Lili Milani
University of Tartu
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Publication
Featured researches published by Lili Milani.
Nature Genetics | 2013
Harm-Jan Westra; Marjolein J. Peters; Tonu Esko; Hanieh Yaghootkar; Johannes Kettunen; Mark W. Christiansen; Benjamin P. Fairfax; Katharina Schramm; Joseph E. Powell; Alexandra Zhernakova; Daria V. Zhernakova; Jan H. Veldink; Leonard H. van den Berg; Juha Karjalainen; Sebo Withoff; André G. Uitterlinden; Albert Hofman; Fernando Rivadeneira; Peter A. C. 't Hoen; Eva Reinmaa; Krista Fischer; Mari Nelis; Lili Milani; David Melzer; Luigi Ferrucci; Andrew Singleton; Dena Hernandez; Michael A. Nalls; Georg Homuth; Matthias Nauck
Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3′ UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.
Nature Communications | 2015
Beben Benyamin; Tonu Esko; Janina S. Ried; Aparna Radhakrishnan; Sita H. Vermeulen; Michela Traglia; Martin Goegele; Denise Anderson; Linda Broer; Clara Podmore; Jian'an Luan; Zoltán Kutalik; Serena Sanna; Peter van der Meer; Toshiko Tanaka; Fudi Wang; Harm-Jan Westra; Lude Franke; Evelin Mihailov; Lili Milani; Jonas Haelldin; Juliane Winkelmann; Thomas Meitinger; Joachim Thiery; Annette Peters; Melanie Waldenberger; Augusto Rendon; Jennifer Jolley; Jennifer Sambrook; Lambertus A. Kiemeney
Corrigendum: Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis
Nature Genetics | 2015
Jian Yang; Andrew Bakshi; Zhihong Zhu; Gibran Hemani; Anna A. E. Vinkhuyzen; Sang Hong Lee; Matthew R. Robinson; John Perry; Ilja M. Nolte; Jana V. van Vliet-Ostaptchouk; Harold Snieder; Tonu Esko; Lili Milani; Reedik Mägi; Andres Metspalu; Anders Hamsten; Patrik K. E. Magnusson; Nancy L. Pedersen; Erik Ingelsson; Nicole Soranzo; Matthew C. Keller; Naomi R. Wray; Michael E. Goddard; Peter M. Visscher
We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ∼97% and ∼68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ∼17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60–70% for height and 30–40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.
International Journal of Epidemiology | 2015
Liis Leitsalu; Toomas Haller; T. Esko; Mari-Liis Tammesoo; Helene Alavere; Harold Snieder; Markus Perola; Pauline C Ng; Reedik Mägi; Lili Milani; Krista Fischer; Andres Metspalu
The Estonian Biobank cohort is a volunteer-based sample of the Estonian resident adult population (aged ≥18 years). The current number of participants-close to 52000--represents a large proportion, 5%, of the Estonian adult population, making it ideally suited to population-based studies. General practitioners (GPs) and medical personnel in the special recruitment offices have recruited participants throughout the country. At baseline, the GPs performed a standardized health examination of the participants, who also donated blood samples for DNA, white blood cells and plasma tests and filled out a 16-module questionnaire on health-related topics such as lifestyle, diet and clinical diagnoses described in WHO ICD-10. A significant part of the cohort has whole genome sequencing (100), genome-wide single nucleotide polymorphism (SNP) array data (20 000) and/or NMR metabolome data (11 000) available (http://www.geenivaramu.ee/for-scientists/data-release/). The data are continuously updated through periodical linking to national electronic databases and registries. A part of the cohort has been re-contacted for follow-up purposes and resampling, and targeted invitations are possible for specific purposes, for example people with a specific diagnosis. The Estonian Genome Center of the University of Tartu is actively collaborating with many universities, research institutes and consortia and encourages fellow scientists worldwide to co-initiate new academic or industrial joint projects with us.
