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Protein–protein functional interactions arise from either transitory or permanent biomolecular associations and often lead to the coevolution of the interacting residues. Although mutual information has traditionally been used to identify coevolving residues within the same protein, its application between coevolving proteins remains largely uncharacterized. Therefore, we developed the Protein Interactions Calculator (PIC) to efficiently identify coevolving residues between two protein sequences using mutual information. We verified the algorithm using 2102 known human protein interactions and 233 known bacterial protein interactions, with a respective 1975 and 252 non-interacting protein controls. The average PIC score for known human protein interactions was 4.5 times higher than non-interacting proteins (p = 1.03 × 10−108) and 1.94 times higher in bacteria (p = 1.22 × 10−35). We then used the PIC scores to determine the probability that two proteins interact. Using those probabilities, we paired 37 Alzheimer’s disease-associated proteins with 8608 other proteins and determined the likelihood that each pair interacts, which we report through a web interface. The PIC had significantly higher sensitivity and residue-specific resolution not available in other algorithms. Therefore, we propose that the PIC can be used to prioritize potential protein interactions, which can lead to a better understanding of biological processes and additional therapeutic targets belonging to protein interaction groups.

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10−13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses.

Microglia are central players in homeostasis and disease. Kang et al. reveal a novel ApoE-driven microglial pathway shared between aging, amyloidosis, and tauopathy that is exacerbated in females, suggesting a convergent mechanism for altering microglial reactivity during Alzheimer’s disease., Alzheimer’s disease (AD) is an age-associated neurodegenerative disease characterized by amyloidosis, tauopathy, and activation of microglia, the brain resident innate immune cells. We show that a RiboTag translational profiling approach can bypass biases due to cellular enrichment/cell sorting. Using this approach in models of amyloidosis, tauopathy, and aging, we revealed a common set of alterations and identified a central APOE-driven network that converged on CCL3 and CCL4 across all conditions. Notably, aged females demonstrated a significant exacerbation of many of these shared transcripts in this APOE network, revealing a potential mechanism for increased AD susceptibility in females. This study has broad implications for microglial transcriptomic approaches and provides new insights into microglial pathways associated with different pathological aspects of aging and AD.

Significant transcriptome alterations are detected in the brain of patients with amyotrophic lateral sclerosis (ALS), including carriers of the C9orf72 repeat expansion and C9orf72-negative sporadic cases. Recently, the expression of repetitive element transcripts has been associated with toxicity and, while increased repetitive element expression has been observed in several neurodegenerative diseases, little is known about their contribution to ALS. To assess whether aberrant expression of repetitive element sequences are observed in ALS, we analysed RNA sequencing data from C9orf72-positive and sporadic ALS cases, as well as healthy controls. Transcripts from multiple classes and subclasses of repetitive elements (LINEs, endogenous retroviruses, DNA transposons, simple repeats, etc.) were significantly increased in the frontal cortex of C9orf72 ALS patients. A large collection of patient samples, representing both C9orf72 positive and negative ALS, ALS/FTLD, and FTLD cases, was used to validate the levels of several repetitive element transcripts. These analyses confirmed that repetitive element expression was significantly increased in C9orf72-positive compared to C9orf72-negative or control cases. While previous studies suggest an important link between TDP-43 and repetitive element biology, our data indicate that TDP-43 pathology alone is insufficient to account for the observed changes in repetitive elements in ALS/FTLD. Instead, we found that repetitive element expression positively correlated with RNA polymerase II activity in postmortem brain, and pharmacologic modulation of RNA polymerase II activity altered repetitive element expression in vitro. We conclude that increased RNA polymerase II activity in ALS/FTLD may lead to increased repetitive element transcript expression, a novel pathological feature of ALS/FTLD.

