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Kauppi, Karolina
Publications (10 of 20) Show all publications
Sanyal, N., Lo, M.-T., Kauppi, K., Djurovic, S., Andreassen, O. A., Johnson, V. E. & Chen, C.-H. (2019). GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies. Bioinformatics, 35(1), 1-11
Open this publication in new window or tab >>GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies
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2019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 1, p. 1-11Article in journal (Refereed) Published
Abstract [en]

Motivation: Multiple marker analysis of the genome-wide association study (GWAS) data has gained ample attention in recent years. However, because of the ultra high-dimensionality of GWAS data, such analysis is challenging. Frequently used penalized regression methods often lead to large number of false positives, whereas Bayesian methods are computationally very expensive. Motivated to ameliorate these issues simultaneously, we consider the novel approach of using non-local priors in an iterative variable selection framework.

Results: We develop a variable selection method, named, iterative non-local prior based selection for GWAS, or GWASinlps, that combines, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors. The hallmark of our method is the introduction of 'structured screen-and-select' strategy, that considers hierarchical screening, which is not only based on response-predictor associations, but also based on response-response associations and concatenates variable selection within that hierarchy. Extensive simulation studies with single nucleotide polymorphisms having realistic linkage disequilibrium structures demonstrate the advantages of our computationally efficient method compared to several frequentist and Bayesian variable selection methods, in terms of true positive rate, false discovery rate, mean squared error and effect size estimation error. Further, we provide empirical power analysis useful for study design. Finally, a real GWAS data application was considered with human height as phenotype.

Place, publisher, year, edition, pages
Oxford University Press, 2019
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:umu:diva-157234 (URN)10.1093/bioinformatics/bty472 (DOI)000459313900001 ()29931045 (PubMedID)
Available from: 2019-03-20 Created: 2019-03-20 Last updated: 2019-03-20Bibliographically approved
Kauppi, K., Fan, C. C., McEvoy, L. K., Holland, D., Tan, C. H., Chen, C.-H., . . . Dale, A. M. (2018). Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease. Frontiers in Neuroscience, 12, Article ID 260.
Open this publication in new window or tab >>Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease
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2018 (English)In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 12, article id 260Article in journal (Refereed) Published
Abstract [en]

Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond APOE. Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months (p = 1.07e-5), and PHS was significantly more predictive than APOE alone (p = 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model p = 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating—Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both APOE and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018
Keywords
pHs, MCI, AD prediction, MRI, genetics
National Category
Neurology
Identifiers
urn:nbn:se:umu:diva-147818 (URN)10.3389/fnins.2018.00260 (DOI)000431178400001 ()29760643 (PubMedID)
Available from: 2018-05-22 Created: 2018-05-22 Last updated: 2018-06-09Bibliographically approved
Kauppi, K., Brin Rosenthal, S., Lo, M.-T., Sanyal, N., Jiang, M., Abagyan, R., . . . Chen, C.-H. (2018). Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia. American Journal of Psychiatry, 175(7), 674-682
Open this publication in new window or tab >>Revisiting Antipsychotic Drug Actions Through Gene Networks Associated With Schizophrenia
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2018 (English)In: American Journal of Psychiatry, ISSN 0002-953X, E-ISSN 1535-7228, Vol. 175, no 7, p. 674-682Article in journal (Refereed) Published
Abstract [en]

Objective: Antipsychotic drugs were incidentally discovered in the 1950s, but their mechanisms of action are still not understood. Better understanding of schizophrenia pathogenesis could shed light on actions of current drugs and reveal novel "druggable" pathways for unmet therapeutic needs. Recent genome-wide association studies offer unprecedented opportunities to characterize disease gene networks and uncover drug-disease relationships. Polygenic overlap between schizophrenia risk genes and antipsychotic drug targets has been demonstrated, but specific genes and pathways constituting this overlap are undetermined. Risk genes of polygenic disorders do not operate in isolation but in combination with other genes through protein-protein interactions among gene product.

Method: The protein interactome was used to map antipsychotic drug targets (N=88) to networks of schizophrenia risk genes (N=328).

Results: Schizophrenia risk genes were significantly localized in the interactome, forming a distinct disease module. Core genes of the module were enriched for genes involved in developmental biology and cognition, which may have a central role in schizophrenia etiology. Antipsychotic drug targets overlapped with the core disease module and comprised multiple pathways beyond dopamine. Some important risk genes like CHRN, PCDH, and HCN families were not connected to existing antipsychotics but may be suitable targets for novel drugs or drug repurposing opportunities to treat other aspects of schizophrenia, such as cognitive or negative symptoms.

