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Posted Content•DOI•

Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

Devanshi Patel1, Xiaoling Zhang1, John J. Farrell1, Jaeyoon Chung1, Thor D. Stein1, Thor D. Stein2, Kathryn L. Lunetta1, Lindsay A. Farrer •
Boston University1, United States Department of Veterans Affairs2
24 Nov 2020-medRxiv (Cold Spring Harbor Laboratory Press)-
TL;DR: This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell- type specific analysis.
Abstract: Because regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

Summary (3 min read)

Jump to: [INTRODUCTION] – [Study cohorts] – [Data processing] – [Cis eQTL mapping] – [Cis ct-eQTL mapping] – [Colocalization analyses] – [RESULTS] – [DISCUSSION] and [Conclusion]

INTRODUCTION

  • Recent expression quantitative trait locus (eQTL) analysis studies suggest that changes in gene expression have a role in the pathogenesis of AD 1, 2.
  • In addition, disease and trait-associated cis-eQTLs were more cell type specific than average cis-eQTLs 7.
  • Microglia, monocytes and macrophages share a similar developmental lineage and are all considered to be myeloid cells 9.

Study cohorts

  • The FHS is a multigenerational study of health and disease in a prospectively followed community-based and primarily non-Hispanic white sample.
  • Requisite information for this study was available for 5,257 participants.
  • ROS enrolled older nuns and priests from across the US, without known dementia for longitudinal clinical analysis and brain donation and MAP enrolled older subjects without dementia from retirement homes who agreed to brain donation at the time of death 14, 15.
  • Preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Data processing

  • Generation and initial quality control (QC) procedures of the FHS GWAS and expression data are described elsewhere and include all genotype QC and preadjustment of gene expression levels for batch effects and other technical covariates 13.
  • ROSMAP gene expression data were log-normalized and adjusted for known and hidden variables detected by surrogate variable analysis (SVA) 17 in order to determine which of these variables should be included as covariates in analysis models for eQTL discovery.
  • Additional filtering steps of FHS and ROSMAP GWAS and gene expression data included eliminating subjects with missing data, restricting gene expression data to protein coding genes, and retaining common variants (MAF ≥ 0.05) with good imputation quality (R2 ≥ 0.3).

Cis eQTL mapping

  • Cis-eQTL mapping was performed using a genome-wide design (Fig. S1).
  • The association of gene expression with SNP genotypes for all cis SNPs within1 Mb of protein-coding genes was evaluated using linear mixed models adjusting for family structure in FHS and linear regression models for unrelated individuals in ROSMAP.
  • The linear model for analysis of FHS can data be expressed as follows: preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
  • Yi is the expression value for gene i, Gj is the genotype dosage for cis SNP j, Aij, Sij, PMij, S2ij, and SV1ij are the covariates for age, sex, PMI, study and SV1 respectively, ij is the residual error, and the βs are regression coefficients.

Cis ct-eQTL mapping

  • Models testing associations with cell type-specific eQTLs (ct-eQTLs) included an interaction term for expression levels of “proxy” genes that represent cell types.
  • These proxy genes for cell types in blood were established previously using BLUEPRINT expression data to validate celltype-specific expression in each cell-type module 5 and the proxy genes for brain cell types have been incorporated in several studies 20-22.
  • Preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
  • The copyright holder for thisthis version posted November 24, 2020.
  • Significance of the pathways was determined by the Fisher's Exact test with False discovery rate (FDR) multiple test correction.

Colocalization analyses

  • Assessment of causal variants shared by adjacent GWAS and eQTL signals was performed using a Bayesian colocalization approach implemented in the R package coloc 24.
  • This analysis incorporated information about significantly associated variants for AD risk obtained from a recent large GWAS 25 and lead eQTL variants each defined as the eSNP showing the strongest association with gene expression.
  • Following recommended guidelines, the variants were deemed to be colocalized by a high posterior probability that a single shared variant is responsible for both signals (PP4 > 0.8) 24, 26.
  • A lower threshold for statistical significance with a false discovery rate (FDR) < 0.05 for eQTL significant results was applied to maximize detection of colocalized pairs.
  • Regional plots were constructed with LocusZoom 27.

