Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

Gloudemans, Michael J.; Antonio, Margaret L.; Abell, Nathan S.; Balliu, Brunilda; Park, YoSon; Martin, Alicia R.; Musharoff, Shaila; Rao, Abhiram; Aguet, François; Barbeira, Alvaro N.; Bonazzola, Rodrigo; Hormozdiari, Farhad; Ardlie, Kristin; Brown, Christopher D.; Im, Hae Kyung; Lappalainen, Tuuli; Wen, Xiaoquan; Montgomery, S

doi:10.1186/s13059-020-02113-0

Cited by 74 publications

(61 citation statements)

References 67 publications

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“…The first two principal components (PCs) were plotted to determine ethnicity ( Figure S1 ). Haplotype structures can vary between populations and, consequently, lead to different gene expression regulation mechanisms [ 28 ]. Therefore, we have manually chosen samples in direct proximity to the European (EUR) reference individuals in the genotype PCA (PC1 < −0.0078 and/or PC2 < −0.0125 in Figure S1 ).…”

Section: Methodsmentioning

confidence: 99%

Assigning Co-Regulated Human Genes and Regulatory Gene Clusters

Strunz

Kellner

Kiel

et al. 2021

Cells

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Elucidating the role of genetic variation in the regulation of gene expression is key to understanding the pathobiology of complex diseases which, in consequence, is crucial in devising targeted treatment options. Expression quantitative trait locus (eQTL) analysis correlates a genetic variant with the strength of gene expression, thus defining thousands of regulated genes in a multitude of human cell types and tissues. Some eQTL may not act independently of each other but instead may be regulated in a coordinated fashion by seemingly independent genetic variants. To address this issue, we combined the approaches of eQTL analysis and colocalization studies. Gene expression was determined in datasets comprising 49 tissues from the Genotype-Tissue Expression (GTEx) project. From about 33,000 regulated genes, over 14,000 were found to be co-regulated in pairs and were assembled across all tissues to almost 15,000 unique clusters containing up to nine regulated genes affected by the same eQTL signal. The distance of co-regulated eGenes was, on average, 112 kilobase pairs. Of 713 genes known to express clinical symptoms upon haploinsufficiency, 231 (32.4%) are part of at least one of the identified clusters. This calls for caution should treatment approaches aim at an upregulation of a haploinsufficient gene. In conclusion, we present an unbiased approach to identifying co-regulated genes in and across multiple tissues. Knowledge of such common effects is crucial to appreciate implications on biological pathways involved, specifically when a treatment option targets a co-regulated disease gene.

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Section: Methodsmentioning

confidence: 99%

Assigning Co-Regulated Human Genes and Regulatory Gene Clusters

Strunz

Kellner

Kiel

et al. 2021

Cells

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“…We expect greater endeavors in TWAS and functional genomic studies for a variety of geographical ancestry groups in the next 10 years, including but not limited to African, Asian, Hispanic or Latin, Greater Middle Eastern, Native American, Oceanian, and admixed populations ( Lavange et al, 2010 ; H3Africa Consortium et al, 2014 ; Kowalski et al, 2019 ; Choudhury et al, 2020 ; Gay et al, 2020 ; Shang et al, 2020 ). Generation of these eQTL data will require resources and efforts from the research communities in different parts of the world.…”

Section: Future Directionsmentioning

confidence: 99%

From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries

Ritchie

2021

Front. Genet.

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Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.

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“…Admixture mapping identifies regions associated with a given trait which must then be fine-mapped to identify the underlying risk variants. Colocalization analysis is not well powered in our study due to the poor representation of non-European populations in large eQTL data sets, as the genetic architecture of eQTLs can be ancestry specific [ 49 , 79 ]. Fine-mapping analyses of whole-genome sequence data collected in this sample may allow the detection of the variants responsible for the admixture mapping signals.…”

Section: Discussionmentioning

confidence: 99%

Admixture mapping reveals the association between Native American ancestry at 3q13.11 and reduced risk of Alzheimer’s disease in Caribbean Hispanics

et al. 2021

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Background Genetic studies have primarily been conducted in European ancestry populations, identifying dozens of loci associated with late-onset Alzheimer’s disease (AD). However, much of AD’s heritability remains unexplained; as the prevalence of AD varies across populations, the genetic architecture of the disease may also vary by population with the presence of novel variants or loci. Methods We conducted genome-wide analyses of AD in a sample of 2565 Caribbean Hispanics to better understand the genetic contribution to AD in this population. Statistical analysis included both admixture mapping and association testing. Evidence for differential gene expression within regions of interest was collected from independent transcriptomic studies comparing AD cases and controls in samples with primarily European ancestry. Results Our genome-wide association study of AD identified no loci reaching genome-wide significance. However, a genome-wide admixture mapping analysis that tests for association between a haplotype’s ancestral origin and AD status detected a genome-wide significant association with chromosome 3q13.11 (103.7–107.7Mb, P = 8.76E−07), driven by a protective effect conferred by the Native American ancestry (OR = 0.58, 95%CI = 0.47−0.73). Within this region, two variants were significantly associated with AD after accounting for the number of independent tests (rs12494162, P = 2.33E−06; rs1731642, P = 6.36E−05). The significant admixture mapping signal is composed of 15 haplotype blocks spanning 5 protein-coding genes (ALCAM, BBX, CBLB, CCDC54, CD47) and four brain-derived topologically associated domains, and includes markers significantly associated with the expression of ALCAM, BBX, CBLB, and CD47 in the brain. ALCAM and BBX were also significantly differentially expressed in the brain between AD cases and controls with European ancestry. Conclusion These results provide multiethnic evidence for a relationship between AD and multiple genes at 3q13.11 and illustrate the utility of leveraging genetic ancestry diversity via admixture mapping for new insights into AD.

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Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx

Cited by 74 publications

References 67 publications

Assigning Co-Regulated Human Genes and Regulatory Gene Clusters

Assigning Co-Regulated Human Genes and Regulatory Gene Clusters

From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries

Admixture mapping reveals the association between Native American ancestry at 3q13.11 and reduced risk of Alzheimer’s disease in Caribbean Hispanics

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