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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.
Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with >5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriched ETFDH stop gain variant p.Trp286Ter (rs1235904433-hexanoylcarnitine) and the MCCC2 stop lost variant p.Ter564GlnextTer3 (rs751970792-carnitine) have been found in patients with glutaric acidemia type II and 3-methylcrotonylglycinuria, respectively, but the loci have not been associated with the respective metabolites in a genome-wide association study (GWAS) previously. We further identified three gene-trait associations, where multiple rare variants contribute to the signal. These results not only provide further evidence for previously described associations, but also describe novel genes and mechanisms for diseases and disease-related traits.
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.
Although the common pathology of Alzheimer’s disease (AD) and white matter hyperintensities (WMH) is disputed, the gene TREML2 has been implicated in both conditions: its whole-blood gene expression was associated with WMH volume and its missense variant rs3747742 with AD risk. We re-examined those associations within one comprehensive dataset of the general population, additionally searched for cross-relations and illuminated the role of the apolipoprotein E (APOE) ε4 status in the associations. For our linear regression and linear mixed effect models, we used 1949 participants from the Study of Health in Pomerania (Germany). AD was assessed using a continuous pre-symptomatic MRI-based score evaluating a participant’s AD-related brain atrophy. In our study, increased whole-blood TREML2 gene expression was significantly associated with reduced WMH volume but not with the AD score. Conversely, rs3747742-C was significantly associated with a reduced AD score but not with WMH volume. The APOE status did not influence the associations. In sum, TREML2 robustly associated with WMH volume and AD-related brain atrophy on different molecular levels. Our results thus underpin TREML2’s role in neurodegeneration, might point to its involvement in AD and WMH via different biological mechanisms, and highlight TREML2 as a worthwhile target for disentangling the two pathologies.
Mendelian randomization indicates causal effects of estradiol levels on kidney function in males
(2023)
Context: Chronic kidney disease (CKD) is a public health burden worldwide. Epidemiological studies observed an association between sex hormones, including estradiol, and kidney function.
Objective: We conducted a Mendelian randomization (MR) study to assess a possible causal effect of estradiol levels on kidney function in males and females.
Design: We performed a bidirectional two-sample MR using published genetic associations of serum levels of estradiol in men (n = 206,927) and women (n = 229,966), and of kidney traits represented by estimated glomerular filtration rate (eGFR, n = 567,460), urine albumin-to-creatinine ratio (UACR, n = 547,361), and CKD (n = 41,395 cases and n = 439,303 controls) using data obtained from the CKDGen Consortium. Additionally, we conducted a genome-wide association study using UK Biobank cohort study data (n = 11,798 men and n = 6,835 women) to identify novel genetic associations with levels of estradiol, and then used these variants as instruments in a one-sample MR.
Results: The two-sample MR indicated that genetically predicted estradiol levels are significantly associated with eGFR in men (beta = 0.077; p = 5.2E-05). We identified a single locus at chromosome 14 associated with estradiol levels in men being significant in the one-sample MR on eGFR (beta = 0.199; p = 0.017). We revealed significant results with eGFR in postmenopausal women and with UACR in premenopausal women, which did not reach statistical significance in the sensitivity MR analyses. No causal effect of eGFR or UACR on estradiol levels was found.
Conclusions: We conclude that serum estradiol levels may have a causal effect on kidney function. Our MR results provide starting points for studies to develop therapeutic strategies to reduce kidney disease.
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.
The Apolipoprotein E (APOE) gene polymorphism (rs429358 and rs7412) shows a well-established association with lipid profiles, but its effect on cardiovascular disease is still conflicting. Therefore, we examined the association of different APOE alleles with common carotid artery intima-media thickness (CCA-IMT), carotid plaques, incident myocardial infarction (MI) and stroke. We analyzed data from 3327 participants aged 20–79 years of the population-based Study of Health in Pomerania (SHIP) from Northeast Germany with a median follow-up time of 14.5 years. Linear, logistic, and Cox-regression models were used to assess the associations of the APOE polymorphism with CCA-IMT, carotid plaques, incident MI and stroke, respectively. In our study, the APOE E2 allele was associated with lower CCA-IMT at baseline compared to E3 homozygotes (β: − 0.02 [95% CI − 0.04, − 0.004]). Over the follow-up, 244 MI events and 218 stroke events were observed. APOE E2 and E4 allele were not associated with incident MI (E2 HR: 1.06 [95% CI 0.68, 1.66]; E4 HR: 1.03 [95% CI 0.73, 1.45]) and incident stroke (E2 HR: 0.79 [95% CI 0.48, 1.30]; E4 HR: 0.96 [95% CI 0.66, 1.38]) in any of the models adjusting for potential confounders. However, the positive association between CCA-IMT and incident MI was more pronounced in E2 carriers than E3 homozygotes. Thus, our study suggests that while APOE E2 allele may predispose individuals to lower CCA-IMT, E2 carriers may be more prone to MI than E3 homozygotes as the CCA-IMT increases. APOE E4 allele had no effect on CCA-IMT, plaques, MI or stroke.
