Role of X11 and ubiquilin as In Vivo Regulators of the Amyloid Precursor Protein in Drosophila

The Amyloid Precursor Protein (APP) undergoes sequential proteolytic cleavages through the action of β- and γ-secretase, which result in the generation of toxic β-amyloid (Aβ) peptides and a C-terminal fragment consisting of the intracellular domain of APP (AICD). Mutations leading to increased APP levels or alterations in APP cleavage cause familial Alzheimer's disease (AD). Thus, identification of factors that regulate APP steady state levels and/or APP cleavage by γ-secretase is likely to provide insight into AD pathogenesis. Here, using transgenic flies that act as reporters for endogenous γ-secretase activity and/or APP levels (GAMAREP), and for the APP intracellular domain (AICDREP), we identified mutations in X11L and ubiquilin (ubqn) as genetic modifiers of APP. Human homologs of both X11L (X11/Mint) and Ubqn (UBQLN1) have been implicated in AD pathogenesis. In contrast to previous reports, we show that overexpression of X11L or human X11 does not alter γ-secretase cleavage of APP or Notch, another γ-secretase substrate. Instead, expression of either X11L or human X11 regulates APP at the level of the AICD, and this activity requires the phosphotyrosine binding (PTB) domain of X11. In contrast, Ubqn regulates the levels of APP: loss of ubqn function leads to a decrease in the steady state levels of APP, while increased ubqn expression results in an increase in APP levels. Ubqn physically binds to APP, an interaction that depends on its ubiquitin-associated (UBA) domain, suggesting that direct physical interactions may underlie Ubqn-dependent regulation of APP. Together, our studies identify X11L and Ubqn as in vivo regulators of APP. Since increased expression of X11 attenuates Aβ production and/or secretion in APP transgenic mice, but does not act on γ-secretase directly, X11 may represent an attractive therapeutic target for AD

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

Genome-wide association study identifies 74 loci associated with educational attainment

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

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

AbstractObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.MethodsWe evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.ResultsWe observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p &lt; 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p &gt; 0.1).ConclusionsGenetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.</jats:sec