INTRODUCTION: The sortilin-related receptor (SORL1) directs APP and Aβ trafficking within the retromer pathway. Cleavage at the cell surface releases soluble SORL1 (sSORL1) into cerebrospinal fluid (CSF). We examined whether CSF-sSORL1 can serve as an in vivo marker of genetically impaired SORL1. METHODS: CSF-sSORL1 was quantified by enzyme-linked immunosorbent assay (ELISA) in 218 participants: 90 carriers of SORL1 variants, 78 SORL1-wildtype (WT) AD patients, and 50 SORL1-WT controls. RESULTS: sSORL1 concentrations were significantly lower in carriers of protein-truncating and damaging missense variants. In SORL1-WT patients, CSF-sSORL1 correlated with pTau181 but not with Aβ42 among AD patients, and did not differ between patients and controls. DISCUSSION: These findings suggest that impaired SORL1 trafficking reduces receptor delivery to the cell surface and thereby decreases sSORL1 shedding, supporting its potential use as a pathway-specific biomarker. Highlights: Enzyme-linked immunosorbent assay (ELISA) enables quantitative measurement of soluble sortilin-related receptor (sSORL1) in cerebrospinal fluid (CSF). sSORL1 levels are reduced in CSF from carriers of a pathogenic SORL1 variant. CSF-sSORL1 levels correlate with tau pathology in Alzheimer's disease. sSORL1 levels represent an in vivo biomarker of SORL1 function.

Tandem repeats (TRs) occupy a significant portion of the human genome and are a source of polymorphisms due to variations in sizes and motif compositions. Some of these variations have been associated with various neuropathological disorders, highlighting the clinical importance of assessing the motif structure of TRs. Moreover, assessing the TR motif variation can offer valuable insights into evolutionary dynamics and population structure. Previously, characterizations of TRs were limited by short-read sequencing technology, which lacks the ability to accurately capture the full TR sequences. As long-read sequencing becomes more accessible and can capture the full complexity of TRs, there is now also a need for tools to characterize and analyze TRs using long-read data across multiple samples. In this study, we present MotifScope, a novel algorithm for the characterization and visualization of TRs based on a de novo k-mer approach for motif discovery. Comparative analysis against established tools reveals that MotifScope can identify a greater number of motifs and more accurately represent the underlying repeat sequences. Moreover, MotifScope has been specifically designed to enable motif composition comparisons across assemblies of different individuals, as well as across long-read sequencing reads within an individual, through combined motif discovery and sequence alignment. We showcase potential applications of MotifScope in diverse fields, including population genetics, clinical settings, and forensic analyses.

Introduction
The field of forensic DNA analysis has undergone rapid advancements in recent decades. The integration of massively parallel sequencing (MPS) has notably expanded the forensic toolkit, moving beyond identity matching to predicting phenotypic traits and biogeographical ancestry. This shift is of particular significance in cases where conventional DNA profiling fails to identify a single suspect. Supplementing forensic analyses with estimated biological age may be valuable but involves a complex and time-consuming DNA methylation analysis. This study explores and validates the performance of a comprehensive forensic third-generation sequencing assay utilizing Oxford Nanopore Technologies (ONT) in an adaptive and direct sequencing approach. We incorporated the most widely used forensic markers, i.e., STRs, SNPs, InDels, mitochondrial DNA (mtDNA), and two methylation-based clock classifiers, thereby combining forensic genetic and epigenetic analysis in one single workflow.

