Polymorphic Inversions Underlie the Shared Genetic Susceptibility of Obesity-Related Diseases

The burden of several common diseases including obesity, diabetes, hypertension, asthma, and depression is increasing in most world populations. However, the mechanisms underlying the numerous epidemiological and genetic correlations among these disorders remain largely unknown. We investigated whether common polymorphic inversions underlie the shared genetic influence of these disorders. We performed an inversion association analysis including 21 inversions and 25 obesity-related traits on a total of 408,898 Europeans and validated the results in 67,299 independent individuals. Seven inversions were associated with multiple diseases while inversions at 8p23.1, 16p11.2, and 11q13.2 were strongly associated with the co-occurrence of obesity with other common diseases. Transcriptome analysis across numerous tissues revealed strong candidate genes for obesity-related traits. Analyses in human pancreatic islets indicated the potential mechanism of inversions in the susceptibility of diabetes by disrupting the cis-regulatory effect of SNPs from their target genes. Our data underscore the role of inversions as major genetic contributors to the joint susceptibility to common complex diseases.This research has received funding from Ministerio de Ciencia, Innovación y Universidades (MICIU), Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional, UE (RTI2018-100789-B-I00) also through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S); and the Catalan Government through the CERCA Program and projects SGR2017/801 and #016FI_B 00272 to CR-A. JG is funded by the European Commission (H2020-ERC-2014-CoG-647900) and the MINECO/AEI/FEDER, EU (BFU2017-82937-P). LAPJ lab was funded by the Spanish Ministry of Science and Innovation (ISCIII-FEDER P13/02481), the Catalan Department of Economy and Knowledge (SGR2014/1468, SGR2017/1974 and ICREA Acadèmia), and also acknowledges support from the Spanish Ministry of Economy and Competiveness “Programa de Excelencia María de Maeztu” (MDM-2014-0370). This research was conducted using the UK Biobank Resource under Application Number 43983. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS.Peer ReviewedPostprint (author's final draft

The impact of non-additive genetic associations on age-related complex diseases

Genome-wide association studies (GWAS) are not fully comprehensive, as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implement an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association. We apply this methodology to 62,281 subjects across 22 age-related diseases and identify 94 genome-wide associated loci, including 26 previously unreported. Moreover, we observe that 27.7% of the 94 loci are missed if we use standard imputation strategies with a single reference panel, such as HRC, and only test the additive model. Among the new findings, we identify three novel low-frequency recessive variants with odds ratios larger than 4, which need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases. Most genome-wide association studies assume an additive model, exclude the X chromosome, and use one reference panel. Here, the authors implement a strategy including non-additive models and find that the number of loci for age-related traits increases as compared to the additive model alone.Peer reviewe

Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort

BACKGROUND: Heritability estimates have revealed an important contribution of SNP variants for most common traits; however, SNP analysis by single-trait genome-wide association studies (GWAS) has failed to uncover their impact. In this study, we applied a multitrait GWAS approach to discover additional factor of the missing heritability of human anthropometric variation. METHODS: We analysed 205 traits, including diseases identified at baseline in the GCAT cohort (Genomes For Life- Cohort study of the Genomes of Catalonia) (n=4988), a Mediterranean adult population-based cohort study from the south of Europe. We estimated SNP heritability contribution and single-trait GWAS for all traits from 15 million SNP variants. Then, we applied a multitrait-related approach to study genome-wide association to anthropometric measures in a two-stage meta-analysis with the UK Biobank cohort (n=336 107). RESULTS: Heritability estimates (eg, skin colour, alcohol consumption, smoking habit, body mass index, educational level or height) revealed an important contribution of SNP variants, ranging from 18% to 77%. Single-trait analysis identified 1785 SNPs with genome-wide significance threshold. From these, several previously reported single-trait hits were confirmed in our sample with LINC01432 (p=1.9×10-9) variants associated with male baldness, LDLR variants with hyperlipidaemia (ICD-9:272) (p=9.4×10-10) and variants in IRF4 (p=2.8×10-57), SLC45A2 (p=2.2×10-130), HERC2 (p=2.8×10-176), OCA2 (p=2.4×10-121) and MC1R (p=7.7×10-22) associated with hair, eye and skin colour, freckling, tanning capacity and sun burning sensitivity and the Fitzpatrick phototype score, all highly correlated cross-phenotypes. Multitrait meta-analysis of anthropometric variation validated 27 loci in a two-stage meta-analysis with a large British ancestry cohort, six of which are newly reported here (p value threshold <5×10-9) at ZRANB2-AS2, PIK3R1, EPHA7, MAD1L1, CACUL1 and MAP3K9. CONCLUSION: Considering multiple-related genetic phenotypes improve associated genome signal detection. These results indicate the potential value of data-driven multivariate phenotyping for genetic studies in large population-based cohorts to contribute to knowledge of complex traits

Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes.

