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Background: Recent Alzheimer’s disease (AD) genetics findings from genome-wide association studies (GWAS) span progressively larger and more diverse populations and outcomes. Currently, there is no up-to-date resource providing harmonized and searchable information on all AD genetic associations found by GWAS, nor linking the reported genetic variants and genes with functional and genomic annotations. Objective: Create an integrated/harmonized, and literature-derived collection of population-specific AD genetic associations. Methods: We developed the Alzheimer’s Disease Variant Portal (ADVP), an extensive collection of associations curated from >200 GWAS publications from Alzheimer’s Disease Genetics Consortium and other consortia. Genetic associations were systematically extracted, harmonized, and annotated from both the genome-wide significant and suggestive loci reported in these publications. To ensure consistent representation of AD genetic findings, all the extracted genetic association information was harmonized across specifically designed publication, variant, and association categories. Results: ADVP V1.0 (February 2021) catalogs 6,990 associations related to disease-risk, expression quantitative traits, endophenotypes, or neuropathology. This extensive harmonization effort led to a catalog containing >900 loci, >1,800 variants, >80 cohorts, and 8 populations. Besides, ADVP provides investigators with a seamless integration of genomic and publicly available functional annotations across multiple databases per harmonized variant and gene records, thus facilitating further understanding and analyses of these genetics findings. Conclusion: ADVP is a valuable resource for investigators to quickly and systematically explore high-confidence AD genetic findings and provides insights into population-specific AD genetic architecture. ADVP is continually maintained and enhanced by NIAGADS and is freely accessible at https://advp.niagads.org.

Genetics play an important role in late-onset Alzheimer’s Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case–control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Background: Genetics play an important role in late-onset Alzheimer’s Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease.Methods: We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants, adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed.Results: We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case-control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable.Conclusions: Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

Over 90% of variants are rare, and 50% of them are singletons in the Alzheimer’s Disease Sequencing Project Whole Exome Sequencing (ADSP WES) data. However, either single variant tests or unit-based tests are limited in the statistical power to detect the association between rare variants and phenotypes. To best utilize rare variants and investigate their biological effect, we exam their association with phenotypes in the context of protein. We developed a protein structure-based approach, POKEMON (Protein Optimized Kernel Evaluation of Missense Nucleotides), which evaluates rare missense variants based on their spatial distribution on the protein rather than allele frequency. The hypothesis behind this is that the three-dimensional spatial distribution of variants within a protein structure provides functional context and improves the power of association tests. POKEMON identified four candidate genes from the ADSP WES data, namely two known Alzheimer’s disease (AD) genes (TREM2 and SORL) and two novel genes (DUSP18 and CSF1R). For known AD genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low frequency risk variants within these genes. DUSP18 has a cluster of variants primarily shared by case subjects around the ligand-binding domain, and this cluster is further validated in a replication dataset with a larger sample size. POKEMON is an open-source tool available at https://github.com/bushlab-genomics/POKEMON.

More than 90% of genetic variants are rare in most modern sequencing studies, such as the Alzheimer's Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data. Furthermore, 54% of the rare variants in ADSP WES are singletons. However, both single variant and unit-based tests are limited in their statistical power to detect an association between rare variants and phenotypes. To best use missense rare variants and investigate their biological effect, we examine their association with phenotypes in the context of protein structures. We developed a protein structure–based approach, protein optimized kernel evaluation of missense nucleotides (POKEMON), which evaluates rare missense variants based on their spatial distribution within a protein rather than their allele frequency. The hypothesis behind this test is that the three-dimensional spatial distribution of variants within a protein structure provides functional context to power an association test. POKEMON identified three candidate genes (TREM2, SORL1, and EXOC3L4) and another suggestive gene from the ADSP WES data. For TREM2 and SORL1, two known Alzheimer's disease (AD) genes, the signal from the spatial cluster is stable even if we exclude known AD risk variants, indicating the presence of additional low-frequency risk variants within these genes. EXOC3L4 is a novel AD risk gene that has a cluster of variants primarily shared by case subjects around the Sec6 domain. This cluster is also validated in an independent replication data set and a validation data set with a larger sample size.

