GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.
GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures.
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DOI：
10.1186/1756-0381-5-16
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发表时间：
2012-10-01
期刊：
影响因子：
4.5
通讯作者：
Moore JH
中科院分区：
文献类型：
--
作者：
Urbanowicz RJ;Kiralis J;Sinnott-Armstrong NA;Heberling T;Fisher JM;Moore JH
那些不局限于单基因座疾病关联的遗传学家需要额外的策略来检测复杂的多基因座效应。上位性，一种多基因座掩盖效应，带来了特殊的挑战，并且一直是生物信息学发展的目标。对新算法的全面评估需要进行模拟研究，以寻找已知的疾病模型。到目前为止，生成模拟多基因座上位性模型的最佳方法依赖于遗传算法。然而，这类方法计算成本高昂，难以适应多个目标，并且不太可能产生我们所指的纯且严格形式的上位性模型。纯且严格的上位性模型在检测疾病关联方面构成了最坏的情况，因为只有当所有n个基因座都包含在疾病模型中时，才可能观察到这种关联。这使得它们成为考虑复杂多基因座效应的模拟研究中有吸引力的黄金标准。
我们介绍GAMETES，这是一个用户友好的软件包和算法，它为模拟研究生成复杂的双等位基因单核苷酸多态性（SNP）疾病模型。GAMETES能够快速且精确地生成具有特定遗传约束的随机、纯、严格的n基因座模型。这些约束包括遗传力、SNP的次要等位基因频率以及人群患病率。GAMETES还包括一种简单的数据集聚类策略，可用于为给定的遗传模型快速生成模拟数据集档案。我们通过一个使用MDR（一种旨在检测上位性的算法）的模拟研究示例来强调GAMETES的用途和局限性。
GAMETES是一种快速、灵活且精确的工具，用于生成具有随机结构的复杂n基因座模型。虽然GAMETES生成具有较高遗传力模型的能力有限，但它擅长生成模拟研究中评估新算法通常使用的较低遗传力模型。此外，GAMETES建模策略可以灵活地与任何数据集聚类策略相结合。除了数据集聚类，GAMETES还可用于对遗传模型和上位性进行理论表征。
Geneticists who look beyond single locus disease associations require additional strategies for the detection of complex multi-locus effects. Epistasis, a multi-locus masking effect, presents a particular challenge, and has been the target of bioinformatic development. Thorough evaluation of new algorithms calls for simulation studies in which known disease models are sought. To date, the best methods for generating simulated multi-locus epistatic models rely on genetic algorithms. However, such methods are computationally expensive, difficult to adapt to multiple objectives, and unlikely to yield models with a precise form of epistasis which we refer to as pure and strict. Purely and strictly epistatic models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n-loci are included in the disease model. This makes them an attractive gold standard for simulation studies considering complex multi-locus effects. We introduce GAMETES, a user-friendly software package and algorithm which generates complex biallelic single nucleotide polymorphism (SNP) disease models for simulation studies. GAMETES rapidly and precisely generates random, pure, strict n-locus models with specified genetic constraints. These constraints include heritability, minor allele frequencies of the SNPs, and population prevalence. GAMETES also includes a simple dataset simulation strategy which may be utilized to rapidly generate an archive of simulated datasets for given genetic models. We highlight the utility and limitations of GAMETES with an example simulation study using MDR, an algorithm designed to detect epistasis. GAMETES is a fast, flexible, and precise tool for generating complex n-locus models with random architectures. While GAMETES has a limited ability to generate models with higher heritabilities, it is proficient at generating the lower heritability models typically used in simulation studies evaluating new algorithms. In addition, the GAMETES modeling strategy may be flexibly combined with any dataset simulation strategy. Beyond dataset simulation, GAMETES could be employed to pursue theoretical characterization of genetic models and epistasis.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
9.8
作者：
Culverhouse, R;Suarez, BK;Reich, T
通讯作者：
Reich, T
影响因子：
9.8
作者：
Ritchie, MD;Hahn, LW;Moore, JH
通讯作者：
Moore, JH
影响因子：
3
作者：
Mailund T;Schierup MH;Pedersen CN;Mechlenborg PJ;Madsen JN;Schauser L
通讯作者：
Schauser L
影响因子：
2.9
作者：
Lemire, Mathieu
通讯作者：
Lemire, Mathieu