Analysis of quantitative genetic traits in a huge data set

海量数据集中的数量遗传性状分析

• 批准号: BB/N006178/1
• 负责人: John Hickey
• 金额: $ 83.83万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FN006178%2F1
• 关键词: Analysis; quantitative; genetic; traits; huge

The genetic basis of quantitative trait variation and covariation is central to human genetics, evolutionary biology, and plant and animal breeding. In medical genetics many diseases, including schizophrenia, heart disease and cancer, are complex traits with continuous phenotypes and liabilities, which have multiple genome variants contributing genetic variance. In evolutionary biology fitness is largely due to such quantitative traits (e.g., fecundity, longevity). In plant and animal breeding most of the economically important traits are quantitative traits (e.g., milk, meat, and grain yields, environmental footprint, fecundity). Huge datasets are needed for statistical genomics because many variants (probably thousands), which can be clustered together, contribute to any individual quantitative trait and their effects can combine in complex ways (additive, dominant, epistatic). Moreover, important portions of the genetic variance of quantitative traits are controlled by variants that are rare, have small effect sizes or are highly correlated with other variants. The effects of such quantitative trait variants can only be separated when very powerful statistical models are used in very large data sets.We will analyse the genetic basis of 25 quantitative traits at the molecular level by creating and analysing a dataset containing genome sequences, pedigrees and trait records of 325000 pigs from the world's biggest commercial breeding programme. The dataset will be created and analysed using imputation and analysis algorithms based on those that we developed to support the breeding programme.The size of the dataset and the quality of the data will allow us to address three big questions:-1. Which genome variants control which quantitative traits, how do they control them and how do the multiple variants that control a single trait interact?2. What kinds of mechanisms cause traits to co-vary? To what extent does pleiotropy and linkage disequilibrium contribute? What is the distribution of the magnitude and sign of joint effects of genomic regions on pairs of traits? 3. To what extent do huge data sets help us address these questions? For the first time we have the technology to generate genome sequence data for hundreds of thousands of individuals at low cost and the computer power to store and analyse such data.The aim of this project is to harvest scientific benefits from a 15 year billion dollar pig breeding program. Our previous projects asked how statistical genomics helps animal breeding; this project asks how animal breeding helps statistical genomics.

定量性状变化和协方差的遗传基础对于人类遗传学，进化生物学以及动植物繁殖至关重要。在医学遗传学中，包括精神分裂症，心脏病和癌症在内的许多疾病都是具有连续表型和负债的复杂特征，它们具有多种基因组变异，促进了遗传方差。在进化生物学中，适应性很大程度上是由于这种定量性状（例如，繁殖力，寿命）。在动植物中，大多数重要的经济特征是定量特征（例如牛奶，肉和谷物产量，环境足迹，繁殖力）。统计基因组学需要巨大的数据集，因为许多可以聚集在一起的变体（可能数千种）有助于任何个体的定量性状及其效果可以以复杂的方式结合（添加剂，主导性，代言）。此外，定量性状的遗传差异的重要部分由罕见，效应大小较小或与其他变体高度相关的变体控制。只有在非常大的数据集中使用非常强大的统计模型时，才能分离这种定量性状变体的效果。我们将通过创建和分析包含来自世界上最大商业品种的325000猪的基因组序列，家谱和特征记录的数据集，分析分子水平上25种定量性状的遗传基础。数据集将根据我们为支持繁殖程序的插入和分析算法而创建和分析数据集。数据集的大小和数据质量将使我们能够解决三个大问题：-1。哪些基因组变体控制哪些定量性状，它们如何控制它们，如何控制单个特征的多个变体相互作用？2。哪些机制导致特征是共同的？多效性和连锁不平衡在多大程度上贡献？基因组区域对成对性状的关节作用的大小和迹象的分布是什么？ 3。大型数据集在多大程度上有助于我们解决这些问题？我们第一次有技术以低成本生成数十万个人的基因组序列数据，并以计算机的能力存储和分析此类数据。该项目的目的是从15年的15亿美元猪育种计划中收获科学收益。我们以前的项目询问统计基因组学如何帮助动物育种。该项目询问动物育种如何帮助统计基因组学。

项目成果

A genome-wide association study for loin depth and muscle pH in pigs from intensely selected purebred lines.

• DOI: 10.1186/s12711-023-00815-0
• 发表时间: 2023-06-15
• 期刊: GENETICS SELECTION EVOLUTION
• 影响因子: 4.1
• 作者: Desire, Suzanne;Johnsson, Martin;Ros-Freixedes, Roger;Chen, Ching-Yi;Holl, Justin W. W.;Herring, William O. O.;Gorjanc, Gregor;Mellanby, Richard J. J.;Hickey, John M. M.;Jungnickel, Melissa K. K.
• 通讯作者: Jungnickel, Melissa K. K.

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

杂交育种项目早期一般配合力基因组预测模型的比较

• DOI: 10.1002/csc2.21105
• 发表时间: 2023
• 期刊: Crop Science
• 影响因子: 2.3
• 作者: De Jong G

A method for the allocation of sequencing resources in genotyped livestock populations.

• DOI: 10.1186/s12711-017-0322-5
• 发表时间: 2017-05-18
• 期刊: Genetics, selection, evolution : GSE
• 作者: Gonen S;Ros-Freixedes R;Battagin M;Gorjanc G;Hickey JM
• 通讯作者: Hickey JM

AlphaSimR: an R package for breeding program simulations.

• DOI: 10.1093/g3journal/jkaa017
• 发表时间: 2021-02-09
• 期刊: G3 (Bethesda, Md.)
• 作者: Gaynor RC;Gorjanc G;Hickey JM
• 通讯作者: Hickey JM

MOESM5 of A hybrid method for the imputation of genomic data in livestock populations

MOESM5 家畜种群基因组数据插补的混合方法

• DOI: 10.6084/m9.figshare.c.3708046_d5
• 发表时间: 2017
• 作者: AntolA­N R