We developed a One-shot Distributed Algorithm for Cox proportional-hazards model to analyze Heterogeneous multi-center time-to-event data (ODACH) circumventing the need for sharing patient-level information across sites. This algorithm implements a surrogate likelihood function to approximate the Cox log-partial likelihood function that is stratified by site using patient-level data from a lead site and aggregated information from other sites, allowing the baseline hazard functions and the distribution of covariates to vary across sites. Simulation studies and application to a real-world opioid use disorder study showed that ODACH provides estimates close to the pooled estimator, which analyzes patient-level data directly from all sites via a stratified Cox model. Compared to the estimator from meta-analysis, the inverse variance-weighted average of the site-specific estimates, ODACH estimator demonstrates less susceptibility to bias, especially when the event is rare. ODACH is thus a valuable privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data.

Deeply phenotyped cohort data can elucidate differences associated with genetic risk for common complex diseases across an age spectrum. Previous work has identified genetic variants associated with Alzheimer’s disease (AD) risk from large-scale genome-wide association study meta-analyses. To explore effects of known AD-risk variants, we performed a phenome-wide association study on ~2000 clinical, proteomic, and metabolic blood-based analytes obtained from 2,831 cognitively normal adult clients of a consumer-based scientific wellness company. Results uncovered statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or nearNYAP1, ABCA7, INPP5D, andAPOE). These effects were detectable from early adulthood. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with thePICALMvariant. Sex-stratified GWAS results from an independent AD case-control meta-analysis supported sexspecific disease effects of thePICALMvariant, highlighting the importance of sex as a biological variable. These analyses support evidence from previous functional genomics studies in the identification of a causal variant within thePILRAgene. Taken together, this study highlights clues to the earliest effects of AD genetic risk variants in individuals where disease symptoms have not (yet) arisen.

Risk for late-onset Alzheimer’s disease (LOAD) is driven by multiple loci primarily identified by genome-wide association studies, many of which are common variants with minor allele frequencies (MAF)> 0.01. To identify additional common and rare LOAD risk variants, we performed a GWAS on 25,170 LOAD subjects and 41,052 cognitively normal controls in 44 datasets from the International Genomics of Alzheimer’s Project (IGAP). Existing genotype data was imputed using the dense, high-resolution Haplotype Reference Consortium (HRC) r1.1 reference panel. Stage 1 associations of P−5 were meta-analyzed with the European Alzheimer’s Disease Biobank (EADB) (n=20,301 cases; 21,839 controls) (stage 2 combined IGAP and EADB). An expanded meta-analysis was performed using a GWAS of parental AD/dementia history in the UK Biobank (UKBB) (n=35,214 cases; 180,791 controls) (stage 3 combined IGAP, EADB, and UKBB). Common variant (MAF≥0.01) associations were identified for 29 loci in stage 2, including novel genome-wide significant associations at TSPAN14 (P=2.33×10−12), SHARPIN (P=1.56×10−9), and ATF5/SIGLEC11 (P=1.03×10−8), and newly significant associations without using AD proxy cases in MTSS1L/IL34 (P=1.80×10−8), APH1B (P=2.10×10−13), and CLNK (P=2.24×10−10). Rare variant (MAFAPOE and TREM2, and a novel association of a rare variant (rs143080277; MAF=0.0054; P=2.69×10−9) in NCK2, further strengthened with the inclusion of UKBB data in stage 3 (P=7.17×10−13). Single-nucleus sequence data shows that NCK2 is highly expressed in amyloid-responsive microglial cells, suggesting a role in LOAD pathology.

Amyotrophic lateral sclerosis (ALS) is a multi‐system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine‐learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post‐mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS., Single nucleotide polymorphisms (SNPs) previously identified through genome‐wide association studies as risk factors for amyotrophic lateral sclerosis (ALS) and/or frontotemporal dementia (FTD) further associate in a polygenic manner with risk for cognitive dysfunction in ALS and related disorders.

PURPOSE OF REVIEW: Neurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable and identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer’s disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) amongst others, interpreting the biological roles of significantly-associated variants in the genetic architecture of these traits remains a significant challenge. Here we review several ‘omics’ approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits. RECENT FINDINGS: Several common ‘omics’ approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically-regulated gene expression (GReX), which aggregates the genetic variants’ effects at the gene-level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits. SUMMARY: GWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology and toxicology as tools for understanding the functional roles of genetics variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.

