A recessive Mendelian model to predict carrier probabilities of DFNB1 for nonsyndromic deafness

Mutations in the DFNB1 locus, where two connexin genes are located (GJB2 and GJB6), account for half of congenital cases of nonsyndromic autosomal recessive deafness. Because of the high frequency of DFNB1 gene mutations and the availability of genetic diagnostic tests involving these genes, they are the best candidates to develop a risk prediction model of being hearing impaired. People undergoing genetic counseling are normally interested in knowing the probability of having a hearing impaired child given his/her family history. To address this, a Mendelian model that predicts the probability of being a carrier of DFNB1 mutations, using family history of deafness, has been developed. This probability will be useful as additional information to decide whether or not a genetic test should be performed. This model incorporates Mendelian mode of inheritance, the age of onset of the disease, and the current age of hearing family members. The carrier probabilities are obtained using Bayes' theorem, in which mutation prevalence is used as the prior distribution. We have validated our model by using information from 305 families affected with congenital or progressive nonsyndromic deafness, in which genetic analysis of GJB2 and GJB6 had already been performed. This model works well, especially in homozygous carriers, showing a high discriminative power. This indicates that our proposed model can be useful in the context of clinical counseling of autosomal recessive disorders.     

Statistical Evaluation of the Use of Concurrent Controls in Treatment Screening Studies

A well‐designed pilot study can advance science by providing essential preliminary data to support or motivate further research, refine study logistics, and demonstrate proof of concept. Often, the outcomes of such studies can be quantified by a success/failure dichotomy. For example, a novel compound may show activation of a neural pathway, or it may not. When an intervention''s efficacy is quantified using a dichotomous outcome, probability mass functions can be enumerated to determine the probability that the observed result from a pilot study supports further evaluation of the intervention since there is only a finite, and often small, number of sample configurations possible. The purpose of this research was to determine the probability of an “efficacy signal” for pilot studies using one‐ and two‐sample pilot study designs. Efficacy signal was defined as the probability of observing a more favorable response proportion relative to a historical control (one‐sample setting) or to a concurrent control (two‐sample setting). An enumeration study (exact simulation) was conducted to calculate the efficacy signal probability. One‐sample study designs yielded higher probability of determining an efficacy signal than the two‐sample setting; however, sampling variation must be accounted for. A 68% score confidence interval is recommended for this purpose.     

Optimal Adaptive Designs for Binary Response Trials With Three Treatments

A fundamental question in response–adaptive randomization is: What allocation proportion should we target to achieve required power while resulting in fewer treatment failures? For comparing two treatments, such optimal allocations are well studied in the literature. However, few authors address the question for multiple treatments and the generalization of optimal allocations is necessary in practice. We are interested in finding the optimal allocation proportion, which achieves a required power of a multivariate test of homogeneity in binary response experiments while minimizing expected treatment failures at the same time. We propose such an optimal allocation for three treatments by giving an analytical solution for the optimization problem. Numerical studies show that a response–adaptive randomization procedure that targets proposed optimal allocation is superior to complete randomization. We also discuss some future research topics and additional issues on optimal adaptive designs.     

Investigating Effects of Plasma Apolipoprotein E on Ischemic Heart Disease Using Mendelian Randomization Study

Background: Observationally plasma apolipoprotein E (apoE) is positively associated with ischemic heart disease (IHD). A Mendelian randomization (MR) study suggesting apoE is unrelated to cardiovascular mortality did not consider specific isoforms. We used MR to obtain estimates of plasma apoE2, apoE3 and apoE4 on IHD, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, triglycerides and apolipoprotein B (apoB). Methods: We obtained independent genetic instruments from proteome genome-wide association studies (GWAS) and applied them to large outcome GWAS. We used univariable MR to assess the role of each isoform and multivariable MR to assess direct effects. Results: In univariable MR, apoE4 was positively associated with IHD (odds ratio (OR) 1.05, 95% confidence interval (CI) 1.01 to 1.09), but apoE2 and apoE3 were less clearly associated. Using multivariable MR an association of apoE2 with IHD (OR 1.16, 95% CI 0.98 to 1.38) could not be excluded, and associations of apoE3 and apoE4 with IHD were not obvious. In univariable MR, apoE2 and apoE4 were positively associated with apoB, and a positive association of apoE2 with LDL cholesterol could not be excluded. Using multivariable MR apoE2 was positively associated with LDL cholesterol, and associations with apoB could not be excluded. After adjusting for apoB, no direct effects of apoE isoforms on IHD were evident. Conclusions: Plasma apoE2 and apoE4 may play a role in lipid modulation and IHD. Whether apoE could be a potential therapeutic target requires further clarification when larger genetic studies of apoE isoforms are available.     

Coffee Consumption and Prostate Cancer Risk: Results from National Health and Nutrition Examination Survey 1999–2010 and Mendelian Randomization Analyses

The aim of this study was to examine the association between coffee and prostate cancer. Firstly, we conducted an observational study using data from National Health and Nutrition Examination Survey (NHANES) 1999–2010. Coffee intake was derived from 24 h dietary recalls. Weighted multivariable-adjusted logistic regression was applied to evaluate the association. Then, we performed Mendelian randomization (MR) to explore the possible causal effect of coffee on prostate cancer risk. Primary and secondary genetic instruments were obtained from genome-wide association studies among 375,833 and 91,462 individuals separately. Prostate cancer summary statistics were extracted from Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome (PRACTICAL) (79,194 cases and 61,112 controls) and FinnGen project (4754 cases and 63,465 controls). Inverse variance weighted (IVW) was the primary analytical method. Through selection, we enrolled 8336 individuals (weighted number = 58,796,070) for our observational study in NHANES. Results suggested that there was no association between coffee and prostate cancer. MR analyses with primary genetic instruments also did not support a causal association between coffee intake and prostate cancer risk, whether using summary data from PRACTICAL (IVW: OR 1.001, 95% CI 0.997–1.005) or FinnGen (IVW: OR 1.005, 95% CI 0.998–1.012). Similar results were observed when using secondary genetic instruments. Therefore, our study did not support a causal association between coffee intake and prostate cancer risk. Further studies with a larger sample size are needed to examine if an association exists by different coffee bean types, roasting procedures, and brewing methods.     

