M. McCarthy

Markedly divergent performance of variant annotation methods for gene-level association testing

Machine learning-based annotation methods are increasingly used to assess the pathogenicity of genetic variants, but their performance at prioritizing variants for gene-level association testing remains poorly characterized. Here, to better understand and optimize for this use case, we assess variant annotations from five methods — CADD v1.6, CADD v1.7, AlphaMissense, ESM-1b, and GPN-MSA — as the basis of four primary gene-based tests and six annotation-level aggregation tests across 14 quantitative traits measured in up to 350,377 UK Biobank participants. Using a novel framework based on optimal transport, we quantify test calibration and power as relative measurements of how methods partition signal across labeled variant sets. These metrics reveal discordant performance characteristics across annotation methods: tests using CADD labels achieved the highest signal separation, while tests using AlphaMissense labels had the lowest calibration. Meanwhile, hits from tests using GPN-MSA labels were strongly enriched among genes with high evolutionary constraint (up to 5.8-fold, versus 2.1- to 2.8-fold using other methods), underscoring variability in how annotation methods assess pathogenicity across the genome. These differences in variant prioritization measurably shifted patterns of genetic discovery, even as tests had low genomic inflation and similar replication rates. Taken together, our results show that (1) no single combination of annotation method and statistical test is currently optimal for this task, (2) results from different methods and tests can and should be aggregated to increase study power, and (3) integrating the information content of variant and gene annotations remains a key open problem for rare variant association testing.

Longitudinal dynamics of gene expression and metabolomics in an aging population cohort

Genome-wide association study of idiopathic pulmonary fibrosis susceptibility using clinically curated European ancestry datasets

Background Idiopathic pulmonary fibrosis (IPF) is a rare, incurable lung disease with a median survival of 3-5 years after diagnosis. Treatment options are limited. Genetic association studies can identify new genes involved in disease that might represent potential new drug targets, and it has been shown that drug targets with support from genetic studies are more likely to be successful in clinical development. Previous genome-wide association studies (GWAS) of IPF susceptibility have identified more than 20 signals implicating genes involved in multiple mechanisms, including telomere dysfunction, cell-cell adhesion, host defence immunity, various signalling pathways and, more recently, mitotic spindle assembly complex. Aim To leverage new datasets and genotype imputation to discover further genes involved in development of IPF that could yield new pathobiological avenues for exploration and to guide future drug target discovery. Methods We conducted a GWAS of IPF susceptibility including seven IPF case-control studies comprising 5,159 IPF cases and 27,459 controls of European ancestry, where IPF diagnosis was made by a respiratory clinician according to international guidelines. Genotypes were obtained from Whole Genome Sequencing (WGS) or from array-based imputation to the TOPMed WGS reference panel. New signals were replicated in independent biobanks with IPF defined using Electronic Healthcare Records. Bayesian fine-mapping was performed to identify the most likely causal variant(s) and bioinformatic investigation undertaken to map associated variants to putative causal genes. Results We identified three novel genetic signals of association with IPF susceptibility. Genes prioritised by functional evidence at these signals included MUC1 , which encodes a large transmembrane glycoprotein and known biomarker of lung fibrosis, and NTN4 encoding Netrin-4 whose known roles include angiogenesis. The third signal may map to SLC6A6 , a taurine and beta-alanine transporter gene, previously implicated in retinal, cardiac and kidney dysfunction. Conclusion Our study has identified new associations not previously identified by previous large biobank-based studies thereby highlighting the value of utilising clinically-curated IPF case-control studies, and new genotype imputation. We present new evidence for disease-driving roles of MUC1 and of endothelial cell and vascular changes in IPF.

Genomic analyses implicate hormonal and metabolic dysregulation in polycystic ovary syndrome

Type 2 diabetes risk alleles in peptidyl-glycine alpha-amidating monooxygenase influence GLP-1 levels and response to GLP-1 receptor agonists

A biological-systems-based analysis using proteomic and metabolic network inference reveals mechanistic insights into hepatic steatosis

OBJECTIVE: To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). METHODS: Bayesian network analyses and complementary two-sample Mendelian randomization were used to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with liver fat in the IMI-DIRECT prospective cohort study. Data included frequently sampled metabolic challenge tests, MRI-derived abdominal and hepatic fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults without diabetes, with harmonized protocols enabling replication. RESULTS: High basal insulin secretion rate (BasalISR), estimated via C-peptide deconvolution, emerged as the primary potential causal driver of liver fat accumulation in both cohorts. BasalISR, a clearance-independent measure of β-cell insulin output distinct from peripheral insulin levels, was independently linked to hepatic steatosis. Visceral adipose tissue exhibited bidirectional associations with liver fat, suggesting a self-reinforcing metabolic loop. Of 446 analyzed proteins, 34 mapped to these metabolic networks (27 in the non-diabetes network, 18 in the T2D network, and 11 shared). Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses identified GUSB in females and LEP in males as the strongest protein predictors of liver fat. CONCLUSIONS: BasalISR may better capture early β-cell-driven disturbances contributing to MASLD. These findings outline a multifactorial, sex- and disease stage-specific proteo-metabolic architecture of hepatic steatosis and identify potential biomarkers or therapeutic targets.

