GWAS on family history of Alzheimer's disease

Age-dependent effect of APOE and polygenic component on Alzheimer's disease

Alzheimer's disease (AD) is a devastating neurodegenerative condition with significant genetic heritability. Several genes have been implicated in the onset of AD with the apolipoprotein E (APOE) gene being the strongest single genetic risk loci. Evidence suggests that the effect of APOE alters with age during disease progression. Here, we aim to investigate the impact of APOE and other variants outside the APOE region on AD risk in younger and older participants. Using data from both the Alzheimer's Disease Neuroimaging Initiative and the UK Biobank, we computed the polygenic risk score of each individual informed by the latest genetic study from the International Genomics of Alzheimer's Project. Our analysis showed that the effect of APOE on the disease risk is greater in younger participants and reduces as participants' age increases. Our findings indicate the increased impact of polygenic risk score as participants' age increases. Therefore, AD in older individuals can potentially be triggered by the cumulative effect of genes which are outside the APOE region.

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Polygenic risk score analysis was used in ADNI and the UK Biobank data sets.

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APOE's effect on Alzheimer's disease risk was greater in the younger group (age<80 years).

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Genes outside APOE region could trigger Alzheimer's disease in older ages (age≥80 years).

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No considerable reduction of APOE ε4 alleles in ages less than and more than 80 years old.

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Age-specific genetic scores could aid in clinical trials and personalized medicine.

Association of CD33 rs3865444:C˃A polymorphism with a reduced risk of late-onset Alzheimer's disease in Slovaks is limited to subjects carrying the APOE ε4 allele

CD33 rs3865444:C>A single nucleotide polymorphism (SNP) has been previously associated with the risk of late-onset Alzheimer's disease (LOAD); however, the results have been inconsistent across different populations. CD33 is a transmembrane receptor that plays an important role in AD pathogenesis by inhibiting amyloid β42 uptake by microglial cells. In this study, we aimed to validate the association between rs3865444 and LOAD risk in the Slovak population and to evaluate whether it was affected by the carrier status of the major LOAD risk allele apolipoprotein (APOE) ε4. CD33 rs3865444 and APOE variants were genotyped in 206 LOAD patients and 487 control subjects using the polymerase chain reaction-restriction fragment length polymorphism method and direct sequencing, respectively. Logistic regression analysis revealed a significant association of rs3865444 A allele with a reduced LOAD risk that was only present in APOE ε4 allele carriers (AA + CA versus CC: p = .0085; OR = 0.45; 95% CI = 0.25-0.82). On the other hand, no such association was found in subjects without the APOE ε4 (p = .75; OR = 0.93; 95% CI = 0.61-1.42). Moreover, regression analysis detected a significant interaction between CD33 rs3865444 A and APOE ε4 alleles (p = .021 for APOE ε4 allele dosage and p = .051 for APOE ε4 carriage status), with synergy factor (SF) value of 0.49 indicating an antagonistic effect between the two alleles in LOAD risk. In conclusion, our results suggest that CD33 rs3865444:C˃A substitution may reduce the risk of LOAD in Slovaks by antagonizing the effect conferred by the major susceptibility allele APOE ε4.

An analysis of genetically regulated gene expression across multiple tissues implicates novel gene candidates in Alzheimer's disease

Introduction

Genome-wide association studies (GWAS) have successfully identified multiple independent genetic loci that harbour variants associated with Alzheimer’s disease, but the exact causal genes and biological pathways are largely unknown.

Methods

To prioritise likely causal genes associated with Alzheimer’s disease, we used S-PrediXcan to integrate expression quantitative trait loci (eQTL) from the Genotype-Tissue Expression (GTEx) study and CommonMind Consortium (CMC) with Alzheimer’s disease GWAS summary statistics. We meta-analysed the GTEx results using S-MultiXcan, prioritised disease-implicated loci using a computational fine-mapping approach, and performed a biological pathway analysis on the gene-based results.

Results

We identified 126 tissue-specific gene-based associations across 48 GTEx tissues, targeting 50 unique genes. Meta-analysis of the tissue-specific associations identified 73 genes whose expression was associated with Alzheimer’s disease. Additional analyses in the dorsolateral prefrontal cortex from the CMC identified 12 significant associations, 8 of which also had a significant association in GTEx tissues. Fine-mapping of causal gene sets prioritised gene candidates in 10 Alzheimer’s disease loci with strong evidence for causality. Biological pathway analyses of the meta-analysed GTEx data and CMC data identified a significant enrichment of Alzheimer’s disease association signals in plasma lipoprotein clearance, in addition to multiple immune-related pathways.

Conclusions

Gene expression data from brain and peripheral tissues can improve power to detect regulatory variation underlying Alzheimer’s disease. However, the associations in peripheral tissues may reflect tissue-shared regulatory variation for a gene. Therefore, future functional studies should be performed to validate the biological meaning of these associations and whether they represent new pathogenic tissues.

Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration

Vitamin D deficiency is a candidate risk factor for a range of adverse health outcomes. In a genome-wide association study of 25 hydroxyvitamin D (25OHD) concentration in 417,580 Europeans we identify 143 independent loci in 112 1-Mb regions, providing insights into the physiology of vitamin D and implicating genes involved in lipid and lipoprotein metabolism, dermal tissue properties, and the sulphonation and glucuronidation of 25OHD. Mendelian randomization models find no robust evidence that 25OHD concentration has causal effects on candidate phenotypes (e.g. BMI, psychiatric disorders), but many phenotypes have (direct or indirect) causal effects on 25OHD concentration, clarifying the epidemiological relationship between 25OHD status and the health outcomes examined in this study.

Interpretation of risk loci from genome-wide association studies of Alzheimer's disease

BACKGROUND

Alzheimer's disease is a debilitating and highly heritable neurological condition. As such, genetic studies have sought to understand the genetic architecture of Alzheimer's disease since the 1990s, with successively larger genome-wide association studies (GWAS) and meta-analyses. These studies started with a small sample size of 1086 individuals in 2007, which was able to identify only the APOE locus. In 2013, the International Genomics of Alzheimer's Project (IGAP) did a meta-analysis of all existing GWAS using data from 74 046 individuals, which stood as the largest Alzheimer's disease GWAS until 2018. This meta-analysis discovered 19 susceptibility loci for Alzheimer's disease in populations of European ancestry.