Genome-wide association studies (GWAS) are used to identify genetic markers linked with at least partially heritable diseases or phenotypes without prior knowledge of any disease-associated genetic loci. In summer 2008, all individuals of the population based cohort Study of Health in Pomerania (SHIP) were individually genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0 microarray. The aim of this work was to establish an efficient workflow for GWAS using the more than 4000 individually genotyped samples of the SHIP cohort as well as pooled samples, focusing exclusively on analyzing genetic variations based on single nucleotide polymorphisms (SNPs). Firstly, an optimal array platform for the genotyping analysis had to be chosen that detected most of the available genetic variants at a high level of accuracy. Secondly, extensive quality controls had to be performed starting from DNA extraction and including tests of the generated array data by the analysis software to obtain the most reliable data for the subsequent association studies. For the identification of loci with smaller genetic influences, individual cohorts were meta-analyzed in large nationally and internationally organized consortia (e.g. CHARGE, BPGen, HaemGen, GIANT, CKD Gen). To participate in those meta-analyses, a comparable common set of genetic data had to be generated. This was done by imputation of the data generated by individual array-based genotyping on the basis of a reference panel using chromosomal linkage information. Due to the extensive phenotype information in the SHIP study, it was possible to perform many genome-wide discovery analyses and replication studies of possible susceptibility loci in a short time once the genetic data was available and processed. This resulted in the necessity to set up an efficient workflow for storing the huge amount of genetic data, converting it into different formats readable for specific analysis software, performing the association analyses and processing the results into a human-readable and clear format. This included replications, GWAS and meta-analyses of several cohorts. Many susceptibility loci were newly identified in different association studies with the SHIP data included and were subsequently published. In this work, genetic association studies with the SHIP data included were performed and published on blood pressure, uric acid concentrations, cardiac structure and function, lipid metabolism, hematological parameters, kidney functions, smoking quantity, circulating IGF-I and IGFBP-3 concentrations and thyroid volume including the risk of goiter development. Besides the SHIP cohort, there was a need to use other, especially patient cohorts for GWAS. Since no genotype information from these patient cohorts was available and the individual genotyping of many probands is still expensive and therefore often not affordable, we established the cost-effective allelotyping method that relied on pooling of DNA samples prior to the hybridization with microarrays. After estimating the pooling-specific error of a case-control allelotyping study, the allelotyping approach was used for identifying genetic susceptibility loci associated with aggressive periodontitis. If not referring to work of collaborators, all statistical analyses, data handling and in silico work concerning the SHIP data described in this context was performed by the author of this dissertation.
The Study of Health in Pomerania (SHIP), a population-based study from a rural state in northeastern Germany with a relatively poor life expectancy, supplemented its comprehensive examination program in 2008 with whole-body MR imaging at 1.5 T (SHIP-MR). We reviewed more than 100 publications that used the SHIP-MR data and analyzed which sequences already produced fruitful scientific outputs and which manuscripts have been referenced frequently. Upon reviewing the publications about imaging sequences, those that used T1-weighted structured imaging of the brain and a gradient-echo sequence for R2* mapping obtained the highest scientific output; regarding specific body parts examined, most scientific publications focused on MR sequences involving the brain and the (upper) abdomen. We conclude that population-based MR imaging in cohort studies should define more precise goals when allocating imaging time. In addition, quality control measures might include recording the number and impact of published work, preferably on a bi-annual basis and starting 2 years after initiation of the study. Structured teaching courses may enhance the desired output in areas that appear underrepresented.