The reanalysis of existing GWAS data represents a powerful and cost-effective opportunity to gain insights into the genetics of complex diseases. By reanalyzing publicly available type 2 diabetes (T2D) genome-wide association studies (GWAS) data for 70,127 subjects, we identify seven novel associated regions, five driven by common variants (LYPLAL1, NEUROG3, CAMKK2, ABO, and GIP genes), one by a low-frequency (EHMT2), and one driven by a rare variant in chromosome Xq23, rs146662057, associated with a twofold increased risk for T2D in males. rs146662057 is located within an active enhancer associated with the expression of Angiotensin II Receptor type 2 gene (AGTR2), a modulator of insulin sensitivity, and exhibits allelic specific activity in muscle cells. Beyond providing insights into the genetics and pathophysiology of T2D, these results also underscore the value of reanalyzing publicly available data using novel genetic resources and analytical approaches

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

A systematic and comprehensive approach for large-scale genome-wide association studies. Unraveling non-additive inheritance models in age-related diseases

[eng] Genome-wide association studies (GWAS) have been proven useful for identifying thousands of associations between genetic variants and human complex diseases and traits. However, the identified loci account for a small proportion of the estimated heritability (i.e., the proportion of variance for a particular phenotype that can be explained by genetic factors). The usually small effect size of common variants and the low frequencies of some variants with potentially larger effect sizes limit the statistical power of GWAS. The identification of common variants with small effects and low-frequency variants with large effects can be overcome with the analysis of larger sample sizes and imputing genotypes using dense reference panels. However, there is still room for improvement beyond increasing the sample size and the number of variants. As current GWAS are predominantly focused on the autosomes and only test the additive model, current strategies still constrain the full potential of GWAS. In this thesis, we hypothesized that performing a comprehensive analysis improving current GWAS strategies by 1) implementing the analysis of the X chromosome alongside the autosomes, 2) including genetic variants from a broader allele frequency spectrum and type of variants, such as small insertions and deletions (INDELs) through genotype imputation using multiple reference panels, and 3) testing different models of inheritance in the association test, would improve our understanding of the genetic architecture of complex diseases. To test these hypotheses we developed an integrated framework including our methodology, called GUIDANCE. Hence, GUIDANCE integrates state-of-the-art tools for GWAS analysis, including the analysis of X chromosome, a two-step imputation with multiple reference panels, the association testing including additive, dominant, recessive, heterodominant and genotypic inheritance models, and cross-phenotype association analysis when more than one disease is available in the cohort under study. We used GUIDANCE to analyze the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, a publicly available cohort that includes 62,281 subjects from European ancestry with an average age of 63 years for 22 diseases, representing the largest cohort for age-related diseases to date. After quality control, we analyzed 56,637 subjects from European descendant populations. Following our methodology, we imputed genotypes using 1000 Genomes Project (1000G) phase 3, the Genome of the Netherlands project (GoNL), the UK10K project22, and the Haplotype Reference Consortium (HRC) as reference panels. Using this strategy, we identified 26 new associated loci for 16 phenotypes (p < 5 × 10-8), with 13 showing significant dominance deviation (p < 0.05). Importantly, we identified three recessive loci with large effects that could not have identified by the additive model. This include a region let by an INDEL associated with cardiovascular disease in CACNB4 (rs201654520, minor allele frequency [MAF] = 0.017, odds ratio [OR] = 19.02, p = 4.32 × 10-8), a lous near PELO associated with type 2 diabetes with the greatest odds ratio for type 2 diabetes in Europeans reported to date (rs77704739, MAF= 0.036, OR = 4.32, p = 1.75 × 10-8), and a rare INDEL associated with age-related macular degeneration near THUMPD2 (rs557998486, MAF= 0.009, OR = 10.5, p = 2.75 × 10-8). Despite the phenotype discrepancies and different demographical characteristics of the GERA cohort and UK Biobank, four of the novel loci were replicated with an equivalent phenotype in UK Biobank, and we found additional supporting associations in related traits, treatments or biomarkers in UK Biobank for the remaining novel loci. Of note, PELO and THUMPD2 recessive loci were replicated using the recessive model in UK Biobank (combined results: PELO, rs77704739, OR = 2.46, p = 4.68 × 10-11, and THUMPD2, rs557998486, OR = 26.51, p = 3.29 × 10-8), which could not have been found with the additive model. Overall, these results highlight the importance of performing a comprehensive analysis of the full spectrum of genetic variation and considering non-additive models when performing GWAS, especially with well-powered biobanks and the increasing ability to impute low-frequency variants. For the benefit of the research community, we make available both GUIDANCE to boost the analysis of existing and ongoing GWAS projects, and the GERA cohort results, which constitute the largest non-additive genetic variation association database to date, through the Type 2 Diabetes Knowledge Portal (http://www.type2diabetesgenetics.org)

