Sonia Moreno-Grau

Genome-wide association studies have successfully identified thousands of genetic associations, yet their predominant reliance on European-descent populations limits insights into the full spectrum of human genetic diversity and its impact on disease. Admixture mapping offers a powerful, complementary approach by leveraging differences in haplotype frequencies across ancestral backgrounds to identify risk loci for complex traits. Here, we perform a large-scale, multi-ancestry admixture mapping study across 415,792 unrelated individuals in the UK Biobank, examining associations between local haplotype ancestry and 108 phenotypes. Our approach identifies 13 genome-wide significant ancestry-phenotype associations, recovering previously reported signals while uncovering four novel ancestry-associated findings, including new risk loci for atrial fibrillation, dermatitis, and angina pectoris. To overcome the limited resolution of traditional admixture mapping, we implemented a conditional fine-mapping framework, which enabled us to localize four putatively causal variants. In silico variant effect prediction and eQTL integration revealed regulatory and missense effects predominantly localized to lung, and immune tissues, aligning with captured phenotypes such as asthma, dermatitis, and hypothyroidism. Notably, our findings demonstrate striking genetic heterogeneity, revealing how the same clinical phenotype can arise through distinct genetic pathways depending on the ancestral background. Overall, this work highlights the critical importance of modeling local ancestry structure to refine genetic associations, uncover novel disease mechanisms, and improve the equitable translation of genomic medicine.

Background: Polygenic risk scores (PRS) derived from European individuals have reduced portability across global populations, limiting their clinical implementation at worldwide scale. Here, we investigate the performance of a wide range of PRS models across four ancestry groups (Africans, Europeans, East Asians, and South Asians) for 14 conditions of high-medical interest. Methods: To select the best-performing model per trait, we first compared PRS performances for publicly available scores, and constructed new models using different methods (LDpred2, PRS-CSx and SNPnet). We used 285K European individuals from the UK Biobank (UKBB) for training and 18K, including diverse ancestries, for testing. We then evaluated PRS portability for the best models in Europeans and compared their accuracies with respect to the best PRS per ancestry. Finally, we validated the selected PRS models using an independent set of 8,417 individuals from Biobank of the Americas-Genomelink (BBofA-GL); and performed a PRS-Phewas. Results: We confirmed a decay in PRS performances relative to Europeans when the evaluation was conducted using the best-PRS model for Europeans (51.3% for South Asians, 46.6% for East Asians and 39.4% for Africans). We observed an improvement in the PRS performances when specifically selecting ancestry specific PRS models (phenotype variance increase: 1.62 for Africans, 1.40 for South Asians and 0.96 for East Asians). Additionally, when we selected the optimal model conditional on ancestry for CAD, HDL-C and LDL-C, hypertension, hypothyroidism and T2D, PRS performance for studied populations was more comparable to what was observed in Europeans. Finally, we were able to independently validate tested models for Europeans, and conducted a PRS-Phewas, identifying cross-trait interplay between cardiometabolic conditions, and between immune-mediated components. Conclusion: Our work comprehensively evaluated PRS accuracy across a wide range of phenotypes, reducing the uncertainty with respect to which PRS model to choose and in which ancestry group. This evaluation has let us identify specific conditions where implementing risk-prioritization strategies could have practical utility across diverse ancestral groups, contributing to democratizing the implementation of PRS.

Introduction: A major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension. Methods: We collected genotyped data from a novel DTC platform where participants upload their genotype data files, and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case-control setting using the BASIL algorithm. Results: We collected data on N=4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D; and N=4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC=0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers. Discussion: DTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly. Conclusions: The genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.

Alzheimer's disease (AD) is the most common form of dementia, currently affecting 35 million people worldwide. Apolipoprotein E (APOE) ε4 allele is the major risk factor for sporadic, late-onset AD (LOAD), which comprises over 95% of AD cases, increasing the risk of AD 4-12 fold. Despite this, the role of APOE in AD pathogenesis is still a mystery. Aiming for a better understanding of APOE-specific effects, the ADAPTED consortium analysed and integrated publicly available data of multiple OMICS technologies from both plasma and brain stratified by APOE haplotype (APOE2, APOE3 and APOE4). Combining genome-wide association studies (GWAS) with differential mRNA and protein expression analyses and single-nuclei transcriptomics, we identified genes and pathways contributing to AD in both APOE dependent and independent fashion. Interestingly, we characterised a set of biomarkers showing plasma and brain consistent protein profiles and opposite trends in APOE2 and APOE4 AD cases that could constitute screening tools for a disease that lacks specific blood biomarkers. Beside the identification of APOE-specific signatures, our findings advocate that this novel approach, based on the concordance across OMIC layers and tissues, is an effective strategy for overcoming the limitations of often underpowered single-OMICS studies.

