Publications

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

BACKGROUND: Cancer cachexia (CCx) presents a multifaceted challenge characterized by negative protein and energy balance and systemic inflammatory response activation. While previous CCx studies predominantly focused on mouse models or human body fluids, there's an unmet need to elucidate the molecular inter-organ cross-talk underlying the pathophysiology of human CCx.

METHODS: Spatial metabolomics were conducted on liver, skeletal muscle, subcutaneous and visceral adipose tissue, and serum from cachectic and control cancer patients. Organ-wise comparisons were performed using component, pathway enrichment and correlation network analyses. Inter-organ correlations in CCx altered pathways were assessed using Circos. Machine learning on tissues and serum established classifiers as potential diagnostic biomarkers for CCx.

RESULTS: Distinct metabolic pathway alteration was detected in CCx, with adipose tissues and liver displaying the most significant (P ≤ 0.05) metabolic disturbances. CCx patients exhibited increased metabolic activity in visceral and subcutaneous adipose tissues and liver, contrasting with decreased activity in muscle and serum compared to control patients. Carbohydrate, lipid, amino acid, and vitamin metabolism emerged as highly interacting pathways across different organ systems in CCx. Muscle tissue showed decreased (P ≤ 0.001) energy charge in CCx patients, while liver and adipose tissues displayed increased energy charge (P ≤ 0.001). We stratified CCx patients by severity and metabolic changes, finding that visceral adipose tissue is most affected, especially in cases of severe cachexia. Morphometric analysis showed smaller (P ≤ 0.05) adipocyte size in visceral adipose tissue, indicating catabolic processes. We developed tissue-based classifiers for cancer cachexia specific to individual organs, facilitating the transfer of patient serum as minimally invasive diagnostic markers of CCx in the constitution of the organs.

CONCLUSIONS: These findings support the concept of CCx as a multi-organ syndrome with diverse metabolic alterations, providing insights into the pathophysiology and organ cross-talk of human CCx. This study pioneers spatial metabolomics for CCx, demonstrating the feasibility of distinguishing cachexia status at the organ level using serum.

Multiplexed assays of variant effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines have led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.

The high prevalence of autoimmune hypothyroidism (AIHT) - more than 5% in human populations - provides a unique opportunity to unlock the most complete picture to date of genetic loci that underlie systemic and organ-specific autoimmunity. Using a meta-analysis of 81,718 AIHT cases in FinnGen and the UK Biobank, we dissect associations along axes of thyroid dysfunction and autoimmunity. This largest-to-date scan of hypothyroidism identifies 418 independent associations (p < 5×10- 8), more than half of which have not previously been documented in thyroid disease. In 48 of these, a protein-coding variant is the lead SNP or is highly correlated (r2 > 0.95) with the lead SNP at the locus, including low-frequency coding variants at LAG3, ZAP70, TG, TNFSF11, IRF3, S1PR4, HABP2, ZNF429 as well as established variants at ADCY7, IFIH1 and TYK2. The variants at LAG3 (P67T), ZAP70 (T155M), and TG (Q655X) are highly enriched in Finland and functional experiments in T-cells demonstrate that the ZAP70:T155M allele reduces T-cell activation. By employing a large-scale scan of non-thyroid autoimmunity and a published meta-analysis of TSH levels, we use a Bayesian classifier to dissect the associated loci into distinct groupings and from this estimate, a significant proportion are involved in systemic (i.e., general to multiple autoimmune conditions) autoimmunity (34%) and another subset in thyroid-specific dysfunction (17%). By comparing these association results further to other common disease endpoints, we identify a noteworthy overlap with skin cancer, with 10% of AIHT loci showing a consistent but opposite pattern of association where alleles that increase the risk of hypothyroidism have protective effects for skin cancer. The association results, including genes encoding checkpoint inhibitors and other genes affecting protein levels of PD1, bolster the causal role of natural variation in autoimmunity influencing cancer outcomes.

