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.

数量性状变异和共变的遗传基础是人类遗传学、进化生物学和动植物育种的核心。在医学遗传学中，许多疾病，包括精神分裂症、心脏病和癌症，都是具有连续表型和责任的复杂性状，它们具有多个基因组变异，导致遗传变异。在进化生物学中，适应性很大程度上取决于这些数量特征（例如，繁殖力、寿命）。在植物和动物育种中，大多数经济上重要的性状都是数量性状（例如，奶、肉和谷物产量、环境足迹、繁殖力）。统计基因组学需要庞大的数据集，因为可以聚集在一起的许多变异（可能是数千个）对任何单个数量性状都有贡献，而且它们的影响可以以复杂的方式（加性、显性、上位性）组合在一起。此外，数量性状遗传变异的重要部分是由罕见的、效应量小的或与其他变异高度相关的变异控制的。只有在非常大的数据集中使用非常强大的统计模型时，才能分离出这种数量性状变异的影响。我们将在分子水平上分析25个数量性状的遗传基础，通过创建和分析包含基因组序列、系谱和来自世界上最大的商业育种计划的325000头猪的性状记录的数据集。数据集将根据我们为支持育种计划而开发的数据集，使用输入和分析算法来创建和分析。数据集的大小和数据的质量将允许我们解决三个大问题：-1。哪些基因组变异控制哪些数量性状，它们是如何控制它们的，以及控制单个性状的多个变异是如何相互作用的？什么样的机制导致特质共变？多效性和连锁不平衡在多大程度上起作用？基因组区域对成对性状的联合效应的大小和符号的分布是什么？3. 庞大的数据集能在多大程度上帮助我们解决这些问题？我们第一次拥有了以低成本为成千上万个人生成基因组序列数据的技术，以及存储和分析这些数据的计算机能力。这个项目的目的是从150亿美元的养猪计划中获得科学效益。我们之前的项目是关于统计基因组学如何帮助动物育种；这个项目探讨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.

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

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

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

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

• DOI: 10.1186/s12711-017-0300-y
• 发表时间: 2017-03-03
• 期刊: Genetics, selection, evolution : GSE
• 作者: Antolín R;Nettelblad C;Gorjanc G;Money D;Hickey JM
• 通讯作者: Hickey JM