MultiBLUP: improved SNP-based prediction for complex traits.
MultiBLUP: improved SNP-based prediction for complex traits.
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DOI：
10.1101/gr.169375.113
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发表时间：
2014-09
期刊：
影响因子：
7
通讯作者：
Balding DJ
中科院分区：
文献类型：
--
作者：
Speed D;Balding DJ
BLUP（最佳线性无偏预测）被广泛用于预测植物和动物育种中的复杂性状，并且越来越多地用于人类遗传学。BLUP的数学模型，其中包括一个单一的随机效应项，是足够的亲属关系时，从家系测量。然而，当使用全基因组SNP来测量亲属关系时，BLUP模型隐含地假设所有SNP具有相同的效应大小分布，这是一个严重且不必要的限制。我们提出了MultiBLUP，它扩展了BLUP模型，包括多个随机效应，当随机效应对应于具有不同效应大小方差的SNP类别时，可以大大提高预测。SNP类别可以预先指定，例如，基于SNP功能注释，并且我们还提供了用于确定SNP的合适分区的自适应程序。我们将MultiBLUP应用于来自Wellcome Trust Case Control Consortium（七种疾病）的全基因组关联数据，以及来自乳糜泻和炎症性肠病的更大研究的数据，发现它始终提供比替代方法更好的预测。此外，MultiBLUP在计算上非常高效;对于最大的数据集，包括12，678个个体和150万个SNP，总分析可以在不到一天的时间内在一台台式PC上运行，并且可以并行化以更快地运行。执行MultiBLUP的工具在我们的软件LDAK中免费提供。
BLUP (best linear unbiased prediction) is widely used to predict complex traits in plant and animal breeding, and increasingly in human genetics. The BLUP mathematical model, which consists of a single random effect term, was adequate when kinships were measured from pedigrees. However, when genome-wide SNPs are used to measure kinships, the BLUP model implicitly assumes that all SNPs have the same effect-size distribution, which is a severe and unnecessary limitation. We propose MultiBLUP, which extends the BLUP model to include multiple random effects, allowing greatly improved prediction when the random effects correspond to classes of SNPs with distinct effect-size variances. The SNP classes can be specified in advance, for example, based on SNP functional annotations, and we also provide an adaptive procedure for determining a suitable partition of SNPs. We apply MultiBLUP to genome-wide association data from the Wellcome Trust Case Control Consortium (seven diseases), and from much larger studies of celiac disease and inflammatory bowel disease, finding that it consistently provides better prediction than alternative methods. Moreover, MultiBLUP is computationally very efficient; for the largest data set, which includes 12,678 individuals and 1.5 M SNPs, the total analysis can be run on a single desktop PC in less than a day and can be parallelized to run even faster. Tools to perform MultiBLUP are freely available in our software LDAK.
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DOI：
10.1097/mpg.0b013e31821a23d0
发表时间：
2012-01-01
影响因子：
2.9
作者：
Husby, S.;Koletzko, S.;Zimmer, K. P.
通讯作者：
Zimmer, K. P.
影响因子：
56.9
作者：
Duerr, Richard H.;Taylor, Kent D.;Cho, Judy H.
通讯作者：
Cho, Judy H.
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
3.5
作者：
Jostins L;Barrett JC
通讯作者：
Barrett JC
影响因子：
3.3
作者：
de Los Campos G;Hickey JM;Pong-Wong R;Daetwyler HD;Calus MP
通讯作者：
Calus MP