Genetic discovery in multi-ethnic populations

Traylor, Matthew; Lewis, Cathryn M.

doi:10.1038/ejhg.2016.38

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…The NOS3 gene is associated with coronary artery disease, migraine, vascular dysfunction, SVD, and ischemic stroke. 22,29,30,34 PLEKHG1 is associated with dementia and ischemic stroke, 35 and SH3PXD2A has been previously associated with total WMH and ischemic stroke. 19,25 The most notable associated vascular gene is COL4A2 that encodes for a subunit of type IV collagen, which has been associated with SVD, ischemic stroke, intracranial hemorrhage, and coronary artery disease.…”

Section: Discussionmentioning

confidence: 96%

“…19,25 The most notable associated vascular gene is COL4A2 that encodes for a subunit of type IV collagen, which has been associated with SVD, ischemic stroke, intracranial hemorrhage, and coronary artery disease. 31,[35][36][37][38] It is a proposed therapeutic target for the prevention of intracranial hemorrhage. 32,39 The association of this vascular gene with PVWMH and deep intracerebral hemorrhage is suggestive of…”

Section: Discussionmentioning

confidence: 99%

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Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Armstrong

Mather

Sargurupremraj

et al. 2020

Stroke

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Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Armstrong

Mather

Sargurupremraj

et al. 2020

Stroke

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“…In addition, certain drugs may be less effective or even unsafe in some racial/ethnic groups due to genetic differences [ 12 ]. For GWAS, studies embracing ethnic diversity have been recognized as more powerful for gene mapping due to differences in allele frequency, effect size, haplotype background of causal variant(s), and LD across ethnicities [ 49 , 50 ], as well as factors that are broadly defined as social determinants of race including but not limited to differential pathogen invasion, environmental exposures, perceived discrimination and associated psychosocial stress [ 51 , 52 , 53 ]. We therefore consider it a pressing task to increase diversity in our genetic studies.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations

Wen

Xie

Rowland

et al. 2021

Genes

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Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants. Results: Our results revealed 24 suggestive signals (p < 1 × 10−4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

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“…This calls into question the generalizability of research findings to ethnic minority groups. As statistical advances facilitate genomic analyses of ancestrally diverse populations [33, 34], we undertook a proof-of-concept GWAS (including the first 10 ancestry-informative PCs) in our ancestrally heterogeneous GENRA/SLESS cohort (supplementary Figs S7 and S8, available at Rheumatology Online). The genomic-inflation factor was 0.99, suggesting appropriate control for population stratification.…”

Section: Discussionmentioning

confidence: 99%

Genetic and environmental risk factors for rheumatoid arthritis in a UK African ancestry population: the GENRA case–control study

et al. 2017

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Objectives. To evaluate whether genetic and environmental factors associated with RA in European and Asian ancestry populations are also associated with RA in African ancestry individuals. Methods. A case–control study was undertaken in 197 RA cases and 868 controls of African ancestry (Black African, Black Caribbean or Black British ethnicity) from South London. Smoking and alcohol consumption data at RA diagnosis was captured. Genotyping was undertaken (Multi-Ethnic Genotyping Array) and human leukocyte antigen (HLA) alleles imputed. The following European/Asian RA susceptibility factors were tested: 99 genome-wide loci combined into a genetic risk score; HLA region [20 haplotypes; shared epitope (SE)]; smoking; and alcohol consumption. The SE was tested for its association with radiological erosions. Logistic regression models were used, including ancestry-informative principal components, to control for admixture. Results. European/Asian susceptibility loci were associated with RA in African ancestry individuals. The genetic risk score provided an odds ratio (OR) for RA of 1.53 (95% CI: 1.31, 1.79; P = 1.3 × 10 −7). HLA haplotype ORs in European and African ancestry individuals were highly correlated (r = 0.83, 95% CI: 0.56, 0.94; P = 1.1 × 10 −4). Ever-smoking increased (OR = 2.36, 95% CI: 1.46, 3.82; P = 4.6 × 10 −4) and drinking alcohol reduced (OR = 0.34, 95% CI: 0.20, 0.56; P = 2.7 × 10 −5) RA risk in African ancestry individuals. The SE was associated with erosions (OR = 2.61, 95% CI: 1.36, 5.01; P = 3.9 × 10 −3). Conclusion. Gene–environment RA risk factors identified in European/Asian ancestry populations are relevant in African ancestry individuals. As modern statistical methods facilitate analysing ancestrally diverse populations, future genetic studies should incorporate African ancestry individuals to ensure their implications for precision medicine are universally applicable.

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Genetic discovery in multi-ethnic populations

Cited by 7 publications

References 13 publications

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations

Genetic and environmental risk factors for rheumatoid arthritis in a UK African ancestry population: the GENRA case–control study

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