Background: While being the most prominent genetic risk factor for Alzheimer’s disease (AD), the effects of APOE variants on the biology and development of AD are still poorly understood. The IMI ADAPTED consortium strives to illuminate the mechanisms underlying the effects of APOE following a holistic approach. In this presentation we describe how the integration of a systematic APOE‐stratified re‐analysis of publicly available GWAS, transcriptomics and proteomics for AD led to new insights in APOE mediated effects in AD.Method: We re‐analysed AD relevant data including 39,186 human genomes, 2,338 transcriptomes, and 2,365 proteomes along with snRNAseq brain data from diverse AD cohorts. Data were obtained from diverse public repositories and AD research consortia (AddNeuroMed,ADGC,ADNI,AMP‐AD,CHARGE,GERAD,GR@ACE,Mount Sinai,NIA‐GENADA,MAYO,ROSMAP and TGEN). Genes showing differences between cases and controls stratified by APOE genotype were identified as well as those showing differences between the APOE genotype carriers. Results of the different OMICS were integrated using the robust rank aggregation (RRA) method to identify effects which are consistent across multiple layers of OMICS data followed by pathway analysis.Results: The stratified GWAS analysis identified genome‐wide significant loci for AD in the APOE ε3 and APOE ε4 genotype strata. After integration with plasma and brain expression profiles, the pathway analysis found enrichment for several processes related to the immune system and glia function among other relevant pathways, of which those differing between APOE strata are of particular interest.Conclusion: The systematic APOE genotype‐stratified analysis of human GWAS, transcriptomics, and proteomics data revealed promising candidate genes and pathways that help to elucidate the mechanisms by which APOE affects the AD risk. OMICS data in APOE relevant cell models will refine these findings. Disclosure: This project has received funding from the Innovative Medicines Initiative 2 JU under grant agreement No 115975. This Joint Undertaking receives support from the EU’s Horizon2020 research and innovation programme and EFPIA. This study was sponsored by AbbVie Deutschland GmbH & Co KG. AbbVie contributed to the study design, research, and interpretation of data, writing, reviewing, and approving the Some authors are currently employed by AbbVie and may own AbbVie stock.

Background: Genome‐wide studies have identified diverse susceptibility genes for Alzheimer’s disease, with the APOE locus being the major risk factor known so far. However, pathogenic mechanisms and the influence of other known AD loci or patient characteristics such as gender have been poorly explored. The ADAPTED IMI consortium, focused on exploring the APOE biology, has conducted the first genome‐wide meta‐analysis of AD stratified according to the three main APOE haplotypes. Additionally, we also explored the effect of gender on AD by performing sex‐stratified meta‐analysis.Methods: APOE stratified meta‐analysis was performed in three stages comprising 39,186 human samples from sixteen datasets including ADNI, AddNeuroMed, ADGC, Mayo, Neocodex‐Murcia, NIA, ROSMAP, TGEN study for Stage I, GR@ACE and GERAD for Stage II and ARIC, CHS, FHS and RS (CHARGE consortium) for Stage III. Sex‐stratified meta‐analysis was performed on Stage I and Stage II datasets.After quality control and imputation, association of genotype dosages with the AD case‐control status for each dataset was investigated through regression models adjusted by age, sex and the first four PC vectors, plus APOE status in the sex stratified analysis. Meta‐analysis was performed using the inverse variance weighing method. Results were summarized on gene‐level and explored for enrichment in known pathways and processes.Results: In the combined analysis, we identified genome‐wide significant signals for APOE, BIN1, CLU, PICALM and a large intergenic region in 4p15 in the APOE ε4 stratum, and for ABCA7, BIN1 and PICALM in the APOE ε3 stratum, whereas no genome‐wide significant results were found in the APOE ε2 stratum. Sex‐stratified analysis identified genome‐wide significant signals for BIN1 and APOE as well as suggestive signals for PICALM, MYLK, SOX5 and SCEL in the female stratum. By contrast in males, only suggestive signals for BIN1, APOE, ZCCHC2, the ABI3BP/IMPG2 locus, ESRRB and the 19q13.4 leukocyte receptor cluster were identified. Accordingly, APOE‐ and sex‐specific pathways were highlighted.Conclusions: This systematic stratified analysis led to the identification of loci associated with AD risk, which are specific for certain APOE haplotypes and specific combinations of APOE haplotypes and gender. This novel insight contributes to unravel the complex architecture of AD pathology.

Long runs of homozygosity (ROH) are contiguous stretches of homozygous genotypes, which are a footprint of recent inbreeding and recessive inheritance. The presence of recessive loci is suggested for Alzheimer’s disease (AD). However, the search for recessive variants has been poorly assessed to date. To investigate homozygosity in AD, we performed a fine-scale ROH analysis including 21,100 individuals from 10 cohorts of European ancestry (11,919 AD cases and 9,181 controls). We detected an increase of homozygosity in AD cases compared to controls [β _FROH (CI95%) = 0.051 (0.023 – 0.078); P = 3.25 x 10 ^-4 ]. ROHs increasing the risk of AD (OR > 1) were significantly overrepresented compared to ROHs increasing protection (p < 2.20 x 10 ^-16 ). The top associated ROH with AD risk (β (CI95%) = 1.09 (0.48 ‒ 1.48), p value = 9.03 x 10 ^-4 ) was detected upstream the HS3ST1 locus (chr4:11,189,482‒11,305,456), previously related to AD. Next, to construct a homozygosity map of AD cases, we selected ROHs shared by inbred AD cases extracted from an outbred population. We used whole-exome sequencing data from 1,449 individuals from the Knight-ADRC-NIA-LOAD (KANL) cohort to identify potential recessive variants in candidate ROHs. We detected a candidate marker, rs117458494, mapped in the SPON1 locus, which has been previously associated with amyloid metabolism. Here, we provide a research framework to look for recessive variants in AD using outbred populations. Our results showed that AD cases have enriched homozygosity, suggesting that recessive effects may explain a proportion of AD heritability.