Background The human genome contains “dark” gene regions that cannot be adequately assembled or aligned using standard short-read sequencing technologies, preventing researchers from identifying mutations within these gene regions that may be relevant to human disease. Here, we identify regions with few mappable reads that we call dark by depth, and others that have ambiguous alignment, called camouflaged. We assess how well long-read or linked-read technologies resolve these regions. Results Based on standard whole-genome Illumina sequencing data, we identify 36,794 dark regions in 6054 gene bodies from pathways important to human health, development, and reproduction. Of these gene bodies, 8.7% are completely dark and 35.2% are ≥ 5% dark. We identify dark regions that are present in protein-coding exons across 748 genes. Linked-read or long-read sequencing technologies from 10x Genomics, PacBio, and Oxford Nanopore Technologies reduce dark protein-coding regions to approximately 50.5%, 35.6%, and 9.6%, respectively. We present an algorithm to resolve most camouflaged regions and apply it to the Alzheimer’s Disease Sequencing Project. We rescue a rare ten-nucleotide frameshift deletion in CR1, a top Alzheimer’s disease gene, found in disease cases but not in controls. Conclusions While we could not formally assess the association of the CR1 frameshift mutation with Alzheimer’s disease due to insufficient sample-size, we believe it merits investigating in a larger cohort. There remain thousands of potentially important genomic regions overlooked by short-read sequencing that are largely resolved by long-read technologies. Electronic supplementary material The online version of this article (10.1186/s13059-019-1707-2) contains supplementary material, which is available to authorized users.

Background:Many neurodegenerative diseases are caused by nucleotide repeat expansions, but most expansions, like theC9orf72‘GGGGCC’ (G4C2) repeat that causes approximately 5-7% of all amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) cases, are too long to sequence using short-read sequencing technologies. It is unclear whether long-read sequencing technologies can traverse these long, challenging repeat expansions. Here, we demonstrate that two long-read sequencing technologies, Pacific Biosciences’ (PacBio) and Oxford Nanopore Technologies’ (ONT), can sequence through disease-causing repeats cloned into plasmids, including the FTD/ALS-causing G4C2repeat expansion. We also report the first long-read sequencing data characterizing theC9orf72G4C2repeat expansion at the nucleotide level in two symptomatic expansion carriers using PacBio whole-genome sequencing and a no-amplification (No-Amp) targeted approach based on CRISPR/Cas9.Results:Both the PacBio and ONT platforms successfully sequenced through the repeat expansions in plasmids. Throughput on the MinlON was a challenge for whole-genome sequencing; we were unable to attain reads covering the humanC9orf72repeat expansion using 15 flow cells. We obtained 8x coverage across theC9orf72locus using the PacBio Sequel, accurately reporting the unexpanded allele at eight repeats, and reading through the entire expansion with 1324 repeats (7941 nucleotides). Using the No-Amp targeted approach, we attained >800x coverage and were able to identify the unexpanded allele, closely estimate expansion size, and assess nucleotide content in a single experiment. We estimate the individual’s repeat region was >99% G4C2content, though we cannot rule out small interruptions.Conclusions:Our findings indicate that long-read sequencing is well suited to characterizing known repeat expansions, and for discovering new disease-causing, disease-modifying, or risk-modifying repeat expansions that have gone undetected with conventional short-read sequencing. The PacBio No-Amp targeted approach may have future potential in clinical and genetic counseling environments. Larger and deeper long-read sequencing studies inC9orf72expansion carriers will be important to determine heterogeneity and whether the repeats are interrupted by non-G4C2content, potentially mitigating or modifying disease course or age of onset, as interruptions are known to do in other repeat-expansion disorders. These results have broad implications across all diseases where the genetic etiology remains unclear.

