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

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Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx
Genome Biology ( IF 12.3 ) Pub Date : 2020-09-11 , DOI: 10.1186/s13059-020-02113-0
Nicole R Gay ₁ , Michael Gloudemans ₂ , Margaret L Antonio ₂ , Nathan S Abell ₁ , Brunilda Balliu ₃ , YoSon Park _{4,

5} , Alicia R Martin _{6,

7} , Shaila Musharoff ₁ , Abhiram S Rao ₈ , François Aguet ₉ , Alvaro N Barbeira ₁₀ , Rodrigo Bonazzola ₁₀ , Farhad Hormozdiari _{9,

11} , , Kristin G Ardlie ₉ , Christopher D Brown ₄ , Hae Kyung Im ₁₀ , Tuuli Lappalainen _{12,

13} , Xiaoquan Wen ₁₄ , Stephen B Montgomery _{1,

15}

Affiliation

Department of Genetics, Stanford University, Stanford, CA, USA.
Biomedical Informatics, Stanford University, Stanford, CA, USA.
Department of Biomathematics, University of California, Los Angeles, Los Angeles, CA, USA.
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA.
Department of Bioengineering, Stanford University, Stanford, CA, USA.
The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
New York Genome Center, New York, NY, USA.
Department of Systems Biology, Columbia University, New York, NY, USA.
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Department of Pathology, Stanford University, Stanford, CA, USA.

Background Population structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization. Results Here, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up. Conclusions We provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach.

中文翻译：

混合和祖先对 GTEx 中 eQTL 分析和 GWAS 共定位的影响

背景研究对象之间的人口结构可能会混淆遗传关联研究，缺乏适当的校正可能会导致虚假的发现。基因型组织表达 (GTEx) 项目主要包含欧洲血统的个体，但 v8 版本还包括高达 15% 的非欧洲血统个体。在 GTEx 中评估基于祖先的调整提高了该研究在人群中的可移植性，并进一步描述了人口结构对 GWAS 共定位的影响。结果在这里，我们在 GTEx (v8) 中确定了 117 个个体的子集，这些个体具有高度的人口混合，并估计了全基因组的本地血统。我们使用七种组织中的混合样本进行全基因组 cis-eQTL 作图，并根据全球或本地血统进行调整。与之前的工作一致，我们观察到通过当地血统调整提高了权力。在两种调整产生不同先导变体的基因座上，我们观察到 31 个基因座 (0.02%)，其中仅使用一种 eQTL 祖先调整方法调用了显着的共定位。值得注意的是，这两种调整在 COLOC 和 FINEMAP 这两种不同的共定位方法中产生了相似数量的显着共定位。最后，我们确定了一小部分与当地血统高度相关的 eQTL 相关变体，为增强功能随访提供了资源。结论我们在 GTEx v8 版本中提供了混合个体的本地血统图谱，并描述了血统和混合对基因表达、eQTL 和 GWAS 共定位的影响。虽然大多数结果在基于本地和全球血统的调整之间是一致的，

更新日期：2020-09-11

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