Methods and results
In our investigation, DNA from six anonymous individuals was sequenced using the ONT standard adaptive direct sequencing approach, reaching a mean percentage of on-target reads ranging from 6.6 % to 7.7 % per sample. ONT data was compared to standard MPS data and Illumina EPIC DNA methylation profiles. Basecalling employed recommended ONT software packages. TREAT was used for ONT-based analysis of autosomal and Y-chromosome STRs, achieving 90–92 % correct calls depending on allelic read depth thresholds. InDel analyses for two lower-quality samples proved challenging due to inadequate read depth, while the remaining four samples significantly contributed to the observed percentage markers (60.9 %) and correct calls (97.8 %). SNP analysis achieved a 98 % call rate, with only two mismatches and two missed alleles. ONT-generated DNA methylation data demonstrated Pearson’s correlation coefficients with EPIC data ranging from 0.67 to 0.97 for Horvath’s clock. Additional age-associated markers exhibited Pearson’s correlation coefficients with chronological age between 0.14 (ELOVL2) and 0.96 (FHL2) at read depths of <30 and <20, respectively. Despite excluding mtDNA from our targeted sequencing approach, adaptive proof-reading fragments covered the complete mtDNA with an average read depth of 21–72, showing 100 % concordance with reference data.

Discussion
Our exploratory study using ONT adaptive sequencing for conventional forensic and age associated DNA methylation markers showed high sequencing accuracy for a significant number of markers, showcasing ONT as a promising (epi)genetic forensic method. Future studies must address three critical aspects: determining clear quantity and quality measures and detection thresholds for accuracy, optimizing input DNA quantity for forensic casework expectations, and addressing ethical considerations associated with phenotype and ancestry analysis to prevent ethnic biases.

Background and ObjectivesIdentifying genetic causes of dementia in patients visiting memory clinics is important for patient care and family planning. Traditional clinical selection criteria for genetic testing may miss carriers of pathogenic variants in dementia-related genes. This study aimed identify how many carriers we are missing and to optimize criteria for selecting patients for genetic counseling in memory clinics.MethodsIn this clinical cohort study, we retrospectively genetically tested patients during 2.5 years (2010-2012) visiting the Alzheimer Center Amsterdam, a specialized memory clinic. Genetic tests consisted of a 54-gene dementia panel, focusing on Class IV/V variants per American College of Medical Genetics and Genomics guidelines, including APP duplications and the C9ORF72 repeat expansion. We determined the prevalence of pathogenic variants and propose new eligibility criteria for genetic testing in memory clinics. The eligibility criteria were prospectively applied for 1 year (2021-2022), and results were compared with the retrospective cohort.ResultsGenetic tests were retrospectively performed in in 1,022 of 1,138 patients (90%) who consecutively visited the memory clinic. Among these, 1,022 patients analyzed (mean age 62.1 ± 8.9 years; 40.4% were female), 34 pathogenic variant carriers were identified (3.3%), with 24 being symptomatic. Previous clinical criteria would have identified only 15 carriers (44% of all carriers, 65% of symptomatic carriers). The proposed criteria increased identification to 22 carriers (62.5% of all carriers, 91% of symptomatic carriers). In the prospective cohort, 148 (28.7%) of 515 patients were eligible for testing under the new criteria. Of the 90 eligible patients who consented to testing, 13 pathogenic carriers were identified, representing a 73% increase compared with the previous criteria.DiscussionWe found that patients who visit a memory clinic and carry a pathogenic genetic variant are often not eligible for genetic testing. The proposed new criteria improve the identification of patients with a genetic cause for their cognitive complaints. In systems without practical or financial barriers to genetic testing, the new criteria can enhance personalized care. In other countries where the health care systems differs and in other genetic ancestry groups, the performance of the criteria may be different.

We constructed a polygenic protective score specific to Alzheimer’s disease (AD PPS) based on the current literature among the participants enrolled in five studies of healthy aging and extreme longevity in the USA, Europe, and Asia. This AD PPS did not include variants on apolipoprotein E (APOE) gene. Comparisons of AD PPS in different data sets of healthy agers and centenarians showed that centenarians have stronger genetic protection against AD compared to individuals without familial longevity. The current study also shows evidence that this genetic protection increases with increasingly older ages in centenarians (centenarians who died before reaching age 105 years, semi-supercentenarians who reached age 105 to 109 years, and supercentenarians who reached age 110 years and older). However, the genetic protection was of modest size: the average increase in AD PPS was approximately one additional protective allele per 5 years of gained lifetime. Additionally, we show that the higher AD PPS was associated with better cognitive function and decreased mortality. Taken together, this analysis suggests that individuals who achieve the most extreme ages, on average, have the greatest protection against AD. This finding is robust to different genetic backgrounds with important implications for universal applicability of therapeutics that target this AD PPS.

