To characterize the role of rare complete human knockouts in autism spectrum disorders (ASDs), we identify genes with homozygous or compound heterozygous loss-of-function (LoF) variants (defined as nonsense and essential splice sites) from exome sequencing of 933 cases and 869 controls. We identify a 2-fold increase in complete knockouts of autosomal genes with low rates of LoF variation (≤ 5% frequency) in cases and estimate a 3% contribution to ASD risk by these events, confirming this observation in an independent set of 563 probands and 4,605 controls. Outside the pseudoautosomal regions on the X chromosome, we similarly observe a significant 1.5-fold increase in rare hemizygous knockouts in males, contributing to another 2% of ASDs in males. Taken together, these results provide compelling evidence that rare autosomal and X chromosome complete gene knockouts are important inherited risk factors for ASD.
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Although neuritic plaques and neurofibrillary tangles in older adults are correlated with cognitive impairment and severity of dementia, it has long been recognized that the relationship is imperfect, as some people exhibit normal cognition despite high levels of Alzheimer’s disease (AD) pathology. We compared the cellular, synaptic, and biochemical composition of midfrontal cortices in female subjects from the Religious Orders Study who were stratified into three subgroups: (1) pathological AD with normal cognition (“AD-Resilient”), (2) pathological AD with AD-typical dementia (“AD-Dementia”), and (3) pathologically normal with normal cognition (“Normal Comparison”). The AD-Resilient group exhibited preserved densities of synaptophysin-labeled presynaptic terminals and synaptopodin-labeled dendritic spines compared with the AD-Dementia group, and increased densities of glial fibrillary acidic protein astrocytes compared with both the AD-Dementia and Normal Comparison groups. Further, in a discovery-type antibody microarray protein analysis, we identified a number of candidate protein abnormalities that were associated with a particular diagnostic group. These data characterize cellular and synaptic features and identify novel biochemical targets that may be associated with resilient cognitive brain aging in the setting of pathological AD.
The frequency and clinical and pathological characteristics associated with the Gly206Ala presenilin 1 (PSEN1) mutation in Puerto Rican and non-Puerto Rican Hispanics were evaluated at the University of Pennsylvania’s Alzheimer’s Disease Center. DNAs from all cohort subjects were genotyped for the Gly206Ala PSEN1 mutation. Carriers and non-carriers with neurodegenerative disease dementias were compared for demographic, clinical, psychometric, and biomarker variables. Nineteen (12.6%) of 151 unrelated subjects with dementia were discovered to carry the PSEN1 Gly206Ala mutation. Microsatellite marker genotyping determined a common ancestral haplotype for all carriers. Carriers were all of Puerto Rican heritage with significantly younger age of onset, but otherwise were clinically and neuropsychologically comparable to those of non-carriers with AD. Three subjects had extensive topographic and biochemical biomarker assessments that were also typical of non-carriers with AD. Neuropathological examination in one subject revealed severe, widespread plaque and tangle pathology without other meaningful disease lesions. The PSEN1 Gly206Ala mutation is notably frequent in unrelated Puerto Rican immigrants with dementia in Philadelphia. Considered together with the increased prevalence and mortality of AD reported in Puerto Rico, these high rates may reflect hereditary risk concentrated in the island which warrants further study.
To discover susceptibility genes of late-onset Alzheimer’s disease (LOAD), we conducted a 3-stage genome-wide association study (GWAS) using three populations: Japanese from the Japanese Genetic Consortium for Alzheimer Disease (JGSCAD), Koreans, and Caucasians from the Alzheimer Disease Genetic Consortium (ADGC). In Stage 1, we evaluated data for 5,877,918 genotyped and imputed SNPs in Japanese cases (n = 1,008) and controls (n = 1,016). Genome-wide significance was observed with 12 SNPs in the APOE region. Seven SNPs from other distinct regions with p-values <2×10(-5) were genotyped in a second Japanese sample (885 cases, 985 controls), and evidence of association was confirmed for one SORL1 SNP (rs3781834, P = 7.33×10(-7) in the combined sample). Subsequent analysis combining results for several SORL1 SNPs in the Japanese, Korean (339 cases, 1,129 controls) and Caucasians (11,840 AD cases, 10,931 controls) revealed genome wide significance with rs11218343 (P = 1.77×10(-9)) and rs3781834 (P = 1.04×10(-8)). SNPs in previously established AD loci in Caucasians showed strong evidence of association in Japanese including rs3851179 near PICALM (P = 1.71×10(-5)) and rs744373 near BIN1 (P = 1.39×10(-4)). The associated allele for each of these SNPs was the same as in Caucasians. These data demonstrate for the first time genome-wide significance of LOAD with SORL1 and confirm the role of other known loci for LOAD in Japanese. Our study highlights the importance of examining associations in multiple ethnic populations.