Meaningful mentor development

Clonal hematopoiesis is associated with reduced response to atezolizumab in non-small cell lung cancer

HIV infection and ART are not linked to higher dysglycaemia prevalence versus HIV-negative controls.

Bayesian network imputation methods applied to multi-omics data identify putative causal relationships in a type 2 diabetes dataset containing incomplete data: An IMI DIRECT Study

Here we report the results from exploratory analysis using a Bayesian network approach of data originally derived from a large North European study of type 2 diabetes (T2D) conducted by the IMI DIRECT consortium. 3029 individuals (795 with T2D and 2234 without) within 7 different study centres provided data comprising genotypes, proteins, metabolites, gene expression measurements and many different clinical variables. The main aim of the current study was to demonstrate the utility of our previously developed method to fit Bayesian networks by performing exploratory analysis of this dataset to identify possible causal relationships between these variables. The data was analysed using the BayesNetty software package, which can handle mixed discrete/continuous data with missing values. The original dataset consisted of over 16,000 variables, which were filtered down to 260 variables for analysis. Even with this reduction, no individual had complete data for all variables, making it impossible to analyse using standard Bayesian network methodology. However, using the recently proposed novel imputation method implemented in BayesNetty we computed a large average Bayesian network from which we could infer possible associations and causal relationships between variables of interest. Our results confirmed many previous findings in connection with T2D, including possible mediating proteins and genes, some of which have not been widely reported. We also confirmed potential causal relationships with liver fat that were identified in an earlier study that used the IMI DIRECT dataset but was limited to a smaller subset of individuals and variables (namely individuals with complete data at pre-defined variables of interest). In addition to providing valuable confirmation, our analyses thus demonstrate a proof-of-principle of the utility of the method implemented within BayesNetty. The full final average Bayesian network generated from our analysis is freely available and can be easily interrogated further to address specific focussed scientific questions of interest.

A Biological-Systems-Based Analyses Using Proteomic and Metabolic Network Inference Reveals Mechanistic Insights into Hepatic Lipid Accumulation: An IMI-DIRECT Study

Objective: To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). Research Design and Methods: We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with fatty liver using data from the IMI-DIRECT prospective cohort study. Measurements were made of glucose and insulin dynamics (using frequently-sampled metabolic challenge tests), MRI-derived abdominal and liver fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults free from diabetes at enrolment. The common protocols used in these two cohorts provided the opportunity for replication analyses to be performed. When the direction of the effect could not be determined with high probability through Bayesian networks, complementary two-sample Mendelian randomization (MR) was employed. Results: High basal insulin secretion rate (BasalISR) was identified as the primary causal driver of liver fat accumulation in both diabetes and non-diabetes. Excess visceral adipose tissue (VAT) was bidirectionally associated with liver fat, indicating a self-reinforcing metabolic loop. Basal insulin clearance (Clinsb) worsened as a consequence of liver fat accumulation to a greater degree before the onset of T2D. Out of 446 analysed proteins, 34 mapped to these metabolic networks and 27 were identified in the non-diabetes network, 18 in the diabetes network, and 11 were common between the two networks. Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses revealed distinct proteomic drivers: GUSB and LEP were most predictive of liver fat in females and males, respectively. Conclusions: Basal insulin hypersecretion is a modifiable, causal driver of MASLD, particularly prior to glycaemic decompensation. Our findings highlight a multifactorial, sex- and disease-stage-specific proteo-metabolic architecture of hepatic steatosis. Proteins such as GUSB, ALDH1A1, LPL, and IGFBPs warrant further investigation as potential biomarkers or therapeutic targets for MASLD prevention and treatment.