Background
Protein truncating variants (PTVs) in SORL1 are observed almost exclusively in Alzheimer’s Disease (AD) cases, but the effect of rare SORL1 missense variants is unclear.

Methods
To identify high-priority missense variants (HPVs), we applied ‘domain mapping of disease mutations’ for the 637 unique coding SORL1 variants detected in 18,959 AD-cases and 21,893 non-demented controls.

Results
In this sample, PTVs and HPVs associated with respectively a 35- and 10-fold increased risk of early onset AD and 17- and 6-fold increased risk of overall AD. The median age at onset (AAO) of PTV- and HPV-carriers was 62 and 64 years, and APOE-genotype contributed to AAO-variability. The median AAO of PTV- and HPV-carriers is ~8–10 years earlier than wild-type SORL1 carriers, matched for APOE-genotype. Specific HPVs are highly penetrant and lead to earlier AAOs than PTVs, suggesting possible dominant negative effects.

Conclusion
Our results justify a debate on whether HPV carriers should be considered for clinical counseling.

The Alzheimer’s disease (AD) genetic landscape identified microglia as a key disease-modifying cell type. Paired immunoglobulin-like type 2 receptor alpha (PILRA) is an immunoreceptor tyrosine–based inhibitory motif domain–containing inhibitory receptor, expressed by myeloid cells such as microglia. The known protective PILRA G78R gene variant reduces AD risk in apolipoprotein E4 (APOE4) carriers and is enriched in a cohort of healthy centenarians. However, mechanisms underlying protective effects in microglia are undefined. Here, we identified biological functions of PILRA in human induced pluripotent stem cell–derived microglia (iMG) and chimeric AD mice. PILRA knockout (KO) in iMG rescued ApoE4-mediated immunometabolic deficits and prevented lipotoxicity through increased lipid storage, improved mitochondrial bioenergetics, and antioxidant activity. PILRA KO also enhanced microglial chemotaxis and attenuated inflammation. With pharmacological inhibitor studies, we showed that peroxisome proliferator–activated receptor and signal transducer and activator of transcription 1/3 mediated PILRA-dependent microglial functions. AD mice transplanted with human PILRA KO microglia exhibited reduced amyloid pathology and rescued synaptic markers. A high-affinity ligand blocking PILRA antibody phenocopied PILRA KO iMG. These findings suggest that PILRA is a pharmacologically tractable therapeutic target for AD.

Background
Many types of dementia have high heritability, which creates opportunities for DNA diagnostics. Clinicians sporadically test for causal genetic variants. However, in addition to causal genetic mutations, an increasing number of both common and rare risk factors are being identified, especially for Alzheimer’s disease (AD). Here, we describe and evaluate diagnostic performance of combining genetic risk factors for AD to assist memory clinic clinicians.

Methods
A retrospective analysis of 998 consecutive patients (mean age 62.1, 40.3% females, 63.3% dementia) was conducted over 2.5 years in a Dutch memory clinic. The patients underwent a complete genetic risk assessment, including whole-exome sequencing and array genotyping. We examined known pathogenic genetic variants for all dementia types and their correlation with clinical diagnoses. We evaluated a combined genetic score (GS) based on all genetic risk factors for AD - namely APOE genotypes, candidate risk rare variants in 11 genes, and a polygenic risk score (PRS) based on 82 common variants. Then, we analyzed the discriminatory characteristics of the GS.

Results
Causal pathogenic variants were rare, present in 3.4% of individuals, but genetic testing would have altered the diagnosis in over half of the carriers. Candidate rare risk variants were more common, identified in 31.6% of patients. Both APOE genotypes and the PRS were independently associated with AD, and gene-specific interaction was found between TREM2 and AD-PRS (β = -1.16, p = 0.015). Patients with a high GS were 7 times more likely receive an AD diagnosis compared to those with a low GS (p = 2.5E-07).