Several recent gene expression studies identified hundreds of genes that are correlated with age in brain and other tissues in human. However, these studies used linear models of age correlation, which are not well equipped to model abrupt changes associated with particular ages. We developed a computational algorithm for age estimation in which the expression of each gene is treated as a dichotomized biomarker for whether the subject is older or younger than a particular age. In addition, for each age-informative gene our algorithm identifies the age threshold with the most drastic change in expression level, which allows us to associate genes with particular age periods. Analysis of human aging brain expression datasets from three frontal cortex regions showed that different pathways undergo transitions at different ages, and the distribution of pathways and age thresholds varies across brain regions. Our study reveals age-correlated expression changes at particular age points and allows one to estimate the age of an individual with better accuracy than previously published methods.
BACKGROUND: Viruses are exceedingly diverse in their evolved strategies to manipulate hosts for viral replication. However, despite these differences, most virus populations will occasionally experience two commonly-encountered challenges: growth in variable host environments, and growth under fluctuating population sizes. We used the segmented RNA bacteriophage ϕ6 as a model for studying the evolutionary genomics of virus adaptation in the face of host switches and parametrically varying population sizes. To do so, we created a bifurcating deme structure that reflected lineage splitting in natural populations, allowing us to test whether phylogenetic algorithms could accurately resolve this ‘known phylogeny’. The resulting tree yielded 32 clones at the tips and internal nodes; these strains were fully sequenced and measured for phenotypic changes in selected traits (fitness on original and novel hosts).
RESULTS: We observed that RNA segment size was negatively correlated with the extent of molecular change in the imposed treatments; molecular substitutions tended to cluster on the Small and Medium RNA chromosomes of the virus, and not on the Large segment. Our study yielded a very large molecular and phenotypic dataset, fostering possible inferences on genotype-phenotype associations. Using further experimental evolution, we confirmed an inference on the unanticipated role of an allelic switch in a viral assembly protein, which governed viral performance across host environments.
CONCLUSIONS: Our study demonstrated that varying complexities can be simultaneously incorporated into experimental evolution, to examine the combined effects of population size, and adaptation in novel environments. The imposed bifurcating structure revealed that some methods for phylogenetic reconstruction failed to resolve the true phylogeny, owing to a paucity of molecular substitutions separating the RNA viruses that evolved in our study.
Rare mutations in the gene encoding for tau (MAPT, microtubule-associated protein tau) cause frontotemporal dementia-spectrum (FTD-s) disorders, including FTD, progressive supranuclear palsy (PSP) and corticobasal syndrome, and a common extended haplotype spanning across the MAPT locus is associated with increased risk of PSP and Parkinson’s disease. We identified a rare tau variant (p.A152T) in a patient with a clinical diagnosis of PSP and assessed its frequency in multiple independent series of patients with neurodegenerative conditions and controls, in a total of 15 369 subjects. Tau p.A152T significantly increases the risk for both FTD-s (n = 2139, OR = 3.0, CI: 1.6-5.6, P = 0.0005) and Alzheimer’s disease (AD) (n = 3345, OR = 2.3, CI: 1.3-4.2, P = 0.004) compared with 9047 controls. Functionally, p.A152T (i) decreases the binding of tau to microtubules and therefore promotes microtubule assembly less efficiently; and (ii) reduces the tendency to form abnormal fibers. However, there is a pronounced increase in the formation of tau oligomers. Importantly, these findings suggest that other regions of the tau protein may be crucial in regulating normal function, as the p.A152 residue is distal to the domains considered responsible for microtubule interactions or aggregation. These data provide both the first genetic evidence and functional studies supporting the role of MAPT p.A152T as a rare risk factor for both FTD-s and AD and the concept that rare variants can increase the risk for relatively common, complex neurodegenerative diseases, but since no clear significance threshold for rare genetic variation has been established, some caution is warranted until the findings are further replicated.