RESULTS

  • Preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
  • The copyright holder for thisthis version posted November 24, 2020.
  • Among the AD-associated SNPs at the GWS level, rs9271058 is a significant eSNP for HLA-DRB1 in both blood and brain cell types (the most significant association by p-value was observed in anti-bacterial cells and microglia) and rs9271192 is a significant ct-eQTL for the gene in multiple brain cell types (Table 1).
  • Among the significant ct-eQTLs in brain, the cell types with the largest proportion that were also significant in monocytes/macrophages were microglia (1.6%) and neurons (1.3%) (Table 4C).

DISCUSSION

  • The authors identified several novel AD-related eQTLs that highlight the importance of cell-type dependent context.
  • Preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
  • Five established AD genes (CR1, ECHDC3, HLA-DRB1, HLA-DRB5, and WWOX 25, 28) were shared eGenes in brain and blood and could be playing a key role in the systemic AD mechanisms.
  • The copyright holder for thisthis version posted November 24, 2020.
  • Also, findings in brain may reflect post-mortem changes unrelated to disease or cell-type different expression 36.

Conclusion

  • The authors observation of cell-type specific expression patterns for established and potentially novel AD genes, finding of additional evidence for the role of myeloid cells in AD risk, and discovery of potential novel blood and brain AD biomarkers highlight the importance All rights reserved.
  • Preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
  • The copyright holder for thisthis version posted November 24, 2020.

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Figures (3)

