Jebsen Center

Oslo, Norway

Jebsen Center

Oslo, Norway

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Lencz T.,Zucker Hillside Hospital | Lencz T.,Feinstein Institute for Medical Research | Knowles E.,Yale University | Davies G.,University of Edinburgh | And 58 more authors.
Molecular Psychiatry | Year: 2014

It has long been recognized that generalized deficits in cognitive ability represent a core component of schizophrenia (SCZ), evident before full illness onset and independent of medication. The possibility of genetic overlap between risk for SCZ and cognitive phenotypes has been suggested by the presence of cognitive deficits in first-degree relatives of patients with SCZ; however, until recently, molecular genetic approaches to test this overlap have been lacking. Within the last few years, large-scale genome-wide association studies (GWAS) of SCZ have demonstrated that a substantial proportion of the heritability of the disorder is explained by a polygenic component consisting of many common single-nucleotide polymorphisms (SNPs) of extremely small effect. Similar results have been reported in GWAS of general cognitive ability. The primary aim of the present study is to provide the first molecular genetic test of the classic endophenotype hypothesis, which states that alleles associated with reduced cognitive ability should also serve to increase risk for SCZ. We tested the endophenotype hypothesis by applying polygenic SNP scores derived from a large-scale cognitive GWAS meta-analysis (∼5000 individuals from nine nonclinical cohorts comprising the Cognitive Genomics consorTium (COGENT)) to four SCZ case-control cohorts. As predicted, cases had significantly lower cognitive polygenic scores compared to controls. In parallel, polygenic risk scores for SCZ were associated with lower general cognitive ability. In addition, using our large cognitive meta-analytic data set, we identified nominally significant cognitive associations for several SNPs that have previously been robustly associated with SCZ susceptibility. Results provide molecular confirmation of the genetic overlap between SCZ and general cognitive ability, and may provide additional insight into pathophysiology of the disorder. © 2014 Macmillan Publishers Limited All rights reserved.


Trampush J.W.,Zucker Hillside Hospital | Trampush J.W.,Feinstein Institute for Medical Research | Lencz T.,Zucker Hillside Hospital | Lencz T.,Feinstein Institute for Medical Research | And 67 more authors.
American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics | Year: 2015

Cognitive deficits and reduced educational achievement are common in psychiatric illness; understanding the genetic basis of cognitive and educational deficits may be informative about the etiology of psychiatric disorders. A recent, large genome-wide association study (GWAS) reported a genome-wide significant locus for years of education, which subsequently demonstrated association to general cognitive ability ("g") in overlapping cohorts. The current study was designed to test whether GWAS hits for educational attainment are involved in general cognitive ability in an independent, large-scale collection of cohorts. Using cohorts in the Cognitive Genomics Consortium (COGENT; up to 20,495 healthy individuals), we examined the relationship between g and variants associated with educational attainment. We next conducted meta-analyses with 24,189 individuals with neurocognitive data from the educational attainment studies, and then with 53,188 largely independent individuals from a recent GWAS of cognition. A SNP (rs1906252) located at chromosome 6q16.1, previously associated with years of schooling, was significantly associated with g (P=1.47×10-4) in COGENT. The first joint analysis of 43,381 non-overlapping individuals for this a priori-designated locus was strongly significant (P=4.94×10-7), and the second joint analysis of 68,159 non-overlapping individuals was even more robust (P=1.65×10-9). These results provide independent replication, in a large-scale dataset, of a genetic locus associated with cognitive function and education. As sample sizes grow, cognitive GWAS will identify increasing numbers of associated loci, as has been accomplished in other polygenic quantitative traits, which may be relevant to psychiatric illness. © 2015 Wiley Periodicals, Inc.


PubMed | Martin Luther University of Halle Wittenberg, Hebrew University of Jerusalem, Jebsen Center, University of Edinburgh and 15 more.
Type: Journal Article | Journal: American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics | Year: 2015

Cognitive deficits and reduced educational achievement are common in psychiatric illness; understanding the genetic basis of cognitive and educational deficits may be informative about the etiology of psychiatric disorders. A recent, large genome-wide association study (GWAS) reported a genome-wide significant locus for years of education, which subsequently demonstrated association to general cognitive ability (g) in overlapping cohorts. The current study was designed to test whether GWAS hits for educational attainment are involved in general cognitive ability in an independent, large-scale collection of cohorts. Using cohorts in the Cognitive Genomics Consortium (COGENT; up to 20,495 healthy individuals), we examined the relationship between g and variants associated with educational attainment. We next conducted meta-analyses with 24,189 individuals with neurocognitive data from the educational attainment studies, and then with 53,188 largely independent individuals from a recent GWAS of cognition. A SNP (rs1906252) located at chromosome 6q16.1, previously associated with years of schooling, was significantly associated with g (P=1.4710(-4) ) in COGENT. The first joint analysis of 43,381 non-overlapping individuals for this a priori-designated locus was strongly significant (P=4.9410(-7) ), and the second joint analysis of 68,159 non-overlapping individuals was even more robust (P=1.6510(-9) ). These results provide independent replication, in a large-scale dataset, of a genetic locus associated with cognitive function and education. As sample sizes grow, cognitive GWAS will identify increasing numbers of associated loci, as has been accomplished in other polygenic quantitative traits, which may be relevant to psychiatric illness.


