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Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. However, the causal estimate may be biased in case the assumptions of MR are violated. The source and the type of this bias are described while providing a summary of the mathematical formulas that should help estimating the magnitude and direction of the potential bias depending on the specific research setting. Finally, methods for relaxing the assumptions and for conducting sensitivity analyses are discussed. Future researches in the field of MR include the assessment of non-linear causal effects, and automatic detection of invalid instruments.
Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. However, the causal estimate may be biased in case the assumptions of MR are violated. The source and the type of this bias are described while providing a summary of the mathematical formulas that should help estimating the magnitude and direction of the potential bias depending on the specific research setting. Finally, methods for relaxing the assumptions and for conducting sensitivity analyses are discussed. Future researches in the field of MR include the assessment of non-linear causal effects, and automatic detection of invalid instruments.
Activation of trace amine-associated receptor 1 (TAAR1) in endocrine pancreas is involved in weight regulation and glucose homeostasis. The purpose of this study was the identification and characterization of potential TAAR1 variants in patients with overweight/obesity and disturbed glucose homeostasis. Screening for TAAR1 variants was performed in 314 obese or overweight patients with impaired insulin secretion. The detected variants were functionally characterized concerning TAAR1 cell surface expression and signaling properties and their allele frequencies were determined in the population-based Study of Health in Pomerania (SHIP). Three heterozygous carriers of the single nucleotide missense variants p.Arg23Cys (R23C, rs8192618), p.Ser49Leu (S49L, rs140960896), and p.Ille171Leu (I171L, rs200795344) were detected in the patient cohort. While p.Ser49Leu and p.Ille171Leu were found in obese/overweight patients with slightly impaired glucose homeostasis, p.Arg23Cys was identified in a patient with a complete loss of insulin production. Functional in vitro characterization revealed a like wild-type function for I171L, partial loss of function for S49L and a complete loss of function for R23C. The frequency of the R23C variant in 2018 non-diabetic control individuals aged 60 years and older in the general population-based SHIP cohort was lower than in the analyzed patient sample. Both variants are rare in the general population indicating a recent origin in the general gene pool and/or the consequence of pronounced purifying selection, in line with the obvious detrimental effect of the mutations. In conclusion, our study provides hints for the existence of naturally occurring TAAR1 variants with potential relevance for weight regulation and glucose homeostasis.
Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method
(2022)
A decreased estimated glomerular filtration rate (eGFR) leading to chronic kidney disease is a significant public health problem. Kidney function is a heritable trait, and recent application of genome-wide association studies (GWAS) successfully identified multiple eGFR-associated genetic loci. To increase statistical power for detecting independent associations in GWAS loci, we improved our recently developed quasi-adaptive method estimating SNP-specific alpha levels for the conditional analysis, and applied it to the GWAS meta-analysis results of eGFR among 783,978 European-ancestry individuals. Among known eGFR loci, we revealed 19 new independent association signals that were subsequently replicated in the United Kingdom Biobank (n = 408,608). These associations have remained undetected by conditional analysis using the established conservative genome-wide significance level of 5 × 10–8. Functional characterization of known index SNPs and novel independent signals using colocalization of conditional eGFR association results and gene expression in cis across 51 human tissues identified two potentially causal genes across kidney tissues: TSPAN33 and TFDP2, and three candidate genes across other tissues: SLC22A2, LRP2, and CDKN1C. These colocalizations were not identified in the original GWAS. By applying our improved quasi-adaptive method, we successfully identified additional genetic variants associated with eGFR. Considering these signals in colocalization analyses can increase the precision of revealing potentially functional genes of GWAS loci.