Content maybe subject to copyright    Report

Cell-type Specific Expression Quantitative Trait Loci Associated with
Alzheimer Disease in Blood and Brain Tissue
Devanshi Patel, PhD
1,2
; Xiaoling Zhang, MD, PhD
2,6
; John J. Farrell, PhD
2
;
Jaeyoon Chung, PhD
2
; Thor D. Stein, MD, PhD
3,8
; Kathryn L. Lunetta, PhD
6
;
and Lindsay A. Farrer, PhD
1,2,4-7
1
Bioinformatics Graduate Program, Boston University, Boston MA;
Departments of
2
Medicine (Biomedical Genetics),
3
Pathology & Laboratory Medicine,
4
Neurology, and
5
Ophthalmology, Boston University School of Medicine, Boston, MA;
Departments of
6
Biostatistics and
7
Epidemiology, Boston University School of Public
Health, Boston, MA
8
VA Boston Healthcare System, Boston MA, Department of
Veterans Affairs Medical Center, Bedford MA
Address correspondence to
: Dr. Lindsay Farrer, Boston University School of Medicine,
Biomedical Genetics E200, 72 East Concord St., Boston, MA, 02118; phone: (617) 358-
3550; farrer@bu.edu
Running Title
: Expression Quantitative Trait Loci for AD
All rights reserved. No reuse allowed without permission.
preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for thisthis version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.23.20237008doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
ABSTRACT
Because regulation of gene expression is heritable and context-dependent, we
investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-
expression quantitative trait locus (eQTL) mapping was performed genome-wide in
blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by
475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The
association of gene expression with genotypes for all cis SNPs within 1Mb of genes was
evaluated using linear regression models for unrelated subjects and linear mixed
models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an
interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-
eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs
in brain and blood, respectively, that were not detected in generic eQTL analysis. Of
note, 386 unique target eGenes of significant eQTLs shared between blood and brain
were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are
established AD loci. The potential importance and relevance to AD of significant results
in myeloid cell-types is supported by the observation that a large portion of GWS ct-
eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant
eGenes in these eQTLs have previously been implicated in AD. This study identified
cell-type specific expression patterns for established and potentially novel AD genes,
found additional evidence for the role of myeloid cells in AD risk, and discovered
potential novel blood and brain AD biomarkers that highlight the importance of cell-type
specific analysis.
All rights reserved. No reuse allowed without permission.
preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for thisthis version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.23.20237008doi: medRxiv preprint
INTRODUCTION
Recent expression quantitative trait locus (eQTL) analysis studies suggest that changes
in gene expression have a role in the pathogenesis of AD
1, 2
. However, regulation of
gene expression, as well as many biological functions, has been shown to be context-
specific (e.g., tissue and cell-types, developmental time point, sex, disease status, and
response to treatment or stimulus)
3-6
. One study of 500 healthy subjects found over-
representation of T cell-specific eQTLs in susceptibility alleles for autoimmune disease
and AD risk alleles polarized for monocyte-specific eQTL effects
7
. In addition, disease
and trait-associated cis-eQTLs were more cell type specific than average cis-eQTLs
7
.
Another study classified 12% of more than 23000 eQTLs in blood as cell-type specific
5
.
Large eQTL studies across multiple human tissues have been conducted by the GTEx
consortium, with a study on genetic effects on gene expression levels across 44 human
tissues collected from the same donors characterizing patterns of tissue specificity
recently published
8
.
Microglia, monocytes and macrophages share a similar developmental lineage and are
all considered to be myeloid cells
9
. It is believed that a large proportion of AD genetic
risk can be explained by genes expressed in myeloid cells and not other cell-types
10
.
Several established AD genes are highly expressed in microglia
9, 11
and a variant in the
AD-associated locus CELF1 has been associated with lower expression of SPI1 in
monocytes and macrophages
10
. AD risk alleles have been shown to be enriched in
myeloid specific epigenomic annotations and in active enhancers of monocytes,
macrophages, and microglia
12
, and to be polarized for cis-eQTL effects in monocytes
7
.
These findings suggest that a cell-type specific analysis in blood and brain tissue may
All rights reserved. No reuse allowed without permission.
preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for thisthis version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.23.20237008doi: medRxiv preprint
identify novel and more precise AD associations that may help elucidate regulatory
networks. In this study, we performed a genome-wide cis ct-eQTL analysis in blood and
brain, respectively, then compared eQTLs and cell-type specific eQTLs (ct-eQTLs)
between brain and blood with a focus on genes, loci, and cell-types previously
implicated in AD risk by genetic approaches.
MATERIALS, SUBJECTS AND METHODS
Study cohorts
Framingham Heart Study (FHS). The FHS is a multigenerational study of health and
disease in a prospectively followed community-based and primarily non-Hispanic white
sample. Procedures for assessing dementia and determining AD status in this cohort
are described elsewhere
13
. Clinical, demographic, and pedigree information, as well as
1000 Genomes Project Phase 1 imputed SNP genotypes and Affymetrix Human Exon
1.0 ST array gene expression data from whole blood, were obtained from dbGaP
(https://www.ncbi.nlm.nih.gov/projects/gap/cgi-
bin/study.cgi?study_id=phs000007.v31.p12) . Requisite information for this study was
available for 5,257 participants. Characteristics of these subjects are provided in Table
S1.
Religious Orders Study (ROS)/ Memory and Aging Project (MAP). ROS enrolled older
nuns and priests from across the US, without known dementia for longitudinal clinical
analysis and brain donation and MAP enrolled older subjects without dementia from
retirement homes who agreed to brain donation at the time of death
14, 15
. RNA-
sequencing brain gene expression and whole-genome sequencing (WGS) genotype
All rights reserved. No reuse allowed without permission.
preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for thisthis version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.23.20237008doi: medRxiv preprint
data were obtained from the AMP-AD knowledge portal
(https://www.synapse.org/#!Synapse:syn3219045
)
16
.
Data processing
Generation and initial quality control (QC) procedures of the FHS GWAS and
expression data are described elsewhere and include all genotype QC and pre-
adjustment of gene expression levels for batch effects and other technical covariates
13
.
ROSMAP gene expression data were log-normalized and adjusted for known and
hidden variables detected by surrogate variable analysis (SVA)
17
in order to determine
which of these variables should be included as covariates in analysis models for eQTL
discovery. Additional filtering steps of FHS and ROSMAP GWAS and gene expression
data included eliminating subjects with missing data, restricting gene expression data to
protein coding genes, and retaining common variants (MAF
≥
0.05) with good
imputation quality (R
2
≥
0.3).
Cis eQTL mapping
Cis-eQTL mapping was performed using a genome-wide design (Fig. S1). The
association of gene expression with SNP genotypes for all cis SNPs within1 Mb of
protein-coding genes was evaluated using linear mixed models adjusting for family
structure in FHS and linear regression models for unrelated individuals in ROSMAP. In
FHS, lmekin function in the R kinship package (version 1.1.3)
18
was applied assuming
an additive genetic model with covariates for age and sex, and family structure modeled
as a random-effects term for kinship - a matrix of kinship coefficients calculated from
pedigree structures. The linear model for analysis of FHS can data be expressed as
follows:
All rights reserved. No reuse allowed without permission.
preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for thisthis version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.23.20237008doi: medRxiv preprint
Citations
More filters
Journal Article•DOI•
Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer’s disease-associated genetic variants