Alzheimer’s Disease (AD) genetics has made substantial progress through genome-wide association studies (GWASs). An up-to-date resource providing harmonized, searchable information on AD genetic variants with linking to genes and supporting functional evidence is needed.We developed the Alzheimer’s Disease Variant Portal (ADVP), an extensive collection of associations curated from >200 GWAS publications from Alzheimer’s Disease Genetics Consortium (ADGC) and other researchers. Publications are reviewed systematically to extract top associations for harmonization and genomic annotation.ADVP V1.0 catalogs 6,990 associations with disease-risk, expression quantitative traits, endophenotypes and neuropathology across >900 loci, >1,800 variants, >80 cohorts, and 8 populations. ADVP integrates with NIAGADS Alzheimer’s GenomicsDB where investigators can cross-reference other functional evidence.ADVP is a valuable resource for investigators to quickly and systematically explore high-confidence AD genetic findings and provides insights into population- and tissue-specific AD genetic architecture. ADVP is continually maintained and enhanced by NIAGADS and is freely accessible (https://advp.niagads.org).

The aim of this study was to explore whether variants in LRP10, recently associated with Parkinson's disease and dementia with Lewy bodies, are observed in 2 large cohorts (discovery and validation cohort) of patients with progressive supranuclear palsy (PSP). A total of 950 patients with PSP were enrolled: 246 patients with PSP (n = 85 possible (35%), n = 128 probable (52%), n = 33 definite (13%)) in the discovery cohort and 704 patients with definite PSP in the validation cohort. Sanger sequencing of all LRP10 exons and exon-intron boundaries was performed in the discovery cohort, and whole-exome sequencing was performed in the validation cohort. Two patients from the discovery cohort and 8 patients from the validation cohort carried a rare, heterozygous, and possibly pathogenic LRP10 variant (p.Gly326Asp, p.Asp389Asn, and p.Arg158His, p.Cys220Tyr, p.Thr278Ala, p.Gly306Asp, p.Glu486Asp, p.Arg554∗, p.Arg661Cys). In conclusion, possibly pathogenic LRP10 variants occur in a small fraction of patients with PSP and may be overrepresented in these patients compared with controls. This suggests that possibly pathogenic LRP10 variants may play a role in the development of PSP.

The Alzheimer’s Disease Sequencing Project (ADSP) undertook whole exome sequencing in 5,740 late-onset Alzheimer disease (AD) cases and 5,096 cognitively normal controls primarily of European ancestry (EA), among whom 218 cases and 177 controls were Caribbean Hispanic (CH). An age-, sex- and APOE based risk score and family history were used to select cases most likely to harbor novel AD risk variants and controls least likely to develop AD by age 85 years. We tested ~1.5 million single nucleotide variants (SNVs) and 50,000 insertion-deletion polymorphisms (indels) for association to AD, using multiple models considering individual variants as well as gene-based tests aggregating rare, predicted functional, and loss of function variants. Sixteen single variants and 19 genes that met criteria for significant or suggestive associations after multiple-testing correction were evaluated for replication in four independent samples; three with whole exome sequencing (2,778 cases, 7,262 controls) and one with genome-wide genotyping imputed to the Haplotype Reference Consortium panel (9,343 cases, 11,527 controls). The top findings in the discovery sample were also followed-up in the ADSP whole-genome sequenced family-based dataset (197 members of 42 EA families and 501 members of 157 CH families). We identified novel and predicted functional genetic variants in genes previously associated with AD. We also detected associations in three novel genes: IGHG3 (p = 9.8 × 10−7), an immunoglobulin gene whose antibodies interact with β-amyloid, a long non-coding RNA AC099552.4 (p = 1.2 × 10−7), and a zinc-finger protein ZNF655 (gene-based p = 5.0 × 10−6). The latter two suggest an important role for transcriptional regulation in AD pathogenesis.

Objective Nearly all adults >50 years of age have evidence for neurofibrillary tau tangles (NFTs) and a significant proportion of individuals additionally develop amyloid plaques (Aβ) consistent with Alzheimer's disease (AD). In an effort to identify the independent genetic risk factors for NFTs and Aβ, we investigated genotypic frequencies of AD susceptibility loci between autopsy‐confirmed AD and primary age‐related tauopathy (PART), a neuropathological condition defined by characteristic neurofibrillary tau tangles (NFTs) with minimal or absent Aβ. Methods General linear models assessed the odds of AD (N = 1190) relative to PART (N = 376) neuropathologically confirmed cases from two independent series: the Penn Brain Bank (PENN; AD N = 312; PART N = 65) and National Alzheimer's Coordinating Center (NACC; AD N = 878; PART N = 311). We also evaluated the odds of Braak stage NFT burden. Results Three genotypes significantly associated with reduced AD risk relative to PART in the PENN (N = 377) and NACC (N = 1189) cohorts including APOE ε4, APOE ε2, and rs6656401 in the CR1 gene. The genotypes rs6733839 in the BIN1 gene and rs28834970 in the PTK2B gene approached significance in the PENN cohort and were significantly associated with reduced AD risk in the NACC cohort. In a combined cohort analysis (N = 1566), APOE ε4 dosage was highly associated with higher Braak stage of NFT burden in Probable PART and AD, but not Definite PART. Interpretation The presence of genotypic differences between PART and AD suggest that PART can provide a genetic model of NFT risk and potential Aβ resistance to inform disease‐modifying therapies.

Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients, however factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N=327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort, and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N=90) as well as post mortem motor cortical neuronal loss (N=87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.

Background Potentially modifiable risk factors have been associated with Alzheimer’s disease (AD). However, the causality of these risk factors on AD is unclear. Using Mendelian randomization we evaluated the causal effect of potentially modifiable risk factors on AD and its associated endophenotypes to inform the development of lifestyle interventions that could reduce risk of developing AD. Methods and Findings Genetic instruments for the exposures were selected from genome wide association studies (GWAS) for traits previously linked to AD in observational studies including alcohol intake, physical activity, lipid traits, blood pressure traits, type 2 diabetes, body-mass index (BMI), depression, sleep, social isolation, smoking, oily fish intake, and educational attainment. The outcomes included AD status, AD age of onset survival (AAOS), hippocampal volume, CSF levels of Aβ 42 , tau, and ptau 181 , and neuropathological burden of neuritic plaques, neurofibrillary tangles, and vascular brain injury (VBI). MR estimates were calculated using an inverse variance weighted approach. Genetically predicted educational attainment (OR [CI]: 0.7 [0.63, 0.78]), diastolic blood pressure (DBP) (OR [CI]: 0.99 [0.98, 0.99]), systolic blood pressure (SBP) (OR [CI]: 0.99 [0.99, 1]) and physical activity (OR [CI]: 2.5 [1.47, 4.23]) were causally associated with AD risk. Genetically predicted BMI (HR [CI]: 1.13 [1.07, 1.2]) and educational attainment (HR [CI]: 0.74 [0.68, 0.82]) were causally associated with AAOS. Genetically predicted alcohol consumption (β [CI]: −0.15 [−0.25, −0.05]), broad depressive symptoms (β [CI]: 0.5 [0.2, 0.8]), major depressive disorder (β [CI]: 0.12 [0.05, 0.18]) and physical activity were causally associated with CSF Aβ 42 (β [CI]: 0.25 [0.1, 0.39]). Genetically predicted DBP was causally associated with CSF total Tau (β [CI]: −0.005 [−0.007, −0.002]). Increased risk of VBI were observed for genetically predicted DBP (OR [CI]: 1.05 [1.02, 1.08]), SBP (OR [CI]: 1.06 [1.03, 1.1]), and pulse pressure (OR [CI]: 1.03 [1.01, 1.05]). Increased risk of neuritic plaque burden were observed for genetically predicted LDL-cholesterol (OR [CI]: 1.87 [1.3, 2.69]) and total cholesterol (OR [CI]: 2.03 [1.44, 2.85]). Genetically predicted insomnia symptoms (β [CI]: −0.2 [−0.34, −0.06]) and total cholesterol were associated (β [CI]: −0.06 [−0.1, −0.03]) with hippocampal volume. Potential limitations include weak instrument bias, non-homogenous samples and other implicit limitations of MR analysis. Conclusions Demonstration of a causal relationship between blood pressure, cholesterol levels, BMI, depression, insomnia symptoms, physical activity and educational attainment on the AD phenome strongly support public health programs to educate the public about these preventable causes of AD.