Accelerating Medicines Partnership in Type 2 Diabetes and Common Metabolic Diseases: Collaborating to Maximize the Value of Genetic and Genomic Data

In the last two decades, significant progress has been made toward understanding the genetic basis of type 2 diabetes. An important supporter of this research has been the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), most recently through the Accelerating Medicines Partnership Program for Type 2 Diabetes (AMP T2D) and Accelerating Medicines Partnership Program for Common Metabolic Diseases (AMP CMD). These public-private partnerships of the National Institutes of Health, multiple biopharmaceutical and life sciences companies, and nonprofit organizations, facilitated and managed by the Foundation for the National Institutes of Health, were designed to improve understanding of therapeutically relevant biological pathways for type 2 diabetes. On the occasion of NIDDK's 75th anniversary, we review the history of NIDDK support for these partnerships, which saw the convergence of research directions prioritized by academic consortia, the pharmaceutical industry, and government funders. Although the NIDDK was not the sole originator or funder of these efforts, its support and leadership have been pivotal to the partnerships' success and have enabled their research to be broadly accessible through the AMP Common Metabolic Diseases Knowledge Portal (CMDKP) and the AMP Common Metabolic Diseases Genome Atlas (CMDGA). Findings from AMP CMD align with NIDDK's mission to conduct research and share results with the goal of improving health and quality of life. ARTICLE HIGHLIGHTS: The Accelerating Medicines Partnership Program for Type 2 Diabetes (AMP T2D) and Accelerating Medicines Partnership Program for Common Metabolic Diseases (AMP CMD) were created to accelerate the translation of genetic and genomic data into knowledge about the biology of disease. Their goal was to gain a better understanding of the mechanisms underlying types 1 and 2 diabetes and prediabetes, obesity, cardiovascular disease, kidney disease, and nonalcoholic steatohepatitis. This work identified multiple genes and pathways underlying these diseases. The findings of AMP T2D and AMP CMD have implications for drug development and improved risk prediction, diagnosis, and treatment for common metabolic diseases.

Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits

Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes. The authors present a multiomics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits in Hispanic/Latino populations. They demonstrate how integrating GWAS with omics characterization can advance precision medicine.

Integration of biobank-scale genetics and plasma proteomics reveals evidence for causal processes in asthma risk and heterogeneity

Hundreds of genetic associations for asthma have been identified, yet translating these findings into mechanistic insights remains challenging. We leveraged plasma proteomics from the UK Biobank Pharma Proteomics Project (UKB-PPP) to identify biomarkers and effectors of asthma risk or heterogeneity using genetic causal inference approaches. We identified 609 proteins associated with asthma status (269 proteins after controlling for body mass index [BMI] and smoking). Analysis of genetically predicted protein levels identified 70 proteins with putative causal roles in asthma risk, including known drug targets and proteins without prior genetic evidence in asthma (e.g., GCHFR, TDRKH, and CLEC7A). The genetic architecture of causally associated proteins provided evidence for a Toll-like receptor (TLR)1-interleukin (IL)-27 asthma axis. Lastly, we identified evidence of causal relationships between proteins and heterogeneous aspects of asthma biology, including between TSPAN8 and neutrophil counts. These findings illustrate that integrating biobank-scale genetics and plasma proteomics can provide a framework to identify therapeutic targets and mechanisms underlying disease risk and heterogeneity.

HLA Class II Alleles are associated with Anti-Drug Antibodies in Ulcerative Colitis Patients Treated with Etrolizumab

Patients treated with etrolizumab, a monoclonal antibody drug for ulcerative colitis, often develop anti-drug antibodies (ADA) and neutralizing antibodies (NAb). To investigate genetic risk factors, we evaluated associations between HLA alleles and ADA/NAb development. HLA-DQB1*06:03 demonstrated the most significant association with both ADA (OR = 7.3, P ≤ 7.6×10 −13 ) and NAb (OR = 13.1, P ≤ 7.7×10 −13 ). After controlling for HLA-DQB1*06:03, HLA-DQA1*03:03 emerged as the next most significant allele associated with ADA (OR = 2.8, P ≤ 1.0×10 −3 ) and NAb (OR = 5.0, P ≤ 1.3×10 −3 ). Notably, all patients carrying both alleles (n=5) developed ADA, suggesting these two alleles are sufficient to induce ADA to etrolizumab.

Optimizing approaches for targeted integration of transgenic cassettes by integrase-mediated cassette exchange in mouse and human stem cells