[...]

l4ahnrb4221, Erik Ode2•
Social Science Research Council1, Nanjing University2
18 Apr 2022-Translational Psychiatry
TL;DR: In this article , a hierarchical Poisson mixed model (HPMM) was proposed to identify 24 allele-specific expression quantitative trait loci (aseQTLs) for 33 ADassociated variants in four brain regions and seven cell types using ~3000 bulk RNA-seq samples and > 0.25 million single nuclei.
Abstract: Elucidating regulatory effects of Alzheimer's disease (AD)-associated genetic variants is critical for unraveling their causal pathways and understanding the pathology. However, their cell-type-specific regulatory mechanisms in the brain remain largely unclear. Here, we conducted an analysis of allele-specific expression quantitative trait loci (aseQTLs) for 33 AD-associated variants in four brain regions and seven cell types using ~3000 bulk RNA-seq samples and >0.25 million single nuclei. We first develop a flexible hierarchical Poisson mixed model (HPMM) and demonstrate its superior statistical power to a beta-binomial model achieved by unifying samples in both allelic and genotype-level expression data. Using the HPMM, we identified 24 (~73%) aseQTLs in at least one brain region, including three new eQTLs associated with CA12, CHRNE, and CASS4. Notably, the APOE ε4 variant reduces APOE expression across all regions, even in AD-unaffected controls. Our results reveal region-dependent and exon-specific effects of multiple aseQTLs, such as rs2093760 with CR1, rs7982 with CLU, and rs3865444 with CD33. In an attempt to pinpoint the cell types responsible for the observed tissue-level aseQTLs using the snRNA-seq data, we detected many aseQTLs in microglia or monocytes associated with immune-related genes, including HLA-DQB1, HLA-DQA2, CD33, FCER1G, MS4A6A, SPI1, and BIN1, highlighting the regulatory role of AD-associated variants in the immune response. These findings provide further insights into potential causal pathways and cell types mediating the effects of the AD-associated variants.

5 citations

Journal Article•DOI•
Set-Based Rare Variant Expression Quantitative Trait Loci in Blood and Brain from Alzheimer Disease Study Participants

[...]

Devanshi Patel1, Xiaoling Zhang1, John J. Farrell1, Kathryn L. Lunetta1, Lindsay A. Farrer •
Boston University1
15 Mar 2021-Genes
TL;DR: In this paper, set-based methods to identify rare expression quantitative trait loci (eQTL) related to Alzheimer disease (AD) were investigated using gene expression data derived from blood donated by 713 Alzheimer's Disease Neuroimaging Initiative participants and from brain tissues donated by 475 Religious Orders Study/Memory and Aging Project participants.
Abstract: Because studies of rare variant effects on gene expression have limited power, we investigated set-based methods to identify rare expression quantitative trait loci (eQTL) related to Alzheimer disease (AD). Gene-level and pathway-level cis rare-eQTL mapping was performed genome-wide using gene expression data derived from blood donated by 713 Alzheimer’s Disease Neuroimaging Initiative participants and from brain tissues donated by 475 Religious Orders Study/Memory and Aging Project participants. The association of gene or pathway expression with a set of all cis potentially regulatory low-frequency and rare variants within 1 Mb of genes was evaluated using SKAT-O. A total of 65 genes expressed in the brain were significant targets for rare expression single nucleotide polymorphisms (eSNPs) among which 17% (11/65) included established AD genes HLA-DRB1 and HLA-DRB5. In the blood, 307 genes were significant targets for rare eSNPs. In the blood and the brain, GNMT, LDHC, RBPMS2, DUS2, and HP were targets for significant eSNPs. Pathway enrichment analysis revealed significant pathways in the brain (n = 9) and blood (n = 16). Pathways for apoptosis signaling, cholecystokinin receptor (CCKR) signaling, and inflammation mediated by chemokine and cytokine signaling were common to both tissues. Significant rare eQTLs in inflammation pathways included five genes in the blood (ALOX5AP, CXCR2, FPR2, GRB2, IFNAR1) that were previously linked to AD. This study identified several significant gene- and pathway-level rare eQTLs, which further confirmed the importance of the immune system and inflammation in AD and highlighted the advantages of using a set-based eQTL approach for evaluating the effect of low-frequency and rare variants on gene expression.