Polygenic scores (or genetic risk scores) quantify the aggregate of small effects from many common genetic loci that have been associated with a trait through genome-wide association. Polygenic scores were first used successfully in schizophrenia and have since been applied to multiple phenotypes including multiple sclerosis, rheumatoid arthritis, and height. Because human height is an easily-measured and complex polygenic trait, polygenic height scores provide exciting insights into the predictability of aggregate common variant effect on the phenotype. Shawn Bradley is an extremely tall former professional basketball player from Brigham Young University and the National Basketball Association (NBA), measuring 2.29 meters (7′6″, 99.99999th percentile for height) tall, with no known medical conditions. Here, we present a case where a rare combination of common SNPs in one individual results in an extremely high polygenic height score that is correlated with an extreme phenotype. While polygenic scores are not clinically significant in the average case, our findings suggest that for extreme phenotypes, polygenic scores may be more successful for the prediction of individuals.

Background Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer’s disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype. Results Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer’s disease phenotype. Conclusions Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1093-7) contains supplementary material, which is available to authorized users.

Background Alzheimer's disease is the leading cause of dementia in the elderly and the third most common cause of death in the United States. A vast number of genes regulate Alzheimer’s disease, including Presenilin 1 (PSEN1). Multiple studies have attempted to locate novel variants in the PSEN1 gene that affect Alzheimer's disease status. A recent study suggested that one of these variants, PSEN1 E318G (rs17125721), significantly affects Alzheimer's disease status in a large case–control dataset, particularly in connection with the APOEε4 allele. Methods Our study looks at the same variant in the Cache County Study on Memory and Aging, a large population-based dataset. We tested for association between E318G genotype and Alzheimer’s disease status by running a series of Fisher’s exact tests. We also performed logistic regression to test for an additive effect of E318G genotype on Alzheimer’s disease status and for the existence of an interaction between E318G and APOEε4. Results In our Fisher’s exact test, it appeared that APOEε4 carriers with an E318G allele have slightly higher risk for AD than those without the allele (3.3 vs. 3.8); however, the 95 % confidence intervals of those estimates overlapped completely, indicating non-significance. Our logistic regression model found a positive but non-significant main effect for E318G (p = 0.895). The interaction term between E318G and APOEε4 was also non-significant (p = 0.689). Conclusions Our findings do not provide significant support for E318G as a risk factor for AD in APOEε4 carriers. Our calculations indicated that the overall sample used in the logistic regression models was adequately powered to detect the sort of effect sizes observed previously. However, the power analyses of our Fisher’s exact tests indicate that our partitioned data was underpowered, particularly in regards to the low number of E318G carriers, both AD cases and controls, in the Cache county dataset. Thus, the differences in types of datasets used may help to explain the difference in effect magnitudes seen. Analyses in additional case–control datasets will be required to understand fully the effect of E318G on Alzheimer's disease status.

Introduction Ebbert et al. reported gene-gene interactions between rs11136000-rs670139 ( CLU - MS4A4E ) and rs3865444-rs670139 ( CD33 - MS4A4E ). We evaluate these interactions in the largest data set for an epistasis study. Methods We tested interactions using 3837 cases and 4145 controls from Alzheimer's Disease Genetics Consortium using meta-analyses and permutation analyses. We repeated meta-analyses stratified by apolipoprotein E ( APOE ) e4 status, estimated combined odds ratio (OR) and population attributable fraction (cPAF), and explored causal variants. Results Results support the CLU - MS4A4E interaction and a dominant effect. An association between CLU - MS4A4E and APOE e4 negative status exists. The estimated synergy factor, OR, and cPAF for rs11136000-rs670139 are 2.23, 2.45, and 8.0, respectively. We identified potential causal variants. Discussion We replicated the CLU - MS4A4E interaction in a large case-control series and observed APOE e4 and possible dominant effect. The CLU - MS4A4E OR is higher than any Alzheimer's disease locus except APOE e4, APP , and TREM2 . We estimated an 8% decrease in Alzheimer's disease incidence without CLU - MS4A4E risk alleles and identified potential causal variants.

Correction The original version of this article [1] unfortunately contained a typographical error. The ‘Alzheimer’s Disease Neuroimaging Initiative’ was erroneously included as ‘Alzheimer’s Disease Neuroimaging Initative’ in the author list of the article.

