A Neighborhood-Wide Association Study (NWAS): Example of prostate cancer aggressiveness.
A Neighborhood-Wide Association Study (NWAS): Example of prostate cancer aggressiveness.
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DOI：
10.1371/journal.pone.0174548
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Rebbeck TR
中科院分区：
文献类型：
--
作者：
Lynch SM;Mitra N;Ross M;Newcomb C;Dailey K;Jackson T;Zeigler-Johnson CM;Riethman H;Branas CC;Rebbeck TR
癌症是生物、个体和社会层面多个变量复杂相互作用的结果。与其他层面相比，在地理空间上发生在社区的社会影响没有得到很好的研究，经验方法来评估这些影响是有限的。我们提出了一种新的邻域关联研究（NWAS），类似于全基因组关联研究（GWAS），利用生物学的高维计算方法来全面和经验性地识别与疾病相关的邻域因素。宾夕法尼亚州癌症登记处的数据与美国人口普查数据相关联。在一个连续的更严格的多阶段方法中，我们评估了邻居（n = 14，663个普查变量）和前列腺癌侵袭性（PCA）之间的关系，其中白色男性中n = 6，416例侵袭性（分期≥3/Gleason分级≥7例）与n = 70，670例非侵袭性（分期<3/Gleason分级<7）病例。分析考虑了年龄、诊断年份、空间相关性和多重检验。我们在第一阶段使用广义估计方程，在第二阶段使用贝叶斯混合效应模型来计算比值比（OR）和置信区间（CI）。在第三阶段，主成分分析对相关变量进行分组。我们确定了17个与PCA相关的新邻域变量。这些变量包括收入、住房、就业、移民、获得护理和社会支助。交通（OR = 1.05;CI = 1.001-1.09）和贫困（OR = 1.07;CI = 1.01-1.12）是影响交通出行的最重要因素。这项研究介绍了应用高维，计算方法的大规模，公开的地理空间数据。虽然NWAS需要进一步测试，但它是假设生成的，并解决了与经验评估有关的地理空间分析中的差距。此外，NWAS可能对许多疾病产生广泛影响，未来的精准医学研究将重点关注疾病的多层次风险因素。
Cancer results from complex interactions of multiple variables at the biologic, individual, and social levels. Compared to other levels, social effects that occur geospatially in neighborhoods are not as well-studied, and empiric methods to assess these effects are limited. We propose a novel Neighborhood-Wide Association Study(NWAS), analogous to genome-wide association studies(GWAS), that utilizes high-dimensional computing approaches from biology to comprehensively and empirically identify neighborhood factors associated with disease. Pennsylvania Cancer Registry data were linked to U.S. Census data. In a successively more stringent multiphase approach, we evaluated the association between neighborhood (n = 14,663 census variables) and prostate cancer aggressiveness(PCA) with n = 6,416 aggressive (Stage≥3/Gleason grade≥7 cases) vs. n = 70,670 non-aggressive (Stage<3/Gleason grade<7) cases in White men. Analyses accounted for age, year of diagnosis, spatial correlation, and multiple-testing. We used generalized estimating equations in Phase 1 and Bayesian mixed effects models in Phase 2 to calculate odds ratios(OR) and confidence/credible intervals(CI). In Phase 3, principal components analysis grouped correlated variables. We identified 17 new neighborhood variables associated with PCA. These variables represented income, housing, employment, immigration, access to care, and social support. The top hits or most significant variables related to transportation (OR = 1.05;CI = 1.001–1.09) and poverty (OR = 1.07;CI = 1.01–1.12). This study introduces the application of high-dimensional, computational methods to large-scale, publically-available geospatial data. Although NWAS requires further testing, it is hypothesis-generating and addresses gaps in geospatial analysis related to empiric assessment. Further, NWAS could have broad implications for many diseases and future precision medicine studies focused on multilevel risk factors of disease.