Haplotype reconstruction error as a classical misclassification problem

Statistically reconstructing haplotypes from single nucleotide polymorphism (SNP) genotypes, can lead to falsely classified haplotypes. This can be an issue when interpreting haplotype association results or when selecting subjects with certain haplotypes for subsequent functional studies. It was our aim to quantify haplotype reconstruction error and to provide tools for it. By numerous simulation scenarios, we systematically investigated several error measures, including discrepancy, error rate, and R(2), and introduced the sensitivity and specificity to this context. We exemplified several measures in the KORA study, a large population-based study from Southern Germany. We find that the specificity is slightly reduced only for common haplotypes, while the sensitivity was decreased for some, but not all rare haplotypes. The overall error rate was generally increasing with increasing number of loci, increasing minor allele frequency of SNPs, decreasing correlation between the alleles and increasing ambiguity. We conclude that, with the analytical approach presented here, haplotype-specific error measures can be computed to gain insight into the haplotype uncertainty. This method provides the information, if a specific risk haplotype can be expected to be reconstructed with rather no or high misclassification and thus on the magnitude of expected bias in association estimates. We also illustrate that sensitivity and specificity separate two dimensions of the haplotype reconstruction error, which completely describe the misclassification matrix and thus provide the prerequisite for methods accounting for misclassification

Investigating Determinants of Multiple Sclerosis in Longitunal Studies: A Bayesian Approach

Modelling data from Multiple Sclerosis longitudinal studies is a challenging topic since the phenotype of interest is typically ordinal; time intervals between two consecutive measurements are nonconstant and they can vary among individuals. Due to these unobservable sources of heterogeneity statistical models for analysis of Multiple Sclerosis severity evolve as a difficult feature. A few proposals have been provided in the biostatistical literature (Heijtan (1991); Albert, (1994)) to address the issue of investigating Multiple Sclerosis course. In this paper Bayesian P-Splines (Brezger and Lang, (2006); Fahrmeir and Lang (2001)) are indicated as an appropriate tool since they account for nonlinear smooth effects of covariates on the change in Multiple Sclerosis disability. By means of Bayesian P-Spline model we investigate both the randomness affecting Multiple Sclerosis data as well as the ordinal nature of the response variable

Misclassification in genetic variants and its impact on genetic association studies

This work focused on misclassification of genetic variants and its impact on genetic association studies. The initial question was, if and in which amount non-replication and inconsistency in these studies can be explained by errors in genotypes and reconstruction of haplotypes. The amount and structure of genotyping errors were estimated via maximum-likelihood method based on double genotype measurements. These measurements were derived within routine quality control from genetic epidemiological association studies. Thereby it was possible to yield realistic error size estimates, as they can be expected in association analyses. Genotyping error per SNP in a high quality laboratory using an established genotyping method has been found to be small (<0.1%). The data suggested the allelic drop out model to be appropriate and to some extent also the symmetric model. The bias due to genotype misclassification, as shown in a re-analysis of the association of SNPs of the APM1 gene on plasma adiponectin concentrations, was found to be negligible. In other settings, e.g. relaxed quality control, genotype error might be higher. Then, a higher bias is expected, which was shown to be efficiently corrected with the MC-SIMEX method, a statistical method to correct for misclassification within a generalized linear model. Regarding the uncertainties in haplotypes induced by statistical reconstruction from genotypes, a classification of the various haplotype error measures was provided, introducing sensitivity and specificity into the context of haplotypes. Results from simulations and analytical derivations emphasized the dependence of the haplotype reconstruction error on the specific situation, particularly on minor allele frequency, correlation between SNPs, number of loci and ambiguity fraction. Generally, the sensitivity was greatly reduced for some rare haplotypes, posing a potential problem of rare haplotypes in association studies. Extension to a full 3x3 misclassification matrix, which has not been performed before in other methodological studies, allowed the inclusion of genotype errors. It could be shown that errors in genotypes add substantially to the pure reconstruction error. The impact of haplotype misclassification, induced by a combination of genotype error and haplotype reconstruction error, on haplotype association analyses was evaluated in simulations as well as in a re-analysis of haplotypes on the APM1 gene. In the case of a high genotype error per allele (1% or more), a rather high bias on haplotype association estimates was observed, which could be corrected using the MC-SIMEX method. The MC-SIMEX was presented as an efficient method to calculate error-corrected association estimates in haplotype association studies. Altogether, assuming good quality standards in the laboratory and thus a small genotype error rate (<0.5%) as it was estimated in this investigation, the impact on haplotype association estimates was rather moderate to negligable. Moreover, calculation of misclassification matrices for specific haplotypes additionally assures the correctness of haplotype assignments and simplifies the interpretation of association estimates. These findings argue that non-replication of genetic association studies are only to a minor extent due to errors in genetic variants, if the genotyping process is performed in experienced laboratories using established methods with sufficient quality control. The multiple testing problem is likely to play the biggest role in the non-replication problem of genetic epidemiological studies

