Locus specific endogenous retroviral expression associated with Alzheimer's disease

Introduction

Human endogenous retroviruses (HERVs) are transcriptionally-active remnants of ancient retroviral infections that may play a role in Alzheimer's disease.

Methods

We combined two, publicly available RNA-Seq datasets with a third, novel dataset for a total cohort of 103 patients with Alzheimer's disease and 45 healthy controls. We use telescope to perform HERV quantification for these samples and simultaneously perform gene expression analysis.

Results

We identify differentially expressed genes and differentially expressed HERVs in Alzheimer's disease patients. Differentially expressed HERVs are scattered throughout the genome; many of them are members of the HERV-K superfamily. A number of HERVs are correlated with the expression of dysregulated genes in Alzheimer's and are physically proximal to genes which drive disease pathways.

Discussion

Dysregulated expression of ancient retroviral insertions in the human genome are present in Alzheimer's disease and show localization patterns that may explain how these elements drive pathogenic gene expression.

Comorbidity and Cancer Disease Rates among Those at High-Risk for Alzheimer's Disease: A Population Database Analysis

(1) Importance: Alzheimer's disease (AD) is complex and only partially understood. Analyzing the relationship between other more treatable or preventable diseases and AD may help in the prevention and the eventual development of treatments for AD. Risk estimation in a high-risk population, rather than a population already affected with AD, may reduce some bias in risk estimates. (2) Objective: To examine the rates of various comorbidities and cancers in individuals at high-risk for AD, but without a clinical diagnosis, relative to individuals from the same population with normal AD risk. (3) Design, Setting, and Participants: We conducted a study using data from the Utah Population Database (UPDB). The UPDB contains linked data from the Utah Cancer Registry, Utah death certificates, the Intermountain Health patient population, and the University of Utah Health patient population. Subjects were selected based on the availability of ancestral data, linked health information, and self-reported biometrics. (4) Results: In total, 75,877 participants who were estimated to be at high risk for AD based on family history, but who did not have an active AD diagnosis, were analyzed. A lower incidence of diabetes (RR = 0.95, 95% CI [0.92,0.97], p < 0.001), hypertension (RR = 0.97, 95% CI [0.95,0.99], p < 0.001), and heart disease (RR = 0.95, 95% CI [0.93,0.98], p < 0.001) was found. There was no difference in rates of cerebrovascular disease or other forms of dementia. Of the 15 types of cancer analyzed: breast (RR = 1.23, 95% CI [1.16, 1.30], p < 0.001); colorectal (RR = 1.30, 95% CI [1.21, 1.39], p < 0.001); kidney (RR = 1.49, 95% CI (1.29, 1.72), p < 0.001); lung (RR = 1.25, 95% CI [1.13, 1.37], p < 0.001); non-Hodgkin's Lymphoma (RR = 1.29, 95% CI [1.15, 1.44], p < 0.001); pancreas (RR = 1.34, 95% CI [1.16, 1.55], p < 0.001); stomach (RR = 1.59, 95% CI [1.36, 1.86], p < 0.001); and bladder (RR = 1.40, 95% CI [1.25, 1.56], p < 0.001), cancers were observed in significant excess among individuals at high-risk for AD after correction for multiple testing. (5) Conclusions and Relevance: Since age is the greatest risk factor for the development of AD, individuals who reach more advanced ages are at increased risk of developing AD. Consistent with this, people with fewer comorbidities earlier in life are more likely to reach an age where AD becomes a larger risk. Our findings show that individuals at high risk for AD have a decreased incidence of various other diseases. This is further supported by our finding that our high-risk group was also found to have an increased incidence of various cancers, which also increase in risk with age. There is the possibility that a more meaningful or etiological relationship exists among these various comorbidities. Further research into the etiological relationship between AD and these comorbidities may elucidate these possible interactions.

The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

The genome of a giant (trevally): Caranx ignobilis

Caranx ignobilis, commonly known as giant kingfish or giant trevally, is a large, reef-associated apex predator. It is a prized sportfish, targeted throughout its tropical and subtropical range in the Indian and Pacific Oceans. It also gained significant interest in aquaculture due to its unusual freshwater tolerance. Here, we present a draft assembly of the estimated 625.92 Mbp nuclear genome of a C. ignobilis individual from Hawaiian waters, which host a genetically distinct population. Our 97.4% BUSCO-complete assembly has a contig NG50 of 7.3 Mbp and a scaffold NG50 of 46.3 Mbp. Twenty-five of the 203 scaffolds contain 90% of the genome. We also present noisy, long-read DNA, Hi-C, and RNA-seq datasets, the latter containing eight distinct tissues and can help with annotations and studies of freshwater tolerance. Our genome assembly and its supporting data are valuable tools for ecological and comparative genomics studies of kingfishes and other carangoid fishes.

Web-Based Protein Interactions Calculator Identifies Likely Proteome Coevolution with Alzheimer’s Disease-Associated Proteins

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 Ramp Atlas: facilitating tissue and cell-specific ramp sequence analyses through an intuitive web interface

Ramp sequences occur when the average translational efficiency of codons near the 5′ end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.

Knowledge Gaps, Challenges, and Opportunities in Health and Prevention Research for Asian Americans, Native Hawaiians, and Pacific Islanders: A Report From the 2021 National Institutes of Health Workshop

Asian Americans (AsA), Native Hawaiians, and Pacific Islanders (NHPI) comprise 7.7% of the U.S. population, and AsA have had the fastest growth rate since 2010. Yet the National Institutes of Health (NIH) has invested only 0.17% of its budget on AsA and NHPI research between 1992 and 2018. More than 40 ethnic subgroups are included within AsA and NHPI (with no majority subpopulation), which are highly diverse culturally, demographically, linguistically, and socioeconomically. However, data for these groups are often aggregated, masking critical health disparities and their drivers. To address these issues, in March 2021, the National Heart, Lung, and Blood Institute, in partnership with 8 other NIH institutes, convened a multidisciplinary workshop to review current research, knowledge gaps, opportunities, barriers, and approaches for prevention research for AsA and NHPI populations. The workshop covered 5 domains: 1) sociocultural, environmental, psychological health, and lifestyle dimensions; 2) metabolic disorders; 3) cardiovascular and lung diseases; 4) cancer; and 5) cognitive function and healthy aging. Two recurring themes emerged: Very limited data on the epidemiology, risk factors, and outcomes for most conditions are available, and most existing data are not disaggregated by subgroup, masking variation in risk factors, disease occurrence, and trajectories. Leveraging the vast phenotypic differences among AsA and NHPI groups was identified as a key opportunity to yield novel clues into etiologic and prognostic factors to inform prevention efforts and intervention strategies. Promising approaches for future research include developing collaborations with community partners, investing in infrastructure support for cohort studies, enhancing existing data sources to enable data disaggregation, and incorporating novel technology for objective measurement. Research on AsA and NHPI subgroups is urgently needed to eliminate disparities and promote health equity in these populations.