4 citations

Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data

[...]

Biao Cai, Jingfei Zhang, Hongyu Li, Changju Su, Hongyu Zhao 
08 Sep 2022
TL;DR: This article introduces a flexible statistical deconvolution framework that allows a general and subject-specific covariance of bulk gene expressions and proposes a decorrelated constrained least squares method called DECALS that estimates cell-type proportions as well as the sampling distribution of the estimates.
Abstract: There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type proportions in a bulk sample. Although many methods have been proposed recently, quantifying the uncertainties associated with the estimated cell-type proportions has not been well studied. Lack of consideration of these uncertainties can lead to missed or false findings in downstream analyses. In this article, we introduce a flexible statistical deconvolution framework that allows a general and subject-specific covariance of bulk gene expressions. Under this framework, we propose a decorrelated constrained least squares method called DECALS that estimates cell-type proportions as well as the sampling distribution of the estimates. Simulation studies demonstrate that DECALS can accurately quantify the uncertainties in the estimated proportions whereas other methods fail. Applying DECALS to analyze bulk gene expression data of post mortem brain samples from the ROSMAP and GTEx projects, we show that taking into account the uncertainties in the estimated cell-type proportions can lead to more accurate identifications of cell-type-specific differentially expressed genes and transcripts between different subject groups, such as between Alzheimer’s disease patients and controls and between males and females.

2 citations

Posted Content•DOI•
omicSynth: an Open Multi-omic Community Resource for Identifying Druggable Targets across Neurodegenerative Diseases

[...]

Chelsea X Alvarado, Mary B. Makarious, Dan Vitale, Mathew J. Koretsky, Sara Bandres-Ciga, Hirotaka Iwaki, Kristin S. Levine, Andrew B. Singleton, Faraz Faghri, Mike A. Nalls, Hampton L. Leonard 
12 Apr 2023-medRxiv
TL;DR: In this article , the authors use Summary-data-based Mendelian Randomization to identify genetic targets for drug discovery and repurposing for neurodegenerative diseases. And they provide mechanistic insights into disease processes and potential networklevel consequences of gene-based therapeutics.
Abstract: Treatments for neurodegenerative disorders remain rare, although recent FDA approvals, such as Lecanemab and Aducanumab for Alzheimer's Disease, highlight the importance of a mechanistic approach in creating disease modifying therapies. As a large portion of the global population is aging, there is an urgent need for therapeutics that can stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence based identification of therapeutic targets for neurodegenerative disease. We use Summary-data-based Mendelian Randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identified 116 Alzheimer's disease, 3 amyotrophic lateral sclerosis, 5 Lewy body dementia, 46 Parkinson's disease, and 9 Progressive supranuclear palsy target genes passing multiple test corrections (pSMR_multi < 2.95E-06 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics - classifying 41 novel targets, 3 known targets, and 115 difficult targets. Our novel class of genes provides a springboard for new opportunities in drug discovery, development and repurposing in the pre-competitive space. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community [https://nih-card-ndd-smr-home-syboky.streamlit.app/].

1 citations

Cell type-specific Expression Quantitative Trait Loci

[...]