Conclusion
Overall, this study highlights the potential of integrating genetic risk factors into clinical practice to enhance AD diagnosis, though the improvement in diagnostic accuracy was moderate. The findings underscore the importance of genetic testing in diagnosis while also recognizing its limitations.

Background: The accumulation of amyloid-β1−42 (Aβ1−42) peptides and phosphorylated-Tau181 (p-Tau181) tangles from the preclinical stages of Alzheimer’s disease (AD) has led to a biological definition of the disease. However, among Aβ1−42-positive individuals, cognitive decline onset varies, and some never develop symptoms. Genetic influences on molecular pathways and their interactions with proteinopathy may underlie this heterogeneity. Leveraging data from a large sample of cognitively intact older adults in the European Prevention of Alzheimer Dementia (EPAD) cohort, we examined how AD-related pathophysiological changes (i.e., Aβ1−42 and p-Tau181), polygenic pathways and their interaction are associated with WM micro- and macrostructural properties. Methods: We selected 803 individuals (mean age = 64.7 ± 7.3 years, 458 [57.0%] females, 275 [34.2%] APOE-ε4 carriers) with CSF-Aβ1−42 and p-Tau181 measurements available, full genotyping, and structural and diffusion MRI. Polygenic risk scores (PRSs) were computed using 85 AD-related genetic variants. These were mapped to their corresponding genes and, after excluding those belonging to the APOE locus, clustered by function into six pathway-specific PRSs (i.e., immune activation, signal transduction, inflammation, lipid, amyloid, and clearance pathways). Diffusion MRIs were processed through the fixel-based analysis framework to derive fiber density (FD) and fiber cross-section (FC) metrics, which were averaged within WM tracts. Linear models assessed the effects of AD-related pathophysiological changes, global and pathway-specific PRSs, and their interactions on FD and FC at both the tract and fixel levels. Models were corrected for multiple comparisons. Results: P-Tau181 was primarily associated with greater FD. The lipid pathway was associated with greater FD and FC, with these effects predominantly occurring in the left hemisphere, consistent with evidence of hemispheric dominance. The clearance pathway moderated the effect of Aβ1−42 on FD, with a positive slope in A + compared to A- individuals. The immune activation pathway moderated the effect of p-Tau181 on FD, with a negative slope in T + compared to T- individuals. Conclusions: Pathway-specific genetic vulnerability to AD is associated with alterations in WM tracts both directly and by moderating the effects of AD-related pathophysiological changes. AD-associated genetic risk should be integrated into the AD diagnostic framework to enable targeted screening and intervention for future preclinical trials aimed at specific biological pathways.

UNC13A (rs12608932-CC) is associated with both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), and shortens survival in ALS. We aim to describe the association for UNC13A and survival in FTD. We included 626 patients with FTD from Dutch memory clinics, including a subcohort of 150 patients with TDP-43 pathology. Survival analyses were performed using Cox proportional hazard models in a recessive manner. Homozygosity for rs12608932-C in UNC13A was associated with a shorter survival compared with other genotypes (hazard ratio [HR] = 1.28, 95% confidence interval [CI] = 1.02–1.60, p = 0.033), which has implications for patient counselling and trial design. ANN NEUROL 2025;97:1062–1066.

Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer’s disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models. ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. They highlight 6 novel loci which replicate in an external dataset, including variants which map to ARHGAP25, LY6H, COG7, SOD1 and ZNF597. They further identify novel association in AP4E1, refining the genetic landscape of the known SPPL2A locus. Our results demonstrate that machine learning methods can achieve predictive performance comparable to classical approaches in genetic epidemiology and have the potential to uncover novel loci that remain undetected by traditional GWAS. These insights provide a complementary avenue for advancing the understanding of AD genetics.