Impact of genotyping errors on the type I error rate and the power of haplotype-based association methods

<p>Abstract</p> <p>Background</p> <p>We investigated the influence of genotyping errors on the type I error rate and empirical power of two haplotype based association methods applied to candidate regions. We compared the performance of the Mantel Statistic Using Haplotype Sharing and the haplotype frequency based score test with that of the Armitage trend test.</p> <p>Our study is based on 1000 replication of simulated case-control data settings with 500 cases and 500 controls, respectively. One of the examined markers was set to be the disease locus with a simulated odds ratio of 3. Differential and non-differential genotyping errors were introduced following a misclassification model with varying mean error rates per locus in the range of 0.2% to 15.6%.</p> <p>Results</p> <p>We found that the type I error rate of all three test statistics hold the nominal significance level in the presence of nondifferential genotyping errors and low error rates. For high and differential error rates, the type I error rate of all three test statistics was inflated, even when genetic markers not in Hardy-Weinberg Equilibrium were removed. The empirical power of all three association test statistics remained high at around 89% to 94% when genotyping error rates were low, but decreased to 48% to 80% for high and nondifferential genotyping error rates.</p> <p>Conclusion</p> <p>Currently realistic genotyping error rates for candidate gene analysis (mean error rate per locus of 0.2%) pose no significant problem for the type I error rate as well as the power of all three investigated test statistics.</p

Cis-epistasis at the LPA locus and risk of cardiovascular diseases

AIMS Coronary artery disease (CAD) has a strong genetic predisposition. However, despite substantial discoveries made by genome-wide association studies (GWAS), a large proportion of heritability awaits identification. Non-additive genetic-effects might be responsible for part of the unaccounted genetic variance. Here we attempted a proof-of-concept study to identify non-additive genetic effects, namely epistatic interactions, associated with CAD. METHODS AND RESULTS We tested for epistatic interactions in ten CAD case-control studies and UK Biobank with focus on 8,068 SNPs at 56 loci with known associations with CAD risk. We identified a SNP pair located in cis at the LPA locus, rs1800769 and rs9458001, to be jointly associated with risk for CAD (odds ratio OR=1.37, p = 1.07 $\times$ 10-11), peripheral arterial disease (OR = 1.22, p = 2.32 $\times$ 10-4), aortic stenosis (OR = 1.47, p = 6.95 $\times$ 10-7), hepatic lipoprotein(a) (Lp(a)) transcript levels (beta = 0.39, p = 1.41 $\times$ 10-8), and Lp(a) serum levels (beta = 0.58, p = 8.7 $\times$ 10-32), while individual SNPs displayed no association. Further exploration of the LPA locus revealed a strong dependency of these associations on a rare variant, rs140570886, that was previously associated with Lp(a) levels. We confirmed increased CAD risk for heterozygous (relative OR = 1.46, p = 9.97 $\times$ 10-32) and individuals homozygous for the minor allele (relative OR = 1.77, p = 0.09) of rs140570886. Using forward model selection, we also show that epistatic interactions between rs140570886, rs9458001, and rs1800769 modulate the effects of the rs140570886 risk allele. CONCLUSIONS These results demonstrate the feasibility of a large-scale knowledge-based epistasis scan and provide rare evidence of an epistatic interaction in a complex human disease. We were directed to a variant (rs140570886) influencing risk through additive genetic as well as epistatic effects. In summary, this study provides deeper insights into the genetic architecture of a locus important for cardiovascular diseases. TRANSLATIONAL PERSPECTIVE Genetic variants identified by GWAS studies explain about a quarter of the heritability of coronary artery disease by additive genetic effects. Our study demonstrates that non-additive effects contribute to the genetic architecture of the disease as well and identifies complex interaction patterns at the LPA locus, which affect LPA expression, Lp(a) plasma levels and risk of atherosclerosis. This proof-of-concept study encourages systematic searches for epistatic interactions in further studies to shed new light on the aetiology of the disease

The Association of Mid-Regional Pro-Adrenomedullin and Mid-Regional Pro-Atrial Natriuretic Peptide with Mortality in an Incident Dialysis Cohort

High levels of the plasma peptides mid-regional pro-adrenomedullin (MR-proADM) and mid-regional pro-atrial natriuretic peptide (MR-proANP) are associated with clinical outcomes in the general population. Data in patients with chronic kidney disease are sparse. We therefore investigated the association of MR-proANP and MR-proADM levels with all-cause and cardiovascular (CV) mortality, CV events and peripheral arterial disease in 201 incident dialysis patients of the INVOR-Study prospectively followed for a period of up to more than 7 years. The overall mortality rate was 43%, thereof 43% due to CV events. Both baseline MR-proANP and MR-proADM were associated with higher risk of all-cause (HR = 1.44, p = 0.001 and HR = 1.32, p = 0.002, respectively) and CV mortality (HR = 1.75, p<0.001 and HR = 1.41, p = 0.007, respectively) after adjustment for age, sex, previous CV events, diabetes mellitus and time-dependent type of renal replacement therapy. We then stratified patients in high risk (both peptides in the upper tertile), intermediate risk (only one of the two peptides in the upper tertile) and low risk (none in the upper tertile). Although demographic, clinical and laboratory variables were similar among the intermediate and high risk group, to be with both parameters in the upper tertile was associated with a 3-fold higher risk for all-cause (HR = 2.87, p<0.001) and CV mortality (HR = 3.58, p = 0.001). In summary, among incident dialysis patients MR-proANP and MR-proADM were shown to be associated with all-cause and CV mortality, with the highest risk when both parameters were in the upper tertiles

Association of HbA1c values with mortality and cardiovascular events in O

Abstract Background: Improved glycemic control reduces complications in patients with diabetes mellitus (DM). However, it is discussed controversially whether patients with diabetes mellitus and end-stage renal disease benefit from strict glycemic control

Use of Analog and Human Insulin in a European Hemodialysis Cohort With Type 2 Diabetes: Associations With Mortality, Hospitalization, MACE, and Hypoglycemia