Paul Little, Vasyl Zhabotynsky, Yun Li, Danyu Lin, Wei Sun 
TL;DR: A statistical method is developed, CSeQTL, to perform cell type-specific eQTL mapping for bulkRNA-seq data while directly modeling RNA-seq count data, which controls false positive probability and reports much more findings than the linear model approach.
Abstract: Genetic regulation of gene expression can vary across cell types. To map cell type-specific gene expression quantitative trait loci (eQTLs), a popular approach is to assess the interaction between genotype and cell type proportions in a linear model. A linear model requires transformation of RNA-seq count data, which distorts the relation between gene expression and cell type proportions and it could lead to reduced power and inflated false positive findings. We have developed a statistical method, CSeQTL, to perform cell type-specific eQTL mapping for bulk RNA-seq data while directly modeling RNA-seq count data. CSeQTL utilizes both total expression and allele-specific expression to improve the power of eQTL mapping. As demonstrated through simulations and applications on human whole blood and brain RNA-seq data, CSeQTL controls false positive probability and reports much more findings than the linear model approach. We have conducted a comprehensive evaluation of the overlap between cell type-specific eQTLs and the genetic loci associated with 21 categories of human traits and identified several interesting findings. For example, the genetic loci associated with substance addiction are enriched among the eQTLs in the excitatory neurons of schizophrenia subjects, but not in healthy controls.

1 citations

References
More filters
Journal Article•DOI•
Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease

[...]

Jean-Charles Lambert1, Jean-Charles Lambert2, Jean-Charles Lambert3, Carla A. Ibrahim-Verbaas4  +212 more•Institutions (75)
01 Dec 2013-Nature Genetics
TL;DR: In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10−8) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.
Abstract: Eleven susceptibility loci for late-onset Alzheimer's disease (LOAD) were identified by previous studies; however, a large portion of the genetic risk for this disease remains unexplained. We conducted a large, two-stage meta-analysis of genome-wide association studies (GWAS) in individuals of European ancestry. In stage 1, we used genotyped and imputed data (7,055,881 SNPs) to perform meta-analysis on 4 previously published GWAS data sets consisting of 17,008 Alzheimer's disease cases and 37,154 controls. In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8,572 Alzheimer's disease cases and 11,312 controls. In addition to the APOE locus (encoding apolipoprotein E), 19 loci reached genome-wide significance (P < 5 × 10−8) in the combined stage 1 and stage 2 analysis, of which 11 are newly associated with Alzheimer's disease.

3,726 citations

Journal Article•DOI•
The sva package for removing batch effects and other unwanted variation in high-throughput experiments

[...]

Jeffrey T. Leek1, W. Evan Johnson2, Hilary S. Parker1, Andrew E. Jaffe1, John D. Storey1 •
Johns Hopkins University1, Boston University2
01 Mar 2012-Bioinformatics
TL;DR: The sva package is described, which supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function.
Abstract: Summary: Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects—when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function. Availability: The R package sva is freely available from http://www.bioconductor.org. Contact: jleek@jhsph.edu Supplementary information:Supplementary data are available at Bioinformatics online.

3,343 citations

Journal Article•DOI•
Genetic effects on gene expression across human tissues.

[...]

Enhancing GTEx (eGTEx) groups1, Nih Common Fund2, Nhgri, Biospecimen Core Resource—VARI, Elsi study, Genome Browser Data Integration Visualization—EBI, Lead analysts, Alexis Battle3, Christopher D. Brown4, Barbara E. Engelhardt1, Stephen B. Montgomery2 •
Princeton University1, Stanford University2, Johns Hopkins University3, University of Pennsylvania4
12 Oct 2017-Nature
TL;DR: It is found that local genetic variation affects gene expression levels for the majority of genes, and inter-chromosomal genetic effects for 93 genes and 112 loci are identified, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
Abstract: Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.

3,289 citations

Journal Article•DOI•
LocusZoom: regional visualization of genome-wide association scan results

[...]