Amino acid substitutions may substantially alter protein stability and function, but the contribution of substitutions arising from alternate translation (deviations from the genetic code) is unknown. To explore it, we analyzed deep proteomic and transcriptomic data from over 1,000 human samples, including 6 cancer types and 26 healthy human tissues. This global analysis identified 60,024 high confidence substitutions corresponding to 8,801 unique sites in proteins derived from 1,990 genes. Some substitutions are shared across samples, while others exhibit strong tissue-type and cancer specificity. Surprisingly, products of alternate translation are more abundant than their canonical counterparts for hundreds of proteins, suggesting sense codon recoding. Recoded proteins include transcription factors, proteases, signaling proteins, and proteins associated with neurodegeneration. Mechanisms contributing to substitution abundance include protein stability, codon frequency, codon-anticodon mismatches, and RNA modifications. We characterize sequence motifs around alternatively translated amino acids and how substitution ratios vary across protein domains, tissue types and cancers. The substitution ratios are positively associated with intrinsically disordered regions and genetic polymorphisms in gnomAD, though the polymorphisms cannot account for the substitutions. Both the sequence and the tissue-specificity of alternatively translated proteins are conserved between human and mouse. These results demonstrate the contribution of alternate translation to diversifying mammalian proteomes, and its association with protein stability, tissue-specific proteomes, and diseases.

Recessive diseases arise when both copies of a gene are impacted by a damaging genetic variant. When a patient carries two potentially causal variants in a gene, accurate diagnosis requires determining that these variants occur on different copies of the chromosome (that is, are in trans) rather than on the same copy (that is, in cis). However, current approaches for determining phase, beyond parental testing, are limited in clinical settings. Here we developed a strategy for inferring phase for rare variant pairs within genes, leveraging genotypes observed in the Genome Aggregation Database (v2, n = 125,748 exomes). Our approach estimates phase with 96% accuracy, both in trio data and in patients with Mendelian conditions and presumed causal compound heterozygous variants. We provide a public resource of phasing estimates for coding variants and counts per gene of rare variants in trans that can aid interpretation of rare co-occurring variants in the context of recessive disease.

Fine-mapping aims to identify causal genetic variants for phenotypes. Bayesian fine-mapping algorithms (for example, SuSiE, FINEMAP, ABF and COJO-ABF) are widely used, but assessing posterior probability calibration remains challenging in real data, where model misspecification probably exists, and true causal variants are unknown. We introduce replication failure rate (RFR), a metric to assess fine-mapping consistency by downsampling. SuSiE, FINEMAP and COJO-ABF show high RFR, indicating potential overconfidence in their output. Simulations reveal that nonsparse genetic architecture can lead to miscalibration, while imputation noise, nonuniform distribution of causal variants and quality control filters have minimal impact. Here we present SuSiE-inf and FINEMAP-inf, fine-mapping methods modeling infinitesimal effects alongside fewer larger causal effects. Our methods show improved calibration, RFR and functional enrichment, competitive recall and computational efficiency. Notably, using our methods' posterior effect sizes substantially increases polygenic risk score accuracy over SuSiE and FINEMAP. Our work improves causal variant identification for complex traits, a fundamental goal of human genetics.

AIMS/HYPOTHESIS: Childhood overweight increases the risk of type 2 diabetes and cardiovascular disease in adulthood. However, the impact of childhood leanness on adult obesity and disease risk has been overlooked. We examined the independent and combined influences of child and adult body size on the risk of type 2 diabetes and cardiovascular disease.

METHODS: Data from the UK Biobank on 364,695 individuals of European ancestry and free of type 2 diabetes and cardiovascular disease were divided into nine categories based on their self-reported body size at age 10 and measured BMI in adulthood. After a median follow-up of 12.8 years, 33,460 individuals had developed type 2 diabetes and/or cardiovascular disease. We used Cox regression models to assess the associations of body size categories with disease incidence.

RESULTS: Individuals with low body size in childhood and high body size in adulthood had the highest risk of type 2 diabetes (HR 4.73; 95% CI 4.50, 4.99), compared to those with average body size in both childhood and adulthood. This was significantly higher than the risk in those with high body size in both childhood and adulthood (HR 4.05; 95% CI 3.84, 4.26). By contrast, cardiovascular disease risk was determined by adult body size, irrespective of childhood body size.

CONCLUSIONS/INTERPRETATION: Low body size in childhood exacerbates the risk of type 2 diabetes associated with adult obesity but not the risk of cardiovascular disease. Thus, promoting healthy weight management from childhood to adulthood, among lean children, is crucial.