Annals of the Rheumatic Diseases | 2014
Evangelos Evangelou; Hanneke J. M. Kerkhof; Unnur Styrkarsdottir; Evangelia E. Ntzani; S.D. Bos; Tonu Esko; Daniel S. Evans; Sarah Metrustry; Kalliope Panoutsopoulou; Y.F. Ramos; Gudmar Thorleifsson; Konstantinos K. Tsilidis; N K Arden; Nadim Aslam; Nicholas Bellamy; Fraser Birrell; F.J. Blanco; Andrew Carr; Kay Chapman; Aaron G. Day-Williams; Panos Deloukas; Michael Doherty; Gunnar Engström; Hafdis T. Helgadottir; Albert Hofman; Thorvaldur Ingvarsson; Helgi Jonsson; Aime Keis; J. Christiaan Keurentjes; Margreet Kloppenburg
Objectives Osteoarthritis (OA) is the most common form of arthritis with a clear genetic component. To identify novel loci associated with hip OA we performed a meta-analysis of genome-wide association studies (GWAS) on European subjects. Methods We performed a two-stage meta-analysis on more than 78 000 participants. In stage 1, we synthesised data from eight GWAS whereas data from 10 centres were used for ‘in silico’ or ‘de novo’ replication. Besides the main analysis, a stratified by sex analysis was performed to detect possible sex-specific signals. Meta-analysis was performed using inverse-variance fixed effects models. A random effects approach was also used. Results We accumulated 11 277 cases of radiographic and symptomatic hip OA. We prioritised eight single nucleotide polymorphism (SNPs) for follow-up in the discovery stage (4349 OA cases); five from the combined analysis, two male specific and one female specific. One locus, at 20q13, represented by rs6094710 (minor allele frequency (MAF) 4%) near the NCOA3 (nuclear receptor coactivator 3) gene, reached genome-wide significance level with p=7.9×10−9 and OR=1.28 (95% CI 1.18 to 1.39) in the combined analysis of discovery (p=5.6×10−8) and follow-up studies (p=7.3×10−4). We showed that this gene is expressed in articular cartilage and its expression was significantly reduced in OA-affected cartilage. Moreover, two loci remained suggestive associated; rs5009270 at 7q31 (MAF 30%, p=9.9×10−7, OR=1.10) and rs3757837 at 7p13 (MAF 6%, p=2.2×10−6, OR=1.27 in male specific analysis). Conclusions Novel genetic loci for hip OA were found in this meta-analysis of GWAS.
PLOS Genetics | 2015
Tianxiao Huan; Tonu Esko; Marjolein J. Peters; Luke C. Pilling; Katharina Schramm; Brian H. Chen; Chunyu Liu; Roby Joehanes; Andrew D. Johnson; Chen Yao; Saixia Ying; Paul Courchesne; Lili Milani; Nalini Raghavachari; Richard Wang; Poching Liu; Eva Reinmaa; Abbas Dehghan; Albert Hofman; André G. Uitterlinden; Dena Hernandez; Stefania Bandinelli; Andrew Singleton; David Melzer; Andres Metspalu; Maren Carstensen; Harald Grallert; Christian Herder; Thomas Meitinger; Annette Peters
Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p<0.05). Among these genes, FOS and PTGS2 have been previously reported to be involved in BP-related processes; the others are novel. The top BP signature genes in aggregate explain 5%–9% of inter-individual variance in BP. Of note, rs3184504 in SH2B3, which was also reported in GWAS to be associated with BP, was found to be a trans regulator of the expression of 6 of the transcripts we found to be associated with BP (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2). Gene set enrichment analysis suggested that the BP-related global gene expression changes include genes involved in inflammatory response and apoptosis pathways. Our study provides new insights into molecular mechanisms underlying BP regulation, and suggests novel transcriptomic markers for the treatment and prevention of hypertension.
PLOS Genetics | 2012
Ruth McQuillan; Niina Eklund; Nicola Pirastu; Maris Kuningas; Brian P. McEvoy; Tonu Esko; Tanguy Corre; Gail Davies; Marika Kaakinen; Leo-Pekka Lyytikäinen; Kati Kristiansson; Aki S. Havulinna; Martin Gögele; Veronique Vitart; Albert Tenesa; Yurii S. Aulchenko; Caroline Hayward; Åsa Johansson; Mladen Boban; Sheila Ulivi; Antonietta Robino; Vesna Boraska; Wilmar Igl; Sarah H. Wild; Lina Zgaga; Najaf Amin; Evropi Theodoratou; Ozren Polasek; Giorgia Girotto; Lorna M. Lopez
Stature is a classical and highly heritable complex trait, with 80%–90% of variation explained by genetic factors. In recent years, genome-wide association studies (GWAS) have successfully identified many common additive variants influencing human height; however, little attention has been given to the potential role of recessive genetic effects. Here, we investigated genome-wide recessive effects by an analysis of inbreeding depression on adult height in over 35,000 people from 21 different population samples. We found a highly significant inverse association between height and genome-wide homozygosity, equivalent to a height reduction of up to 3 cm in the offspring of first cousins compared with the offspring of unrelated individuals, an effect which remained after controlling for the effects of socio-economic status, an important confounder (χ2 = 83.89, df = 1; p = 5.2×10−20). There was, however, a high degree of heterogeneity among populations: whereas the direction of the effect was consistent across most population samples, the effect size differed significantly among populations. It is likely that this reflects true biological heterogeneity: whether or not an effect can be observed will depend on both the variance in homozygosity in the population and the chance inheritance of individual recessive genotypes. These results predict that multiple, rare, recessive variants influence human height. Although this exploratory work focuses on height alone, the methodology developed is generally applicable to heritable quantitative traits (QT), paving the way for an investigation into inbreeding effects, and therefore genetic architecture, on a range of QT of biomedical importance.
