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Sökning: WFRF:(Lekman M)

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  • Karlsson, Robert, et al. (författare)
  • MAGI1 Copy Number Variation in Bipolar Affective Disorder and Schizophrenia
  • 2012
  • Ingår i: Biological Psychiatry. - 0006-3223 .- 1873-2402. ; 71:10, s. 922-930
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Bipolar affective disorder (BPAD) and schizophrenia (SZ) are devastating psychiatric disorders that each affect about 1% of the population worldwide. Identification of new drug targets is an important step toward better treatment of these poorly understood diseases. Methods: Genome-wide copy number variation (CNV) was assessed and variants were ranked by co-occurrence with disease in 48 BPAD families. Additional support for involvement of the highest-ranking CNV from the family-based analysis in psychiatric disease was obtained through analysis of 4084 samples with BPAD, SZ, or schizoaffective disorder. Finally, a pooled analysis of in-house and published datasets was carried out including 10,925 cases with BPAD, SZ, or schizoaffective disorder and 16,747 controls. Results: In the family-based analysis, an approximately 200 kilobase (kb) deletion in the first intron of the MAGI1 gene was identified that segregated with BPAD in a pedigree (six out of six affected individuals; parametric logarithm of the odds score = 1.14). In the pooled analysis, seven additional insertions or deletions over 100 kb were identified in MAGI1 in cases, while only two such CNV events were identified in the same gene in controls (p = .023; Fisher's exact test). Because earlier work had identified a CNV in the close relative MAGI2 in SZ, the study was extended to include MAGI2. In the pooled analysis of MAGI2, two large deletions were found in cases, and two duplications were detected in controls. Conclusions: Results presented herein provide further evidence for a role of MAGI1 and MAGI2 in BPAD and SZ etiology.
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3.
  • Lekman, Magnus, et al. (författare)
  • A significant risk locus on 19q13 for bipolar disorder identified using a combined genome-wide linkage and copy number variation analysis
  • 2015
  • Ingår i: BioData Mining. - 1756-0381 .- 1756-0381. ; 8
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: The genetic background to bipolar disorder (BPD) has been attributed to different genetic and genomic risk factors. In the present study we hypothesized that inherited copy number variations (CNVs) contribute to susceptibility of BPD. We screened 637 BP-pedigrees from the NIMH Genetic Initiative and gave priority to 46 pedigrees. In this subsample we performed parametric and non-parametric genome-wide linkage analyses using similar to 21,000 SNP-markers. We developed an algorithm to test for linkage restricted to regions with CNVs that are shared within and across families. Results: For the combined CNV and linkage analysis, one region on 19q13 survived correction for multiple comparisons and replicates a previous BPD risk locus. The shared CNV map to the pregnancy-specific glycoprotein (PSG) gene, a gene-family not previously implicated in BPD etiology. Two SNPs in the shared CNV are likely transcription factor binding sites and are linked to expression of an F-box binding gene, a key regulator of neuronal pathways suggested to be involved in BPD etiology. Conclusions: Our CNV-weighted linkage approach identifies a risk locus for BPD on 19q13 and forms a useful tool to future studies to unravel part of the genetic vulnerability to BPD.
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4.
  • Lekman, Magnus, et al. (författare)
  • The genetic interacting landscape of 63 candidate genes in Major Depressive Disorder : an explorative study
  • 2014
  • Ingår i: BioData Mining. - 1756-0381 .- 1756-0381. ; 7, s. 19-
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Genetic contributions to major depressive disorder (MDD) are thought to result from multiple genes interacting with each other. Different procedures have been proposed to detect such interactions. Which approach is best for explaining the risk of developing disease is unclear. This study sought to elucidate the genetic interaction landscape in candidate genes for MDD by conducting a SNP-SNP interaction analysis using an exhaustive search through 3,704 SNP-markers in 1,732 cases and 1,783 controls provided from the GAIN MDD study. We used three different methods to detect interactions, two logistic regressions models (multiplicative and additive) and one data mining and machine learning (MDR) approach. Results: Although none of the interaction survived correction for multiple comparisons, the results provide important information for future genetic interaction studies in complex disorders. Among the 0.5% most significant observations, none had been reported previously for risk to MDD. Within this group of interactions, less than 0.03% would have been detectable based on main effect approach or an a priori algorithm. We evaluated correlations among the three different models and conclude that all three algorithms detected the same interactions to a low degree. Although the top interactions had a surprisingly large effect size for MDD (e. g. additive dominant model P-uncorrected = 9.10E-9 with attributable proportion (AP) value = 0.58 and multiplicative recessive model with P-uncorrected = 6.95E-5 with odds ratio (OR estimated from beta 3) value = 4.99) the area under the curve (AUC) estimates were low (< 0.54). Moreover, the population attributable fraction (PAF) estimates were also low (< 0.15). Conclusions: We conclude that the top interactions on their own did not explain much of the genetic variance of MDD. The different statistical interaction methods we used in the present study did not identify the same pairs of interacting markers. Genetic interaction studies may uncover previously unsuspected effects that could provide novel insights into MDD risk, but much larger sample sizes are needed before this strategy can be powerfully applied.
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