【佳學(xué)基因檢測】精神病基因組協(xié)會如何解碼疾病發(fā)生的基因原因并應(yīng)用基因檢測？
什么是影像表型？
利用英國生物銀行的大規(guī)模圖像數(shù)據(jù)和精神病基因組協(xié)會的大規(guī)模GWAS數(shù)據(jù)的方法有可能開啟對精神疾病生物學(xué)的許多洞察。在本文中，我們提出了一種這樣的方法，BrainXcan，它利用這兩種數(shù)據(jù)資源來解決小規(guī)模MRI研究中的一些不足。以英國生物銀行的數(shù)據(jù)為參考，我們建立了從基因數(shù)據(jù)預(yù)測大腦IDPs的模型。這些模型可以應(yīng)用于全基因組關(guān)聯(lián)研究。例如，使用精神病基因組協(xié)會收集的精神分裂癥GWAS數(shù)據(jù)，我們的方法測試了精神分裂癥與許多不同功能、結(jié)構(gòu)和擴(kuò)散MR模式之間的關(guān)聯(lián)，大小為∼ 70000個案例和∼ 24萬個控件。此外，通過應(yīng)用孟德爾隨機(jī)方法，我們推斷出因果關(guān)系的方向：IDP的變化是疾病的原因還是后果。Methods that leverage UK Biobank’s large scale image data and the PGC’s large scale GWAS data have the potential to unlock many insights into the biology of mental disorders. In this paper we propose one such method, BrainXcan, which leverages these two data resources to address some of the deficiencies in small scale MRI studies. Using UK Biobank data as a reference, we build models to predict brain IDPs from genetic data. These models can then be applied to from genome-wide association studies. For example, using the schizophrenia GWAS data collected by the PGC, our method tests for association between schizophrenia and a number of different functional, structural and diffusion MR modalities with size of ∼ 70, 000 cases and ∼ 240, 000 controls. Furthermore, by applying a Mendelian randomization approach we infer the direction of causality: whether the changes in IDP are the cause of disease or a consequence of it.
影像表型（IDP）相關(guān)遺傳標(biāo)記已被用于因果推斷，并采用孟德爾隨機(jī)法等方法，在大樣本量和防止反向因果關(guān)系的情況下，研究大腦特征對行為表型的中介作用。例如，Jansen等人（2020年）研究了腦容量IDP和智力之間共享的基因組位點和相應(yīng)基因，他們確定了92個共享基因，為腦容量和智力的共享遺傳病因?qū)W提供了見解。Shen等人（2020年）對抑郁癥和dMRI IDPs進(jìn)行了雙向MR分析，發(fā)現(xiàn)提示性證據(jù)表明丘腦輻射平均擴(kuò)散率的變化可能是抑郁癥的后果。一種相關(guān)的方法是將遺傳預(yù)測的大腦IDP/表型與復(fù)雜性狀相關(guān)聯(lián)，這是基于轉(zhuǎn)錄組的方法（Gamazon等人，2015；Gusev等人，2016）對IDP的延伸?；谶@一想法，Knutson等人（2020年）利用阿爾茨海默病神經(jīng)成像倡議的14個大腦特征開展了成像廣泛關(guān)聯(lián)研究（IWAS）。他們還使用標(biāo)準(zhǔn)PRS方法，使用Elliott等人（2018年）（n=8428）的GWAS匯總結(jié)果生成預(yù)測權(quán)重。
IDP-associated genetic markers have been used for causal inference with methods such as Mendelian Randomization to investigate the mediating role of brain features on behavioral phenotypes with both large sample sizes and protection from reverse causality. For instance, Jansen et al. (2020) studied the genomic loci and corresponding genes that are shared between brain volume IDPs and intelligence and they identified 92 shared genes which provided insight of the shared genetic etiology of brain volume and intelligence. Shen et al. (2020) performed bi-directional MR analysis with depression and dMRI IDPs finding suggestive evidence that the change of the mean diffusivity in thalamic radiations could be a consequence of major depressive disorder. A related approach is one that correlates genetically predicted brain IDP/phenotype and the complex trait, an extension of transcriptome-based methods (Gamazon et al., 2015; Gusev et al., 2016) to IDPs. Based on this idea, Knutson et al. (2020) developed imaging-wide association study (IWAS) using 14 brain features from the Alzheimer’s Disease Neuroimaging Initiative. They also used standard PRS approaches to generate prediction weights using the GWAS summary results from Elliott et al. (2018) (n=8,428).
In this paper, we perform an in-depth analysis of the genetic architecture of IDPs and further process UK Biobank’s IDPs to develop a framework that maximizes interpretability, robustness, computational efficiency, and user friendliness.
The high polygenicity of brain features imposes several challenges to existing methods limiting the power to detect their link to diseases; strong genetic instruments needed for Mendelian randomization based approaches are difficult to identify. We address these challenges by developing polygenic predictors of IDPs informed by their complex genetic architecture and correlation structure. To facilitate interpretation of the results, we develop region-specific and brain-wide predictors providing an in-depth analysis and quantification of potential biases. We make sure that the implementation is computationally efficient and scalable to genome-wide Biobank-scale data. We develop an extension of the association method that can infer the association using the increasingly available GWAS summary results, i.e. without the need to use individual level data. We add a Mendelian Randomization module to estimate the direction of the causal flow. We illustrate the power of the approach by applying it to 44 human traits. Finally, we provide the software, the recommended pipeline, and automated reports to improve usability and lower the barrier to adoption for users less familiar with genetic studies.
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