Carlo Sidore

Background and Aims: Distinct autoimmune diseases can share symptoms and autoantibodies, but the molecular pathways underlying this shared etiology remain poorly understood. We assessed genetic support for IL12 signaling across 27 diseases. Approach and Results: We identified a rare variant upstream of IL12A with large risk-increasing effects on autoimmune hepatitis [AIH; per-allele odds ratio (OR)=2.98, p =3×10 ⁻⁹ ], primary biliary cholangitis (PBC; OR=2.80, p =2×10 ⁻⁸ ), systemic sclerosis (SSc; OR=2.64, p =2×10 ⁻¹¹ ), Sjogren's disease (SjD; OR=1.94, p =10 ⁻¹¹ ), systemic lupus erythematosus (SLE; OR=1.83, p =5×10 ⁻¹⁰ ) and multiple sclerosis (OR=1.79, p =10 ⁻¹¹ ). Lifetime risk for developing at least one of these conditions increased 6-fold in homozygote carriers (OR=5.73, p =0.003). We then evaluated genetic support for all 4 IL12 signaling genes and found that: (i) a non-coding variant downstream of IL12A (linked to lower gene expression) protected against 5 of the 6 diseases; (ii) an intronic variant in IL12RB2 (linked to higher gene expression) increased risk of all 6 diseases; and (iii) heterozygote carriers of rare predicted loss-of-function or missense variants in IL12RB2 had 18% lower odds of SLE (OR=0.82, p =2×10 ⁻⁵ ). IL23 signaling, which shares ligand and receptor subunits with IL12, was not associated with these conditions. In epidemiologic analyses of co-prevalence, 5 of the 6 diseases strongly co-occurred with each other: AIH, PBC, SSc, SjD and SLE. Conclusions: AIH, PBC, SSc, SjD and SLE represent a distinct subset of autoimmune diseases that share genetic associations with the IL12 pathway, as well as profound co-prevalence, suggesting they may have a shared etiology and may respond to the same targeted therapeutic intervention(s).

Background: Growth differentiation factor 15 (GDF‐15) levels are emerging as a candidate biomarker of aging. The present study aimed to: (1) characterize the association of GDF‐15 with the continuum of arterial stiffening, assessed as carotid‐femoral pulse wave velocity, as age increases; (2) determine the predictive role of serum GDF‐15 levels on mortality; and (3) identify genetic determinants of serum GDF‐15 levels. Methods and Results: Serum levels of GDF‐15 and established cardiovascular risk factors, including pulse wave velocity, were assessed in a large (4736 individual) Sardinian population. Serum levels of GDF‐15, which can be reliably measured repeatedly over time, increase with age; are associated with a stiffer aorta; “mediate” a large proportion of the age‐associated increase in arterial stiffness; pose risks because of their association with greater mortality; and are significantly associated with the variant rs11549407, which causes thalassemia major in homozygosity. Conclusions: Because of its consistent ability to predict functional and clinical outcomes, including all‐cause mortality, we conclude that GDF‐15 serum levels serve as a robust biomarker for the continuum from health to the emergence of clinical disease during aging and, subsequently, to the likelihood of mortality.

Meta-analysis of gene-based tests using single variant summary statistics is a powerful strategy for associating genes with disease. However, current approaches require sharing the covariance matrix between variants for each study and trait of interest. For large-scale studies with many phenotypes, these matrices can be cumbersome to calculate, store, and share. To address this challenge, we present REMETA, an efficient tool for meta-analysis of gene-based tests. REMETA uses a single sparse covariance reference file per study that is rescaled for each phenotype using single variant summary statistics. We develop methods to apply REMETA to binary traits with case-control imbalance, and estimate allele frequencies, genotype counts and effect sizes of burden tests. We demonstrate the performance and advantages of our approach via meta-analysis of 5 traits in 469,376 samples in UK Biobank. The open-source REMETA software tools and framework will facilitate meta-analysis across large scale exome sequencing studies from diverse studies that cannot easily be brought together.

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Few genome-wide association studies (GWAS) analyzing genetic regulation of morphological traits of white blood cells have been reported. We carried out a GWAS of 12 morphological traits in 869 individuals from the general population of Sardinia, Italy. These traits, included measures of cell volume, conductivity and light scatter in four white-cell populations (eosinophils, lymphocytes, monocytes, neutrophils). This analysis yielded seven statistically significant signals, four of which were novel (four novel, PRG2, P2RX3, two of CDK6). Five signals were replicated in the independent INTERVAL cohort of 11 822 individuals. The most interesting signal with large effect size on eosinophil scatter (P-value = 8.33 x 10⁻³², beta = −1.651, se = 0.1351) falls within the innate immunity cluster on chromosome 11, and is located in the PRG2 gene. Computational analyses revealed that a rare, Sardinian-specific PRG2:p.Ser148Pro mutation modifies PRG2 amino acid contacts and protein dynamics in a manner that could possibly explain the changes observed in eosinophil morphology. Our discoveries shed light on genetics of morphological traits. For the first time, we describe such large effect size on eosinophils morphology that is relatively frequent in Sardinian population.

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use¹. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels², heart disease remains the leading cause of death worldwide³. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS^{4,5,300,000 individuals. Nat. Genet. 49, 1758–1766 (2017)." href="#ref-CR6" id="ref-link-section-d425582245e17109_2">6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23} have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns²⁴. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine²⁵, we anticipate that increased diversity of participants will lead to more accurate and equitable²⁶ application of polygenic scores in clinical practice.

Aims/hypothesis: Given the potential shared aetiology between type 1 and type 2 diabetes, we aimed to identify any genetic regions associated with both diseases. For associations where there is a shared signal and the allele that increases risk to one disease also increases risk to the other, inference about shared aetiology could be made, with the potential to develop therapeutic strategies to treat or prevent both diseases simultaneously. Alternatively, if a genetic signal co-localises with divergent effect directions, it could provide valuable biological insight into how the association affects the two diseases differently. Methods: Using publicly available type 2 diabetes summary statistics from a genome-wide association study (GWAS) meta-analysis of European ancestry individuals (74,124 cases and 824,006 controls) and type 1 diabetes GWAS summary statistics from a meta-analysis of studies on individuals from the UK and Sardinia (7467 cases and 10,218 controls), we identified all regions of 0.5 Mb that contained variants associated with both diseases (false discovery rate coloc. Any association with a co-localisation posterior probability of ≥0.9 was considered a genuine shared association with both diseases. Results: Of the 81 association signals from 42 genetic regions that showed association with both type 1 and type 2 diabetes, four association signals co-localised between both diseases (posterior probability ≥0.9): (1) chromosome 16q23.1, near CTRB1/BCAR1, which has been previously identified; (2) chromosome 11p15.5, near the INS gene; (3) chromosome 4p16.3, near TMEM129 and (4) chromosome 1p31.3, near PGM1. In each of these regions, the effect of genetic variants on type 1 diabetes was in the opposite direction to the effect on type 2 diabetes. Use of additional datasets also supported the previously identified co-localisation on chromosome 9p24.2, near the GLIS3 gene, in this case with a concordant direction of effect. Conclusions/interpretation: Four of five association signals that co-localise between type 1 diabetes and type 2 diabetes are in opposite directions, suggesting a complex genetic relationship between the two diseases. Graphical

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10⁻⁸) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.