RECENT DEVELOPMENTS

Three new Alzheimer's disease GWAS published in 2018 and 2019, which used larger sample sizes and proxy phenotypes from biobanks, have substantially increased the number of known susceptibility loci in Alzheimer's disease to 40. The first, an updated GWAS from IGAP, included 94 437 individuals and discovered 24 susceptibility loci. Although IGAP sought to increase sample size by recruiting additional clinical cases and controls, the two other studies used parental family history of Alzheimer's disease to define proxy cases and controls in the UK Biobank for a genome-wide association by proxy, which was meta-analysed with data from GWAS of clinical Alzheimer's disease to attain sample sizes of 388 324 and 534 403 individuals. These two studies identified 27 and 29 susceptibility loci, respectively. However, the three studies were not independent because of the large overlap in their participants, and interpretation can be challenging because different variants and genes were highlighted by each study, even in the same locus. Furthermore, neither the variant with the strongest Alzheimer's disease association nor the nearest gene are necessarily causal. This situation presents difficulties for experimental studies, drug development, and other future research. WHERE NEXT?: The ultimate goal of understanding the genetic architecture of Alzheimer's disease is to characterise novel biological pathways that underly Alzheimer's disease pathogenesis and to identify novel drug targets. GWAS have successfully contributed to the characterisation of the genetic architecture of Alzheimer's disease, with the identification of 40 susceptibility loci; however, this does not equate to the discovery of 40 Alzheimer's disease genes. To identify Alzheimer's disease genes, these loci need to be mapped to variants and genes through functional genomics studies that combine annotation of variants, gene expression, and gene-based or pathway-based analyses. Such studies are ongoing and have validated several genes at Alzheimer's disease loci, but greater sample sizes and cell-type specific data are needed to map all GWAS loci.

Leveraging mouse chromatin data for heritability enrichment informs common disease architecture and reveals cortical layer contributions to schizophrenia

Genome-wide association studies have implicated thousands of noncoding variants across common human phenotypes. However, they cannot directly inform the cellular context in which disease-associated variants act. Here, we use open chromatin profiles from discrete mouse cell populations to address this challenge. We applied stratified linkage disequilibrium score regression and evaluated heritability enrichment in 64 genome-wide association studies, emphasizing schizophrenia. We provide evidence that mouse-derived human open chromatin profiles can serve as powerful proxies for difficult to obtain human cell populations, facilitating the illumination of common disease heritability enrichment across an array of human phenotypes. We demonstrate that signatures from discrete subpopulations of cortical excitatory and inhibitory neurons are significantly enriched for schizophrenia heritability with maximal enrichment in cortical layer V excitatory neurons. We also show that differences between schizophrenia and bipolar disorder are concentrated in excitatory neurons in cortical layers II-III, IV, and V, as well as the dentate gyrus. Finally, we leverage these data to fine-map variants in 177 schizophrenia loci nominating variants in 104/177. We integrate these data with transcription factor binding site, chromatin interaction, and validated enhancer data, placing variants in the cellular context where they may modulate risk.

Shared genetic etiology underlying Alzheimer's disease and major depressive disorder

Patients with late-onset Alzheimer's disease (LOAD) frequently manifest comorbid neuropsychiatric symptoms with depression and anxiety being most frequent, and individuals with major depressive disorder (MDD) have an increased prevalence of LOAD. This suggests shared etiologies and intersecting pathways between LOAD and MDD. We performed pleiotropy analyses using LOAD and MDD GWAS data sets from the International Genomics of Alzheimer's Project (IGAP) and the Psychiatric Genomics Consortium (PGC), respectively. We found a moderate enrichment for SNPs associated with LOAD across increasingly stringent levels of significance with the MDD GWAS association (LOAD|MDD), of maximum four and eightfolds, including and excluding the APOE-region, respectively. Association analysis excluding the APOE-region identified numerous SNPs corresponding to 40 genes, 9 of which are known LOAD-risk loci primarily in chromosome 11 regions that contain the SPI1 gene and MS4A genes cluster, and others were novel pleiotropic risk-loci for LOAD conditional with MDD. The most significant associated SNPs on chromosome 11 overlapped with eQTLs found in whole-blood and monocytes, suggesting functional roles in gene regulation. The reverse conditional association analysis (MDD|LOAD) showed a moderate level, ~sevenfold, of polygenic overlap, however, no SNP showed significant association. Pathway analyses replicated previously reported LOAD biological pathways related to immune response and regulation of endocytosis. In conclusion, we provide insights into the overlapping genetic signatures underpinning the common phenotypic manifestations and inter-relationship between LOAD and MDD. This knowledge is crucial to the development of actionable targets for novel therapies to treat depression preceding dementia, in an effort to delay or ultimately prevent the onset of LOAD.

Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology

Polygenic risk scores have identified that genetic variants without genome-wide significance still add to the genetic risk of developing Alzheimer's disease (AD). Whether and how subthreshold risk loci translate into relevant disease pathways is unknown. We investigate here the involvement of AD risk variants in the transcriptional responses of two mouse models: APPswe/PS1L166P and Thy-TAU22. A unique gene expression module, highly enriched for AD risk genes, is specifically responsive to Aβ but not TAU pathology. We identify in this module 7 established AD risk genes (APOE, CLU, INPP5D, CD33, PLCG2, SPI1, and FCER1G) and 11 AD GWAS genes below the genome-wide significance threshold (GPC2, TREML2, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN, and BLNK), that become significantly upregulated when exposed to Aβ. Single microglia sequencing confirms that Aβ, not TAU, pathology induces marked transcriptional changes in microglia, including increased proportions of activated microglia. We conclude that genetic risk of AD functionally translates into different microglia pathway responses to Aβ pathology, placing AD genetic risk downstream of the amyloid pathway but upstream of TAU pathology.

Omics in Neurodegenerative Disease: Hope or Hype?

The past 15 years have seen a boom in the use and integration of 'omic' approaches (limited here to genomic, transcriptomic, and epigenomic techniques) to study neurodegenerative disease in an unprecedented way. We first highlight advances in and the limitations of using such approaches in the neurodegenerative disease literature, with a focus on Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal lobar degeneration (FTLD), and amyotrophic lateral sclerosis (ALS). We next discuss how these studies can advance human health in the form of generating leads for downstream mechanistic investigation or yielding polygenic risk scores (PRSs) for prognostication. However, we argue that these approaches constitute a new form of molecular description, analogous to clinical or pathological description, that alone does not hold the key to solving these complex diseases.

