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Article ; Online: Genotype Imputation in Genome-Wide Association Studies.

Naj, Adam C

Current protocols in human genetics

2019 Volume 102, Issue 1, Page(s) e84

Abstract: 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 ...

Abstract	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.
MeSH term(s)	Gene Frequency ; Genetic Linkage ; Genome, Human ; Genome-Wide Association Study/methods ; Genotyping Techniques/methods ; Haplotypes ; Humans ; Polymorphism, Single Nucleotide ; Software ; Whole Genome Sequencing
Language	English
Publishing date	2019-07-01
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ISSN	1934-8258
ISSN (online)	1934-8258
DOI	10.1002/cphg.84
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Article: Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease.

Mews, Makaela A / Naj, Adam C / Griswold, Anthony J / Below, Jennifer E / Bush, William S

medRxiv : the preprint server for health sciences

2024

Abstract: Introduction: Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from : ... ...

Abstract	Introduction: Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from Methods: We implemented the OTTERS TWAS pipeline, leveraging Results: We identified and validated five novel gene associations in cortical brain tissue ( Discussion: Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.
Language	English
Publishing date	2024-04-19
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.04.17.24305737
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Article ; Online: Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.

Woodward, Alexa A / Urbanowicz, Ryan J / Naj, Adam C / Moore, Jason H

Genetic epidemiology

2022 Volume 46, Issue 8, Page(s) 555–571

Abstract: 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 ...

Abstract	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.
MeSH term(s)	Humans ; Genetic Heterogeneity ; Precision Medicine/methods ; Machine Learning ; Phenotype
Language	English
Publishing date	2022-08-04
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22497
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Article ; Online: An X Chromosome Transcriptome Wide Association Study Implicates ARMCX6 in Alzheimer's Disease.

Zhang, Xueyi / Gomez, Lissette / Below, Jennifer E / Naj, Adam C / Martin, Eden R / Kunkle, Brian W / Bush, William S

Journal of Alzheimer's disease : JAD

2024 Volume 98, Issue 3, Page(s) 1053–1067

Abstract: Background: The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer's disease (AD).: Objective: To model the expression ... ...

Abstract	Background: The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer's disease (AD). Objective: To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner. Methods: Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. Results: Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002). Conclusions: We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
MeSH term(s)	Humans ; Male ; Female ; Alzheimer Disease/genetics ; Transcriptome ; Genetic Predisposition to Disease/genetics ; X Chromosome ; Brain ; Polymorphism, Single Nucleotide/genetics ; Genome-Wide Association Study
Language	English
Publishing date	2024-03-14
Publishing country	Netherlands
Document type	Meta-Analysis ; Journal Article
ZDB-ID	1440127-7
ISSN	1875-8908 ; 1387-2877
ISSN (online)	1875-8908
ISSN	1387-2877
DOI	10.3233/JAD-231075
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Article ; Online: ODACH: a one-shot distributed algorithm for Cox model with heterogeneous multi-center data.

Luo, Chongliang / Duan, Rui / Naj, Adam C / Kranzler, Henry R / Bian, Jiang / Chen, Yong

Scientific reports

2022 Volume 12, Issue 1, Page(s) 6627

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Algorithms ; Bias ; Computer Simulation ; Humans ; Likelihood Functions ; Proportional Hazards Models
Language	English
Publishing date	2022-04-22
Publishing country	England
Document type	Journal Article ; Meta-Analysis ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-09069-0
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Article ; Online: Detecting Familial Aggregation.

Naj, Adam C / Beaty, Terri H

Methods in molecular biology (Clifton, N.J.)

2017 Volume 1666, Page(s) 133–169

Abstract: In addition to characterizing the distribution of genetic features of populations (mutation and allele frequencies; measures of Hardy-Weinberg equilibrium), genetic epidemiology and statistical genetics aim to explore and define the role of genomic ... ...

Abstract	In addition to characterizing the distribution of genetic features of populations (mutation and allele frequencies; measures of Hardy-Weinberg equilibrium), genetic epidemiology and statistical genetics aim to explore and define the role of genomic variation in risk of disease or variation in traits of interest. To facilitate this kind of exploration, genetic epidemiology and statistical genetics address a series of questions: 1. Does the disease tend to cluster in families more than expected by chance alone? 2. Does the disease appear to follow a particular genetic model of transmission in families? 3. Does variation at a particular genomic position tend to cosegregate with disease in families? 4. Do specific genetic variants tend to be carried more frequently by those with disease than by those without these variants in a given population (or across families)? The first question can be examined using studies of familial aggregation or correlation. An ancillary question: "how much of the susceptibility to disease (or variation in disease-related traits) might be accounted for by genetic factors?" is typically answered by estimating heritability, the proportion of variance in a trait or in risk to a disease attributable to genetics. The second question can be formally tested using pedigrees for which disease affection status or trait values are available through a modeling approach known as segregation analysis. The third question can be answered with data on genomic markers in pedigrees with affected members informative for linkage, where meiotic cross-over events are estimated or assessed. The fourth question is answerable using genotype data on genomic markers on unrelated affected and unaffected individuals and/or families with affected members and unaffected members. All of these questions can also be explored for quantitative (or continuously distributed) traits by examining variation in trait values between family members or between unrelated individuals. While each of these questions and the analytical approaches for answering them is explored extensively in subsequent chapters (heritability in Chapters 8 and 9 segregation in Chapter 12 linkage in Chapters 13 - 17 and association in Chapters 18 - 20 ), this chapter focuses on statistical methods to address questions of familial aggregation of qualitative phenotypes (e.g., disease status) or quantitative phenotypes.While studies exploring genotype-phenotype correlations are arguably the most important and common type of statistical genetic study performed, these studies are performed under the assumption that genetic contributors at least partially explain risk of a disease or a trait of interest. This may not always be the case, especially with diseases or traits known to be strongly influenced by environmental factors. For this reason, before any of the last three questions described above can be answered, it is important to ask first whether the disease clusters among family members more than unrelated persons, as this constitutes evidence of a possible heritable contribution to disease, justifying the pursuit of studies answering the other questions. In this chapter, the underlying principles of familial aggregation studies are addressed to provide an understanding and set of analytical tools to help answer the question if diseases or traits of interest are likely to be heritable and therefore justify subsequent statistical genetic studies to identify specific genetic causes.
MeSH term(s)	Gene-Environment Interaction ; Genetic Predisposition to Disease ; Genetic Variation ; Humans ; Logistic Models ; Models, Genetic ; Molecular Epidemiology/methods ; Odds Ratio ; Pedigree ; Software
Language	English
Publishing date	2017-10-04
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-7274-6_8
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Article ; Online: Genomic variants, genes, and pathways of Alzheimer's disease: An overview.

