Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power.

Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power.
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拖拉机使用当地血统,使混血的个人能够纳入GWA,并提高权力。

DOI:
10.1038/s41588-020-00766-y
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发表时间:
2021-03
期刊:
影响因子:
30.8
通讯作者:
Neale BM
Neale BM
中科院分区:
生物学1区
文献类型:
--
作者:
Atkinson EG;Maihofer AX;Kanai M;Martin AR;Karczewski KJ;Santoro ML;Ulirsch JC;Kamatani Y;Okada Y;Finucane HK;Koenen KC;Nievergelt CM;Daly MJ;Neale BM

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由于对种群结构的担忧,混合种群通常被排除在基因组研究之外。在这里,我们提出了一个统计框架和软件包,拖拉机,以促进包括混合个人协会的研究,利用当地的祖先。我们测试拖拉机与模拟和经验的双向混合非洲-欧洲的同伙。Tractor生成准确的祖先特异性效应大小估计值和p值,可以提高GWAS功效,并提高关联信号的分辨率。使用本地祖先感知回归模型,我们复制已知的血脂命中,发现标准GWAS错过的新命中,并将信号定位为更接近推定的因果变异。
Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical 2-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect size estimates and p values, can boost GWAS power, and improves the resolution of association signals. Using a local ancestry aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS, and localize signals closer to putative causal variants.
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