A systematic and comprehensive approach for large-scale genome-wide association studies. Unraveling non-additive inheritance models in age-related diseases

Genome-wide association studies (GWAS) have been proven useful for identifying thousands of associations between genetic variants and human complex diseases and traits. However, the identified loci account for a small proportion of the estimated heritability (i.e., the proportion of variance for a particular phenotype that can be explained by genetic factors). The usually small effect size of common variants and the low frequencies of some variants with potentially larger effect sizes limit the statistical power of GWAS. The identification of common variants with small effects and low-frequency variants with large effects can be overcome with the analysis of larger sample sizes and imputing genotypes using dense reference panels. However, there is still room for improvement beyond increasing the sample size and the number of variants. As current GWAS are predominantly focused on the autosomes and only test the additive model, current strategies still constrain the full potential of GWAS. In this thesis, we hypothesized that performing a comprehensive analysis improving current GWAS strategies by 1) implementing the analysis of the X chromosome alongside the autosomes, 2) including genetic variants from a broader allele frequency spectrum and type of variants, such as small insertions and deletions (INDELs) through genotype imputation using multiple reference panels, and 3) testing different models of inheritance in the association test, would improve our understanding of the genetic architecture of complex diseases. To test these hypotheses we developed an integrated framework including our methodology, called GUIDANCE. Hence, GUIDANCE integrates state-of-the-art tools for GWAS analysis, including the analysis of X chromosome, a two-step imputation with multiple reference panels, the association testing including additive, dominant, recessive, heterodominant and genotypic inheritance models, and cross-phenotype association analysis when more than one disease is available in the cohort under study. We used GUIDANCE to analyze the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, a publicly available cohort that includes 62,281 subjects from European ancestry with an average age of 63 years for 22 diseases, representing the largest cohort for age-related diseases to date. After quality control, we analyzed 56,637 subjects from European descendant populations. Following our methodology, we imputed genotypes using 1000 Genomes Project (1000G) phase 3, the Genome of the Netherlands project (GoNL), the UK10K project22, and the Haplotype Reference Consortium (HRC) as reference panels. Using this strategy, we identified 26 new associated loci for 16 phenotypes (p < 5 × 10-8), with 13 showing significant dominance deviation (p < 0.05). Importantly, we identified three recessive loci with large effects that could not have identified by the additive model. This include a region let by an INDEL associated with cardiovascular disease in CACNB4 (rs201654520, minor allele frequency [MAF] = 0.017, odds ratio [OR] = 19.02, p = 4.32 × 10-8), a lous near PELO associated with type 2 diabetes with the greatest odds ratio for type 2 diabetes in Europeans reported to date (rs77704739, MAF= 0.036, OR = 4.32, p = 1.75 × 10-8), and a rare INDEL associated with age-related macular degeneration near THUMPD2 (rs557998486, MAF= 0.009, OR = 10.5, p = 2.75 × 10-8). Despite the phenotype discrepancies and different demographical characteristics of the GERA cohort and UK Biobank, four of the novel loci were replicated with an equivalent phenotype in UK Biobank, and we found additional supporting associations in related traits, treatments or biomarkers in UK Biobank for the remaining novel loci. Of note, PELO and THUMPD2 recessive loci were replicated using the recessive model in UK Biobank (combined results: PELO, rs77704739, OR = 2.46, p = 4.68 × 10-11, and THUMPD2, rs557998486, OR = 26.51, p = 3.29 × 10-8), which could not have been found with the additive model. Overall, these results highlight the importance of performing a comprehensive analysis of the full spectrum of genetic variation and considering non-additive models when performing GWAS, especially with well-powered biobanks and the increasing ability to impute low-frequency variants. For the benefit of the research community, we make available both GUIDANCE to boost the analysis of existing and ongoing GWAS projects, and the GERA cohort results, which constitute the largest non-additive genetic variation association database to date, through the Type 2 Diabetes Knowledge Portal (http://www.type2diabetesgenetics.org)

Dysregulation of Placental miRNA in Maternal Obesity Is Associated With Pre- and Postnatal Growth

Human placenta exhibits a specific microRNA (miRNA) expression pattern. Some of these miRNAs are dysregulated in pregnancy disorders such as preeclampsia and intrauterine growth restriction and are potential biomarkers for these pathologies.status: publishe