Naj, Adam C / Schellenberg, Gerard D

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics

2016 Volume 174, Issue 1, Page(s) 5–26

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Alzheimer Disease/genetics ; Alzheimer Disease/physiopathology ; Dementia/genetics ; Genetic Linkage/genetics ; Genetic Predisposition to Disease/genetics ; Genetic Variation/genetics ; Genome-Wide Association Study/methods ; Genomics ; Humans ; Polymorphism, Single Nucleotide/genetics
Language	English
Publishing date	2016-11-25
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural
ZDB-ID	2108616-3
ISSN	1552-485X ; 1552-4841 ; 0148-7299
ISSN (online)	1552-485X
ISSN	1552-4841 ; 0148-7299
DOI	10.1002/ajmg.b.32499
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Article: GWAS and Beyond: Using Omics Approaches to Interpret SNP Associations.

Chen, Hung-Hsin / Petty, Lauren E / Bush, William / Naj, Adam C / Below, Jennifer E

Current genetic medicine reports

2019 Volume 7, Issue 1, Page(s) 30–40

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2019-02-14
Publishing country	United States
Document type	Journal Article
ISSN	2167-4876
ISSN	2167-4876
DOI	10.1007/s40142-019-0159-z
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Article ; Online: Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci.

Chen, Hung-Hsin / Petty, Lauren E / Sha, Jin / Zhao, Yi / Kuzma, Amanda / Valladares, Otto / Bush, William / Naj, Adam C / Gamazon, Eric R / Below, Jennifer E

Translational psychiatry

2021 Volume 11, Issue 1, Page(s) 618

Abstract: Late-onset Alzheimer disease (LOAD) is highly polygenic, with a heritability estimated between 40 and 80%, yet risk variants identified in genome-wide studies explain only ~8% of phenotypic variance. Due to its increased power and interpretability, ... ...

Abstract	Late-onset Alzheimer disease (LOAD) is highly polygenic, with a heritability estimated between 40 and 80%, yet risk variants identified in genome-wide studies explain only ~8% of phenotypic variance. Due to its increased power and interpretability, genetically regulated expression (GReX) analysis is an emerging approach to investigate the genetic mechanisms of complex diseases. Here, we conducted GReX analysis within and across 51 tissues on 39 LOAD GWAS data sets comprising 58,713 cases and controls from the Alzheimer's Disease Genetics Consortium (ADGC) and the International Genomics of Alzheimer's Project (IGAP). Meta-analysis across studies identified 216 unique significant genes, including 72 with no previously reported LOAD GWAS associations. Cross-brain-tissue and cross-GTEx models revealed eight additional genes significantly associated with LOAD. Conditional analysis of previously reported loci using established LOAD-risk variants identified eight genes reaching genome-wide significance independent of known signals. Moreover, the proportion of SNP-based heritability is highly enriched in genes identified by GReX analysis. In summary, GReX-based meta-analysis in LOAD identifies 216 genes (including 72 novel genes), illuminating the role of gene regulatory models in LOAD.
MeSH term(s)	Alzheimer Disease/genetics ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Multifactorial Inheritance ; Polymorphism, Single Nucleotide
Language	English
Publishing date	2021-12-06
Publishing country	United States
Document type	Journal Article ; Meta-Analysis ; Research Support, N.I.H., Extramural
ZDB-ID	2609311-X
ISSN	2158-3188 ; 2158-3188
ISSN (online)	2158-3188
ISSN	2158-3188
DOI	10.1038/s41398-021-01677-0
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Article ; Online: Manifestations of Alzheimer's disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90.

Heath, Laura / Earls, John C / Magis, Andrew T / Kornilov, Sergey A / Lovejoy, Jennifer C / Funk, Cory C / Rappaport, Noa / Logsdon, Benjamin A / Mangravite, Lara M / Kunkle, Brian W / Martin, Eden R / Naj, Adam C / Ertekin-Taner, Nilüfer / Golde, Todd E / Hood, Leroy / Price, Nathan D

Scientific reports

2022 Volume 12, Issue 1, Page(s) 6117

Abstract: 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 ...

Abstract	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.
MeSH term(s)	ATP-Binding Cassette Transporters/genetics ; Adult ; Alzheimer Disease/genetics ; Apolipoprotein E2/genetics ; Female ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Genomics ; Humans ; Male ; Polymorphism, Single Nucleotide
Chemical Substances	ATP-Binding Cassette Transporters ; Apolipoprotein E2
Language	English
Publishing date	2022-04-12
Publishing country	England
Document type	Journal Article ; Meta-Analysis ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-09825-2
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