Lower baseline performance but greater plasticity of working memory for carriers of the val allele of the COMT Val158Met polymorphism

Objective: Little is known about genetic contributions to individual differences in cognitive plasticity. Given that the neurotransmitter dopamine is critical for cognition and associated with cognitive plasticity, we investigated the effects of 3 polymorphisms of dopamine-related genes (LMX1A, DRD2, COMT) on baseline performance and plasticity of working memory (WM), perceptual speed, and reasoning. Method: One hundred one younger and 103 older adults underwent approximately 100 days of cognitive training, and extensive testing before and after training. We analyzed the baseline and posttest data using latent change score models. Results: For working memory, carriers of the val allele of the COMT polymorphism had lower baseline performance and larger performance gains from training than carriers of the met allele. There was no significant effect of the other genes or on other cognitive domains. Conclusions: We relate this result to available evidence indicating that met carriers perform better than val carriers in WM tasks taxing maintenance, whereas val carriers perform better at updating tasks. We suggest that val carriers may show larger training gains because updating operations carry greater potential for plasticity than maintenance operations. (DIPF/Orig.

Neurosci Lett

Recently, the P86L alteration in CALHM1 (calcium homeostasis modulator-1) was reported to be associated with Alzheimer's disease (AD). Moreover, the risk allele increased amyloid-beta (A beta) levels in conditioned media from cultured cells. Therefore, we hypothesized that CALHM1 P86L may modulate A beta or tau levels in cerebrospinal fluid (CSF). Nearly 200 individuals with AD or other cognitive disorders were included for CSF analysis and CALHM1 genotyping. No significant differences in CSF levels of A beta 42, tau or phospho-tau were found across the various CALHM1 genotypes. In conclusion, we found no evidence that CALHM1 P86L is associated with altered CSF levels of the investigated AD biomarkers

MicroRNA-138 is a potential regulator of memory performance in humans

Genetic factors underlie a substantial proportion of individual differences in cognitive functions in humans, including processes related to episodic and working memory. While genetic association studies have proposed several candidate "memory genes," these currently explain only a minor fraction of the phenotypic variance. Here, we performed genome-wide screening on 13 episodic and working memory phenotypes in 1318 participants of the Berlin Aging Study II aged 60 years or older. The analyses highlight a number of novel single nucleotide polymorphisms (SNPs) associated with memory performance, including one located in a putative regulatory region of microRNA (miRNA) hsa-mir-138-5p (rs9882688, P-value = 7.8 x 10(-9)). Expression quantitative trait locus analyses on next-generation RNA-sequencing data revealed that rs9882688 genotypes show a significant correlation with the expression levels of this miRNA in 309 human lymphoblastoid cell lines (P-value = 5 x 10(-4)). In silico modeling of other top-ranking GWAS signals identified an additional memory-associated SNP in the 3' untranslated region (3' UTR) of DCP1B, a gene encoding a core component of the mRNA decapping complex in humans, predicted to interfere with hsa-mir-138-5p binding. This prediction was confirmed in vitro by luciferase assays showing differential binding of hsa-mir-138-5p to 3' UTR reporter constructs in two human cell lines (HEK293: P-value = 0.0470; SH-SY5Y: P-value = 0.0866). Finally, expression profiling of hsa-mir-138-5p and DCP1B mRNA in human post-mortem brain tissue revealed that both molecules are expressed simultaneously in frontal cortex and hippocampus, suggesting that the proposed interaction between hsa-mir-138-5p and DCP1B may also take place in vivo. In summary, by combining unbiased genome-wide screening with extensive in silico modeling, in vitro functional assays, and gene expression profiling, our study identified miRNA-138 as a potential molecular regulator of human memory function

Loss of Egg Yolk Genes in Mammals and the Origin of Lactation and Placentation

Embryonic development in nonmammalian vertebrates depends entirely on nutritional reserves that are predominantly derived from vitellogenin proteins and stored in egg yolk. Mammals have evolved new resources, such as lactation and placentation, to nourish their developing and early offspring. However, the evolutionary timing and molecular events associated with this major phenotypic transition are not known. By means of sensitive comparative genomics analyses and evolutionary simulations, we here show that the three ancestral vitellogenin-encoding genes were progressively lost during mammalian evolution (until around 30–70 million years ago, Mya) in all but the egg-laying monotremes, which have retained a functional vitellogenin gene. Our analyses also provide evidence that the major milk resource genes, caseins, which have similar functional properties as vitellogenins, appeared in the common mammalian ancestor ∼200–310 Mya. Together, our data are compatible with the hypothesis that the emergence of lactation in the common mammalian ancestor and the development of placentation in eutherian and marsupial mammals allowed for the gradual loss of yolk-dependent nourishment during mammalian evolution