RATIONALE & OBJECTIVE: Poor glycemic control may contribute to the high mortality rate in patients with type 2 diabetes receiving hemodialysis. Insulin type may influence glycemic control, and its choice may be an opportunity to improve outcomes. This study assessed whether treatment with analog insulin compared with human insulin is associated with different outcomes in people with type 2 diabetes and kidney failure receiving hemodialysis. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: People in the Analyzing Data, Recognizing Excellence and Optimizing Outcomes (AROii) study with kidney failure commencing hemodialysis and type 2 diabetes being treated with insulin within 288 dialysis facilities between 2007 and 2009 across 7 European countries. Study participants were followed for 3 years. People with type 1 diabetes were excluded using an established administrative data algorithm. EXPOSURE: Treatment with an insulin analog or human insulin. OUTCOME: All-cause mortality, major adverse cardiovascular events (MACE), all-cause hospitalization, and confirmed hypoglycemia (blood glucose<3.0mmol/L sampled during hemodialysis). ANALYTICAL APPROACH: Inverse probability weighted Cox proportional hazards models to estimate hazard ratios for analog insulin compared with human insulin. RESULTS: There were 713 insulin analog and 733 human insulin users. Significant variation in insulin type by country was observed. Comparing analog with human insulin at 3 years, the percentage of patients experiencing end points and adjusted hazard ratios (AHR) were 22.0% versus 31.4% (AHR, 0.808 [95% CI, 0.66-0.99], P=0.04) for all-cause mortality, 26.8% versus 35.9% (AHR, 0.817 [95% CI, 0.68-0.98], P=0.03) for MACE, and 58.2% versus 75.0% (AHR, 0.757 [95% CI, 0.67-0.86], P<0.001) for hospitalization. Hypoglycemia was comparable between insulin types at 14.1% versus 15.0% (AHR, 1.169 [95% CI, 0.80-1.72], P=0.4). Consistent strength and direction of the associations were observed across sensitivity analyses. LIMITATIONS: Residual confounding, lack of more detailed glycemia data. CONCLUSIONS: In this large multinational cohort of people with type 2 diabetes and kidney failure receiving maintenance hemodialysis, treatment with analog insulins was associated with better clinical outcomes when compared with human insulin. PLAIN-LANGUAGE SUMMARY: People with diabetes who are receiving dialysis for kidney failure are at high risk of cardiovascular disease and death. This study uses information from 1,446 people with kidney failure from 7 European countries who are receiving dialysis, have type 2 diabetes, and are prescribed either insulin identical to that made in the body (human insulin) or insulins with engineered extra features (insulin analog). After 3 years, fewer participants receiving analog insulins had died, had been admitted to the hospital, or had a cardiovascular event (heart attack, stroke, heart failure, or peripheral vascular disease). These findings suggest that analog insulins should be further explored as a treatment leading to better outcomes for people with diabetes on dialysis

Lipoprotein(a) plasma levels are not associated with incident microvascular complications in type 2 diabetes mellitus

Aims/hypothesis: Microvascular disease in type 2 diabetes is a significant cause of end-stage renal disease, blindness and peripheral neuropathy. The strict control of known risk factors, e.g. lifestyle, hyperglycaemia, hypertension and dyslipidaemia, reduces the incidence of microvascular complications, but a residual risk remains. Lipoprotein (a) [Lp(a)] is a strong risk factor for macrovascular disease in the general population. We hypothesised that plasma Lp(a) levels and the LPA gene SNPs rs10455872 and rs3798220 are associated with the incident development of microvascular complications in type 2 diabetes. Methods: Analyses were performed of data from the DiaGene study, a prospective study for complications of type 2 diabetes, collected in the city of Eindhoven, the Netherlands (n = 1886 individuals with type 2 diabetes, mean follow-up time = 6.97 years). To assess the relationship between plasma Lp(a) levels and the LPA SNPs with each newly developed microvascular complication (retinopathy n = 223, nephropathy n = 246, neuropathy n = 236), Cox proportional hazards models were applied and adjusted for risk factors for microvascular complications (age, sex, mean arterial pressure, non-HDL-cholesterol, HDL-cholesterol, BMI, duration of type 2 diabetes, HbA1c and smoking). Results: No significant associations of Lp(a) plasma levels and the LPA SNPs rs10455872 and rs3798220 with prevalent or incident microvascular complications in type 2 diabetes were found. In line with previous observations the LPA SNPs rs10455872 and rs3798220 did influence the plasma Lp(a) levels. Conclusions/interpretation: Our data show no association between Lp(a) plasma levels and the LPA SNPs with known effect on Lp(a) plasma levels with the development of microvascular complications in type 2 diabetes. This indicates that Lp(a) does not play a major role in the development of microvascular complications. However, larger studies are needed to exclude minimal effects of Lp(a) on the development of microvascular complications