The multiplex model of the genetics of Alzheimer's disease

Genes play a strong role in Alzheimer's disease (AD), with late-onset AD showing heritability of 58-79% and early-onset AD showing over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 50 loci are now implicated for AD, suggesting that AD is a disease of multiple components, as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD heritability has been captured, allowing researchers to calculate the accumulation of AD genetic risk through polygenic risk scores. A polygenic risk score predicts disease with up to 90% accuracy and is an exciting tool in our research armory that could allow selection of those with high polygenic risk scores for clinical trials and precision medicine. It could also allow cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.

Enhancing α-secretase Processing for Alzheimer's Disease-A View on SFRP1

Amyloid β (Aβ) peptides generated via sequential β- and γ-secretase processing of the amyloid precursor protein (APP) are major etiopathological agents of Alzheimer's disease (AD). However, an initial APP cleavage by an α-secretase, such as the a disintegrin and metalloproteinase domain-containing protein ADAM10, precludes β-secretase cleavage and leads to APP processing that does not produce Aβ. The latter appears to underlie the disease symptom-attenuating effects of a multitude of experimental therapeutics in AD animal models. Recent work has indicated that an endogenous inhibitor of ADAM10, secreted-frizzled-related protein 1 (SFRP1), is elevated in human AD brains and associated with amyloid plaques in mouse AD models. Importantly, genetic or functional attenuation of SFRP1 lowered Aβ accumulation and improved AD-related histopathological and neurological traits. Given SFRP1's well-known activity in attenuating Wnt signaling, which is also commonly impaired in AD, SFRP1 appears to be a promising therapeutic target for AD. This idea, however, needs to be addressed with care because of cancer enhancement potentials resulting from a systemic loss of SFRP1 activity, as well as an upregulation of ADAM10 activity. In this focused review, I shall discuss α-secretase-effected APP processing in AD with a focus on SFRP1, and explore the contrasting perspectives arising from the recent findings.

Genetic architecture of neurodegenerative dementias

Molecular genetics has been an invaluable tool to help understand the molecular basis of neurodegenerative dementias. In this review, we provide an overview of the genetic architecture underlying some of the most prevalent causes of dementia, including Alzheimer's dementia, frontotemporal lobar degeneration, Lewy body dementia, and prion diseases. We also discuss the complexity of the human genome and how the novel technologies have revolutionized and accelerated the way we screen the variety of our DNA. Finally, we also provide some examples about how this genetic knowledge is being transferred into the clinic through personalized medicine. This article is part of the special issue entitled 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'.

Impact of Vitamin D Binding Protein Levels on Alzheimer's Disease: A Mendelian Randomization Study

Until now, observational studies, randomized controlled trials (RCTs), and Mendelian randomization (MR) studies have explored the impact of vitamin D on Alzheimer's disease (AD), and reported inconsistent findings. In MR studies, the sensitivity analysis by removing GC rs2282679 variant highlighted no association of 25OHD levels with AD risk, which indicates that vitamin D-binding protein (DBP) encoded by GC may have distinct effects on AD risk. Here, we aim to clarify this assumption. We selected the GC rs2282679 variant associated with DBP levels (p = 3.30E-76) as the instrumental variable, and extracted the summary statistics of rs2282679 variant in multiple AD GWAS datasets from IGAP, Complex Trait Genetics (CTG) lab, and UK Biobank. We then performed a MR study to investigate the causal association between DBP levels and AD. In IGAP, MR analysis showed that the genetically DBP levels (per 1 standard deviation (SD) increase 50 mg/L) were significantly associated with reduced AD risk (OR = 0.63, 95% CI: 0.45-0.89, p = 0.009). Importantly, the estimates from two sensitivity analyses were consistent with the main estimate in terms of direction and magnitude. Meanwhile, we found no causal association between DBP levels and other four AD phenotypes in CTG lab and UK Biobank. In summary, we highlight the role of DBP levels in AD risk, and provide strong support evidence that DBP may be the therapeutic agent for the treatment of AD. Meanwhile, our findings clarify the assumption that DBP may drive the observed relationship between 25OHD levels and AD.

Dickkopf-1 Overexpression in vitro Nominates Candidate Blood Biomarkers Relating to Alzheimer's Disease Pathology

BACKGROUND

Previous studies suggest that Dickkopf-1 (DKK1), an inhibitor of Wnt signaling, plays a role in amyloid-induced toxicity and hence Alzheimer's disease (AD). However, the effect of DKK1 expression on protein expression, and whether such proteins are altered in disease, is unknown.

OBJECTIVE

We aim to test whether DKK1 induced protein signature obtained in vitro were associated with markers of AD pathology as used in the amyloid/tau/neurodegeneration (ATN) framework as well as with clinical outcomes.

METHODS

We first overexpressed DKK1 in HEK293A cells and quantified 1,128 proteins in cell lysates using aptamer capture arrays (SomaScan) to obtain a protein signature induced by DKK1. We then used the same assay to measure the DKK1-signature proteins in human plasma in two large cohorts, EMIF (n = 785) and ANM (n = 677).

RESULTS

We identified a 100-protein signature induced by DKK1 in vitro. Subsets of proteins, along with age and apolipoprotein E ɛ4 genotype distinguished amyloid pathology (A + T-N-, A+T+N-, A+T-N+, and A+T+N+) from no AD pathology (A-T-N-) with an area under the curve of 0.72, 0.81, 0.88, and 0.85, respectively. Furthermore, we found that some signature proteins (e.g., Complement C3 and albumin) were associated with cognitive score and AD diagnosis in both cohorts.

CONCLUSIONS

Our results add further evidence for a role of DKK regulation of Wnt signaling in AD and suggest that DKK1 induced signature proteins obtained in vitro could reflect theATNframework as well as predict disease severity and progression in vivo.