OBJECTIVE: To use values of cerebrospinal fluid tau and β-amyloid obtained from 2 different analytical immunoassays to differentiate Alzheimer disease (AD) from frontotemporal lobar degeneration (FTLD).
DESIGN: Cerebrospinal fluid values of total tau (T-tau) and β-amyloid 1-42 (Aβ 1-42) obtained using the Innotest enzyme-linked immunosorbent assay were transformed using a linear regression model to equivalent values obtained using the INNO-BIA AlzBio3 (xMAP; Luminex) assay. Cutoff values obtained from the xMAP assay were developed in a series of autopsy-confirmed cases and cross validated in another series of autopsy-confirmed samples using transformed enzyme-linked immunosorbent assay values to assess sensitivity and specificity for differentiating AD from FTLD.
SETTING: Tertiary memory disorder clinics and neuropathologic and biomarker core centers.
PARTICIPANTS: Seventy-five samples from patients with cerebrospinal fluid data obtained from both assays were used for transformation of enzyme-linked immunosorbent assay values. Forty autopsy-confirmed cases (30 with AD and 10 with FTLD) were used to establish diagnostic cutoff values and then cross validated in a second sample set of 21 autopsy-confirmed cases (11 with AD and 10 with FTLD) with transformed enzyme-linked immunosorbent assay values.
MAIN OUTCOME MEASURE: Diagnostic accuracy using transformed biomarker values.
RESULTS: Data obtained from both assays were highly correlated. The T-tau to Aβ 1-42 ratio had the highest correlation between measures (r = 0.928, P < .001) and high reliability of transformation (intraclass correlation coefficient= 0.89). A cutoff of 0.34 for the T-tau to Aβ 1-42 ratio had 90% and 100% sensitivity and 96.7% and 91% specificity to differentiate FTLD cases in the validation and cross-validation samples, respectively.
CONCLUSIONS: Values from 2 analytical platforms can be transformed into equivalent units, which can distinguish AD from FTLD more accurately than the clinical diagnosis.
RNA secondary structure is required for the proper regulation of the cellular transcriptome. This is because the functionality, processing, localization and stability of RNAs are all dependent on the folding of these molecules into intricate structures through specific base pairing interactions encoded in their primary nucleotide sequences. Thus, as the number of RNA sequencing (RNA-seq) data sets and the variety of protocols for this technology grow rapidly, it is becoming increasingly pertinent to develop tools that can analyze and visualize this sequence data in the context of RNA secondary structure. Here, we present Sequencing Annotation and Visualization of RNA structures (SAVoR), a web server, which seamlessly links RNA structure predictions with sequencing data and genomic annotations to produce highly informative and annotated models of RNA secondary structure. SAVoR accepts read alignment data from RNA-seq experiments and computes a series of per-base values such as read abundance and sequence variant frequency. These values can then be visualized on a customizable secondary structure model. SAVoR is freely available at http://tesla.pcbi.upenn.edu/savor.
MOTIVATION: While phylogenetic analyses of datasets containing 1000-5000 sequences are challenging for existing methods, the estimation of substantially larger phylogenies poses a problem of much greater complexity and scale.
METHODS: We present DACTAL, a method for phylogeny estimation that produces trees from unaligned sequence datasets without ever needing to estimate an alignment on the entire dataset. DACTAL combines iteration with a novel divide-and-conquer approach, so that each iteration begins with a tree produced in the prior iteration, decomposes the taxon set into overlapping subsets, estimates trees on each subset, and then combines the smaller trees into a tree on the full taxon set using a new supertree method. We prove that DACTAL is guaranteed to produce the true tree under certain conditions. We compare DACTAL to SATé and maximum likelihood trees on estimated alignments using simulated and real datasets with 1000-27 643 taxa.
RESULTS: Our studies show that on average DACTAL yields more accurate trees than the two-phase methods we studied on very large datasets that are difficult to align, and has approximately the same accuracy on the easier datasets. The comparison to SATé shows that both have the same accuracy, but that DACTAL achieves this accuracy in a fraction of the time. Furthermore, DACTAL can analyze larger datasets than SATé, including a dataset with almost 28 000 sequences.
AVAILABILITY: DACTAL source code and results of dataset analyses are available at www.cs.utexas.edu/users/phylo/software/dactal.