【佳學(xué)基因檢測(cè)】如何應(yīng)用大腦影像資料提高神經(jīng)系統(tǒng)疾病的基因檢測(cè)正確性？

腦磁共振成像的進(jìn)展使神經(jīng)科學(xué)有了許多發(fā)現(xiàn)。病例組和對(duì)照組之間腦部MRI特征的比較突出了精神和行為特征的潛在原因。然而，由于收集MRI數(shù)據(jù)的成本和招募特定患者群體的困難，大多數(shù)研究樣本量小，限制了其高效性。此外，反向因果關(guān)系使解釋變得復(fù)雜，許多觀察到的大腦差異是疾病的結(jié)果，而不是原因。在此，我們提出了一種方法（BrainXcan），該方法利用大規(guī)模全基因組關(guān)聯(lián)研究（GWAS）、參考腦MRI數(shù)據(jù)以及使用遺傳儀器進(jìn)行因果推斷的方法學(xué)進(jìn)展，以發(fā)現(xiàn)疾病病因的新機(jī)制并驗(yàn)證現(xiàn)有機(jī)制。BrainXcan測(cè)試復(fù)雜性狀與大腦MRI衍生表型的遺傳預(yù)測(cè)因子的關(guān)聯(lián)，以正確定位相關(guān)的區(qū)域特異性和跨大腦特征。它還使用孟德爾隨機(jī)法評(píng)估因果流的一致性和方向。由于這種方法只需要遺傳數(shù)據(jù)，BrainXcan允許我們使用現(xiàn)有的公共數(shù)據(jù)資源，測(cè)試關(guān)于精神疾病的一系列假設(shè)，跨越許多疾病和MRI模式。我們的方法表明，整個(gè)大腦的軸突密度降低與精神分裂癥的風(fēng)險(xiǎn)相關(guān)，這與斷連假說一致。我們還發(fā)現(xiàn)海馬、杏仁核和前扣帶回皮質(zhì)等與精神分裂癥風(fēng)險(xiǎn)相關(guān)的結(jié)構(gòu)特征，突出了我們的方法帶來正交證據(jù)的潛力，以告知復(fù)雜特征的生物學(xué)。
Advances in brain MRI have enabled many discoveries in neuroscience. Comparison of brain MRI features between cases and controls have highlighted potential causes of psychiatric and behavioral traits. However, due to the cost of collecting MRI data and the difficulty in recruiting particular patient groups, most studies have small sample sizes, limiting their reliability. Furthermore, interpretation is complicated by reverse causality, where many observed brain differences are the result of disease rather than the cause. Here we propose a method (BrainXcan) that leverages the power of large-scale genome-wide association studies (GWAS), reference brain MRI data, and methodological advances in causal inference using genetic instruments to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests complex traits for association with genetic predictors of brain MRI derived phenotypes to pinpoint relevant region-specific and cross-brain features. It also evaluates consistency and direction of the causal flow with Mendelian Randomization. As this approach requires only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many disorders and MRI modalities, using existing public data resources. Our method shows that reduced axonal density across the brain is associated with the risk of schizophrenia, consistent with the disconnectivity hypothesis. We also find structural features hippocampus, amygdala, and anterior cingulate cortex among others associated with schizophrenia risk highlighting the potential of our approach to bring orthogonal lines of evidence to inform the biology of complex traits.

腦磁共振成像的進(jìn)展使神經(jīng)科學(xué)有了許多發(fā)現(xiàn)。然而，在很大程度上，由于樣本量小，全腦關(guān)聯(lián)研究（BWAS）的再現(xiàn)性較低（Marek et al.，2020）。這些小樣本是由于收集MRI掃描的高成本，以及招募特定精神疾病患者的困難。此外，與疾病狀態(tài)不會(huì)改變種系遺傳變異的全基因組關(guān)聯(lián)研究不同，大腦特征可以通過疾病狀態(tài)和治療而改變，這可能由于反向因果關(guān)系而產(chǎn)生顯著關(guān)聯(lián)。

Advances in brain MRI have enabled many discoveries in neuroscience. However, reproducibility of brain-wide associations studies (BWAS) is low due, in large part, to small sample sizes (Marek et al., 2020). These small sample sizes are the result of the high cost of collecting MRI scans, as well as the difficulty in recruiting patients with particular mental illnesses. Also, unlike genome-wide association studies where disease status does not alter germline genetic variation, brain features can be altered by disease status and treatments, which can yield significant associations due to reverse causality.

英國生物銀行正在對(duì)100000人的腦部MRI進(jìn)行測(cè)量（Littlejohns等人，2020年）。史無前例的數(shù)據(jù)規(guī)模、數(shù)據(jù)的自動(dòng)化統(tǒng)一處理、基因數(shù)據(jù)的可用性和無數(shù)的表型數(shù)據(jù)無疑將促進(jìn)未來幾年的許多發(fā)現(xiàn)。對(duì)腦MRI圖像衍生表型（IDPs）的中期分析發(fā)現(xiàn)了許多與之相關(guān)的全基因組重要位點(diǎn)，并確定大多數(shù)IDPs是可遺傳的（Smith等人，2021年）。Zhao等人利用19629名英國生物銀行參與者的數(shù)據(jù)，對(duì)101種腦體積表型進(jìn)行了多基因風(fēng)險(xiǎn)評(píng)分，結(jié)果表明，他們可以解釋四項(xiàng)獨(dú)立研究中6%以上的表型變異（Zhao等人，2019年）。

The UK Biobank is in the process of measuring brain MRI in 100,000 individuals (Littlejohns et al., 2020). The unprecedented scale of the data, the automated uniform processing of the data, the availability of genetic and a myriad of phenotypic data will undoubtedly catalyze many discoveries in the coming years. The interim analysis of brain MRI image derived phenotypes (IDPs) found many genome-wide significant loci associated and established that most IDPs are heritable (Smith et al., 2021). Zhao et al generated polygenic risk scores of 101 brain volumetric phenotypes using 19,629 UK Biobank participant data and showed they could explain more than 6% of the phenotypic variance in four independent studies (Zhao et al., 2019).

精神病基因組學(xué)協(xié)會(huì)是世界各地研究人員的合作成果，它結(jié)合了許多精神疾病的研究，并促成了在每項(xiàng)研究中都不可能實(shí)現(xiàn)的發(fā)現(xiàn)。他們所有的GWAS總結(jié)結(jié)果都是公開的，以允許其他研究者測(cè)試他們自己的假設(shè)并提取新的生物學(xué)見解。PGC研究11種精神疾病，包括ADHD、阿爾茨海默病、孤獨(dú)癥、雙相情感障礙和精神分裂癥。
The Psychiatric Genomics Consortium is a cooperative effort of investigators across the world that combines studies of many mental disorders and has enabled discoveries that would not have been possible within each of the studies. All their GWAS summary results are publicly available to allow other investigators to test their own hypotheses and extract new biological insight. The PGC studies 11 psychiatric disorders including ADHD, Alzheimer’s disease, autism, bipolar disorder, and schizophrenia.

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