Circulating Vitamin D Levels and Alzheimer's Disease: A Mendelian Randomization Study in the IGAP and UK Biobank

Observational studies strongly supported the association of low levels of circulating 25-hydroxyvitamin D (25OHD) and cognitive impairment or dementia in aging populations. However, randomized controlled trials have not shown clear evidence that vitamin D supplementation could improve cognitive outcomes. In fact, some studies reported the association between vitamin D and cognitive impairment based on individuals aged 60 years and over. However, it is still unclear that whether vitamin D levels are causally associated with Alzheimer's disease (AD) risk in individuals aged 60 years and over. Here, we performed a Mendelian randomization (MR) study to investigate the causal association between vitamin D levels and AD using a large-scale vitamin D genome-wide association study (GWAS) dataset and two large-scale AD GWAS datasets from the IGAP and UK Biobank with individuals aged 60 years and over. Our results showed that genetically increased 25OHD levels were significantly associated with reduced AD risk in individuals aged 60 years and over. Hence, our findings in combination with previous literature indicate that maintaining adequate vitamin D status in older people especially aged 60 years and over, may contribute to slow down cognitive decline and forestall AD. Long-term randomized controlled trials are required to test whether vitamin D supplementation may prevent AD in older people especially those aged 60 years and may be recommended as preventive agents.

Investigating Causal Relations Between Risk Tolerance, Risky Behaviors, and Alzheimer's Disease: A Bidirectional Two-Sample Mendelian Randomization Study

BACKGROUND

Several studies have shown risky behaviors and risk tolerance are associated with Alzheimer's disease. However, the underlying causality remains unclear. Risky behavior and risk tolerance may induce the onset of Alzheimer's disease, and/or vulnerability to Alzheimer's disease may result in more risky behaviors.

OBJECTIVE

To examine bidirectional relationships between risky behavior, risk tolerance, and Alzheimer's disease using Mendelian randomization method for assessing potential causal inference.

METHODS

This bidirectional two-sample Mendelian randomization study used independent genetic variants associated with risky behaviors and risk tolerance (n = 370, 771- 939, 908), and Alzheimer's disease (n = 71, 880 - 37, 613) as genetic instruments from large meta-analyses of genome-wide association studies.

RESULTS

Our results support a strong protective casual effect of risk-taking tendency on AD (odds ratio [OR] = 0.79; 95% CI, 0.67- 0.94, p = 0.007). There was weak statistically significant relationship between number of sexual partners and AD (OR = 0.50, 95% CI, 0.27- 0.93, p = 0.04), and between family history of AD and automobile speeding propensity (OR = 1.018, 95% CI, 1.005 to 1.031; p = 0.007). Contrary to expectations, there was no statistically significant causal effect of AD on risk-taking tendency (β=  0.015, 95% CI, - 0.005 to 0.04; p = 0.14).

CONCLUSION

Under Mendelian randomization assumptions, our results suggest a protective relationship between risk-taking tendency and the risk of AD. This finding may provide valuable insights into Alzheimer's disease pathogenesis and the development of preventive strategies.

DNA Methylation Signatures of Breastfeeding in Buccal Cells Collected in Mid-Childhood

Breastfeeding has long-term benefits for children that may be mediated via the epigenome. This pathway has been hypothesized, but the number of empirical studies in humans is small and mostly done by using peripheral blood as the DNA source. We performed an epigenome-wide association study (EWAS) in buccal cells collected around age nine (mean = 9.5) from 1006 twins recruited by the Netherlands Twin Register (NTR). An age-stratified analysis examined if effects attenuate with age (median split at 10 years; n<10 = 517, mean age = 7.9; n>10 = 489, mean age = 11.2). We performed replication analyses in two independent cohorts from the NTR (buccal cells) and the Avon Longitudinal Study of Parents and Children (ALSPAC) (peripheral blood), and we tested loci previously associated with breastfeeding in epigenetic studies. Genome-wide DNA methylation was assessed with the Illumina Infinium MethylationEPIC BeadChip (Illumina, San Diego, CA, USA) in the NTR and with the HumanMethylation450 Bead Chip in the ALSPAC. The duration of breastfeeding was dichotomized ('never' vs. 'ever'). In the total sample, no robustly associated epigenome-wide significant CpGs were identified (α = 6.34 × 10-8). In the sub-group of children younger than 10 years, four significant CpGs were associated with breastfeeding after adjusting for child and maternal characteristics. In children older than 10 years, methylation differences at these CpGs were smaller and non-significant. The findings did not replicate in the NTR sample (n = 98; mean age = 7.5 years), and no nearby sites were associated with breastfeeding in the ALSPAC study (n = 938; mean age = 7.4). Of the CpG sites previously reported in the literature, three were associated with breastfeeding in children younger than 10 years, thus showing that these CpGs are associated with breastfeeding in buccal and blood cells. Our study is the first to show that breastfeeding is associated with epigenetic variation in buccal cells in children. Further studies are needed to investigate if methylation differences at these loci are caused by breastfeeding or by other unmeasured confounders, as well as what mechanism drives changes in associations with age.

Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits

Volumetric variations of the human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) of 101 brain volumetric phenotypes using the UK Biobank sample including 19,629 participants. GWAS identified 365 independent genetic variants exceeding a significance threshold of 4.9 × 10-10, adjusted for testing multiple phenotypes. A gene-based association study found 157 associated genes (124 new), and functional gene mapping analysis linked 146 additional genes. Many of the discovered genetic variants and genes have previously been implicated in cognitive and mental health traits. Through genome-wide polygenic-risk-score prediction, more than 6% of the phenotypic variance (P = 3.13 × 10-24) in four other independent studies could be explained by the UK Biobank GWAS results. In conclusion, our study identifies many new genetic associations at the variant, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.

Genetic architecture of subcortical brain structures in 38,851 individuals

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

Integrate GWAS, eQTL, and mQTL Data to Identify Alzheimer's Disease-Related Genes

It is estimated that the impact of related genes on the risk of Alzheimer's disease (AD) is nearly 70%. Identifying candidate causal genes can help treatment and diagnosis. The maturity of sequencing technology and the reduction of cost make genome-wide association study (GWAS) become an important means to find disease-related mutation sites. Because of linkage disequilibrium (LD), neither the gene regulated by SNP nor the specific SNP can be determined. Because GWAS is affected by sample size and interaction, we introduced empirical Bayes (EB) to make a meta-analysis of GWAS to greatly eliminate the bias caused by sample and the interaction of SNP. In addition, most SNPs are in the noncoding region, so it is not clear how they relate to phenotype. In this paper, expression quantitative trait locus (eQTL) studies and methylation quantitative trait locus (mQTL) studies are combined with GWAS to find the genes associated with Alzheimer disease in expression levels by pleiotropy. Summary data-based Mendelian randomization (SMR) is introduced to integrate GWAS and eQTL/mQTL data. Finally, we prioritized 274 significant SNPs, which belong to 20 genes by eQTL analysis and 379 significant SNPs, which belong to seven known genes by mQTL. Among them, 93 SNPs and 2 genes are overlapped. Finally, we did 10 case studies to prove the effectiveness of our method.