Conclusions: The network medicine approach provides a platform to collate information of disease genetics and drug-gene interactions to shift focus from development of antipsychotics to multitarget antischizophrenia drugs. This approach is transferable to other diseases.

Place, publisher, year, edition, pages
AMER PSYCHIATRIC PUBLISHING, INC, 2018
National Category
Psychiatry
Identifiers
urn:nbn:se:umu:diva-150746 (URN)10.1176/appi.ajp.2017.17040410 (DOI)000437319200013 ()29495895 (PubMedID)2-s2.0-85049391454 (Scopus ID)
Available from: 2018-08-20 Created: 2018-08-20 Last updated: 2019-01-11Bibliographically approved
Athanasiu, L., Giddaluru, S., Fernandes, C., Christoforou, A., Reinvang, I., Lundervold, A. J., . . . Le Hellard, S. (2017). A genetic association study of CSMD1 and CSMD2 with cognitive function. Brain, behavior, and immunity, 61, 209-216
Open this publication in new window or tab >>A genetic association study of CSMD1 and CSMD2 with cognitive function
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2017 (English)In: Brain, behavior, and immunity, ISSN 0889-1591, E-ISSN 1090-2139, Vol. 61, p. 209-216Article in journal (Refereed) Published
Abstract [en]

The complement cascade plays a role in synaptic pruning and synaptic plasticity, which seem to be involved in cognitive functions and psychiatric disorders. Genetic variants in the closely related CSMD1 and CSMD2 genes, which are implicated in complement regulation, are associated with schizophrenia. Since patients with schizophrenia often show cognitive impairments, we tested whether variants in CSMD1 and CSMD2 are also associated with cognitive functions per se. We took a discovery-replication approach, using well-characterized Scandinavian cohorts. A total of 1637 SNPs in CSMD1 and 206 SNPs in CSMD2 were tested for association with cognitive functions in the NCNG sample (Norwegian Cognitive NeuroGenetics; n = 670). Replication testing of SNPs with p-value < 0.001 (7 in CSMD1 and 3 in CSMD2) was carried out in the TOP sample (Thematically Organized Psychosis; n =1025) and the BETULA sample (Betula Longitudinal Study on aging, memory and dementia; n = 1742). Finally, we conducted a meta-analysis of these SNPs using all three samples. The previously identified schizophrenia marker in CSMD1 (SNP rs10503253) was also included. The strongest association was observed between the CSMDI SNP rs2740931 and performance in immediate episodic memory (p-value = 5 Chi 10(-6), minor allele A, MAF 0.48-0.49, negative direction of effect). This association reached the study-wide significance level (p <= 1.2 Chi 10(-5)). SNP rs10503253 was not significantly associated with cognitive functions in our samples. In conclusion, we studied n = 3437 individuals and found evidence that a variant in CSMD1 is associated with cognitive function. Additional studies of larger samples with cognitive phenotypes will be needed to further clarify the role of CSMD1 in cognitive phenotypes in health and disease.

Keywords
Complement, Immunity, CSMD1, CSMD2, Schizophrenia, Cognition, Psychiatry, Memory, GWAS
National Category
Psychiatry Neurosciences
Identifiers
urn:nbn:se:umu:diva-133700 (URN)10.1016/j.bbi.2016.11.026 (DOI)000395365900023 ()27890662 (PubMedID)
Available from: 2017-04-18 Created: 2017-04-18 Last updated: 2018-11-12Bibliographically approved
Lo, M.-T., Hinds, D. A., Tung, J. Y., Franz, C., Fan, C.-C., Wang, Y., . . . Chen, C.-H. (2017). Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders [Letter to the editor]. Nature Genetics, 49(1), 152-156
Open this publication in new window or tab >>Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders
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2017 (English)In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 49, no 1, p. 152-156Article in journal, Letter (Refereed) Published
Abstract [en]

Personality is influenced by genetic and environmental factors(1) and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci(2,3), significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132-260,861). Of these genome-wide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422-18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit- hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion-introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety).

National Category
Psychiatry Medical Genetics
Identifiers
urn:nbn:se:umu:diva-131883 (URN)10.1038/ng.3736 (DOI)000390976600022 ()27918536 (PubMedID)
Available from: 2017-02-24 Created: 2017-02-24 Last updated: 2018-06-09Bibliographically approved
Lo, M.-T., Fan, C.-C., Kauppi, K., Sanyal, N., Desikan, R. S., Farrer, L. A., . . . Chen, C.-H. (2017). Identification of Genetic Heterogeneity of Alzheimer's Disease Across Age. Paper presented at Annual Meeting of the International-Genetic-Epidemiology-Society (IGES), SEP 09-11, 2017, Queens Coll, Cambridge, ENGLAND. Genetic Epidemiology, 41(7), 706-707
Open this publication in new window or tab >>Identification of Genetic Heterogeneity of Alzheimer's Disease Across Age
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2017 (English)In: Genetic Epidemiology, ISSN 0741-0395, E-ISSN 1098-2272, Vol. 41, no 7, p. 706-707Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
John Wiley & Sons, 2017
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-141467 (URN)000413035000171 ()
Conference
Annual Meeting of the International-Genetic-Epidemiology-Society (IGES), SEP 09-11, 2017, Queens Coll, Cambridge, ENGLAND
Note