Genome assembly of the roundjaw bonefish (Albula glossodonta), a vulnerable circumtropical sportfish

The roundjaw bonefish, Albula glossodonta, is the most widespread albulid in the Indo-Pacific and is vulnerable to extinction. We assembled the genome of a roundjaw bonefish from Hawai'i, USA, which will be instrumental for effective transboundary management and conservation when paired with population genomics datasets. The 1.05 gigabase pair (Gbp) contig-level assembly had a 4.75 megabase pair (Mbp) NG50 and a maximum contig length of 28.2 Mbp. Scaffolding yielded an LG50 of 20 and an NG50 of 14.49 Mbp, with the longest scaffold reaching 42.29 Mbp. The genome comprised 6.5% repetitive elements and was annotated with 28.3 K protein-coding genes. We then evaluated population genetic connectivity between six atolls in the Western Indian Ocean with 38,355 SNP loci across 66 A. glossodonta individuals. We discerned shallow population structure and observed genetic homogeneity between atolls in Seychelles and reduced gene flow between Seychelles and Mauritius. The South Equatorial Current might be the limiting mechanism of this reduced gene flow. The genome assembly will be useful for addressing taxonomic uncertainties of bonefishes globally.

Association between WWOX/MAF variants and dementia-related neuropathologic endophenotypes

The genetic locus containing the WWOX and MAF genes was implicated as a clinical Alzheimer's disease (AD) risk locus in two recent large meta-analytic genome wide association studies (GWAS). In a prior GWAS, we identified a variant in WWOX as a suggestive risk allele for hippocampal sclerosis. We hypothesized that the WWOX/MAF locus may be preferentially associated with non-plaque- and non-tau-related neuropathological changes (NC). Data from research participants with GWAS and autopsy measures from the National Alzheimer's Coordinating Center and the Religious Orders Study and the Rush Memory and Aging Project were meta-analyzed. Notably, no variants in the locus were significantly associated with ADNC. However, several WWOX/MAF variants had significant adjusted associations with limbic-predominant age-related TDP-43 encephalopathy NC (LATE-NC), HS, and brain arteriolosclerosis. These associations remained largely unchanged after adjustment for ADNC (operationalized with standard semiquantitative staging), suggesting that these associations are independent of ADNC. Thus, WWOX genetic variants were associated pathologically with LATE-NC, not ADNC.

Pairwise Correlation Analysis of the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

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.

GenoRisk: A polygenic risk score for Alzheimer's disease

INTRODUCTION

Recent clinical trials are considering inclusion of more than just apolipoprotein E (APOE) ε4 genotype as a way of reducing variability in analysis of outcomes.

METHODS

Case-control data were used to compare the capacity of age, sex, and 58 Alzheimer's disease (AD)-associated single nucleotide polymorphisms (SNPs) to predict AD status using several statistical models. Model performance was assessed with Brier scores and tenfold cross-validation. Genotype and sex × age estimates from the best performing model were combined with age and intercept estimates from the general population to develop a personalized genetic risk score, termed age, and sex-adjusted GenoRisk.

RESULTS

The elastic net model that included age, age x sex interaction, allelic APOE terms, and 29 additional SNPs performed the best. This model explained an additional 19% of the heritable risk compared to APOE genotype alone and achieved an area under the curve of 0.747.

DISCUSSION

GenoRisk could improve the risk assessment of individuals identified for prevention studies.

De novo genome assembly of the marine teleost, bluefin trevally (Caranx melampygus)

The bluefin trevally, Caranx melampygus, also known as the bluefin kingfish or bluefin jack, is known for its remarkable, bright-blue fins. This marine teleost is a widely prized sportfish, but few resources have been devoted to the genomics and conservation of this species because it is not targeted by large-scale commercial fisheries. Population declines from recreational and artisanal overfishing have been observed in Hawai'i, USA, resulting in both an interest in aquaculture and concerns about the long-term conservation of this species. Most research to-date has been performed in Hawai'i, raising questions about the status of bluefin trevally populations across its Indo-Pacific range. Genomic resources allow for expanded research on stock status, genetic diversity, and population demography. We present a high quality, 711 Mb nuclear genome assembly of a Hawaiian bluefin trevally from noisy long-reads with a contig NG50 of 1.2 Mb and longest contig length of 8.9 Mb. As measured by single-copy orthologs, the assembly was 95% complete, and the genome is comprised of 16.9% repetitive elements. The assembly was annotated with 33.1 K protein-coding genes, 71.4% of which were assigned putative functions, using RNA-seq data from eight tissues from the same individual. This is the first whole-genome assembly published for the carangoid genus Caranx. Using this assembled genome, a multiple sequentially Markovian coalescent model was implemented to assess population demography. Estimates of effective population size suggest population expansion has occurred since the Late Pleistocene. This genome will be a valuable resource for comparative phylogenomic studies of carangoid fishes and will help elucidate demographic history and delineate stock structure for bluefin trevally populations throughout the Indo-Pacific.

Analysis of genes (TMEM106B, GRN, ABCC9, KCNMB2, and APOE) implicated in risk for LATE-NC and hippocampal sclerosis provides pathogenetic insights: a retrospective genetic association study

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is the most prevalent subtype of TDP-43 proteinopathy, affecting up to 1/3rd of aged persons. LATE-NC often co-occurs with hippocampal sclerosis (HS) pathology. It is currently unknown why some individuals with LATE-NC develop HS while others do not, but genetics may play a role. Previous studies found associations between LATE-NC phenotypes and specific genes: TMEM106B, GRN, ABCC9, KCNMB2, and APOE. Data from research participants with genomic and autopsy measures from the National Alzheimer’s Coordinating Center (NACC; n = 631 subjects included) and the Religious Orders Study and Memory and the Rush Aging Project (ROSMAP; n = 780 included) were analyzed in the current study. Our goals were to reevaluate disease-associated genetic variants using newly collected data and to query whether the specific genotype/phenotype associations could provide new insights into disease-driving pathways. Research subjects included in prior LATE/HS genome-wide association studies (GWAS) were excluded. Single nucleotide variants (SNVs) within 10 kb of TMEM106B, GRN, ABCC9, KCNMB2, and APOE were tested for association with HS and LATE-NC, and separately for Alzheimer’s pathologies, i.e. amyloid plaques and neurofibrillary tangles. Significantly associated SNVs were identified. When results were meta-analyzed, TMEM106B, GRN, and APOE had significant gene-based associations with both LATE and HS, whereas ABCC9 had significant associations with HS only. In a sensitivity analysis limited to LATE-NC + cases, ABCC9 variants were again associated with HS. By contrast, the associations of TMEM106B, GRN, and APOE with HS were attenuated when adjusting for TDP-43 proteinopathy, indicating that these genes may be associated primarily with TDP-43 proteinopathy. None of these genes except APOE appeared to be associated with Alzheimer’s-type pathology. In summary, using data not included in prior studies of LATE or HS genomics, we replicated several previously reported gene-based associations and found novel evidence that specific risk alleles can differentially affect LATE-NC and HS.

Alzheimer's disease alters oligodendrocytic glycolytic and ketolytic gene expression

INTRODUCTION

Sporadic Alzheimer's disease (AD) is strongly correlated with impaired brain glucose metabolism, which may affect AD onset and progression. Ketolysis has been suggested as an alternative pathway to fuel the brain.