Genetic variability in response to amyloid beta deposition influences Alzheimer's disease risk

Genome-wide association studies of late-onset Alzheimer's disease risk have previously identified genes primarily expressed in microglia that form a transcriptional network. Using transgenic mouse models of amyloid deposition, we previously showed that many of the mouse orthologues of these risk genes are co-expressed and associated with amyloid pathology. In this new study, we generate an improved RNA-seq-derived network that is expressed in amyloid-responsive mouse microglia and we statistically compare this with gene-level variation in previous human Alzheimer's disease genome-wide association studies to predict at least four new risk genes for the disease (OAS1, LAPTM5, ITGAM/CD11b and LILRB4). Of the mouse orthologues of these genes Oas1a is likely to respond directly to amyloid at the transcriptional level, similarly to established risk gene Trem2, because the increase in Oas1a and Trem2 transcripts in response to amyloid deposition in transgenic mice is significantly higher than both the increase of the average microglial transcript and the increase in microglial number. In contrast, the mouse orthologues of LAPTM5, ITGAM/CD11b and LILRB4 (Laptm5, Itgam/CD11b and Lilra5) show increased transcripts in the presence of amyloid plaques similar in magnitude to the increase of the average microglial transcript and the increase in microglia number, except that Laptm5 and Lilra5 transcripts increase significantly quicker than the average microglial transcript as the plaque load becomes dense. This work suggests that genetic variability in the microglial response to amyloid deposition is a major determinant for Alzheimer's disease risk, and identification of these genes may help to predict the risk of developing Alzheimer's disease. These findings also provide further insights into the mechanisms underlying Alzheimer's disease for potential drug discovery.

Genetic risk for Alzheimer's dementia predicts motor deficits through multi-omic systems in older adults

Alzheimer's disease manifests with both cognitive and motor deficits. However, the degree to which genetic risk of Alzheimer's dementia contributes to late-life motor impairment, and the specific molecular systems underlying these associations, are uncertain. Here, we adopted an integrative multi-omic approach to assess genetic influence on motor impairment in older adults and identified key molecular pathways that may mediate this risk. We built a polygenic risk score for clinical diagnosis of Alzheimer's dementia (AD-PRS) and examined its relationship to several motor phenotypes in 1885 older individuals from two longitudinal aging cohorts. We found that AD-PRS was associated with a previously validated composite motor scores and their components. The major genetic risk factor for sporadic Alzheimer's dementia, the APOE/TOMM40 locus, was not a major driver of these associations. To identify specific molecular features that potentially medicate the genetic risk into motor dysfunction, we examined brain multi-omics, including transcriptome, DNA methylation, histone acetylation (H3K9AC), and targeted proteomics, as well as diverse neuropathologies. We found that a small number of factors account for the majority of the influence of AD-PRS on motor function, which comprises paired helical filament tau-tangle density, H3K9AC in specific chromosomal regions encoding genes involved in neuromuscular process. These multi-omic factors have the potential to elucidate key molecular mechanisms developing motor impairment in the context of Alzheimer's dementia.

Microglia in Brain Development, Homeostasis, and Neurodegeneration

Advances in human genetics have implicated a growing number of genes in neurodegenerative diseases, providing insight into pathological processes. For Alzheimer disease in particular, genome-wide association studies and gene expression studies have emphasized the pathogenic contributions from microglial cells and motivated studies of microglial function/dysfunction. Here, we summarize recent genetic evidence for microglial involvement in neurodegenerative disease with a focus on Alzheimer disease, for which the evidence is most compelling. To provide context for these genetic discoveries, we discuss how microglia influence brain development and homeostasis, how microglial characteristics change in disease, and which microglial activities likely influence the course of neurodegeneration. In all, we aim to synthesize varied aspects of microglial biology and highlight microglia as possible targets for therapeutic interventions in neurodegenerative disease.

Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain

Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number of environmental factors at high granularity might not always be feasible. Instead, here we propose extracting cellular embedding of environmental factors from gene expression data by using latent variable (LV) analysis and taking these LVs as environmental proxies in detecting gene-by-environment (GxE) interaction effects on gene expression, i.e., GxE expression quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset. Combining the two samples via meta-analysis, 895 GxE eQTLs are identified. On average, GxE effect explains an additional ∼4% variation in expression of each gene that displays a GxE effect. Ten of these 52 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional. Furthermore, after substituting LVs with expression of transcription factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain GxE eQTLs.

Pleiotropic Roles of P2X7 in the Central Nervous System

The purinergic receptor P2X7 is expressed in neural and immune cells known to be involved in neurological diseases. Its ligand, ATP, is a signaling molecule that can act as a neurotransmitter in physiological conditions or as a danger signal when released in high amount by damaged/dying cells or activated glial cells. Thus, ATP is a danger-associated molecular pattern. Binding of ATP by P2X7 leads to the activation of different biochemical pathways, depending on the physiological or pathological environment. The aim of this review is to discuss various functions of P2X7 in the immune and central nervous systems. We present evidence that P2X7 may have a detrimental or beneficial role in the nervous system, in the context of neurological pathologies: epilepsy, Alzheimer’s disease, multiple sclerosis, amyotrophic lateral sclerosis, age-related macular degeneration and cerebral artery occlusion.

Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer's Disease

The genetic component of many common traits is associated with the gene expression and several variants act as expression quantitative loci, regulating the gene expression in a tissue specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction models on the genotype of 808 samples including controls, subjects with mild cognitive impairment, and patients with Alzheimer's Disease. We then dissected the imputed transcriptomic profiles by means of different unsupervised and supervised machine learning approaches to identify potential biological associations. Our analysis suggests that unsupervised and supervised methods can provide complementary information, which can be integrated for a better characterization of the underlying biological system. In particular, a variational autoencoder representation of the transcriptomic profiles, followed by a support vector machine classification, has been used for tissue-specific gene prioritizations. Interestingly, the achieved gene prioritizations can be efficiently integrated as a feature selection step for improving the accuracy of deep learning classifier networks. The identified gene-tissue information suggests a potential role for inflammatory and regulatory processes in gut-brain axis related tissues. In line with the expected low heritability that can be apportioned to eQTL variants, we were able to achieve only relatively low prediction capability with deep learning classification models. However, our analysis revealed that the classification power strongly depends on the network structure, with recurrent neural networks being the best performing network class. Interestingly, cross-tissue analysis suggests a potentially greater role of models trained in brain tissues also by considering dementia-related endophenotypes. Overall, the present analysis suggests that the combination of supervised and unsupervised machine learning techniques can be used for the evaluation of high dimensional omics data.

Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer's disease

BACKGROUND

Alzheimer's disease results from a neurodegenerative process that starts well before the diagnosis can be made. New prognostic or diagnostic markers enabling early intervention into the disease process would be highly valuable. Environmental and lifestyle factors largely modulate the disease risk and may influence the pathogenesis through epigenetic mechanisms, such as DNA methylation. As environmental and lifestyle factors may affect multiple tissues of the body, we hypothesized that the disease-associated DNA methylation signatures are detectable in the peripheral blood of discordant twin pairs.

RESULTS

Comparison of 23 disease discordant Finnish twin pairs with reduced representation bisulfite sequencing revealed peripheral blood DNA methylation differences in 11 genomic regions with at least 15.0% median methylation difference and FDR adjusted p value ≤ 0.05. Several of the affected genes are primarily associated with neuronal functions and pathologies and do not display disease-associated differences in gene expression in blood. The DNA methylation mark in ADARB2 gene was found to be differentially methylated also in the anterior hippocampus, including entorhinal cortex, of non-twin cases and controls. Targeted bisulfite pyrosequencing of the DNA methylation mark in ADARB2 gene in 62 Finnish and Swedish twin pairs revealed that, in addition to the disease status, DNA methylation of this region is influenced by gender, age, zygosity, APOE genotype, and smoking. Further analysis of 120 Swedish twin pairs indicated that this specific DNA methylation mark is not predictive for Alzheimer's disease and becomes differentially methylated after disease onset.

CONCLUSIONS

DNA methylation differences can be detected in the peripheral blood of twin pairs discordant for Alzheimer's disease. These DNA methylation signatures may have value as disease markers and provide insights into the molecular mechanisms of pathogenesis. We found no evidence that the DNA methylation marks would be associated with gene expression in blood. Further studies are needed to elucidate the potential importance of the associated genes in neuronal functions and to validate the prognostic or diagnostic value of the individual marks or marker panels.

HENA, heterogeneous network-based data set for Alzheimer's disease

Alzheimer's disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. These experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. The results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for Alzheimer's disease (HENA). Additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze their own results in the broader context of Alzheimer's disease research.

Bioinformatics strategy to advance the interpretation of Alzheimer's disease GWAS discoveries: The roads from association to causation

INTRODUCTION

Genome-wide association studies (GWAS) discovered multiple late-onset Alzheimer's disease (LOAD)-associated SNPs and inferred the genes based on proximity; however, the actual causal genes are yet to be identified.

METHODS

We defined LOAD-GWAS regions by the most significantly associated SNP ±0.5 Mb and developed a bioinformatics pipeline that uses and integrates chromatin state segmentation track to map active enhancers and virtual 4C software to visualize interactions between active enhancers and gene promoters. We augmented our pipeline with biomedical and functional information.

RESULTS

We applied the bioinformatics pipeline using three ∼1 Mb LOAD-GWAS loci: BIN1, PICALM, CELF1. These loci contain 10-24 genes, an average of 106 active enhancers and 80 CTCF sites. Our strategy identified all genes corresponding to the promoters that interact with the active enhancer that is closest to the LOAD-GWAS-SNP and generated a shorter list of prioritized candidate LOAD genes (5-14/loci), feasible for post-GWAS investigations of causality.

DISCUSSION

Interpretation of LOAD-GWAS discoveries requires the integration of brain-specific functional genomic data sets and information related to regulatory activity.

A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

The genetic variant rs72824905-G (minor allele) in the PLCG2 gene was previously associated with a reduced Alzheimer's disease risk (AD). The role of PLCG2 in immune system signaling suggests it may also protect against other neurodegenerative diseases and possibly associates with longevity. We studied the effect of the rs72824905-G on seven neurodegenerative diseases and longevity, using 53,627 patients, 3,516 long-lived individuals and 149,290 study-matched controls. We replicated the association of rs72824905-G with reduced AD risk and we found an association with reduced risk of dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). We did not find evidence for an effect on Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) risks, despite adequate sample sizes. Conversely, the rs72824905-G allele was associated with increased likelihood of longevity. By-proxy analyses in the UK Biobank supported the associations with both dementia and longevity. Concluding, rs72824905-G has a protective effect against multiple neurodegenerative diseases indicating shared aspects of disease etiology. Our findings merit studying the PLCγ2 pathway as drug-target.

Genome-wide analysis of dental caries and periodontitis combining clinical and self-reported data

Dental caries and periodontitis account for a vast burden of morbidity and healthcare spending, yet their genetic basis remains largely uncharacterized. Here, we identify self-reported dental disease proxies which have similar underlying genetic contributions to clinical disease measures and then combine these in a genome-wide association study meta-analysis, identifying 47 novel and conditionally-independent risk loci for dental caries. We show that the heritability of dental caries is enriched for conserved genomic regions and partially overlapping with a range of complex traits including smoking, education, personality traits and metabolic measures. Using cardio-metabolic traits as an example in Mendelian randomization analysis, we estimate causal relationships and provide evidence suggesting that the processes contributing to dental caries may have undesirable downstream effects on health.

High-Diversity Mouse Populations for Complex Traits

Contemporary mouse genetic reference populations are a powerful platform to discover complex disease mechanisms. Advanced high-diversity mouse populations include the Collaborative Cross (CC) strains, Diversity Outbred (DO) stock, and their isogenic founder strains. When used in systems genetics and integrative genomics analyses, these populations efficiently harnesses known genetic variation for precise and contextualized identification of complex disease mechanisms. Extensive genetic, genomic, and phenotypic data are already available for these high-diversity mouse populations and a growing suite of data analysis tools have been developed to support research on diverse mice. This integrated resource can be used to discover and evaluate disease mechanisms relevant across species.

Enhancing face validity of mouse models of Alzheimer's disease with natural genetic variation

Classical laboratory strains show limited genetic diversity and do not harness natural genetic variation. Mouse models relevant to Alzheimer's disease (AD) have largely been developed using these classical laboratory strains, such as C57BL/6J (B6), and this has likely contributed to the failure of translation of findings from mice to the clinic. Therefore, here we test the potential for natural genetic variation to enhance the translatability of AD mouse models. Two widely used AD-relevant transgenes, APPswe and PS1de9 (APP/PS1), were backcrossed from B6 to three wild-derived strains CAST/EiJ, WSB/EiJ, PWK/PhJ, representative of three Mus musculus subspecies. These new AD strains were characterized using metabolic, functional, neuropathological and transcriptional assays. Strain-, sex- and genotype-specific differences were observed in cognitive ability, neurodegeneration, plaque load, cerebrovascular health and cerebral amyloid angiopathy. Analyses of brain transcriptional data showed strain was the greatest driver of variation. We identified significant variation in myeloid cell numbers in wild type mice of different strains as well as significant differences in plaque-associated myeloid responses in APP/PS1 mice between the strains. Collectively, these data support the use of wild-derived strains to better model the complexity of human AD.

Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases

Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies.

The Major Risk Factors for Alzheimer's Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques

Gene expression profiles of more than 10,000 individual microglial cells isolated from cortex and hippocampus of male and female AppNL-G-F mice over time demonstrate that progressive amyloid-β accumulation accelerates two main activated microglia states that are also present during normal aging. Activated response microglia (ARMs) are composed of specialized subgroups overexpressing MHC type II and putative tissue repair genes (Dkk2, Gpnmb, and Spp1) and are strongly enriched with Alzheimer's disease (AD) risk genes. Microglia from female mice progress faster in this activation trajectory. Similar activated states are also found in a second AD model and in human brain. Apoe, the major genetic risk factor for AD, regulates the ARMs but not the interferon response microglia (IRMs). Thus, the ARMs response is the converging point for aging, sex, and genetic AD risk factors.

Microglia in Alzheimer's Disease: Exploring How Genetics and Phenotype Influence Risk

Research into the function of microglia has dramatically accelerated during the last few years, largely due to recent genetic findings implicating microglia in virtually every neurodegenerative disorder. In Alzheimer's disease (AD), a majority of risk loci discovered through genome-wide association studies were found in or near genes expressed most highly in microglia leading to the hypothesis that microglia play a much larger role in disease progression than previously thought. From this body of work produced in the last several years, we find that almost every function of microglia has been proposed to influence the progression of AD from altered phagocytosis and synaptic pruning to cytokine secretion and changes in trophic support. By studying key Alzheimer's risk genes such as TREM2, CD33, ABCA7, and MS4A6A, we will be able to distinguish true disease-modulatory pathways from the full range of microglial-related functions. To successfully carry out these experiments, more advanced microglial models are needed. Microglia are quite sensitive to their local environment, suggesting the need to more fully recapitulate an in vivo environment to study this highly plastic cell type. Likely only by combining the above approaches will the field fully elucidate the molecular pathways that regulate microglia and influence neurodegeneration, in turn uncovering potential new targets for future therapeutic development.

Immune Signaling in Neurodegeneration

Neurodegenerative diseases of the central nervous system progressively rob patients of their memory, motor function, and ability to perform daily tasks. Advances in genetics and animal models are beginning to unearth an unexpected role of the immune system in disease onset and pathogenesis; however, the role of cytokines, growth factors, and other immune signaling pathways in disease pathogenesis is still being examined. Here we review recent genetic risk and genome-wide association studies and emerging mechanisms for three key immune pathways implicated in disease, the growth factor TGF-β, the complement cascade, and the extracellular receptor TREM2. These immune signaling pathways are important under both healthy and neurodegenerative conditions, and recent work has highlighted new functional aspects of their signaling. Finally, we assess future directions for immune-related research in neurodegeneration and potential avenues for immune-related therapies.

Integrated DNA methylation and gene expression profiling across multiple brain regions implicate novel genes in Alzheimer's disease

Late-onset Alzheimer's disease (AD) is a complex age-related neurodegenerative disorder that likely involves epigenetic factors. To better understand the epigenetic state associated with AD, we surveyed 420,852 DNA methylation (DNAm) sites from neurotypical controls (N = 49) and late-onset AD patients (N = 24) across four brain regions (hippocampus, entorhinal cortex, dorsolateral prefrontal cortex and cerebellum). We identified 858 sites with robust differential methylation collectively annotated to 772 possible genes (FDR < 5%, within 10 kb). These sites were overrepresented in AD genetic risk loci (p = 0.00655) and were enriched for changes during normal aging (p < 2.2 × 10^-16), and nearby genes were enriched for processes related to cell-adhesion, immunity, and calcium homeostasis (FDR < 5%). To functionally validate these associations, we generated and analyzed corresponding transcriptome data to prioritize 130 genes within 10 kb of the differentially methylated sites. These 130 genes were differentially expressed between AD cases and controls and their expression was associated with nearby DNAm (p < 0.05). This integrated analysis implicates novel genes in Alzheimer's disease, such as ANKRD30B. These results highlight DNAm differences in Alzheimer's disease that have gene expression correlates, further implicating DNAm as an epigenetic mechanism underlying pathological molecular changes associated with AD. Furthermore, our framework illustrates the value of integrating epigenetic and transcriptomic data for understanding complex disease.

A Quarter Century of APOE and Alzheimer's Disease: Progress to Date and the Path Forward

Alzheimer's disease (AD) is considered a polygenic disorder. This view is clouded, however, by lingering uncertainty over how to treat the quasi "monogenic" role of apolipoprotein E (APOE). The APOE4 allele is not only the strongest genetic risk factor for AD, it also affects risk for cardiovascular disease, stroke, and other neurodegenerative disorders. This review, based mostly on data from human studies, ranges across a variety of APOE-related pathologies, touching on evolutionary genetics and risk mitigation by ethnicity and sex. The authors also address one of the most fundamental question pertaining to APOE4 and AD: does APOE4 increase AD risk via a loss or gain of function? The answer will be of the utmost importance in guiding future research in AD.

Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk

Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology 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.

Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of complex traits and disease

Integrative approaches that harness large-scale molecular datasets can help develop mechanistic insight into findings from genome-wide association studies (GWAS). We have performed extensive analyses to uncover transcriptional and epigenetic processes which may play a role in complex trait variation. This was undertaken by applying Bayesian multiple-trait colocalization systematically across the genome to identify genetic variants responsible for influencing intermediate molecular phenotypes as well as complex traits. In this analysis, we leveraged high-dimensional quantitative trait loci data derived from the prefrontal cortex tissue (concerning gene expression, DNA methylation and histone acetylation) and GWAS findings for five complex traits (Neuroticism, Schizophrenia, Educational Attainment, Insomnia and Alzheimer's disease). There was evidence of colocalization for 118 associations, suggesting that the same underlying genetic variant influenced both nearby gene expression as well as complex trait variation. Of these, 73 associations provided evidence that the genetic variant also influenced proximal DNA methylation and/or histone acetylation. These findings support previous evidence at loci where epigenetic mechanisms may putatively mediate effects of genetic variants on traits, such as KLC1 and schizophrenia. We also uncovered evidence implicating novel loci in disease susceptibility, including genes expressed predominantly in the brain tissue, such as MDGA1, KIRREL3 and SLC12A5. An inverse relationship between DNA methylation and gene expression was observed more than can be accounted for by chance, supporting previous findings implicating DNA methylation as a transcriptional repressor. Our study should prove valuable in helping future studies prioritize candidate genes and epigenetic mechanisms for in-depth functional follow-up analyses.

Genetic Analyses of Alzheimer's Disease in China: Achievements and Perspectives

Since 2010, the Chinese have become one of the most aged populations in the world, leading to a severe burden of neurodegenerative disorders. Alzheimer's disease (AD) is the most prevalent neurodegenerative disease and has a high genetic heritability. In the past two decades, numerous genetic analyses, from linkage analyses and candidate gene studies to genome-wide association studies (GWASs) and next-generation sequencing studies, have identified dozens of AD susceptibility or causal genes. These studies have provided a comprehensive genetic view and contributed to the understanding of the pathological and molecular mechanisms of the disease. However, most of the recognized AD genetic risk factors have been reported in studies based on European populations or populations of European ancestry, and data about the genetics of AD from other populations has been very limited. As China has the largest AD population in the world and because of the remarkable genetic differences between the East and the West, deciphering the genetic basis and molecular mechanism in Chinese patients with AD may add key points to the full characterization of AD. In this review, we present an overview of the current state of AD genetic research in China, with an emphasis on genome-level studies. We also describe the challenges and opportunities for future advances, especially for in-depth collaborations, brain bank construction, and primate animal modeling. There is an urgent need to promote public awareness and increase our collaborations and data sharing.

The metalloprotease ADAMTS4 generates N-truncated Aβ4-x species and marks oligodendrocytes as a source of amyloidogenic peptides in Alzheimer's disease

Brain accumulation and aggregation of amyloid-β (Aβ) peptides is a critical step in the pathogenesis of Alzheimer's disease (AD). Full-length Aβ peptides (mainly Aβ1-40 and Aβ1-42) are produced through sequential proteolytic cleavage of the amyloid precursor protein (APP) by β- and γ-secretases. However, studies of autopsy brain samples from AD patients have demonstrated that a large fraction of insoluble Aβ peptides are truncated at the N-terminus, with Aβ4-x peptides being particularly abundant. Aβ4-x peptides are highly aggregation prone, but their origin and any proteases involved in their generation are unknown. We have identified a recognition site for the secreted metalloprotease ADAMTS4 (a disintegrin and metalloproteinase with thrombospondin motifs 4) in the Aβ peptide sequence, which facilitates Aβ4-x peptide generation. Inducible overexpression of ADAMTS4 in HEK293 cells resulted in the secretion of Aβ4-40 but unchanged levels of Aβ1-x peptides. In the 5xFAD mouse model of amyloidosis, Aβ4-x peptides were present not only in amyloid plaque cores and vessel walls, but also in white matter structures co-localized with axonal APP. In the ADAMTS4^-/- knockout background, Aβ4-40 levels were reduced confirming a pivotal role of ADAMTS4 in vivo. Surprisingly, in the adult murine brain, ADAMTS4 was exclusively expressed in oligodendrocytes. Cultured oligodendrocytes secreted a variety of Aβ species, but Aβ4-40 peptides were absent in cultures derived from ADAMTS4^-/- mice indicating that the enzyme was essential for Aβ4-x production in this cell type. These findings establish an enzymatic mechanism for the generation of Aβ4-x peptides. They further identify oligodendrocytes as a source of these highly amyloidogenic Aβ peptides.

Centenarian controls increase variant effect sizes by an average twofold in an extreme case-extreme control analysis of Alzheimer's disease

The detection of genetic loci associated with Alzheimer's disease (AD) requires large numbers of cases and controls because variant effect sizes are mostly small. We hypothesized that variant effect sizes should increase when individuals who represent the extreme ends of a disease spectrum are considered, as their genomes are assumed to be maximally enriched or depleted with disease-associated genetic variants. We used 1,073 extensively phenotyped AD cases with relatively young age at onset as extreme cases (66.3 ± 7.9 years), 1,664 age-matched controls (66.0 ± 6.5 years) and 255 cognitively healthy centenarians as extreme controls (101.4 ± 1.3 years). We estimated the effect size of 29 variants that were previously associated with AD in genome-wide association studies. Comparing extreme AD cases with centenarian controls increased the variant effect size relative to published effect sizes by on average 1.90-fold (SE = 0.29, p = 9.0 × 10-4). The effect size increase was largest for the rare high-impact TREM2 (R74H) variant (6.5-fold), and significant for variants in/near ECHDC3 (4.6-fold), SLC24A4-RIN3 (4.5-fold), NME8 (3.8-fold), PLCG2 (3.3-fold), APOE-ε2 (2.2-fold), and APOE-ε4 (twofold). Comparing extreme phenotypes enabled us to replicate the AD association for 10 variants (p < 0.05) in relatively small samples. The increase in effect sizes depended mainly on using centenarians as extreme controls: the average variant effect size was not increased in a comparison of extreme AD cases and age-matched controls (0.94-fold, p = 6.8 × 10-1), suggesting that on average the tested genetic variants did not explain the extremity of the AD cases. Concluding, using centenarians as extreme controls in AD case-control studies boosts the variant effect size by on average twofold, allowing the replication of disease-association in relatively small samples.