Meeting Abstract: 172

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2018-06-09Bibliographically approved
Smeland, O. B., Frei, O., Kauppi, K., Hill, W. D., Li, W., Wang, Y., . . . Andreassen, O. A. (2017). Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function. JAMA psychiatry, 74(10), 1065-1075
Open this publication in new window or tab >>Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function
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2017 (English)In: JAMA psychiatry, ISSN 2168-6238, E-ISSN 2168-622X, Vol. 74, no 10, p. 1065-1075Article in journal (Refereed) Published
Abstract [en]

IMPORTANCE Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction. OBJECTIVE To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, aswell as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains. DESIGN, SETTING, AND PARTICIPANTS Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [ cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888). MAIN OUTCOMES AND MEASURES Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined. RESULTS Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.2), and was shared between schizophrenia (z score, 5.01; P = 5.53 x 10(-7)), general cognitive function (z score, - 4.43; P = 9.42 x 10(-6)), and verbal-numerical reasoning (z score, - 5.43; P = 5.64 x 10(-8)). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain. CONCLUSIONS AND RELEVANCE The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.

Place, publisher, year, edition, pages
American Medical Association, 2017
National Category
Psychiatry
Identifiers
urn:nbn:se:umu:diva-142914 (URN)10.1001/jamapsychiatry.2017.1986 (DOI)000412386900014 ()28746715 (PubMedID)
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2019-01-11Bibliographically approved
Chen, C.-H., Wang, Y., Lo, M.-T., Schork, A., Fan, C.-C., Holland, D., . . . Dale, A. M. (2017). Leveraging genome characteristics to improve gene discovery for putamen subcortical brain structure. Scientific Reports, 7, Article ID 15736.
Open this publication in new window or tab >>Leveraging genome characteristics to improve gene discovery for putamen subcortical brain structure
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 15736Article in journal (Refereed) Published
Abstract [en]

Discovering genetic variants associated with human brain structures is an on-going effort. The ENIGMA consortium conducted genome-wide association studies (GWAS) with standard multi-study analytical methodology and identified several significant single nucleotide polymorphisms (SNPs). Here we employ a novel analytical approach that incorporates functional genome annotations (e.g., exon or 5′UTR), total linkage disequilibrium (LD) scores and heterozygosity to construct enrichment scores for improved identification of relevant SNPs. The method provides increased power to detect associated SNPs by estimating stratum-specific false discovery rate (FDR), where strata are classified according to enrichment scores. Applying this approach to the GWAS summary statistics of putamen volume in the ENIGMA cohort, a total of 15 independent significant SNPs were identified (conditional FDR < 0.05). In contrast, 4 SNPs were found based on standard GWAS analysis (P < 5 × 10−8). These 11 novel loci include GATAD2B, ASCC3, DSCAML1, and HELZ, which are previously implicated in various neural related phenotypes. The current findings demonstrate the boost in power with the annotation-informed FDR method, and provide insight into the genetic architecture of the putamen.

Place, publisher, year, edition, pages
Nature Publishing Group, 2017
National Category
Medical Genetics
Identifiers
urn:nbn:se:umu:diva-142456 (URN)10.1038/s41598-017-15705-x (DOI)000415282900051 ()29147026 (PubMedID)
Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-06-09Bibliographically approved
Lo, M.-T., Wang, Y., Kauppi, K., Sanyal, N., Fan, C.-C., Smeland, O. B., . . . Chen, C.-H. (2017). Modeling prior information of common genetic variants improves gene discovery for neuroticism. Human Molecular Genetics, 26(22), 4530-4539
Open this publication in new window or tab >>Modeling prior information of common genetic variants improves gene discovery for neuroticism
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2017 (English)In: Human Molecular Genetics, ISSN 0964-6906, E-ISSN 1460-2083, Vol. 26, no 22, p. 4530-4539Article in journal (Refereed) Published
Abstract [en]