Alzheimer’s disease affects millions of people worldwide and incidence is expected to rise as the population ages, but no effective therapies exist despite decades of research and more than 20 known disease markers. Research has shown that Alzheimer’s disease’s missing heritability remains extensive with an estimated 25% of phenotypic variance unexplained by known variants. The missing heritability may be explained by missing variants or by epistasis. Researchers often focus on individual loci rather than epistatic interactions, which is likely an oversimplification of the underlying biology since most phenotypes are affected by multiple genes. Focusing research efforts on epistasis will be critical to resolving Alzheimer’s disease etiology, and a major key to identifying and properly interpreting key epistatic interactions will be bridging the gap between statistical and biological epistasis. This review covers the current state of epistasis research in Alzheimer’s disease and how researchers can bridge the gap between statistical and biological epistasis to help resolve Alzheimer’s disease etiology.

Reported odds ratios and population attributable fractions (PAF) for late-onset Alzheimer's disease (LOAD) risk loci (BIN1, ABCA7, CR1, MS4A4E, CD2AP, PICALM, MS4A6A, CD33, and CLU) come from clinically ascertained samples. Little is known about the combined PAF for these LOAD risk alleles and the utility of these combined markers for case-control prediction. Here we evaluate these loci in a large population-based sample to estimate PAF and explore the effects of additive and nonadditive interactions on LOAD status prediction performance.2419 samples from the Cache County Memory Study were genotyped for APOE and nine LOAD risk loci from AlzGene.org. We used logistic regression and receiver operator characteristic analysis to assess the LOAD status prediction performance of these loci using additive and nonadditive models and compared odds ratios and PAFs between AlzGene.org and Cache County.Odds ratios were comparable between Cache County and AlzGene.org when identical single nucleotide polymorphisms were genotyped. PAFs from AlzGene.org ranged from 2.25% to 37%; those from Cache County ranged from .05% to 20%. Including non-APOE alleles significantly improved LOAD status prediction performance (area under the curve = .80) over APOE alone (area under the curve = .78) when not constrained to an additive relationship (p.03). We identified potential allelic interactions (p values uncorrected): CD33-MS4A4E (synergy factor = 5.31; p.003) and CLU-MS4A4E (synergy factor = 3.81; p.016).Although nonadditive interactions between loci significantly improve diagnostic ability, the improvement does not reach the desired sensitivity or specificity for clinical use. Nevertheless, these results suggest that understanding gene-gene interactions may be important in resolving Alzheimer's disease etiology.

Alzheimer’s disease is the most common form of dementia and is the only top 10 cause of death in the United States that lacks disease-altering treatments. It is a complex disorder with environmental and genetic components. There are two major types of Alzheimer’s disease, early onset and the more common late onset. The genetics of early-onset Alzheimer’s disease are largely understood with variants in three different genes leading to disease. In contrast, while several common alleles associated with late-onset Alzheimer’s disease, including APOE, have been identified using association studies, the genetics of late-onset Alzheimer’s disease are not fully understood. Here we review the known genetics of early- and late-onset Alzheimer’s disease.

Alzheimer’s disease is the most common form of dementia and is the only top 10 cause of death in the United States that lacks disease-altering treatments. It is a complex disorder with environmental and genetic components. There are two major types of Alzheimer’s disease, early onset and the more common late onset. The genetics of early-onset Alzheimer’s disease are largely understood with variants in three different genes leading to disease. In contrast, while several common alleles associated with late-onset Alzheimer’s disease, including APOE, have been identified using association studies, the genetics of late-onset Alzheimer’s disease are not fully understood. Here we review the known genetics of early- and late-onset Alzheimer’s disease.

