China 
Africa 
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Personalizing Environmental Health for the Military—Striving for Precision
Published in Kirk A. Phillips, Dirk P. Yamamoto, LeeAnn Racz, Total Exposure Health, 2020
While the advancements have been exciting, there have also been unrealized expectations. From 2001 to 2015, the primary workhorse of genotypic variation has not been genomic sequencing, but rather, single-nucleotide polymorphism (SNP) genotyping arrays. SNP genotyping arrays are an older technology (developed between the late 1990s and early 2000s) and have generated almost all of the hundreds of GWAS-defined trait associations curated by the NHGRI and the European Bioinformatics Institute (EBI) (US National Library of Medicine 2019). However, SNPs are not very predictive for most traits and phenotypes. There are several reasons for this: (1) SNPs do not represent all of the variations of the human genome. In fact, they are less than half of the variations by most estimates (National Human Genome Research Institute 2019). Other forms of genomic variation include insertions and deletions (INDELS), copy number variants (CNVs), segmental duplications, and others. These ‘structural variants’ are not readily assayable by standard SNP array technologies. (2) SNPs do not account for much of the heritability of most complex traits (Fisher 1918). The highest estimated heritability that can be explained by SNPs for a non-Mendelian, complex disease is age-related macular degeneration (~50% from 5 SNPs), which has made it a prime candidate for several focused gene therapy treatments currently underway (Venter et al. 2001), but most other complex diseases are not this straightforward. (3) Genetics (SNPs, etc.) does not account for the majority of the heritability of most human chronic diseases (Green & Guyer 2011). In fact, most have a larger environmental, causative component.
Association of genetic polymorphisms with mercapturic acids in the urine of young healthy subjects before and after exposure to outdoor air pollution
Published in International Journal of Environmental Health Research, 2023
Wenping Song, Lingjie Bian, Mengran Xiong, Yuanyuan Duan, Yi Wang, Xia Zhang, Biao Li, Yulong Dai, Jiawei Lu, Meng Li, Zhiguo Liu, Shigang Liu, Li Zhang, Hongjuan Yao, Rongguang Shao, Guangxi Li, Liang Li
Assessment of physiological factors, including age, sex and body mass index (BMI), for the participants was performed using Student’s t-test and presented as the mean ± SEM. Correlation analysis of SNP genotyping and changes in MAs was conducted using the R package to obtain Pearson’s correlation coefficient and the P value. A P value ≤.05 was considered to be a significant difference.