Neuroticism reflects emotional instability, and is related to various mental and physical health issues. However, the majority of genetic variants associated with neuroticism remain unclear. Inconsistent genetic variants identified by different genome-wide association studies (GWAS) may be attributable to low statistical power. We proposed a novel framework to improve the power for gene discovery by incorporating prior information of single nucleotide polymorphisms (SNPs) and combining two relevant existing tools, relative enrichment score (RES) and conditional false discovery rate (FDR). Here, SNP's conditional FDR was estimated given its RES based on SNP prior information including linkage disequilibrium (LD)-weighted genic annotation scores, total LD scores and heterozygosity. A known significant locus in chromosome 8p was excluded before estimating FDR due to long-range LD structure. Only one significant LD-independent SNP was detected by analyses of unconditional FDR and traditional GWAS in the discovery sample (N = 59 225), and notably four additional SNPs by conditional FDR. Three of the five SNPs, all identified by conditional FDR, were replicated (P < 0.05) in an independent sample (N = 170 911). These three SNPs are located in intronic regions of CADM2, LINGO2 and EP300 which have been reported to be associated with autism, Parkinson's disease and schizophrenia, respectively. Our approach using a combination of RES and conditional FDR improved power of traditional GWAS for gene discovery providing a useful framework for the analysis of GWAS summary statistics by utilizing SNP prior information, and helping to elucidate the links between neuroticism and complex diseases from a genetic perspective.

Place, publisher, year, edition, pages
OXFORD UNIV PRESS, 2017
National Category
Medical Genetics
Identifiers
urn:nbn:se:umu:diva-142238 (URN)10.1093/hmg/ddx340 (DOI)000414403900019 ()
Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2018-06-09Bibliographically approved
Smeland, O. B., Kauppi, K., Wang, Y., Hill, W. D., Davies, G., Frei, O., . . . Andreassen, O. A. (2017). Shared genetic variants between schizophrenia and general cognitive function indicate common molecular genetic mechanisms. Paper presented at 24th World Congress of Psychiatric Genetics (WCPG), OCT 30-NOV 04, 2016, Jerusalem, ISRAEL. European Neuropsychopharmacology, 27, S410-S410
Open this publication in new window or tab >>Shared genetic variants between schizophrenia and general cognitive function indicate common molecular genetic mechanisms
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2017 (English)In: European Neuropsychopharmacology, ISSN 0924-977X, E-ISSN 1873-7862, Vol. 27, p. S410-S410Article in journal, Meeting abstract (Other academic) Published
Abstract [en]

Background: Schizophrenia (SCZ) is a severe mental disorder characterized by widespread cognitive impairments including deficits in learning, memory, processing speed, attention and executive functioning. Although cognitive deficits are a strong predictor of functional outcome in SCZ, current treatment strategies largely fail to ameliorate these impairments. Thus, in order to develop more efficient treatment strategies in SCZ, a better understanding of the pathogenesis of these cognitive deficits is needed. Given that both SCZ and cognitive ability are substantially heritable, we here aimed to determine whether SCZ share genetic influences with general cognitive function (COG), a phenotype that captures the shared variation in performance across several cognitive domains. Methods: We analyzed GWAS results in the form of summary statistics (p-values and z-scores) from SCZ (the Psychiatric Genomics Consortium; n=82 315) and COG (CHARGE Consortium; n=53 949). We applied a conditional false discovery rate (FDR) framework. By leveraging SNP-associations in a secondary trait (SCZ or COG), the conditional FDR approach increases power to detect loci in the primary trait (COG or SCZ), regardless of the directions of allelic effects of the risk loci. We then applied the conjunction FDR to identify shared loci between the phenotypes. The conjunction FDR is defined as the maximum of the conditional FDRs for both directions, and we used an overall FDR threshold of 0.05. Results: To visualize pleiotropic enrichment, we constructed conditional Q-Q plots which indicate substantial polygenetic overlap between SCZ and COG. For progressively stringent p-value thresholds for SCZ SNPs, we found approximately 150-fold enrichment for COG. For progressively stringent p-value thresholds for COG SNPs, we found approximately 100-fold enrichment for SCZ. We then used the conjunction FDR and identified fourteen independent loci shared between SCZ and COG. The majority of the shared loci show inverse associations in SCZ and COG, in line with the observed cognitive dysfunction in SCZ. Discussion: Our preliminary findings indicate shared molecular genetic mechanisms between SCZ and COG, which may provide important new insights into the pathogenesis of cognitive dysfunction in SCZ.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2017
National Category
Medical Genetics
Identifiers
urn:nbn:se:umu:diva-143152 (URN)000413847300081 ()
Conference
24th World Congress of Psychiatric Genetics (WCPG), OCT 30-NOV 04, 2016, Jerusalem, ISRAEL
Available from: 2017-12-20 Created: 2017-12-20 Last updated: 2018-11-12Bibliographically approved
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