Panagiota Kyratzi, Oswald Matika, Amey H Brassington, Connie E Clare, Juan Xu, David A Barrett, Richard D Emes, Alan L Archibald, Andras Paldi, Kevin D Sinclair, Jonathan Wattis, Cyril Rauch
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引用次数: 0
Abstract
Identifying associations between phenotype and genotype is the fundamental basis of genetic analyses. Inspired by frequentist probability and the work of R. A. Fisher, genome-wide association studies (GWAS) extract information using averages and variances from genotype-phenotype datasets. Averages and variances are legitimated upon creating distribution density functions obtained through the grouping of data into categories. However, as data from within a given category cannot be differentiated, the investigative power of such methodologies is limited. Genomic informational field theory (GIFT) is a method specifically designed to circumvent this issue. The way GIFT proceeds is opposite to that of GWAS. Although GWAS determines the extent to which genes are involved in phenotype formation (bottom-up approach), GIFT determines the degree to which the phenotype can select microstates (genes) for its subsistence (top-down approach). Doing so requires dealing with new genetic concepts, a.k.a. genetic paths, upon which significance levels for genotype-phenotype associations can be determined. By using different datasets obtained in Ovis aries related to bone growth (dataset 1) and to a series of linked metabolic and epigenetic pathways (dataset 2), we demonstrate that removing the informational barrier linked to categories enhances the investigative and discriminative powers of GIFT, namely that GIFT extracts more information than GWAS. We conclude by suggesting that GIFT is an adequate tool to study how phenotypic plasticity and genetic assimilation are linked.NEW & NOTEWORTHY The genetic basis of complex traits remains challenging to investigate using classic genome-wide association studies (GWASs). Given the success of gene editing technologies, this point needs to be addressed urgently since there can only be useful editing technologies whether precise genotype-phenotype mapping information is available initially. Genomic informational field theory (GIFT) is a new mapping method designed to increase the investigative power of biological/medical datasets suggesting, in turn, the need to rethink the conceptual bases of quantitative genetics.
确定表型与基因型之间的关联是遗传分析的基础。全基因组关联研究(GWAS)受频繁概率论和费雪(R.A. Fisher)著作的启发,利用基因型-表型数据集的平均值和方差提取信息。平均值和方差是通过对数据进行分类而得到的分布密度函数来确定的。然而,由于给定类别内的数据无法区分,这种方法的研究能力有限。基因组信息场理论(GIFT)是专门为规避这一问题而设计的方法。GIFT 的工作方式与 GWAS 相反。GWAS 确定基因参与表型形成的程度(自下而上的方法),而 GIFT 则确定表型选择微观状态(基因)以维持其生存的程度(自上而下的方法)。要做到这一点,就需要处理新的遗传概念,即遗传路径,在此基础上才能确定基因型与表型关联的显著性水平。通过使用在羱羊身上获得的与骨骼生长(数据集-1)以及一系列相关的代谢和表观遗传途径(数据集-2)有关的不同数据集,我们证明了消除与类别相关的信息障碍可增强 GIFT 的研究和鉴别能力,即 GIFT 比 GWAS 提取出更多的信息。最后,我们认为 GIFT 是研究表型可塑性与遗传同化之间联系的适当工具。
期刊介绍:
The Physiological Genomics publishes original papers, reviews and rapid reports in a wide area of research focused on uncovering the links between genes and physiology at all levels of biological organization. Articles on topics ranging from single genes to the whole genome and their links to the physiology of humans, any model organism, organ, tissue or cell are welcome. Areas of interest include complex polygenic traits preferably of importance to human health and gene-function relationships of disease processes. Specifically, the Journal has dedicated Sections focused on genome-wide association studies (GWAS) to function, cardiovascular, renal, metabolic and neurological systems, exercise physiology, pharmacogenomics, clinical, translational and genomics for precision medicine, comparative and statistical genomics and databases. For further details on research themes covered within these Sections, please refer to the descriptions given under each Section.