Randall Pruim1, Ryan P. Welch2, Serena Sanna2, Tanya M. Teslovich2, Peter S. Chines2, Terry P. Gliedt2, Michael Boehnke2, Gonçalo R. Abecasis2, Cristen J. Willer2 •
Calvin College1, National Institutes of Health2
15 Sep 2010-Bioinformatics
TL;DR: LocusZoom is a web-based plotting tool that provides fast visual display of GWAS results in a publication-ready format that visually displays regional information such as the strength and extent of the association signal relative to genomic position, local linkage disequilibrium (LD) and recombination patterns and the positions of genes in the region.
Abstract: Summary: Genome-wide association studies (GWAS) have revealed hundreds of loci associated with common human genetic diseases and traits. We have developed a web-based plotting tool that provides fast visual display of GWAS results in a publication-ready format. LocusZoom visually displays regional information such as the strength and extent of the association signal relative to genomic position, local linkage disequilibrium (LD) and recombination patterns

2,454 citations

Journal Article•DOI•
Regulation of type I interferon responses

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Lionel B. Ivashkiv1, Laura T. Donlin2•
Cornell University1, Hospital for Special Surgery2
01 Jan 2014-Nature Reviews Immunology
TL;DR: In this paper, the authors summarize the signalling and epigenetic mechanisms that regulate type I IFN-induced STAT activation and ISG transcription and translation and conclude that these regulatory mechanisms determine the biological outcomes of type I ILN responses and whether pathogens are cleared effectively or chronic infection or autoimmune disease ensues.
Abstract: Type I interferons (IFNs) activate intracellular antimicrobial programmes and influence the development of innate and adaptive immune responses. Canonical type I IFN signalling activates the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway, leading to transcription of IFN-stimulated genes (ISGs). Host, pathogen and environmental factors regulate the responses of cells to this signalling pathway and thus calibrate host defences while limiting tissue damage and preventing autoimmunity. Here, we summarize the signalling and epigenetic mechanisms that regulate type I IFN-induced STAT activation and ISG transcription and translation. These regulatory mechanisms determine the biological outcomes of type I IFN responses and whether pathogens are cleared effectively or chronic infection or autoimmune disease ensues.

2,273 citations

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Frequently Asked Questions (12)
Q1. What are the contributions mentioned in the paper "Cell-type specific expression quantitative trait loci associated with alzheimer disease in blood and brain tissue" ?

Because regulation of gene expression is heritable and context-dependent, the authors investigated AD-related gene expression patterns in cell-types in blood and brain. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis. Preprint ( which was not certified by peer review ) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS cteQTLs map within 1Mb of established AD loci and 58 % ( 23/40 ) of the most significant eGenes in these eQTLs have previously been implicated in AD. 

Future studies that compare cell type specific differential gene expression among AD cases and controls using single cell RNA-sequencing or cell count data, as well as functional experiments, are needed to validate and extend their findings. 

In addition, the most widely used marker to examine activated microglia in normal and diseased human brains is HLA-DR and microglia activation increases with the progression of AD 49, 50. 

The human leukocyte antigen (HLA) region is the key susceptibility locus in many immunological diseases and many associations have been reported between neurodegenerative diseases and HLA haplotypes 48. 

Because many AD risk genes are expressed in myeloid cells including microglia 10, the large number of microglia ct-eQTLs is consistent with the high proportion of AD subjects in the ROSMAP dataset. 

Additional filtering steps of FHS and ROSMAP GWAS and gene expression data included eliminating subjects with missing data, restricting gene expression data to protein coding genes, and retaining common variants (MAF ≥ 0.05) with good imputation quality (R2 ≥ 0.3). 

Rs6557994 is also correlated with a GWAS SNP in CLU, located approximately 150 kb from PTK2B, that is not significantly associated with expression of any gene. 

Rs6557994 is the most significant eSNP for and located in PTK2B (blood ct-eQTL P=2.58x10-9) and is moderately correlated with the PTK2B GWAS SNP (rs28834970, r2=0.78, P=1.58x10-9). 

Many GWS SNPs for AD risk are eSNPs affecting expression of the nearest gene, which is usually recognized as the causative gene, but several GWS SNPs target other genes (Table S9). 

Five established AD genes (CR1, ECHDC3, HLA-DRB1, HLA-DRB5, and WWOX 25, 28) were shared eGenes in brain and blood and could be playing a key role in the systemic AD mechanisms. 

Approximately 52% of significant cteQTLs in microglia are located in AD regions including five of the 20 most significant cteQTLs in this group (Table 4B). 

rs113986870 also significantly influenced expression of another gene in this region, ARL17A, that was previously linked to progressive supranuclear palsy by analysis of gene expression and methylation 83.