A genome-wide association study coupled with machine learning approaches to identify influential demographic and genomic factors underlying Parkinson's disease.
A genome-wide association study coupled with machine learning approaches to identify influential demographic and genomic factors underlying Parkinson's disease.
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DOI：
10.3389/fgene.2023.1230579
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发表时间：
2023
影响因子：
3.7
通讯作者：
Liu, Jinling
中科院分区：
文献类型：
--
作者：
Rahman, Md Asad;Liu, Jinling
关键词：
背景资料：尽管最近全基因组关联研究（GWAS）在确定帕金森病（PD）的90个独立风险位点方面取得了成功，但PD的基因组基础在很大程度上仍然未知。同时，在临床上需要利用基因组或人口统计学特征的准确和可靠的预测模型来预测帕金森病的风险。 研究方法：为了确定与PD相关的有影响力的人口统计学和基因组因素，并进一步开发预测模型，我们利用人口统计学数据，纳入33，473名参与者的200个变量，沿着涉及8，840个样本的447，089个SNP的基因组数据，两者均来自Fox Insight在线研究。我们首先应用相关性和GWAS分析分别找到与PD相关的最重要的人口统计学和基因组因素。我们进一步开发和比较了各种机器学习（ML）模型来预测PD。从开发的ML模型中，我们进行了特征重要性分析，以揭示PD的每个人口统计学或基因组输入特征的可预测性。最后，我们对GWAS结果进行了基因集富集分析，以确定PD相关通路。 结果：在我们的研究中，我们确定了与PD相关的新的和众所周知的人口统计学和遗传因素（沿着丰富的途径）。此外，我们开发了表现稳健的预测模型，人口统计数据的AUC = 0.89，基因组数据的AUC = 0.74。我们的GWAS分析确定了几个新的和重要的变体和基因位点，包括LMNA中的三个内含子变体（p值小于4.0e-21）和SEMA 4A中的一个错义变体（p值= 1.11e-26）。我们对PD预测ML模型的特征重要性分析突出了我们GWAS分析中的一些重要和新颖的变体（例如，RIT 1基因中的内含子变体rs 1749409），并帮助鉴定GWAS遗漏的潜在致病变体，如rs 11264300，DCST 1基因中的错义变体，和rs 11584630，KCNN 3基因中的内含子变体。 结论：总之，通过将GWAS与先进的机器学习模型相结合，我们确定了已知和新的人口统计学和基因组因素，并建立了用于预测帕金森病的性能良好的ML模型。
Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson’s disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson’s disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. We first applied correlation and GWAS analyses to find the top demographic and genomic factors associated with PD, respectively. We further developed and compared a variety of machine learning (ML) models for predicting PD. From the developed ML models, we performed feature importance analysis to reveal the predictability of each demographic or the genomic input feature for PD. Finally, we performed gene set enrichment analysis on our GWAS results to identify PD-associated pathways. Results: In our study, we identified both novel and well-known demographic and genetic factors (along with the enriched pathways) related to PD. In addition, we developed predictive models that performed robustly, with AUC = 0.89 for demographic data and AUC = 0.74 for genomic data. Our GWAS analysis identified several novel and significant variants and gene loci, including three intron variants in LMNA (p-values smaller than 4.0e-21) and one missense variant in SEMA4A (p-value = 1.11e-26). Our feature importance analysis from the PD-predictive ML models highlighted some significant and novel variants from our GWAS analysis (e.g., the intron variant rs1749409 in the RIT1 gene) and helped identify potentially causative variants that were missed by GWAS, such as rs11264300, a missense variant in the gene DCST1, and rs11584630, an intron variant in the gene KCNN3. Conclusion: In summary, by combining a GWAS with advanced machine learning models, we identified both known and novel demographic and genomic factors as well as built well-performing ML models for predicting Parkinson’s disease.
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影响因子：
15.9
作者：
Lin, Ling;Lesnick, Timothy G.;Maraganore, Demetrius M.;Isacson, Ole
通讯作者：
Isacson, Ole
影响因子：
3.7
作者：
Nuruddin S;Syverstad GH;Lillehaug S;Leergaard TB;Nilsson LN;Ropstad E;Krogenæs A;Haraldsen IR;Torp R
通讯作者：
Torp R
影响因子：
4.4
作者：
Kaler, Avjinder S.;Purcell, Larry C.
通讯作者：
Purcell, Larry C.
帕金森氏病的遗传结构。
DOI：
10.1016/s1474-4422(19)30287-x
发表时间：
2020-02
期刊：
The Lancet. Neurology
影响因子：
--
作者：
Blauwendraat C;Nalls MA;Singleton AB
通讯作者：
Singleton AB
影响因子：
30.8
作者：
Chang D;Nalls MA;Hallgrímsdóttir IB;Hunkapiller J;van der Brug M;Cai F;International Parkinson's Disease Genomics Consortium;23andMe Research Team;Kerchner GA;Ayalon G;Bingol B;Sheng M;Hinds D;Behrens TW;Singleton AB;Bhangale TR;Graham RR
通讯作者：
Graham RR