Tripartite motif–containing 29 (TRIM29) is a member of the TRIM protein family that has been implicated in hematologic and solid tumor cancers. We found that TRIM29 functions as a tumor suppressor in both the nontumorigenic MCF10A [estrogen receptor (ER)−/TRIM29+] breast cell line and the invasive MCF7 (ER+/TRIM29−) breast cell line. Silencing TRIM29 in MCF10A cells resulted in preneoplastic changes that included loss of polarity in three-dimensional culture, increased proliferation, anchorage-independent growth, and increased migration and invasion. Conversely, the introduction of TRIM29 into MCF7 cells caused reversion to a less aggressive phenotype by antagonizing the growth effect of 17β-estradiol. The interaction between TRIM29 and ER signaling in MCF7 cells was supported by a reduction in ERE binding in the presence of TRIM29 and suppression of ER-dependent gene expression of TFF1, FOS, and GREB1. By microarray analyses, we showed that younger women (55 years of age) and thus may be due to menopause and loss of circulating estrogens. Our results suggest that loss of TRIM29 expression in normal breast luminal cells can contribute to malignant transformation and lead to progression of ER+ breast cancer in premenopausal women.

Background Many methodologies have been used in research to identify the “intrinsic” subtypes of breast cancer commonly known as Luminal A, Luminal B, HER2-Enriched (HER2-E) and Basal-like. The PAM50 gene set is often used for gene expression-based subtyping; however, surrogate subtyping using panels of immunohistochemical (IHC) markers are still widely used clinically. Discrepancies between these methods may lead to different treatment decisions. Methods We used the PAM50 RT-qPCR assay to expression profile 814 tumors from the GEICAM/9906 phase III clinical trial that enrolled women with locally advanced primary invasive breast cancer. All samples were scored at a single site by IHC for estrogen receptor (ER), progesterone receptor (PR), and Her2/neu (HER2) protein expression. Equivocal HER2 cases were confirmed by chromogenic in situ hybridization (CISH). Single gene scores by IHC/CISH were compared with RT-qPCR continuous gene expression values and “intrinsic” subtype assignment by the PAM50. High, medium, and low expression for ESR1, PGR, ERBB2, and proliferation were selected using quartile cut-points from the continuous RT-qPCR data across the PAM50 subtype assignments. Results ESR1, PGR, and ERBB2 gene expression had high agreement with established binary IHC cut-points (area under the curve (AUC) ≥ 0.9). Estrogen receptor positivity by IHC was strongly associated with Luminal (A and B) subtypes (92%), but only 75% of ER negative tumors were classified into the HER2-E and Basal-like subtypes. Luminal A tumors more frequently expressed PR than Luminal B (94% vs 74%) and Luminal A tumors were less likely to have high proliferation (11% vs 77%). Seventy-seven percent (30/39) of ER-/HER2+ tumors by IHC were classified as the HER2-E subtype. Triple negative tumors were mainly comprised of Basal-like (57%) and HER2-E (30%) subtypes. Single gene scoring for ESR1, PGR, and ERBB2 was more prognostic than the corresponding IHC markers as shown in a multivariate analysis. Conclusions The standard immunohistochemical panel for breast cancer (ER, PR, and HER2) does not adequately identify the PAM50 gene expression subtypes. Although there is high agreement between biomarker scoring by protein immunohistochemistry and gene expression, the gene expression determinations for ESR1 and ERBB2 status was more prognostic.

We conducted a multi-stage, genome-wide association study of bladder cancer with a primary scan of 591,637 SNPs in 3,532 affected individuals (cases) and 5,120 controls of European descent from five studies followed by a replication strategy, which included 8,382 cases and 48,275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P = 8 x 10(-12)) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 x 10(-11)) on 19q12 maps to CCNE1 and rs11892031 (P = 1 x 10(-7)) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3, validated previous candidate associations for the GSTM1 deletion (P = 4 x 10(-11)) and a tag SNP for NAT2 acetylation status (P = 4 x 10(-11)), and found interactions with smoking in both regions. Our findings on common variants associated with bladder cancer risk should provide new insights into the mechanisms of carcinogenesis.