INTRODUCTION: Cognitive resilience refers to maintaining cognitive function despite Alzheimer's disease (AD) pathophysiology. METHODS: We analyzed amyloid-positive individuals across clinical stages of AD in two cohorts: the Amsterdam Dementia Cohort (ADC, N = 1036) and Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 685). Cognitive resilience was conceptualized from a canonical correlation analysis of magnetic resonance imaging and neuropsychological data in each cohort separately. Model validation involved education as a resilience proxy and key genetic factors (apolipoprotein E [APOE] ε4 and APOE ε2) of AD. We explored associations between 83 AD risk loci and cognitive resilience. RESULTS: Resilience was correlated with education (ADC: β = 0.144, p < 0.001; ADNI: β = 0.149, p < 0.001) and APOE ε4 (β_{meta-analysis}= –0.052, p = 0.014). Exploratory single nucleotide polymorphism meta-analysis identified potential involvement of genetic variants around genes UNC5CL, USP6NL, and TPCN1 in lower, and genes COX7C and MINDY2 in higher resilience. DISCUSSION: Our novel resilience approach showed conceptual validity and potential for future discovery of resilience-related genetic variants. Highlights: ·We define a novel approach to resilience using canonical correlation analysis (CCA). ·Apolipoprotein E ε4 is linked to lower resilience, suggesting increased vulnerability. ·Genetic loci around COX7C and MINDY2 are potentially involved in higher resilience. ·This novel approach may be used for multi-cohort studies such as genome-wide association studies in the future.

Alzheimer's disease and related dementias (ADRD) are complex neurodegenerative disorders of which the genetic basis remains incompletely understood. Hippocampal volume loss is a core hallmark of AD. Hippocampal volume also has a strong heritable component and its genetic underpinnings may help us to understand the complex biological mechanism underlying ADRD. To identify shared genetic risk loci across late-onset ADRD and bilateral hippocampal volumes, we conducted a cross-trait analysis of existing GWAS data on the two traits using the conjunctional false discovery rate (conjFDR) framework. Functional annotation and phenome-wide association studies (PheWAS) were performed on the identified shared loci to characterize their biological relevance. We identified 11 unique lead genetic loci, of which 7 loci showed discordant directional effects (loci associated with increased risk for ADRD and smaller hippocampal volumes). We found that SHARPIN and TNIP1 genes play a role in ADRD by affecting hippocampal volumes. In addition, we observed 9 novel ADRD-hippocampus loci in genes previously implicated in AD (IGIP and ACE) and novel ADRD-genes (KCTD13, HINT1, SH3TC2, FAM53B, TPM1, IL34 and SSH2). PheWAS results show that most shared loci associated with neuroimaging measurements, white blood cell markers, red blood cell markers, and lipids. This study shows a shared genetic basis between ADRD and bilateral hippocampal volumes. By integrating summary statistics for these two traits, we identified both novel and previously reported ADRD-hippocampus loci. Functional analysis highlights the role of immune cells and lipid markers in the shared loci, suggesting a shared neurobiological basis for ADRD and bilateral hippocampal volumes.

Background and Objectives
More than 200 genetic variants have been associated with multiple sclerosis (MS) susceptibility. However, it is unclear to what extent genetic factors influence lifetime risk of MS. Using a population-based birth-year cohort, we investigate the effect of genetics on lifetime risk of MS.

Methods
In the Project Y study, we tracked down almost all persons with MS (pwMS) from birth year 1966 in the Netherlands. As control participants, we included non-MS participants from the Project Y cohort (born 1965–1967 in the Netherlands) and non-MS participants from the Amsterdam Dementia Cohort born between 1963 and 1969. Genetic variants associated with MS were determined in pwMS and control participants using genotyping or imputation methods. Polygenic risk scores (PRSs) based on variants and weights from the largest genetic study in MS were calculated for each participant and assigned into deciles based on the PRS distribution in the control participants. We examined the lifetime risk for each decile and the association between PRS and MS disease variables, including age at onset and time to secondary progression.