PubMed | Queensland Institute of Medical Research Berghofer, Martin Luther University of Halle Wittenberg, Jebsen Center, University of Edinburgh and 13 more.
Type: Journal Article | Journal: Molecular psychiatry | Year: 2016

Inbreeding depression refers to lower fitness among offspring of genetic relatives. This reduced fitness is caused by the inheritance of two identical chromosomal segments (autozygosity) across the genome, which may expose the effects of (partially) recessive deleterious mutations. Even among outbred populations, autozygosity can occur to varying degrees due to cryptic relatedness between parents. Using dense genome-wide single-nucleotide polymorphism (SNP) data, we examined the degree to which autozygosity associated with measured cognitive ability in an unselected sample of 4854 participants of European ancestry. We used runs of homozygosity-multiple homozygous SNPs in a row-to estimate autozygous tracts across the genome. We found that increased levels of autozygosity predicted lower general cognitive ability, and estimate a drop of 0.6 s.d. among the offspring of first cousins (P=0.003-0.02 depending on the model). This effect came predominantly from long and rare autozygous tracts, which theory predicts as more likely to be deleterious than short and common tracts. Association mapping of autozygous tracts did not reveal any specific regions that were predictive beyond chance after correcting for multiple testing genome wide. The observed effect size is consistent with studies of cognitive decline among offspring of known consanguineous relationships. These findings suggest a role for multiple recessive or partially recessive alleles in general cognitive ability, and that alleles decreasing general cognitive ability have been selected against over evolutionary time.


News Article | November 20, 2016
Site: www.sciencedaily.com

Despite its high value in assessment of risk, fitness is not routinely measured in clinical practice. The likely reason for this is the costly and time consuming testing procedure that requires trained personnel and expensive equipment. Therefore, research has recently turned to non-exercise algorithms, which, without the need for expensive equipment or trained personnel, estimate fitness from available clinical information and information provided by the patient. It is well known that individuals who are unfit are at substantially greater risk for lifestyle-related diseases and premature death. Despite its high value in assessment of risk, fitness is not routinely measured in clinical practice. The likely reason for this is the costly and time consuming testing procedure that requires trained personnel and expensive equipment. Therefore, research has recently turned to non-exercise algorithms, which, without the need for expensive equipment or trained personnel, estimate fitness from available clinical information and information provided by the patient. In a new study published in Mayo Clinic Proceedings, researchers from K.G. Jebsen Center for Exercise in Medicine, at Norwegian University of Science and Technology tested the value of estimated fitness in predicting the risk of premature death from either heart disease or any other cause, alone or in combination with other risk factors such as high blood pressure, smoking status, alcohol consumption, family history of heart disease, and diabetes. In other words, they investigated whether adding estimated fitness to traditional risk factors could improve the reliability of predicting premature death. In order to test their hypothesis, the researchers analyzed data available on 38,480 men and women who participated in the second wave of the Nord-Trondelag Health Study (HUNT2), followed up for up to 16 years. "We found that estimating fitness was enough to predict future risk of premature death from all causes. There was no need to perform complicated risk score algorithms that traditionally are used to calculate risk," explained Javaid Nauman, PhD, and Bjarne M. Nes, PhD, first co-authors of the study. "With the increase in lifestyle-related diseases around the world, estimated fitness is an easy, cost-effective method that could significantly help medical professionals identify people at high risk and improve patient management," commented co-author Carl J. Lavie, MD, from the John Ochsner Heart and Vascular Institute, New Orleans, LA.


News Article | November 17, 2016
Site: www.eurekalert.org

Rochester, MN, November 17, 2016 - It is well known that individuals who are unfit are at substantially greater risk for lifestyle-related diseases and premature death. Despite its high value in assessment of risk, fitness is not routinely measured in clinical practice. The likely reason for this is the costly and time consuming testing procedure that requires trained personnel and expensive equipment. Therefore, research has recently turned to non-exercise algorithms, which, without the need for expensive equipment or trained personnel, estimate fitness from available clinical information and information provided by the patient. In a new study published in Mayo Clinic Proceedings, researchers from K.G. Jebsen Center for Exercise in Medicine, at Norwegian University of Science and Technology tested the value of estimated fitness in predicting the risk of premature death from either heart disease or any other cause, alone or in combination with other risk factors such as high blood pressure, smoking status, alcohol consumption, family history of heart disease, and diabetes. In other words, they investigated whether adding estimated fitness to traditional risk factors could improve the reliability of predicting premature death. In order to test their hypothesis, the researchers analyzed data available on 38,480 men and women who participated in the second wave of the Nord-Trondelag Health Study (HUNT2), followed up for up to 16 years. "We found that estimating fitness was enough to predict future risk of premature death from all causes. There was no need to perform complicated risk score algorithms that traditionally are used to calculate risk," explained Javaid Nauman, PhD, and Bjarne M. Nes, PhD, first co-authors of the study. "With the increase in lifestyle-related diseases around the world, estimated fitness is an easy, cost-effective method that could significantly help medical professionals identify people at high risk and improve patient management," commented co-author Carl J. Lavie, MD, from the John Ochsner Heart and Vascular Institute, New Orleans, LA. "And just as importantly, it is a test that individuals can easily use to assess his/her own Fitness Number and Fitness Age, and in cases of low fitness do something about it! The only thing needed is access to the Internet and/or a smartphone as we have made this tool freely available (worldfitnesslevel.org and as apps on Google Play and Apple Store)," noted Ulrik Wisløff, PhD, lead investigator of the study. Video: https:/ Title: Knowing Your Fitness Number Predicts Your Risk for Future Ill Health Caption: Drs Javaid Nauman and Bjarne Nes from the Norwegian University of Science and Technology in Trondheim, Norway, discuss their article appearing in the February 2017 issue of Mayo Clinic Proceedings, which reported on an algorithm created to predict cardiovascular mortality. They found that this estimated measure was accurate in determining risk of death based on low levels of estimated cardiorespiratory fitness and suggests this as an effective tool for clinicians in patient discussions on cardiovascular health and exercise. Credit: Mayo Clinic Proceedings

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