Biology subjects

Chikowore, T.

Publications and source records attributed to Chikowore, T..

5 recordsLinked to original sources

bioRxiv2025

Multi-ancestry fine-mapping accounting for ancestral and environmental heterogeneity improves resolution and interpretation

Amongst genome-wide association studies (GWAS) across diverse populations, allelic effect heterogeneity may arise due to differences in genetic ancestry and environmental exposures. This heterogeneity impacts the refinement of sets of potential causal variants underlying genetic associations through statistical fine-mapping. We introduce two multi-ancestry fine-mapping methods, MR-MEGAfm and env-MR-MEGAfm, allowing for multiple causal variants in a genomic region. Both methods integrate GWAS summary statistics and differing linkage disequilibrium from multiple cohorts; env-MR-MEGAfm additionally incorporates summary-level environmental covariates. Through simulations, we show that, when allelic heterogeneity is correlated with environmental exposures and ancestry, env-MR-MEGAfm yields improved resolution over MR-MEGAfm and similar resolution to SuSiEx. In twelve sex-stratified African GWAS of low-density lipoprotein cholesterol in 19,589 individuals, MR-MEGAfm and env-MR-MEGAFM (adjusting for urban status) identify five variants with posterior probability > 0.5 within two loci. One variant showed heterogeneity only due to ancestry, while three showed heterogeneity due only to urban status.

genetics↗

bioRxiv2024

Accounting for heterogeneity due to environmental sources in meta-analysis of genome-wide association studies

Meta-analysis of genome-wide association studies (GWAS) across diverse populations offers power gains to identify loci associated with complex traits and diseases. Often heterogeneity in effect sizes across populations will be correlated with genetic ancestry and environmental exposures (e.g. lifestyle factors). We present an environment-adjusted meta-regression model (env-MR-MEGA) to detect genetic associations by adjusting for and quantifying environmental and ancestral heterogeneity between populations. In simulations, env-MR-MEGA had similar or greater association power than MR-MEGA, with notable gains when the environmental factor had a greater correlation with the trait than ancestry. In our analysis of low-density lipoprotein cholesterol in [~]19,000 individuals across twelve sex-stratified GWAS from Africa, adjusting for sex, BMI, and urban status, we identified additional heterogeneity beyond ancestral effects for nine variants. Env-MR-MEGA provides an approach to account for environmental effects using summary-level data, making it a useful tool for meta-analyses without the need to share individual-level data.

genetics↗

bioRxiv2023

Leveraging information between multiple population groups and traits improves fine-mapping resolution

Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP>0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.

genomics↗

bioRxiv2021

Polygenic Prediction of Type 2 Diabetes in Continental Africa

ObjectivePolygenic prediction of type 2 diabetes in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of type 2 diabetes from Africa, and the poor transferability of European derived polygenic risk scores (PRS) in diverse ethnicities. We set out to evaluate if African American or multi-ethnic derived PRSs would improve polygenic prediction in continental Africans. Research Design and MethodsUsing the PRSice software, ethnic-specific PRSs were computed with weights from the type 2 diabetes GWAS of the Million Veteran Program (MVP) study. The South African Zulu study (1602 cases and 976 controls) was used as the target data set. Replication and assessment of the best predictive PRS association with age at diagnosis was done in the Africa America Diabetes Mellitus (AADM) study (1031 cases and 738 controls). ResultsThe African American derived PRS was more predictive of type 2 diabetes compared to the European and multi-ethnic derived scores. Notably, participants in the 10th decile of this PRS had a 3.19-fold greater risk (OR 3.19; 95%CI (1.94-5.29), p = 5.33 x10-6) of developing diabetes and were diagnosed 2.6 years earlier compared to those in the first decile. ConclusionsAfrican American derived PRS enhances polygenic prediction of type 2 diabetes in continental Africans. Improved representation of non-Europeans populations (including Africans) in GWAS, promises to provide better tools for precision medicine interventions in type 2 diabetes.

genetics↗

bioRxiv2020

Discovery and fine-mapping of kidney function loci in first genome-wide association study in Africans

Genome-wide association studies (GWAS) for kidney function have uncovered hundreds of risk loci, primarily in populations of European ancestry. We conducted the first GWAS of estimated glomerular filtration rate (eGFR) in Africa in 3288 Ugandans and replicated the findings in 8224 African Americans. We identified two loci associated with eGFR at genome-wide significance (p<5x10-8). The most significantly associated variant (rs2433603, p=2.4x10-9) in GATM was distinct from previously reported signals. A second association signal mapping near HBB (rs141845179, p=3.0x10-8) was not significant after conditioning on a previously reported SNP (rs334) for eGFR. However, fine-mapping analyses highlighted rs141845179 to be the most likely causal variant at the HBB locus (posterior probability of 0.61). A trans-ethnic GRS of eGFR constructed from previously reported lead SNPs was not predictive into the Ugandan population, indicating that additional large-scale efforts in Africa are necessary to gain further insight into the genetic architecture of kidney disease.

genetics↗