IMPORTANCE: Previous genome-wide association studies of common variants identified associations for Alzheimer disease (AD) loci evident only among individuals with particular APOE alleles. OBJECTIVE: To identify APOE genotype-dependent associations with infrequent and rare variants using whole-exome sequencing. DESIGN, SETTING, AND PARTICIPANTS: The discovery stage included 10 441 non-Hispanic white participants in the Alzheimer Disease Sequencing Project. Replication was sought in 2 independent, whole-exome sequencing data sets (1766 patients with AD, 2906 without AD [controls]) and a chip-based genotype imputation data set (8728 patients with AD, 9808 controls). Bioinformatics and functional analyses were conducted using clinical, cognitive, neuropathologic, whole-exome sequencing, and gene expression data obtained from a longitudinal cohort sample including 402 patients with AD and 647 controls. Data were analyzed between March 2017 and September 2018. MAIN OUTCOMES AND MEASURES: Score, Firth, and sequence kernel association tests were used to test the association of AD risk with individual variants and genes in subgroups of APOE ε4 carriers and noncarriers. Results with P ≤ 1 × 10(−5) were further evaluated in the replication data sets and combined by meta-analysis. RESULTS: Among 3145 patients with AD and 4213 controls lacking ε4 (mean [SD] age, 83.4 [7.6] years; 4363 [59.3.%] women), novel genome-wide significant associations were obtained in the discovery sample with rs536940594 in AC099552 (odds ratio [OR], 88.0; 95% CI, 9.08-852.0; P = 2.22 × 10(−7)) and rs138412600 in GPAA1 (OR, 1.78; 95% CI, 1.44-2.2; meta-P = 7.81 × 10(−8)). GPAA1 was also associated with expression in the brain of GPAA1 (β = −0.08; P = .03) and its repressive transcription factor, FOXG1 (β = 0.13; P = .003), and global cognition function (β = −0.53; P = .009). Significant gene-wide associations (threshold P ≤ 6.35 × 10(−7)) were observed for OR8G5 (P = 4.67 × 10(−7)), IGHV3-7 (P = 9.75 × 10(−16)), and SLC24A3 (P = 2.67 × 10(−12)) in 2377 patients with AD and 706 controls with ε4 (mean [SD] age, 75.2 [9.6] years; 1668 [54.1%] women). CONCLUSIONS AND RELEVANCE: The study identified multiple possible novel associations for AD with individual and aggregated rare variants in groups of individuals with and without APOE ε4 alleles that reinforce known and suggest additional pathways leading to AD.

CpG‐related single nucleotide polymorphisms (CGS) have the potential to perturb DNA methylation; however, their effects on Alzheimer disease (AD) risk have not been evaluated systematically. We conducted a genome‐wide association study using a sliding‐window approach to measure the combined effects of CGSes on AD risk in a discovery sample of 24 European ancestry cohorts (12,181 cases, 12,601 controls) from the Alzheimer's Disease Genetics Consortium (ADGC) and replication sample of seven European ancestry cohorts (7,554 cases, 27,382 controls) from the International Genomics of Alzheimer's Project (IGAP). The potential functional relevance of significant associations was evaluated by analysis of methylation and expression levels in brain tissue of the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP), and in whole blood of Framingham Heart Study participants (FHS). Genome‐wide significant (p

Genotype imputation infers missing genotypes in silico using haplotype information from reference samples with genotypes from denser genotyping arrays or sequencing. This approach can confer a number of improvements on genome-wide association studies: it can improve statistical power to detect associations by reducing the number of missing genotypes; it can simplify data harmonization for meta-analyses by improving overlap of genomic variants between differently-genotyped sample sets; and it can increase the overall number and density of genomic variants available for association testing. This article reviews the general concepts behind imputation, describes imputation approaches and methods for various types of genotype data, including family-based data, and identifies web-based resources that can be used in different steps of the imputation process. For practical application, it provides a step-by-step guide to implementation of a two-step imputation process consisting of phasing of the study genotypes and the imputation of reference panel genotypes into the study haplotypes. In addition, this review describes recently developed haplotype reference panel resources and online imputation servers that are capable of remotely and securely implementing an imputation workflow on uploaded genotype array data. © 2019 by John Wiley & Sons, Inc.

Alzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia. With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets. Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately ∼20 genes with common variants contributing to LOAD risk. In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses. Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple low-frequency and rare variant contributors to AD, and discuss on-going efforts with next-generation sequencing studies to develop statistically well- powered and comprehensive genomic studies of AD. Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD. © 2016 Wiley Periodicals, Inc.

Summary Work in the past 10 years has greatly expanded our understanding of Alzheimer's disease (AD) genetics, with more than 20 susceptibility genetic loci discovered in large genome-wide association and sequencing studies. The NIA Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is funded by National Institute on Aging to serve as a one-stop portal for the community to access research data and findings generated from AD genetics projects. In this article, we describe the mission of NIAGADS, available resources, and data sets including the Alzheimer's Disease Sequencing Project, and how researchers may apply for access to genetic data.