Results
MS-PRS was calculated for 285 pwMS (mean age 53.0 ± 0.9 years, 72.3% female) and 267 control participants (mean age 51.8 ± 3.2 years, 58.1% female). Based on the lifetime risk estimation, we observed that 1:2,739 of the women with the lowest 30% genetic risk developed MS, whereas 1:92 of the women with the top 10% highest risk developed MS. For men, only 1:7,900 developed MS in the lowest 30% genetic risk group, compared with 1:293 men with the top 10% genetic risk. The PRS was not significantly associated with age at onset and time to secondary progression in both sexes.

Discussion
Our results show that the lifetime risk of MS is strongly influenced by genetic factors. Our findings have the potential to support diagnostic certainty in individuals with suspected MS: a high PRS could strengthen a diagnosis, but especially a PRS from the lowest tail of the PRS distribution should be considered a red flag and could prevent misdiagnosing conditions that mimic MS.

Alzheimer’s disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood–brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine.

Tandem repeats (TRs) play important roles in genomic variation and disease risk in humans. Long-read sequencing allows for the accurate characterization of TRs; however, the underlying bioinformatics perspectives remain challenging. We present otter and TREAT: otter is a fast targeted local assembler, cross-compatible across different sequencing platforms. It is integrated in TREAT, an end-to-end workflow for TR characterization, visualization, and analysis across multiple genomes. In a comparison with existing tools based on long-read sequencing data from both Oxford Nanopore Technology (ONT, Simplex and Duplex) and Pacific Bioscience (PacBio, Sequel II and Revio), otter and TREAT achieve state-of-the-art genotyping and motif characterization accuracy. Applied to clinically relevant TRs, TREAT/otter significantly identify individuals with pathogenic TR expansions. When applied to a case-control setting, we replicate previously reported associations of TRs with Alzheimer's disease, including those near or within APOC1 (P = 2.63 × 10−9), SPI1 (P = 6.5 × 10−3), and ABCA7 (P = 0.04) genes. Finally, we use TREAT/otter to systematically evaluate potential biases when genotyping TRs using diverse ONT and PacBio long-read sequencing data sets. We show that, in rare cases (0.06%), long-read sequencing from coverage drops in TRs, including the disease-associated TRs in ABCA7 and RFC1 genes. Such coverage drops can lead to TR misgenotyping, hampering the accurate characterization of TR alleles. Taken together, our tools can accurately genotype TRs across different sequencing technologies and with minimal requirements, allowing end-to-end analysis and comparisons of TRs in human genomes, with broad applications in research and clinical fields.

Introduction
Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine.

Methods
We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity.

Results
CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication.

Discussion
This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies.

Highlights
- Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers.
- Inflammatory pathways are mostly related to cerebrovascular burden.
- White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.

Introduction Both late-onset Alzheimer’s disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability. Methods We compute heritability in five large independent cohorts (N = 7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability a) for all SNPs, b) excluding APOE region, c) excluding both APOE and genome-wide association study hit regions, and d) SNPs overlapping a microglia gene-set. Results SNP-based heritability of late onset Alzheimer’s disease is between 38 and 66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD = 8%] on average when the APOE region is excluded and an additional 1% [SD = 3%] when genome-wide significant regions were removed. A microglia gene-set explains 69–84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts. Conclusion The heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes.

The authors of the manuscript Identification of five potential predictive biomarkers for Alzheimer’s disease by integrating the unified test for molecular signatures and weighted gene co-expression network analysis in this issue of the Medical Sciences Section of the Journals of Gerontology Series A have exploited a number of different bioinformatic tools to analyze the vast amount of data that they were confronted with (1). This is increasingly happening as it is becoming easier and cheaper to generate comprehensive molecular and phenotypic data with which biological hypotheses can be sharpened. Here, we will give a basic understanding of the methods used by Zhou et al., which are becoming standard practices when analyzing high-throughput biological data (1).

Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage
genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele.