Genome-wide association and Mendelian randomization study of blood copper levels and 213 deep phenotypes in humans

Yang, Wenjun; Li, Longman; Feng, Xiuming; Cheng, Hong; Ge, Xiaoting; Bao, Yu; Huang, Lulu; Wang, Fei; Liu, Chaoqun; Chen, Xing; Yang, Xiaobo

doi:10.1038/s42003-022-03351-7

Cited by 16 publications

(6 citation statements)

References 109 publications

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“…In total, we found 210 shared significant SNPs that were composed of 18 loci linking 19 representative pairs of possibly correlated traits (Supplementary Table S5). Some of the findings are well-established, such as PP:Vitamin_D─MM:Vitamin_D3 with GC gene (PP4 = 99.9%) 41–43 , PP:MCH─MM: Element_Fe with TMPRSS6 gene (PP4 = 100%) 11,44,45 , and PP:BMI─EHR:Obesity with FTO gene (PP4 = 96.7%) 46–49 , where MCH is mean corpuscular hemoglobin. Here, PP4 indicates posterior probability of H4: one common causal variant of two GWAS studies.…”

Section: Resultsmentioning

confidence: 98%

Genetic analysis of 104 pregnancy phenotypes in 39,194 Chinese women

Xiao,

Li,

Yang

et al. 2023

Preprint

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Maternity is a special period in a woman’s life that involves substantial physiological, psychological, and hormonal changes. These changes may cause alterations in many clinical measurements during pregnancy, which can be used to monitor and diagnose maternal disorders and adverse postnatal outcomes. Exploring the genetic background of these phenotypes is key to elucidating the pathogenesis of pregnancy disorders. In this study, we conducted a large-scale molecular biology analysis of 104 pregnancy phenotypes based on genotype data from 39,194 Chinses women. Genome- wide association analysis identified a total of 407 trait-locus associations, of which 75.18% were previously reported. Among the 101 novel associations for 37 phenotypes, some were potentially pregnancy-specific and worth further experimental investigation. For example,ESR1with fasting glucose, hemoglobin, hematocrit, and several leukocytoses;ZSCAN31with blood urea nitrogen. We further performed pathway- based analysis and uncovered at least one significant pathway for 24 traits, in addition to previously known functional pathways, novel findings included birthweight with “Reactome signaling by NODAL”, twin pregnancy with “Reactome mitotic G1-G1/S phases”. The partitioning heritability analysis recapitulated known trait-relevant tissue/cell types, and also discovered interesting results including twin pregnancy with “embryoid bodies” cell-type enrichment, the delivery type cesarean section with “fallopian tube”, and birth weight with “ovary and embryonic stem cells”. In terms of both sample size and the variety of phenotypes, our work is one of the largest genetic studies of pregnancy phenotypes across all populations. We believe that this study will provide a valuable resource for exploring the genetic background of pregnancy phenotypes and also for further research on pregnancy-related diseases and adverse neonatal outcomes.

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Section: Resultsmentioning

confidence: 98%

Genetic analysis of 104 pregnancy phenotypes in 39,194 Chinese women

Xiao,

Li,

Yang

et al. 2023

Preprint

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“…The Asian GWAS data, which pertained to heavy metal exposures, were sourced from a study involving 2,488 Chinese participants. Specifically, this data comprised the serum and plasma levels of 16 heavy metals: barium (Ba), lead (Pb), cobalt (Co), molybdenum (Mo), cadmium (Cd), vanadium (V), chromium (Cr), aluminum (Al), manganese (Mn), nickel (Ni), tin (Sn), titanium (Ti), rubidium (Rb), strontium (Sr), copper (Cu), and zinc (Zn) ( 35 ). The East Asian GWAS data for DKD were collected from a Japanese study ( 36 ).…”

Section: Methodsmentioning

confidence: 99%

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis

Zhao,

Lin,

Han

et al. 2024

Front. Public Health

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Background and objectiveHeavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Our objective was to explore the correlation between heavy metal exposures and the incidence of DKD.MethodsWe analyzed data from the NHANES (2005–2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis.ResultsMachine learning reveals correlation coefficients of −0.5059 and − 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (p = 0.023), 0.70 (0.53, 0.92) (p = 0.012), 0.53 (0.34, 0.82) (p = 0.005), and 0.76 (0.64, 0.90) (p = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03, 1.32) (p = 0.018) and 1.17 (1.00, 1.36) (p = 0.044) for blood Cd and blood Mn, respectively.ConclusionIn the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

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“…Speci cally, this data comprised the serum and plasma levels of 16 heavy metals: barium (Ba), lead (Pb), cobalt (Co), molybdenum (Mo), cadmium (Cd), vanadium (V), chromium (Cr), aluminum (Al), manganese (Mn), nickel (Ni), tin (Sn), titanium (Ti), rubidium (Rb), strontium (Sr), copper (Cu), and zinc (Zn). [30] The East Asian GWAS data for DKD were collected from a Japanese study. [31] For the European cohort, we utilized GWAS data from a Swedish study of 949 participants, which included whole blood levels of 11 heavy metals (Al, Cd, Co, Cu, Cr, Hg, Mn, Mo, Ni, Pb, and Zn).…”

Section: De Nition and Assessment Of Dkd In Nhanesmentioning

confidence: 99%

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: A Cross-Sectional Survey and Mendelian Randomization Analysis

Zhao,

Lin,

Han

et al. 2024

Preprint

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Background & objective: Heavy metals, ubiquitous in the environment, pose a global public health concern. The correlation between these and diabetic kidney disease (DKD) remains unclear. Methods: We analyzed data from the NHANES (2005–2020), using machine learning, and cross-sectional survey. Our study also involved a bidirectional two-sample Mendelian randomization (MR) analysis. Results: Machine learning reveals correlation coefficients of -0.5059 and − 0.6510 for urinary Ba and urinary Tl with DKD, respectively. Multifactorial logistic regression implicates urinary Ba, urinary Pb, blood Cd, and blood Pb as potential associates of DKD. When adjusted for all covariates, the odds ratios and 95% confidence intervals are 0.87 (0.78, 0.98) (P = 0.023), 0.70 (0.53, 0.92) (P = 0.012), 0.53 (0.34, 0.82) (P = 0.005), and 0.76 (0.64, 0.90) (P = 0.002) in order. Furthermore, multiplicative interactions between urinary Ba and urinary Sb, urinary Cd and urinary Co, urinary Cd and urinary Pb, and blood Cd and blood Hg might be present. Among the diabetic population, the OR of urinary Tl with DKD is a mere 0.10, with a 95%CI of (0.01, 0.74), urinary Co 0.73 (0.54, 0.98) in Model 3, and urinary Pb 0.72 (0.55, 0.95) in Model 2. Restricted Cubic Splines (RCS) indicate a linear linkage between blood Cd in the general population and urinary Co, urinary Pb, and urinary Tl with DKD among diabetics. An observable trend effect is present between urinary Pb and urinary Tl with DKD. MR analysis reveals odds ratios and 95% confidence intervals of 1.16 (1.03,1.32) (P = 0.018) and 1.17 (1.00,1.36) (P = 0.044) for blood Cd and blood Mn, respectively. Conclusions: In the general population, urinary Ba demonstrates a nonlinear inverse association with DKD, whereas in the diabetic population, urinary Tl displays a linear inverse relationship with DKD.

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Genome-wide association and Mendelian randomization study of blood copper levels and 213 deep phenotypes in humans

Cited by 16 publications

References 109 publications

Genetic analysis of 104 pregnancy phenotypes in 39,194 Chinese women

Genetic analysis of 104 pregnancy phenotypes in 39,194 Chinese women

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: a cross-sectional survey and Mendelian randomization analysis

Association between machine learning-assisted heavy metal exposures and diabetic kidney disease: A Cross-Sectional Survey and Mendelian Randomization Analysis

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