Human Molecular Genetics | 2013
John Perry; Tanguy Corre; Tonu Esko; Daniel I. Chasman; Krista Fischer; Nora Franceschini; Chunyan He; Zoltán Kutalik; Massimo Mangino; Lynda M. Rose; Albert V. Smith; Lisette Stolk; Patrick Sulem; Michael N. Weedon; Wei V. Zhuang; Alice M. Arnold; Alan Ashworth; Sven Bergmann; Julie E. Buring; Andrea Burri; Constance Chen; Marilyn C. Cornelis; David Couper; Mark O. Goodarzi; Vilmundur Gudnason; Tamara B. Harris; Albert Hofman; Michael P. Jones; P. Kraft; Lenore J. Launer
Early menopause (EM) affects up to 10% of the female population, reducing reproductive lifespan considerably. Currently, it constitutes the leading cause of infertility in the western world, affecting mainly those women who postpone their first pregnancy beyond the age of 30 years. The genetic aetiology of EM is largely unknown in the majority of cases. We have undertaken a meta-analysis of genome-wide association studies (GWASs) in 3493 EM cases and 13 598 controls from 10 independent studies. No novel genetic variants were discovered, but the 17 variants previously associated with normal age at natural menopause as a quantitative trait (QT) were also associated with EM and primary ovarian insufficiency (POI). Thus, EM has a genetic aetiology which overlaps variation in normal age at menopause and is at least partly explained by the additive effects of the same polygenic variants. The combined effect of the common variants captured by the single nucleotide polymorphism arrays was estimated to account for ∼30% of the variance in EM. The association between the combined 17 variants and the risk of EM was greater than the best validated non-genetic risk factor, smoking.
BMC Genomics | 2014
Marc Jan Bonder; Silva Kasela; Mart Kals; Riin Tamm; Kaie Lokk; Isabel Barragan; Wim A. Buurman; Patrick Deelen; Jan-Willem M. Greve; Maxim Ivanov; Sander S. Rensen; Jana V. van Vliet-Ostaptchouk; Marcel G. M. Wolfs; Jingyuan Fu; Marten H. Hofker; Cisca Wijmenga; Alexandra Zhernakova; Magnus Ingelman-Sundberg; Lude Franke; Lili Milani
BackgroundThe liver plays a central role in the maintenance of homeostasis and health in general. However, there is substantial inter-individual variation in hepatic gene expression, and although numerous genetic factors have been identified, less is known about the epigenetic factors.ResultsBy analyzing the methylomes and transcriptomes of 14 fetal and 181 adult livers, we identified 657 differentially methylated genes with adult-specific expression, these genes were enriched for transcription factor binding sites of HNF1A and HNF4A. We also identified 1,000 genes specific to fetal liver, which were enriched for GATA1, STAT5A, STAT5B and YY1 binding sites. We saw strong liver-specific effects of single nucleotide polymorphisms on both methylation levels (28,447 unique CpG sites (meQTL)) and gene expression levels (526 unique genes (eQTL)), at a false discovery rate (FDR) < 0.05. Of the 526 unique eQTL associated genes, 293 correlated significantly not only with genetic variation but also with methylation levels. The tissue-specificities of these associations were analyzed in muscle, subcutaneous adipose tissue and visceral adipose tissue. We observed that meQTL were more stable between tissues than eQTL and a very strong tissue-specificity for the identified associations between CpG methylation and gene expression.ConclusionsOur analyses generated a comprehensive resource of factors involved in the regulation of hepatic gene expression, and allowed us to estimate the proportion of variation in gene expression that could be attributed to genetic and epigenetic variation, both crucial to understanding differences in drug response and the etiology of liver diseases.
Genome Biology | 2013
Maxim Ivanov; Mart Kals; Marina Kacevska; Isabel Barragan; Kie Kasuga; Anders Rane; Andres Metspalu; Lili Milani; Magnus Ingelman-Sundberg
BackgroundInterindividual differences in liver functions such as protein synthesis, lipid and carbohydrate metabolism and drug metabolism are influenced by epigenetic factors. The role of the epigenetic machinery in such processes has, however, been barely investigated. 5-hydroxymethylcytosine (5hmC) is a recently re-discovered epigenetic DNA modification that plays an important role in the control of gene expression.ResultsIn this study, we investigate 5hmC occurrence and genomic distribution in 8 fetal and 7 adult human liver samples in relation to ontogeny and function. LC-MS analysis shows that in the adult liver samples 5hmC comprises up to 1% of the total cytosine content, whereas in all fetal livers it is below 0.125%. Immunohistostaining of liver sections with a polyclonal anti-5hmC antibody shows that 5hmC is detected in most of the hepatocytes. Genome-wide mapping of the distribution of 5hmC in human liver samples by next-generation sequencing shows significant differences between fetal and adult livers. In adult livers, 5hmC occupancy is overrepresented in genes involved in active catabolic and metabolic processes, whereas 5hmC elements which are found in genes exclusively in fetal livers and disappear in the adult state, are more specific to pathways for differentiation and development.ConclusionsOur findings suggest that 5-hydroxymethylcytosine plays an important role in the development and function of the human liver and might be an important determinant for development of liver diseases as well as of the interindividual differences in drug metabolism and toxicity.