METHODS

RNA-seq profiles of post mortem AD brains were used to determine whether dysfunctional AD brain metabolism can be determined by impairments in glycolytic and ketolytic gene expression. Data were obtained from the Knight Alzheimer's Disease Research Center (62 cases; 13 controls), Mount Sinai Brain Bank (110 cases; 44 controls), and the Mayo Clinic Brain Bank (80 cases; 76 controls), and were normalized to cell type: astrocytes, microglia, neurons, oligodendrocytes.

RESULTS

In oligodendrocytes, both glycolytic and ketolytic pathways were significantly impaired in AD brains. Ketolytic gene expression was not significantly altered in neurons, astrocytes, and microglia.

DISCUSSION

Oligodendrocytes may contribute to brain hypometabolism observed in AD. These results are suggestive of a potential link between hypometabolism and dysmyelination in disease physiology. Additionally, ketones may be therapeutic in AD due to their ability to fuel neurons despite impaired glycolytic metabolism.

Analysis of high-risk pedigrees identifies 11 candidate variants for Alzheimer's disease

Introduction

Analysis of sequence data in high‐risk pedigrees is a powerful approach to detect rare predisposition variants.

Methods

Rare, shared candidate predisposition variants were identified from exome sequencing 19 Alzheimer's disease (AD)‐affected cousin pairs selected from high‐risk pedigrees. Variants were further prioritized by risk association in various external datasets. Candidate variants emerging from these analyses were tested for co‐segregation to additional affected relatives of the original sequenced pedigree members.

Results

AD‐affected high‐risk cousin pairs contained 564 shared rare variants. Eleven variants spanning 10 genes were prioritized in external datasets: rs201665195 (ABCA7), and rs28933981 (TTR) were previously implicated in AD pathology; rs141402160 (NOTCH3) and rs140914494 (NOTCH3) were previously reported; rs200290640 (PIDD1) and rs199752248 (PIDD1) were present in more than one cousin pair; rs61729902 (SNAP91), rs140129800 (COX6A2, AC026471), and rs191804178 (MUC16) were not present in a longevity cohort; and rs148294193 (PELI3) and rs147599881 (FCHO1) approached significance from analysis of AD‐related phenotypes. Three variants were validated via evidence of co‐segregation to additional relatives (PELI3, ABCA7, and SNAP91).

Discussion

These analyses support ABCA7 and TTR as AD risk genes, expand on previously reported NOTCH3 variant identification, and prioritize seven additional candidate variants.

A comprehensive analysis of the phylogenetic signal in ramp sequences in 211 vertebrates

Ramp sequences increase translational speed and accuracy when rare, slowly-translated codons are found at the beginnings of genes. Here, the results of the first analysis of ramp sequences in a phylogenetic construct are presented. Ramp sequences were compared from 247 vertebrates (114 Mammalian and 133 non-mammalian), where the presence and absence of ramp sequences was analyzed as a binary character in a parsimony and maximum likelihood framework. Additionally, ramp sequences were mapped to the Open Tree of Life synthetic tree to determine the number of parallelisms and reversals that occurred, and those results were compared to random permutations. Parsimony and maximum likelihood analyses of the presence and absence of ramp sequences recovered phylogenies that are highly congruent with established phylogenies. Additionally, 81% of vertebrate mammalian ramps and 81.2% of other vertebrate ramps had less parallelisms and reversals than the mean from 1000 randomly permuted trees. A chi-square analysis of completely orthologous ramp sequences resulted in a p-value < 0.001 as compared to random chance. Ramp sequences recover comparable phylogenies as other phylogenomic methods. Although not all ramp sequences appear to have a phylogenetic signal, more ramp sequences track speciation than expected by random chance. Therefore, ramp sequences may be used in conjunction with other phylogenomic approaches if many orthologs are taken into account. However, phylogenomic methods utilizing few orthologs should be cautious in incorporating ramp sequences because individual ramp sequences may provide conflicting signals.

CUBAP: an interactive web portal for analyzing codon usage biases across populations

Synonymous codon usage significantly impacts translational and transcriptional efficiency, gene expression, the secondary structure of both mRNA and proteins, and has been implicated in various diseases. However, population-specific differences in codon usage biases remain largely unexplored. Here, we present a web server, https://cubap.byu.edu, to facilitate analyses of codon usage biases across populations (CUBAP). Using the 1000 Genomes Project, we calculated and visually depict population-specific differences in codon frequencies, codon aversion, identical codon pairing, co-tRNA codon pairing, ramp sequences, and nucleotide composition in 17,634 genes. We found that codon pairing significantly differs between populations in 35.8% of genes, allowing us to successfully predict the place of origin for African and East Asian individuals with 98.8% and 100% accuracy, respectively. We also used CUBAP to identify a significant bias toward decreased CTG pairing in the immunity related GTPase M (IRGM) gene in East Asian and African populations, which may contribute to the decreased association of rs10065172 with Crohn's disease in those populations. CUBAP facilitates in-depth gene-specific and codon-specific visualization that will aid in analyzing candidate genes identified in genome-wide association studies, identifying functional implications of synonymous variants, predicting population-specific impacts of synonymous variants and categorizing genetic biases unique to certain populations.

Distinct clinicopathologic clusters of persons with TDP-43 proteinopathy

To better understand clinical and neuropathological features of TDP-43 proteinopathies, data were analyzed from autopsied research volunteers who were followed in the National Alzheimer's Coordinating Center (NACC) data set. All subjects (n = 495) had autopsy-proven TDP-43 proteinopathy as an inclusion criterion. Subjects underwent comprehensive longitudinal clinical evaluations yearly for 6.9 years before death on average. We tested whether an unsupervised clustering algorithm could detect coherent groups of TDP-43 immunopositive cases based on age at death and extensive neuropathologic data. Although many of the brains had mixed pathologies, four discernible clusters were identified. Key differentiating features were age at death and the severity of comorbid Alzheimer's disease neuropathologic changes (ADNC), particularly neuritic amyloid plaque densities. Cluster 1 contained mostly cases with a pathologic diagnosis of frontotemporal lobar degeneration (FTLD-TDP), consistent with enrichment of frontotemporal dementia clinical phenotypes including appetite/eating problems, disinhibition and primary progressive aphasia (PPA). Cluster 2 consisted of elderly limbic-predominant age-related TDP-43 encephalopathy (LATE-NC) subjects without severe neuritic amyloid plaques. Subjects in Cluster 2 had a relatively slow cognitive decline. Subjects in both Clusters 3 and 4 had severe ADNC + LATE-NC; however, Cluster 4 was distinguished by earlier disease onset, swifter disease course, more Lewy body pathology, less neocortical TDP-43 proteinopathy, and a suggestive trend in a subgroup analysis (n = 114) for increased C9orf72 risk SNP rs3849942 T allele (Fisher's exact test p value = 0.095). Overall, clusters enriched with neocortical TDP-43 proteinopathy (Clusters 1 and 2) tended to have lower levels of neuritic amyloid plaques, and those dying older (Clusters 2 and 3) had far less PPA or disinhibition, but more apathy. Indeed, 98% of subjects dying past age 85 years lacked clinical features of the frontotemporal dementia syndrome. Our study revealed discernible subtypes of LATE-NC and underscored the importance of age of death for differentiating FTLD-TDP and LATE-NC.

Interaction Between Physical Activity and Genes Related to Neurotrophin Signaling in Late-Life Cognitive Performance: The Cache County Study

Research indicates that lifestyle and genetic factors influence the course of cognitive impairment in aging, but their interactions have not been well-examined. This study examined the relationship between physical activity and genotypes related to brain-derived neurotrophic factor (BDNF) in predicting cognitive performance in a sample of older adults with up to 12 years of follow-up. Physical activity levels (sedentary, light, and moderate/vigorous) were determined for the sample of 3,591 participants (57% female) without dementia. The genotypes examined included BDNF gene single nucleotide polymorphisms (SNPs) (rs6265 and rs56164415) and receptor gene SNPs (NTRK2 rs2289656 and NGFR rs2072446). Cognition was assessed triennially using the Modified Mini-Mental State Exam. Unadjusted linear mixed models indicated that sedentary (β = -5.05) and light (β = -2.41) groups performed worse than moderate-vigorous (p < .001). Addition of interaction effects showed significant differences in rate of decline between activity levels, particularly among males (p = .006). A three-way interaction with sex, NGFR SNP rs2072446, and physical activity suggested that the C/C allele was associated with better cognitive performance among males engaging in light activity only (p = .004). Physical activity and sex, but not BDNF-related SNPs, predicted rate of cognitive decline in older adults, while NGFR rs2072446 may modify main effects.

Identification and genomic analysis of pedigrees with exceptional longevity identifies candidate rare variants

BACKGROUND

Longevity as a phenotype entails living longer than average and typically includes living without chronic age-related diseases. Recently, several common genetic components to longevity have been identified. This study aims to identify additional genetic variants associated with longevity using unique and powerful analyses of pedigrees with a statistical excess of healthy elderly individuals identified in the Utah Population Database (UPDB).

METHODS

From an existing biorepository of Utah pedigrees, six independent cousin pairs were selected from four extended pedigrees that exhibited an excess of healthy elderly individuals; whole exome sequencing (WES) was performed on two elderly individuals from each pedigree who were either first cousins or first cousins once removed. Rare (<.01 population frequency) variants shared by at least one elderly cousin pair in a region likely to be identical by descent were identified as candidates. Ingenuity Variant Analysis was used to prioritize putative causal variants based on quality control, frequency, and gain or loss of function. The variant frequency was compared in healthy cohorts and in an Alzheimer's disease cohort. Remaining variants were filtered based on their presence in genes reported to have an effect on the aging process, aging of cells, or the longevity process. Validation of these candidate variants included tests of segregation on other elderly relatives.

RESULTS

Fifteen rare candidate genetic variants spanning 17 genes shared within cousins were identified as having passed prioritization criteria. Of those variants, six were present in genes that are known or predicted to affect the aging process: rs78408340 (PAM), rs112892337 (ZFAT), rs61737629 (ESPL1), rs141903485 (CEBPE), rs144369314 (UTP4), and rs61753103 (NUP88 and RABEP1). ESPL1 rs61737629 and CEBPE rs141903485 show additional evidence of segregation with longevity in expanded pedigree analyses (p-values = .001 and .0001, respectively).

DISCUSSION

This unique pedigree analysis efficiently identified several novel rare candidate variants that may affect the aging process and added support to seven genes that likely contribute to longevity. Further analyses showed evidence for segregation for two rare variants, ESPL1 rs61737629 and CEBPE rs141903485, in the original longevity pedigrees in which they were initially observed. These candidate genes and variants warrant further investigation.

Predicting Clinical Dementia Rating Using Blood RNA Levels

The Clinical Dementia Rating (CDR) is commonly used to assess cognitive decline in Alzheimer's disease patients and is included in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We divided 741 ADNI participants with blood microarray data into three groups based on their most recent CDR assessment: cognitive normal (CDR = 0), mild cognitive impairment (CDR = 0.5), and probable Alzheimer's disease (CDR ≥ 1.0). We then used machine learning to predict cognitive status using only blood RNA levels. Only one probe for chloride intracellular channel 1 (CLIC1) was significant after correction. However, by combining individually nonsignificant probes with p-values less than 0.1, we averaged 87.87% (s = 1.02) predictive accuracy for classifying the three groups, compared to a 55.46% baseline for this study due to unequal group sizes. The best model had an overall precision of 0.902, recall of 0.895, and a receiver operating characteristic (ROC) curve area of 0.904. Although we identified one significant probe in CLIC1, CLIC1 levels alone were not sufficient to predict dementia status and cannot be used alone in a clinical setting. Additional analyses combining individually suggestive, but nonsignificant, blood RNA levels were significantly predictive and may improve diagnostic accuracy for Alzheimer's disease. Therefore, we propose that patient features that do not individually predict cognitive status might still contribute to overall cognitive decline through interactions that can be elucidated through machine learning.

Codon Pairs are Phylogenetically Conserved: A comprehensive analysis of codon pairing conservation across the Tree of Life

Identical codon pairing and co-tRNA codon pairing increase translational efficiency within genes when two codons that encode the same amino acid are translated by the same tRNA before it diffuses from the ribosome. We examine the phylogenetic signal in both identical and co-tRNA codon pairing across 23 428 species using alignment-free and parsimony methods. We determined that conserved codon pairing typically has a smaller window size than the length of a ribosome, and codon pairing tracks phylogenies across various taxonomic groups. We report a comprehensive analysis of codon pairing, including the extent to which each codon pairs. Our parsimony method generally recovers phylogenies that are more congruent with the established phylogenies than our alignment-free method. However, four of the ten taxonomic groups did not have sufficient orthologous codon pairings and were therefore analyzed using only the alignment-free methods. Since the recovered phylogenies using only codon pairing largely match phylogenies from the Open Tree of Life and the NCBI taxonomy, and are comparable to trees recovered by other algorithms, we propose that codon pairing biases are phylogenetically conserved and should be considered in conjunction with other phylogenomic techniques.

Failure to detect synergy between variants in transferrin and hemochromatosis and Alzheimer's disease in large cohort

Alzheimer's disease (AD) is the most common cause of dementia and, despite decades of effort, there is no effective treatment. In the last decade, many association studies have identified genetic markers that are associated with AD status. Two of these studies suggest that an epistatic interaction between variants rs1049296 in the transferrin (TF) gene and rs1800562 in the homeostatic iron regulator (HFE) gene, commonly known as hemochromatosis, is in genetic association with AD. TF and HFE are involved in the transport and regulation of iron in the brain, and disrupting these processes exacerbates AD pathology through increased neurodegeneration and oxidative stress. However, by using a significantly larger data set from the Alzheimer's Disease Genetics Consortium, we fail to detect an association between TF rs1049296 or HFE rs1800562 with AD risk (TF rs1049296 p = 0.38 and HFE rs1800562 p = 0.40). In addition, logistic regression with an interaction term and a synergy factor analysis both failed to detect epistasis between TF rs1049296 and HFE rs1800562 (SF = 0.94; p = 0.48) in AD cases. Each of these analyses had sufficient statistical power (power > 0.99), suggesting that previously reported associations may be the result of more complex epistatic interactions, genetic heterogeneity, or false-positive associations because of limited sample sizes.

Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight

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.

Association study of rs3846662 with Alzheimer's disease in a population-based cohort: the Cache County Study

3-Hydroxy-3-methylglutaryl coenzyme A reductase is associated with monitoring cholesterol levels. The presence of the single-nucleotide polymorphism rs3846662 introduces alternative splicing at exon 13; the exclusion of this exon leads to a reduction in total cholesterol levels. Lower cholesterol levels are linked to a reduction in Alzheimer's disease (AD) risk. The major allele of rs3846662, which encourages the splicing of exon 13, has recently been shown to act as a preventative allele for AD, especially in women. The purpose of our research was to replicate and confirm this finding. Using logistic regressions and survival curves, we found a significant association between AD and rs3846662, with a stronger association in individuals who carry the APOE e4 allele, supporting previously published work. The effect of rs3846662 on women is insignificant in our cohort. We confirmed that rs3846662 is associated with reduced risk for AD without gender differences; however, we failed to detect association between rs3846662 and delayed mild cognitive impairment conversion to AD for either of the APOE e4 allelic groups.

Relative risk for Alzheimer disease based on complete family history

OBJECTIVE

The inherited component for Alzheimer disease (AD) risk has focused on close relatives; consideration of the full family history may improve accuracy and utility of risk estimates.

METHODS

A population resource including a genealogy of Utah pioneers from the 1800s linked to Utah death certificates was used to estimate relative risk for AD based on specific family history constellations, including from first- to third-degree relatives.

RESULTS

Any affected first-degree relatives (FDR) significantly increased risk of AD (≥1 FDRs: relative risk [RR] 1.73, 95% confidence interval [CI] [1.59-1.87]; ≥2 FDRs: RR 3.98 [3.26-4.82]; ≥3 FDRs: RR 2.48 [1.07-4.89]; ≥4 FDRs: RR 14.77 [5.42-32.15]). Affected second-degree relatives (SDR) increased risk even in the presence of affected FDRs (FDR = 1 with SDR = 2: RR 21.29 [5.80-54.52]). AD only in third-degree relatives (TDR) also increased risk (FDR = 0, SDR = 0, TDR ≥3: RR 1.43 [1.21-1.68]). Mixed evidence was observed for differences in risk based on maternal compared to paternal inheritance; higher risks for men than women with equivalent family history, and higher risk for individuals with at least one affected FDR regardless of the relative's age at death, were observed.

CONCLUSIONS

This population-based estimation of RRs for AD based on family history ascertained from extended genealogy data indicates that inherited genetic factors have a broad influence, extending beyond immediate relatives. Providers should consider the full constellation of family history when counseling patients and families about their risk of AD.

Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Risk for late-onset Alzheimer's disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer's or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer's disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10-7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.

Association of Rare Coding Mutations With Alzheimer Disease and Other Dementias Among Adults of European Ancestry

Importance

Some of the unexplained heritability of Alzheimer disease (AD) may be due to rare variants whose effects are not captured in genome-wide association studies because very large samples are needed to observe statistically significant associations.

Objective

To identify genetic variants associated with AD risk using a nonstatistical approach.

Design, Setting, and Participants

Genetic association study in which rare variants were identified by whole-exome sequencing in unrelated individuals of European ancestry from the Alzheimer's Disease Sequencing Project (ADSP). Data were analyzed between March 2017 and September 2018.

Main Outcomes and Measures

Minor alleles genome-wide and in 95 genes previously associated with AD, AD-related traits, or other dementias were tabulated and filtered for predicted functional impact and occurrence in participants with AD but not controls. Support for several findings was sought in a whole-exome sequencing data set comprising 19 affected relative pairs from Utah high-risk pedigrees and whole-genome sequencing data sets from the ADSP and Alzheimer's Disease Neuroimaging Initiative.

Results

Among 5617 participants with AD (3202 [57.0%] women; mean [SD] age, 76.4 [9.3] years) and 4594 controls (2719 [59.0%] women; mean [SD] age, 86.5 [4.5] years), a total of 24 variants with moderate or high functional impact from 19 genes were observed in 10 or more participants with AD but not in controls. These variants included a missense mutation (rs149307620 [p.A284T], n = 10) in NOTCH3, a gene in which coding mutations are associated with cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), that was also identified in 1 participant with AD and 1 participant with mild cognitive impairment in the whole genome sequencing data sets. Four participants with AD carried the TREM2 rs104894002 (p.Q33X) high-impact mutation that, in homozygous form, causes Nasu-Hakola disease, a rare disorder characterized by early-onset dementia and multifocal bone cysts, suggesting an intermediate inheritance model for the mutation. Compared with controls, participants with AD had a significantly higher burden of deleterious rare coding variants in dementia-associated genes (2314 vs 3354 cumulative variants, respectively; P = .006).

Conclusions and Relevance

Different mutations in the same gene or variable dose of a mutation may be associated with result in distinct dementias. These findings suggest that minor differences in the structure or amount of protein may be associated with in different clinical outcomes. Understanding these genotype-phenotype associations may provide further insight into the pathogenic nature of the mutations, as well as offer clues for developing new therapeutic targets.

Population genealogy resource shows evidence of familial clustering for Alzheimer disease

OBJECTIVE

To show the potential of a resource consisting of a genealogy of the US record linked to National Veterans Health Administration (VHA) patient data for investigation of the genetic contribution to health-related phenotypes, we present an analysis of familial clustering of VHA patients diagnosed with Alzheimer disease (AD).

METHODS

Patients with AD were identified by the International Classification of Diseases code. The Genealogical Index of Familiality method was used to compare the average relatedness of VHA patients with AD with expected relatedness. Relative risks for AD were estimated in first- to fifth- degree relatives of patients with AD using population rates for AD.

RESULTS

Evidence for significant excess relatedness and significantly elevated risks for AD in relatives was observed; multiple pedigrees with a significant excess of VHA patients with AD were identified.

CONCLUSIONS

This analysis of AD shows the nascent power of the US Veterans Genealogy Resource, in early stages, to provide evidence for familial clustering of multiple phenotypes, and shows the utility of this VHA genealogic resource for future genetic studies.

Analysis of shared heritability in common disorders of the brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

Discovery and Confirmation of Diagnostic Serum Lipid Biomarkers for Alzheimer's Disease Using Direct Infusion Mass Spectrometry

Alzheimer's disease (AD) is a neurodegenerative disorder lacking early biochemical diagnosis and treatment. Lipids have been implicated in neurodegenerative disorders including AD. A shotgun lipidomic approach was undertaken to determine if lipid biomarkers exist that can discriminate AD cases from controls. The discovery study involved sera from 29 different stage AD cases and 32 controls. Lipid extraction was performed using organic solvent and the samples were directly infused into a time-of-flight mass spectrometer. Differences between AD cases and controls were detected with 87 statistically significant lipid candidate markers found. These potential lipid markers were reevaluated in a second confirmatory study involving 27 cases and 30 controls. Of the 87 candidates from the first study, 35 continued to be statistically significant in the second confirmatory set. Tandem MS studies were performed and almost all confirmed markers were characterized and classified. Using a Bayesian lasso probit regression model on the confirmed markers, a multi-marker set with AUC = 0.886 was developed comparing all stages of AD with controls. Additionally, using confirmed biomarkers, multi-marker sets with AUCs >0.90 were developed for each specific AD Clinical Dementia Rating versus controls, including the earliest stage of AD. More conservative and likely more realistic statistical analyses still found multi-marker sets that appeared useful in diagnosing AD. Finally, using ordinal modeling a set of markers was developed that staged AD accurately 70% of the time, p = 0.0079. These results suggest that these serum lipidomic biomarkers may help diagnose and perhaps even stage AD.

Assembly of 809 whole mitochondrial genomes with clinical, imaging, and fluid biomarker phenotyping

INTRODUCTION

Mitochondrial genetics are an important but largely neglected area of research in Alzheimer's disease. A major impediment is the lack of data sets.

METHODS

We used an innovative, rigorous approach, combining several existing tools with our own, to accurately assemble and call variants in 809 whole mitochondrial genomes.

RESULTS

To help address this impediment, we prepared a data set that consists of 809 complete and annotated mitochondrial genomes with samples from the Alzheimer's Disease Neuroimaging Initiative. These whole mitochondrial genomes include rich phenotyping, such as clinical, fluid biomarker, and imaging data, all of which is available through the Alzheimer's Disease Neuroimaging Initiative website. Genomes are cleaned, annotated, and prepared for analysis.

DISCUSSION

These data provide an important resource for investigating the impact of mitochondrial genetic variation on risk for Alzheimer's disease and other phenotypes that have been measured in the Alzheimer's Disease Neuroimaging Initiative samples.

Mitochondria and Alzheimer's Disease: the Role of Mitochondrial Genetic Variation

Purpose of Review

Alzheimer’s disease (AD) is the most common form of dementia, affects an increasing number of people worldwide, has a rapidly increasing incidence, and is fatal. In the past several years, significant progress has been made towards solving the genetic architecture of AD, but our understanding remains incomplete and has not led to treatments that either cure or slow disease. There is substantial evidence that mitochondria are involved in AD: mitochondrial functional declines in AD, mitochondrial encoded gene expression changes, mitochondria are morphologically different, and mitochondrial fusion/fission are modified. While a majority of mitochondrial proteins are nuclear encoded and could lead to malfunction in mitochondria, the mitochondrial genome encodes numerous proteins important for the electron transport chain, which if damaged could possibly lead to mitochondrial changes observed in AD. Here, we review publications that describe a relationship between the mitochondrial genome and AD and make suggestions for analysis approaches and data acquisition, from existing datasets, to study the mitochondrial genetics of AD.

Recent Findings

Numerous mitochondrial haplogroups and SNPs have been reported to influence risk for AD, but the majority of these have not been replicated, nor experimentally validated.

Summary

The role of the mitochondrial genome in AD remains elusive, and several impediments exist to fully understand the relationship between the mitochondrial genome and AD. Yet, by leveraging existing datasets and implementing appropriate analysis approaches, determining the role of mitochondrial genetics in risk for AD is possible.

Sex Differences in Risk for Alzheimer's Disease Related to Neurotrophin Gene Polymorphisms: The Cache County Memory Study

Neurotrophins, including nerve-growth factor and brain-derived neurotrophic factor, have been implicated in Alzheimer's disease (AD). Associations between AD and neurotrophin signaling genes have been inconsistent, with few studies examining sex differences in risk. We examined four single-nucleotide polymorphisms (SNPs) involved in neurotrophin signaling (rs6265, rs56164415, rs2289656, rs2072446) and risk for AD by sex in a population-based sample of older adults. Three thousand four hundred and ninety-nine individuals without dementia at baseline [mean (standard deviation) age = 74.64 (6.84), 58% female] underwent dementia screening and assessment over four triennial waves. Cox regression was used to examine time to AD or right censoring for each SNP. Female carriers of the minor T allele for rs2072446 and rs56164415 had a 60% (hazard ratio [HR] = 1.60, 95% confidence interval [CI] = 1.02-2.51) and 93% (HR = 1.93, 95% CI = 1.30-2.84) higher hazard for AD, respectively, than male noncarriers of the T allele. Furthermore, male carriers of the T allele of rs2072446 had a 61% lower hazard (HR = 0.39, 95% CI = 0.14-1.06) than male noncarriers at trend-level significance (p = .07). The association between certain neurotrophin gene polymorphisms and AD differs by sex and may explain inconsistent findings in the literature.

A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease

A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and 14 novel loci associated with age at onset. Linkage disequilibrium score regression of 220 cell types implicated the regulation of myeloid gene expression in AD risk. The minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability was enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

Transethnic genome-wide scan identifies novel Alzheimer's disease loci

INTRODUCTION

Genetic loci for Alzheimer's disease (AD) have been identified in whites of European ancestry, but the genetic architecture of AD among other populations is less understood.

METHODS

We conducted a transethnic genome-wide association study (GWAS) for late-onset AD in Stage 1 sample including whites of European Ancestry, African-Americans, Japanese, and Israeli-Arabs assembled by the Alzheimer's Disease Genetics Consortium. Suggestive results from Stage 1 from novel loci were followed up using summarized results in the International Genomics Alzheimer's Project GWAS dataset.

RESULTS

Genome-wide significant (GWS) associations in single-nucleotide polymorphism (SNP)-based tests (P < 5 × 10-8) were identified for SNPs in PFDN1/HBEGF, USP6NL/ECHDC3, and BZRAP1-AS1 and for the interaction of the (apolipoprotein E) APOE ε4 allele with NFIC SNP. We also obtained GWS evidence (P < 2.7 × 10-6) for gene-based association in the total sample with a novel locus, TPBG (P = 1.8 × 10-6).

DISCUSSION

Our findings highlight the value of transethnic studies for identifying novel AD susceptibility loci.

Genome-wide association study identifies four novel loci associated with Alzheimer's endophenotypes and disease modifiers

More than 20 genetic loci have been associated with risk for Alzheimer's disease (AD), but reported genome-wide significant loci do not account for all the estimated heritability and provide little information about underlying biological mechanisms. Genetic studies using intermediate quantitative traits such as biomarkers, or endophenotypes, benefit from increased statistical power to identify variants that may not pass the stringent multiple test correction in case-control studies. Endophenotypes also contain additional information helpful for identifying variants and genes associated with other aspects of disease, such as rate of progression or onset, and provide context to interpret the results from genome-wide association studies (GWAS). We conducted GWAS of amyloid beta (Aβ42), tau, and phosphorylated tau (ptau181) levels in cerebrospinal fluid (CSF) from 3146 participants across nine studies to identify novel variants associated with AD. Five genome-wide significant loci (two novel) were associated with ptau181, including loci that have also been associated with AD risk or brain-related phenotypes. Two novel loci associated with Aβ42 near GLIS1 on 1p32.3 (β = -0.059, P = 2.08 × 10-8) and within SERPINB1 on 6p25 (β = -0.025, P = 1.72 × 10-8) were also associated with AD risk (GLIS1: OR = 1.105, P = 3.43 × 10-2), disease progression (GLIS1: β = 0.277, P = 1.92 × 10-2), and age at onset (SERPINB1: β = 0.043, P = 4.62 × 10-3). Bioinformatics indicate that the intronic SERPINB1 variant (rs316341) affects expression of SERPINB1 in various tissues, including the hippocampus, suggesting that SERPINB1 influences AD through an Aβ-associated mechanism. Analyses of known AD risk loci suggest CLU and FERMT2 may influence CSF Aβ42 (P = 0.001 and P = 0.009, respectively) and the INPP5D locus may affect ptau181 levels (P = 0.009); larger studies are necessary to verify these results. Together the findings from this study can be used to inform future AD studies.

Fine-mapping of the human leukocyte antigen locus as a risk factor for Alzheimer disease: A case-control study

BACKGROUND

Alzheimer disease (AD) is a progressive disorder that affects cognitive function. There is increasing support for the role of neuroinflammation and aberrant immune regulation in the pathophysiology of AD. The immunoregulatory human leukocyte antigen (HLA) complex has been linked to susceptibility for a number of neurodegenerative diseases, including AD; however, studies to date have failed to consistently identify a risk HLA haplotype for AD. Contributing to this difficulty are the complex genetic organization of the HLA region, differences in sequencing and allelic imputation methods, and diversity across ethnic populations.

METHODS AND FINDINGS

Building on prior work linking the HLA to AD, we used a robust imputation method on two separate case-control cohorts to examine the relationship between HLA haplotypes and AD risk in 309 individuals (191 AD, 118 cognitively normal [CN] controls) from the San Francisco-based University of California, San Francisco (UCSF) Memory and Aging Center (collected between 1999-2015) and 11,381 individuals (5,728 AD, 5,653 CN controls) from the Alzheimer's Disease Genetics Consortium (ADGC), a National Institute on Aging (NIA)-funded national data repository (reflecting samples collected between 1984-2012). We also examined cerebrospinal fluid (CSF) biomarker measures for patients seen between 2005-2007 and longitudinal cognitive data from the Alzheimer's Disease Neuroimaging Initiative (n = 346, mean follow-up 3.15 ± 2.04 y in AD individuals) to assess the clinical relevance of identified risk haplotypes. The strongest association with AD risk occurred with major histocompatibility complex (MHC) haplotype A*03:01~B*07:02~DRB1*15:01~DQA1*01:02~DQB1*06:02 (p = 9.6 x 10-4, odds ratio [OR] [95% confidence interval] = 1.21 [1.08-1.37]) in the combined UCSF + ADGC cohort. Secondary analysis suggested that this effect may be driven primarily by individuals who are negative for the established AD genetic risk factor, apolipoprotein E (APOE) ɛ4. Separate analyses of class I and II haplotypes further supported the role of class I haplotype A*03:01~B*07:02 (p = 0.03, OR = 1.11 [1.01-1.23]) and class II haplotype DRB1*15:01- DQA1*01:02- DQB1*06:02 (DR15) (p = 0.03, OR = 1.08 [1.01-1.15]) as risk factors for AD. We followed up these findings in the clinical dataset representing the spectrum of cognitively normal controls, individuals with mild cognitive impairment, and individuals with AD to assess their relevance to disease. Carrying A*03:01~B*07:02 was associated with higher CSF amyloid levels (p = 0.03, β ± standard error = 47.19 ± 21.78). We also found a dose-dependent association between the DR15 haplotype and greater rates of cognitive decline (greater impairment on the 11-item Alzheimer's Disease Assessment Scale cognitive subscale [ADAS11] over time [p = 0.03, β ± standard error = 0.7 ± 0.3]; worse forgetting score on the Rey Auditory Verbal Learning Test (RAVLT) over time [p = 0.02, β ± standard error = -0.2 ± 0.06]). In a subset of the same cohort, dose of DR15 was also associated with higher baseline levels of chemokine CC-4, a biomarker of inflammation (p = 0.005, β ± standard error = 0.08 ± 0.03). The main study limitations are that the results represent only individuals of European-ancestry and clinically diagnosed individuals, and that our study used imputed genotypes for a subset of HLA genes.

CONCLUSIONS

We provide evidence that variation in the HLA locus-including risk haplotype DR15-contributes to AD risk. DR15 has also been associated with multiple sclerosis, and its component alleles have been implicated in Parkinson disease and narcolepsy. Our findings thus raise the possibility that DR15-associated mechanisms may contribute to pan-neuronal disease vulnerability.

Systems biology approach to late-onset Alzheimer's disease genome-wide association study identifies novel candidate genes validated using brain expression data and Caenorhabditis elegans experiments

INTRODUCTION

We sought to determine whether a systems biology approach may identify novel late-onset Alzheimer's disease (LOAD) loci.

METHODS

We performed gene-wide association analyses and integrated results with human protein-protein interaction data using network analyses. We performed functional validation on novel genes using a transgenic Caenorhabditis elegans Aβ proteotoxicity model and evaluated novel genes using brain expression data from people with LOAD and other neurodegenerative conditions.

RESULTS

We identified 13 novel candidate LOAD genes outside chromosome 19. Of those, RNA interference knockdowns of the C. elegans orthologs of UBC, NDUFS3, EGR1, and ATP5H were associated with Aβ toxicity, and NDUFS3, SLC25A11, ATP5H, and APP were differentially expressed in the temporal cortex.

DISCUSSION

Network analyses identified novel LOAD candidate genes. We demonstrated a functional role for four of these in a C. elegans model and found enrichment of differentially expressed genes in the temporal cortex.

CSF protein changes associated with hippocampal sclerosis risk gene variants highlight impact of GRN/PGRN

OBJECTIVE

Hippocampal sclerosis of aging (HS-Aging) is a common cause of dementia in older adults. We tested the variability in cerebrospinal fluid (CSF) proteins associated with previously identified HS-Aging risk single nucleotide polymorphisms (SNPs).

METHODS

Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n=237) data, combining both multiplexed proteomics CSF and genotype data, were used to assess the association between CSF analytes and risk SNPs in four genes (SNPs): GRN (rs5848), TMEM106B (rs1990622), ABCC9 (rs704180), and KCNMB2 (rs9637454). For controls, non-HS-Aging SNPs in APOE (rs429358/rs7412) and MAPT (rs8070723) were also analyzed against Aβ1-42 and total tau CSF analytes.

