Publications by authors named "Letizia Marullo"

13 Publications

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Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability.

Nat Commun 2021 01 5;12(1):24. Epub 2021 Jan 5.

Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
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http://dx.doi.org/10.1038/s41467-020-19366-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785747PMC
January 2021

Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals.

Sci Rep 2019 07 1;9(1):9439. Epub 2019 Jul 1.

Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.

Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.
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http://dx.doi.org/10.1038/s41598-019-45823-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602949PMC
July 2019

Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.

Nat Genet 2019 05 1;51(5):804-814. Epub 2019 May 1.

Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.
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http://dx.doi.org/10.1038/s41588-019-0403-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6522365PMC
May 2019

A meta-analysis of Italian and Estonian individuals shows an effect of common variants in on blood apoB levels.

Biomark Med 2019 08 7;13(11):931-940. Epub 2018 Sep 7.

Genomics of Common Disease, Division of Diabetes, Endocrinology & Metabolism, Department of Medicine, Imperial College London, London, UK.

The aim of the study was to explore the effects of variants at on a range of cardio-metabolic phenotypes. We analyzed the range of variants within Genetics in Brisighella Health Study and genes using an additive genetic model on 18 cardiometabolic phenotypes in a sample of 1645 individuals from the Genetics in Brisighella Health Study and replicated in 10,662 individuals from the Estonian Genome Center University of Tartu. We defined directly the effects of rs3846662:C>A at on apoB levels. The analysis also confirmed effects of on low-density lipoprotein-cholesterol and total cholesterol levels. Variants in gene did not reveal any associations with cardiometabolic phenotypes. This study highlights effect of locus on assay-determined apoB levels, an infrequent measure of blood lipids in large studies.
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http://dx.doi.org/10.2217/bmm-2017-0431DOI Listing
August 2019

Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics.

Hum Mol Genet 2018 02;27(4):742-756

FIMM Institute for Molecular Medicine Finland, Helsinki University, Helsinki FI-00014, Finland.

Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother-child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P < 5 × 10-8. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.
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http://dx.doi.org/10.1093/hmg/ddx429DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5886200PMC
February 2018

An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans.

Diabetes 2017 11 31;66(11):2888-2902. Epub 2017 May 31.

MRC Epidemiology Unit, University of Cambridge, Cambridge, U.K.

To characterize type 2 diabetes (T2D)-associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D case and 132,532 control subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference panel. Promising association signals were followed up in additional data sets (of 14,545 or 7,397 T2D case and 38,994 or 71,604 control subjects). We identified 13 novel T2D-associated loci ( < 5 × 10), including variants near the , , and genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common single nucleotide variants. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion and in adipocytes, monocytes, and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.
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http://dx.doi.org/10.2337/db16-1253DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5652602PMC
November 2017

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.

PLoS Genet 2015 Jul 1;11(7):e1005230. Epub 2015 Jul 1.

Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom.

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.
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http://dx.doi.org/10.1371/journal.pgen.1005230DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488845PMC
July 2015

The impact of low-frequency and rare variants on lipid levels.

Nat Genet 2015 Jun 11;47(6):589-97. Epub 2015 May 11.

1] Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. [2] German Center for Diabetes Research (DZD), Neuherberg, Germany. [3] Institute of Epidemiology II, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.

Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples, we identify association to lipid traits in 93 loci, including 79 previously identified loci with new lead SNPs and 10 new loci, 15 loci with a low-frequency lead SNP and 10 loci with a missense lead SNP, and 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC and APOE) or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2) explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for low-density lipoprotein cholesterol and total cholesterol. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to resequencing.
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http://dx.doi.org/10.1038/ng.3300DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757735PMC
June 2015

Insights into the genetic susceptibility to type 2 diabetes from genome-wide association studies of glycaemic traits.

Curr Diab Rep 2014 ;14(11):551

Department of Life Sciences and Biotechnology, Genetic Section, University of Ferrara, Via L. Borsari 46, 44121, Ferrara, Italy.

Over the past 8 years, the genetics of complex traits have benefited from an unprecedented advancement in the identification of common variant loci for diseases such as type 2 diabetes (T2D). The ability to undertake genome-wide association studies in large population-based samples for quantitative glycaemic traits has permitted us to explore the hypothesis that models arising from studies in non-diabetic individuals may reflect mechanisms involved in the pathogenesis of diabetes. Amongst 88 T2D risk and 72 glycaemic trait loci, only 29 are shared and show disproportionate magnitudes of phenotypic effects. Important mechanistic insights have been gained regarding the physiological role of T2D loci in disease predisposition through the elucidation of their contribution to glycaemic trait variability. Further investigation is warranted to define causal variants within these loci, including functional characterisation of associated variants, to dissect their role in disease mechanisms and to enable clinical translation.
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http://dx.doi.org/10.1007/s11892-014-0551-8DOI Listing
June 2015

Pleiotropic genes for metabolic syndrome and inflammation.

Mol Genet Metab 2014 Aug 9;112(4):317-38. Epub 2014 May 9.

Department of Health Studies, University of Chicago, IL, USA. Electronic address:

Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation.
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http://dx.doi.org/10.1016/j.ymgme.2014.04.007DOI Listing
August 2014

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.

Nat Genet 2012 Sep 12;44(9):991-1005. Epub 2012 Aug 12.

Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.
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http://dx.doi.org/10.1038/ng.2385DOI Listing
September 2012

A common ancestor more than 10,000 years old for patients with R854Q-related type 2N von Willebrand's disease in Italy.

Haematologica 2013 Jan 8;98(1):147-52. Epub 2012 Aug 8.

Department of Cardiologic, Thoracic and Vascular Sciences, University of Padua Medical School, Padua, France.

The impaired capacity of von Willebrand factor to carry factor VIII is identified as type 2N von Willebrand's disease. R854Q is the most common type 2N mutation, and almost the only one identified in Italy. This aim of this study was to ascertain whether R854Q mutations in a cohort of Italian patients with type 2N von Willebrand's disease originated from a single event or recurrent events. Thirteen unrelated Italian families were investigated, analyzing the von Willebrand factor gene haplotype associated with the R854Q mutation. A common haplotype emerged in all the families, extending from single nucleotide polymorphisms rs2166902 to rs216293 over 48.2 kb and including five intragenic markers. This haplotype is infrequent in the healthy Italian population (17% versus 100%, P<0.0001) and each genetic marker within the said haplotype is similarly rare. These data strongly suggest a founder effect, with a single R854Q mutation event being the cause of the type 2N von Willebrand's disease in our cohort of patients. Using DMLE+ software and the mathematical model of Bengtsson and Thomson, it was estimated that the R854Q mutation occurred from 10,000 to 40,000 years ago, which is consistent with the short dimension of the haplotype shared by our patients. Together with the fact that the R854Q mutation seems to be limited to Caucasian populations, these findings suggest that a single mutational event took place after human populations moved from Africa towards Europe.
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http://dx.doi.org/10.3324/haematol.2012.066019DOI Listing
January 2013
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