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Tilahun Abebe

Researcher at University of Northern Iowa

Publications -  9
Citations -  719

Tilahun Abebe is an academic researcher from University of Northern Iowa. The author has contributed to research in topics: Population & Health equity. The author has an hindex of 8, co-authored 9 publications receiving 550 citations.

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Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities

TL;DR: The limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research are explored and "ancestry” (or biogeographical ancestry) is suggested to describe actual genetic variation in the context of health disparities.
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Genotype-environment interactions and their translational implications

TL;DR: How human population structure can obscure the resolution of GEI is described and how emerging biobanks across the globe can be coordinated to further the understanding of genotype-phenotype associations within the context of varying environment is discussed.
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Drought response in the spikes of barley: gene expression in the lemma, palea, awn, and seed.

TL;DR: Despite expressing more drought-associated genes, many genes for amino acid, amino acid derivative, and carbohydrate metabolism, as well as for photosynthesis, respiration, and stress response, were down-regulated in the awn compared with the lemma, palea, and seed.
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Comparison of measures of marker informativeness for ancestry and admixture mapping

TL;DR: Various methods for selecting ancestry informative SNPs using simulations as well as SNP genotype data from samples of admixed populations are compared and it is shown that the In measure estimates ancestry proportion with lower bias and mean square error.
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Rank-based genome-wide analysis reveals the association of Ryanodine receptor-2 gene variants with childhood asthma among human populations

TL;DR: The replication of top-ranked asthma genes across populations suggests that such loci are less likely to be false positives and could indicate true associations, and Variants that are associated with asthma across populations could be used to identify individuals at high risk for asthma regardless of genetic ancestry.