A Computational Method Based on the Integration of Heterogeneous Networks for Predicting Disease-Gene Associations

The identification of disease-causing genes is a fundamental challenge in human health and of great importance in improving medical care, and provides a better understanding of gene functions. Recent computational approaches based on the interactions among human proteins and disease similarities have shown their power in tackling the issue. In this paper, a novel systematic and global method that integrates two heterogeneous networks for prioritizing candidate disease-causing genes is provided, based on the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein interactions. In this method, the association score function between a query disease and a candidate gene is defined as the weighted sum of all the association scores between similar diseases and neighbouring genes. Moreover, the topological correlation of these two heterogeneous networks can be incorporated into the definition of the score function, and finally an iterative algorithm is designed for this issue. This method was tested with 10-fold cross-validation on all 1,126 diseases that have at least a known causal gene, and it ranked the correct gene as one of the top ten in 622 of all the 1,428 cases, significantly outperforming a state-of-the-art method called PRINCE. The results brought about by this method were applied to study three multi-factorial disorders: breast cancer, Alzheimer disease and diabetes mellitus type 2, and some suggestions of novel causal genes and candidate disease-causing subnetworks were provided for further investigation

Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10−8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10−8). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies

Genome-wide significant association with seven novel multiple sclerosis risk loci

Objective: A recent large-scale study in multiple sclerosis (MS) using the ImmunoChip platform reported on 11 loci that showed suggestive genetic association with MS. Additional data in sufficiently sized and independent data sets are needed to assess whether these loci represent genuine MS risk factors. Methods: The lead SNPs of all 11 loci were genotyped in 10 796 MS cases and 10 793 controls from Germany, Spain, France, the Netherlands, Austria and Russia, that were independent from the previously reported cohorts. Association analyses were performed using logistic regression based on an additive model. Summary effect size estimates were calculated using fixed-effect meta-analysis. Results: Seven of the 11 tested SNPs showed significant association with MS susceptibility in the 21 589 individuals analysed here. Meta-analysis across our and previously published MS case-control data (total sample size n=101 683) revealed novel genome-wide significant association with MS susceptibility (p<5×10−8) for all seven variants. This included SNPs in or near LOC100506457 (rs1534422, p=4.03×10−12), CD28 (rs6435203, p=1.35×10−9), LPP (rs4686953, p=3.35×10−8), ETS1 (rs3809006, p=7.74×10−9), DLEU1 (rs806349, p=8.14×10−12), LPIN3 (rs6072343, p=7.16×10−12) and IFNGR2 (rs9808753, p=4.40×10−10). Cis expression quantitative locus effects were observed in silico for rs6435203 on CD28 and for rs9808753 on several immunologically relevant genes in the IFNGR2 locus. Conclusions: This study adds seven loci to the list of genuine MS genetic risk factors and further extends the list of established loci shared across autoimmune diseases

Genome-wide significant association with seven novel multiple sclerosis risk loci

Objective: A recent large-scale study in multiple sclerosis (MS) using the ImmunoChip platform reported on 11 loci that showed suggestive genetic association with MS. Additional data in sufficiently sized and independent data sets are needed to assess whether these loci represent genuine MS risk factors. Methods: The lead SNPs of all 11 loci were genotyped in 10 796 MS cases and 10 793 controls from Germany, Spain, France, the Netherlands, Austria and Russia, that were independent from the previously reported cohorts. Association analyses were performed using logistic regression based on an additive model. Summary effect size estimates were calculated using fixed-effect meta-analysis. Results: Seven of the 11 tested SNPs showed significant association with MS susceptibility in the 21 589 individuals analysed here. Meta-analysis across our and previously published MS case-control data (total sample size n=101 683) revealed novel genome-wide significant association with MS susceptibility (p<5×10−8) for all seven variants. This included SNPs in or near LOC100506457 (rs1534422, p=4.03×10−12), CD28 (rs6435203, p=1.35×10−9), LPP (rs4686953, p=3.35×10−8), ETS1 (rs3809006, p=7.74×10−9), DLEU1 (rs806349, p=8.14×10−12), LPIN3 (rs6072343, p=7.16×10−12) and IFNGR2 (rs9808753, p=4.40×10−10). Cis expression quantitative locus effects were observed in silico for rs6435203 on CD28 and for rs9808753 on several immunologically relevant genes in the IFNGR2 locus. Conclusions: This study adds seven loci to the list of genuine MS genetic risk factors and further extends the list of established loci shared across autoimmune diseases