To enable robust expression of transgenes in stem cells, recombinase-mediated cassette exchange at safe harbor loci is frequently adopted. The choice of recombinase enzyme is a critical parameter to ensure maximum efficiency and accuracy of the integration event. We have explored the serine recombinase family of site-specific integrases and have directly compared the efficiency of PhiC31, W-beta, and Bxb1 integrase for targeted transgene integration at the Gt(ROSA)26Sor locus in mouse embryonic stem cells. All 3 integrases were found to be suitable for efficient engineering and long-term expression of each integrase was compatible with pluripotency, as evidenced by germline transmission. Bxb1 integrase was found to be 2-3 times more efficient than PhiC31 and W-beta. The Bxb1 system was adapted for cassette exchange at the AAVS1 locus in human induced pluripotent stem (iPS) cells, and the 2 commonly used ubiquitous promoters, CAG and Ef1α (EIF1A), were tested for their suitability in driving expression of the integrated transgenic cargo. AAVS1-integrated Ef1α promoter led to a very mosaic pattern of expression in targeted hiPS cells, whereas the AAVS1-integrated CAG promoter drove consistent and stable expression. To validate the system for the integration of functional machinery, the Bxb1 integrase system was used to integrate CAG-driven CRISPR-activation and CRISPR-inhibition machinery in human iPS cells and robust sgRNA-induced up- and downregulation of target genes was demonstrated.

Genetic associations of neuropathic pain and sensory profile in a deeply phenotyped neuropathy cohort

We aimed to investigate the genetic associations of neuropathic pain in a deeply phenotyped cohort. Participants with neuropathic pain were cases and compared with those exposed to injury or disease but without neuropathic pain as control subjects. Diabetic polyneuropathy was the most common aetiology of neuropathic pain. A standardised quantitative sensory testing protocol was used to categorize participants based on sensory profile. We performed genome-wide association study, and in a subset of participants, we undertook whole-exome sequencing targeting analyses of 45 known pain-related genes. In the genome-wide association study of diabetic neuropathy (N = 1541), a top significant association was found at the KCNT2 locus linked with pain intensity (rs114159097, P = 3.55 × 10 -8 ). Gene-based analysis revealed significant associations between LHX8 and TCF7L2 and neuropathic pain. Polygenic risk score for depression was associated with neuropathic pain in all participants. Polygenic risk score for C-reactive protein showed a positive association, while that for fasting insulin showed a negative association with neuropathic pain, in individuals with diabetic polyneuropathy. Gene burden analysis of candidate pain genes supported significant associations between rare variants in SCN9A and OPRM1 and neuropathic pain. Comparison of individuals with the "irritable" nociceptor profile to those with a "nonirritable" nociceptor profile identified a significantly associated variant (rs72669682, P = 4.39 × 10 -8 ) within the ANK2 gene. Our study on a deeply phenotyped cohort with neuropathic pain has confirmed genetic associations with the known pain-related genes KCNT2 , OPRM1 , and SCN9A and identified novel associations with LHX8 and ANK2 , genes not previously linked to pain and sensory profiles, respectively.

SLC16A8 is a causal contributor to age-related macular degeneration risk

Age-related macular degeneration (AMD), a complex neurodegenerative disease, is a leading cause of visual impairment worldwide with a strong genetic component. Genetic studies have identified several loci, but few causal genes with functional characterization. Here we highlight multiple lines of evidence which show a causal role in AMD for SLC16A8, which encodes MCT3, a retinal pigment epithelium (RPE) specific lactate transporter. First, in an unbiased, genome-wide analysis of rare coding variants we show multiple SLC16A8 rare variants are associated with AMD risk, corroborating previous borderline significant reports from AMD rare variant studies. Second, we report a novel SLC16A8 mutation in a three-generation family with early onset macular degeneration. Finally, mis-expression in multiple model organisms shows functional and anatomic retinal consequences. This study highlights the important role for SLC16A8 and lactate regulation towards outer retina/RPE health and highlights a potential new therapeutic opportunity for the treatment of AMD.

Very high fire danger in UK in 2022 at least 6 times more likely due to human-caused climate change

The UK experienced an unprecedented heatwave in 2022, with temperatures reaching 40°C for the first time in recorded history. This extreme heat was accompanied by widespread fires across London and elsewhere in England, which destroyed houses and prompted evacuations. While attribution studies have identified a strong human fingerprint contributing to the heatwave, no studies have attributed the associated fires to anthropogenic influence. In this study, we assess the contribution of human-induced climate change to fire weather conditions over the summer of 2022 using simulations from the HadGEM3-A model with and without anthropogenic emissions, and apply the Canadian Fire Weather Index. Our analysis reveals at least a 6-fold increase in the probability of very high fire weather in the UK due to human influence, most of which is driven by high fire conditions across England. These findings highlight the significant role of human-induced climate change in the emerging threat of wildfires in the UK. As we experience more hotter and drier summers as temperatures continue to rise, the frequency and severity of fires are likely to increase, posing significant risks to both natural ecosystems and human populations. This study underscores the need for further research to quantify the changing fire risk due to our changing climate, and the urgent requirement for mitigation and adaptation efforts to address the growing wildfire threat in the UK.

Integrated clinical risk prediction of type 2 diabetes with a multifactorial polygenic risk score