RESULTS

The GRN risk SNP (rs5848) status correlated with variation in CSF proteins, with the risk allele (T) associated with increased levels of AXL Receptor Tyrosine Kinase (AXL), TNF-Related Apoptosis-Inducing Ligand Receptor 3 (TRAIL-R3), Vascular Cell Adhesion Molecule-1 (VCAM-1) and clusterin (CLU) (all p<0.05 after Bonferroni correction). The TRAIL-R3 correlation was significant in meta-analysis with an additional dataset (p=5.05×10^-5). Further, the rs5848 SNP status was associated with increased CSF tau protein - a marker of neurodegeneration (p=0.015). These data are remarkable since this GRN SNP has been found to be a risk factor for multiple types of dementia-related brain pathologies.

Evaluating the necessity of PCR duplicate removal from next-generation sequencing data and a comparison of approaches

Background

Analyzing next-generation sequencing data is difficult because datasets are large, second generation sequencing platforms have high error rates, and because each position in the target genome (exome, transcriptome, etc.) is sequenced multiple times. Given these challenges, numerous bioinformatic algorithms have been developed to analyze these data. These algorithms aim to find an appropriate balance between data loss, errors, analysis time, and memory footprint. Typical analysis pipelines require multiple steps. If one or more of these steps is unnecessary, it would significantly decrease compute time and data manipulation to remove the step. One step in many pipelines is PCR duplicate removal, where PCR duplicates arise from multiple PCR products from the same template molecule binding on the flowcell. These are often removed because there is concern they can lead to false positive variant calls. Picard (MarkDuplicates) and SAMTools (rmdup) are the two main softwares used for PCR duplicate removal.

Results

Approximately 92 % of the 17+ million variants called were called whether we removed duplicates with Picard or SAMTools, or left the PCR duplicates in the dataset. There were no significant differences between the unique variant sets when comparing the transition/transversion ratios (p = 1.0), percentage of novel variants (p = 0.99), average population frequencies (p = 0.99), and the percentage of protein-changing variants (p = 1.0). Results were similar for variants in the American College of Medical Genetics genes. Genotype concordance between NGS and SNP chips was above 99 % for all genotype groups (e.g., homozygous reference).

Conclusions

Our results suggest that PCR duplicate removal has minimal effect on the accuracy of subsequent variant calls.

Presenilin E318G variant and Alzheimer's disease risk: the Cache County study

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.

Variants in ACPP are associated with cerebrospinal fluid Prostatic Acid Phosphatase levels

BACKGROUND

Prostatic Acid Phosphatase (PAP) is an enzyme that is produced primarily in the prostate and functions as a cell growth regulator and potential tumor suppressor. Understanding the genetic regulation of this enzyme is important because PAP plays an important role in prostate cancer and is expressed in other tissues such as the brain.

METHODS

We tested association between 5.8 M SNPs and PAP levels in cerebrospinal fluid across 543 individuals in two datasets using linear regression. We then performed meta-analyses using METAL =with a significance threshold of p < 5 × 10(-8) and removed SNPs where the direction of the effect was different between the two datasets, identifying 289 candidate SNPs that affect PAP cerebrospinal fluid levels. We analyzed each of these SNPs individually and prioritized SNPs that had biologically meaningful functional annotations in wANNOVAR (e.g. non-synonymous, stop gain, 3' UTR, etc.) or had a RegulomeDB score less than 3.

RESULTS

Thirteen SNPs met our criteria, suggesting they are candidate causal alleles that underlie ACPP regulation and expression.

CONCLUSIONS

Given PAP's expression in the brain and its role as a cell-growth regulator and tumor suppressor, our results have important implications in brain health such as cancer and other brain diseases including neurodegenerative diseases (e.g., Alzheimer's disease and Parkinson's disease) and mental health (e.g., anxiety, depression, and schizophrenia).

Variants in CCL16 are associated with blood plasma and cerebrospinal fluid CCL16 protein levels

Background

CCL16 is a chemokine predominantly expressed in the liver, but is also found in the blood and brain, and is known to play important roles in immune response and angiogenesis. Little is known about the gene’s regulation.

Methods

Here, we test for potential causal SNPs that affect CCL16 protein levels in both blood plasma and cerebrospinal fluid in a genome-wide association study across two datasets. We then use METAL to performed meta-analyses with a significance threshold of p < 5x10⁻⁸. We removed SNPs where the direction of the effect was different between the two datasets.

Results

We identify 10 SNPs associated with increased CCL16 protein levels in both biological fluids.

Conclusions

Our results will help understand CCL16’s regulation, allowing researchers to better understand the gene’s effects on human health.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2788-x) contains supplementary material, which is available to authorized users.

Assessment of the genetic variance of late-onset Alzheimer's disease

Alzheimer's disease (AD) is a complex genetic disorder with no effective treatments. More than 20 common markers have been identified, which are associated with AD. Recently, several rare variants have been identified in Amyloid Precursor Protein (APP), Triggering Receptor Expressed On Myeloid Cells 2 (TREM2) and Unc-5 Netrin Receptor C (UNC5C) that affect risk for AD. Despite the many successes, the genetic architecture of AD remains unsolved. We used Genome-wide Complex Trait Analysis to (1) estimate phenotypic variance explained by genetics; (2) calculate genetic variance explained by known AD single nucleotide polymorphisms (SNPs); and (3) identify the genomic locations of variation that explain the remaining unexplained genetic variance. In total, 53.24% of phenotypic variance is explained by genetics, but known AD SNPs only explain 30.62% of the genetic variance. Of the unexplained genetic variance, approximately 41% is explained by unknown SNPs in regions adjacent to known AD SNPs, and the remaining unexplained genetic variance outside these regions.

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Genetically predicted body mass index and Alzheimer's disease-related phenotypes in three large samples: Mendelian randomization analyses

Observational research shows that higher body mass index (BMI) increases Alzheimer's disease (AD) risk, but it is unclear whether this association is causal. We applied genetic variants that predict BMI in Mendelian randomization analyses, an approach that is not biased by reverse causation or confounding, to evaluate whether higher BMI increases AD risk. We evaluated individual-level data from the AD Genetics Consortium (ADGC: 10,079 AD cases and 9613 controls), the Health and Retirement Study (HRS: 8403 participants with algorithm-predicted dementia status), and published associations from the Genetic and Environmental Risk for AD consortium (GERAD1: 3177 AD cases and 7277 controls). No evidence from individual single-nucleotide polymorphisms or polygenic scores indicated BMI increased AD risk. Mendelian randomization effect estimates per BMI point (95% confidence intervals) were as follows: ADGC, odds ratio (OR) = 0.95 (0.90-1.01); HRS, OR = 1.00 (0.75-1.32); GERAD1, OR = 0.96 (0.87-1.07). One subscore (cellular processes not otherwise specified) unexpectedly predicted lower AD risk.

Interaction between variants in CLU and MS4A4E modulates Alzheimer's disease risk

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) ε4 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 ε4 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 ε4 and possible dominant effect. The CLU-MS4A4E OR is higher than any Alzheimer's disease locus except APOE ε4, APP, and TREM2. We estimated an 8% decrease in Alzheimer's disease incidence without CLU-MS4A4E risk alleles and identified potential causal variants.

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans

INTRODUCTION

Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans.

METHODS

Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated.

RESULTS

ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD.

DISCUSSION

Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.