wrote a general (non-scientific) summary on it for a blog on the MRC Biostatistics Unit's News page. To reach a larger audience, I circulated the blog on twitter (re-tweeted from the MRC Biostatistics Unit). The tweet of the publication generated 6319 impressions and 289 engagements.
I have received a few emails asking questions on the use of the associated flashfm software, as well as this interactive visualisation tool flashfm-ivis, so i am aware of others using the software, who are outside of my network.
Year(s) Of Engagement Activity 2022
URL https://www.mrc-bsu.cam.ac.uk/blog/plot-and-play-a-new-tool-to-explore-statistical-evidence-for-gene...
 
Description Cambridge Science Fesitval 2019 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Public/other audiences
Results and Impact Participation in Cambridge Science Festival 2019 - presenting bespoke hands-on activity based on statistical research taking place at the BSU.
Year(s) Of Engagement Activity 2019
 
Description Invited Mentoring of Trainees (IGES conference) 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Postgraduate students
Results and Impact I was invited as a mentor to co-host a 1-hour session on Research and Grants during the annual International Genetic Epidemiology Society (IGES) meeting - this was via Zoom and was aimed at trainees (postgraduate and early postdoctoral); the session title was "Mentoring Session #1". We had five attendees. I gave a brief description of my research and gave advice on how to prepare grant applications for fellowships. This was a very interactive session and at the end the attendees said that they felt better prepared to write their grants.
Year(s) Of Engagement Activity 2021
URL https://pheedloop.com/IGES2021/site/schedule/
 
Description NIHR Biomedical Research Centre (BRC) annual theme meeting 2023 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Other audiences
Results and Impact This was an internal University meeting that was attended by theme co-leads, research administrators and finance, and one PI from our BSU and two other university units. An early career researcher from each of the three units was also selected to present translational research. For BSU, Feng Zhou (RA under my CDA funding) was selected to present our recently published multi-trait multi-group fine-mapping method (MGflashfm, Nature Communications 2023).

Our results on two genetic regions that are drug targets for cardiovascular disease were highlighted, which demonstrated multiple causal variants that are shared between genetic ancestry groups and also causal variants that are ancestry-specific (being polymorphic in only some genetic ancestry groups). This sparked a discussion and plenty of positive feedback, as MGflashfm is the first multi-trait multi-group fine-mapping method.
Year(s) Of Engagement Activity 2023
 
Description Quinquennial Review (QQR) of the MRC Biostatistics Unit 2023 
Form Of Engagement Activity Participation in an open day or visit at my research institution
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact I am a member of the senior leadership team for the Causal Mechanisms (CM) research theme in our Unit and was part of the CM discussion panel at the Unit's QQR. In our session, I responded to questions from the assessment panel regarding research in the Unit, particularly my plans for additional multi-ancestry methods development and large-scale analyses with collaborators.

As an example of research in the Unit, Feng Zhou (RA hired under my CDA funding) presented a poster to the assessment panel on a user-friendly interactive web tool that allows researchers with little programming experience to explore genetic associations of traits within UK Biobank and to run their own multi-trait analyses (flashfm method developed in my group) to prioritise likely causal variants that are shared and distinct between traits. Both the method and initiative to make such analyses easily accessible to a wider range of researchers were of high interest.

Within the Unit's QQR progress review, my flashfm multi-trait fine-mapping method and its applications was a featured case study. Working towards increasing health equity for understudied populations from Africa, and with collaborators from Africa, we conducted joint multi-trait fine-mapping with flashfm of lipids traits among 125,000 individuals of African ancestry. This resulted in a mean 18% reduction in credible set size and revealed potential causal variants not detected by single-trait fine-mapping. Our interpretations and plots in this paper were simplified by our flashfm-ivis R shiny app (Zhou Bioinfor 2022), which enables interactive visualisations of flashfm results and may be used by professionals with little programming experience. Despite initial publication in only 2021, flashfm is already in use by at least seven research groups from four countries, including Barroso (Exeter) for the joint analysis of glycaemic traits (Soenksen Diab 2021).
Year(s) Of Engagement Activity 2023