Christa Meisinger

Prof. Christa Meisinger


Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 January 2010 in Nature Genetics
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JDJosée Dupuis
Josée Dupuis
CLClaudia Langenberg
Claudia Langenberg
IPInga Prokopenko
Inga Prokopenko
RSRicha Saxena
Richa Saxena
NSNicole Soranzo
Nicole Soranzo
IBInês Barroso
Inês Barroso
The MAGIC investigators report results of a large genome-wide association study meta-analysis to identify common variants influencing fasting glucose homeostasis. They further show that several of the newly discovered loci influencing glycemic traits are also associated with risk of type 2 diabetes. Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.

6 March 2011 in Nature Genetics
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JCJohn F. Carlquist Cardiogenics
John F. Carlquist Cardiogenics
HSHeribert Schunkert
Heribert Schunkert
TCthe CARDIoGRAM Consortium
the CARDIoGRAM Consortium
IKInke R König
Inke R König
SKSekar Kathiresan
Sekar Kathiresan
Jeanette ErdmannJeanette Erdmann
Jeanette Erdmann

2 December 2012 in Nature Genetics
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PDPanos Deloukas
Panos Deloukas
Stavroula KanoniStavroula Kanoni
Stavroula Kanoni
CWChristina Willenborg
Christina Willenborg
MFMartin Farrall
Martin Farrall
TAThemistocles L Assimes
Themistocles L Assimes
NSNilesh J Samani
Nilesh J Samani

11 May 2016 in Nature
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AOAysu Okbay
Aysu Okbay
JBJonathan P. Beauchamp
Jonathan P. Beauchamp
MFMark Alan Fontana
Mark Alan Fontana
JLJames J. Lee
James J. Lee
TPTune H. Pers
Tune H. Pers
DBDaniel J. Benjamin
Daniel J. Benjamin
Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals1. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.

31 August 2011 in Nature
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CMC A R D Io G R A M
C A R D Io G R A M
Karsten SuhreKarsten Suhre
Karsten Suhre
SSSo-Youn Shin
So-Youn Shin
APAnn-Kristin Petersen
Ann-Kristin Petersen
RMRobert P. Mohney
Robert P. Mohney
CGChristian Gieger
Christian Gieger

11 July 2016 in Nature
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CFChristian Fuchsberger
Christian Fuchsberger
JFJason Flannick
Jason Flannick
TTTanya M. Teslovich
Tanya M. Teslovich
AMAnubha Mahajan
Anubha Mahajan
VAVineeta Agarwala
Vineeta Agarwala
MMMark I. McCarthy
Mark I. McCarthy
The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.