The Homocysteine and Metabolic Syndrome: A Mendelian Randomization Study

Homocysteine (Hcy) is well known to be increased in the metabolic syndrome (MetS) incidence. However, it remains unclear whether the relationship is causal or not. Recently, Mendelian Randomization (MR) has been popularly used to assess the causal influence. In this study, we adopted MR to investigate the causal influence of Hcy on MetS in adults using three independent cohorts. We considered one-sample MR and two-sample MR. We analyzed one-sample MR in 5902 individuals (2090 MetS cases and 3812 controls) from the KARE and two-sample MR from the HEXA (676 cases and 3017 controls) and CAVAS (1052 cases and 764 controls) datasets to evaluate whether genetically increased Hcy level influences the risk of MetS. In observation studies, the odds of MetS increased with higher Hcy concentrations (odds ratio (OR) 1.17, 95%CI 1.12–1.22, p < 0.01). One-sample MR was performed using two-stage least-squares regression, with an MTHFR C677T and weighted Hcy generic risk score as an instrument. Two-sample MR was performed with five genetic variants (rs12567136, rs1801133, rs2336377, rs1624230, and rs1836883) by GWAS data as the instrumental variables. For sensitivity analysis, weighted median and MR–Egger regression were used. Using one-sample MR, we found an increased risk of MetS (OR 2.07 per 1-SD Hcy increase). Two-sample MR supported that increased Hcy was significantly associated with increased MetS risk by using the inverse variance weighted (IVW) method (beta 0.723, SE 0.119, and p < 0.001), the weighted median regression method (beta 0.734, SE 0.097, and p < 0.001), and the MR–Egger method (beta 2.073, SE 0.843, and p = 0.014) in meta-analysis. The MR–Egger slope showed no evidence of pleiotropic effects (intercept −0.097, p = 0.107). In conclusion, this study represented the MR approach and elucidates the significant relationship between Hcy and the risk of MetS in the Korean population.     

Evaluating causal associations between chronotype and fatty acids and between fatty acids and type 2 diabetes: A Mendelian randomization study

Background and aimsPreference for activity in the morning or evening (chronotype) may impact type 2 diabetes (T2D) risk factors. Our objective was to use Mendelian randomization (MR) to evaluate whether there are causal links between chronotype and one potential T2D risk factor, total fatty acids (TOTFA), and between TOTFA and T2D.Methods and resultsWe estimated the causal effect of: 1) morning chronotype on TOTFA; and 2) higher TOTFA on T2D. We found that: a) morning compared to evening chronotype was associated with lower TOTFA levels (inverse-weighted variance (IVW) estimate −0.21; 95% CI −0.38, −0.03; raw P = 0.02; FDR-corrected P 0.04) and b) elevated TOTFA levels were protective against T2D (IVW estimate −0.23; 95% CI −0.41, −0.05; raw P = 0.01; FDR-corrected P = 0.03). Based on this finding, we further hypothesized that healthy fats would show a similar pattern and performed MR of a) morning chronotype on omega-3 (Omega-3), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids; and b) MR of each of these fat types on T2D. We observed the same mediating-type pattern for chronotype, MUFA, and T2D as we had for chronotype, TOTFA, and T2D, and morning chronotype was associated with lower Omega-3.ConclusionOur findings provide suggestive, new information about relationships among chronotype, TOTFA, and T2D and about chronotype as a factor influencing Omega-3, MUFA, and TOTFA levels. In addition, we validated previous knowledge about MUFA and T2D. Morning chronotypes may predispose towards lower levels of TOTFA and some healthy fats, whereas higher levels of TOTFA and MUFA may protect against T2D.     

Lipoprotein(a): An underrecognized genetic risk factor for malignant coronary artery disease in young Indians

Malignant coronary artery disease (CAD) refers to a severe and extensive atherosclerotic process involving multiple coronary arteries in young individuals (aged <45 years in men and <50 years in women) with a low or no burden of established risk factors. Indians, in general, develop acute myocardial infarction (AMI) about 10 years earlier; AMI rates are threefold to fivefold higher in young Indians than in other populations. Although established CAD risk factors have a predictive value, they do not fully account for the excessive burden of CAD in young Indians. Lipoprotein(a) (Lp(a)) is increasingly recognized as the strongest known genetic risk factor for premature CAD, with high levels observed in Indians with malignant CAD. High Lp(a) levels confer a twofold to threefold risk of CAD—a risk similar to that of established risk factors, including diabetes. South Asians have the second highest Lp(a) levels and the highest risk of AMI from the elevated levels, more than double the risk observed in people of European descent. Approximately 25% of Indians and other South Asians have elevated Lp(a) levels (≥50 mg/dl), rendering Lp(a) a risk factor of great importance, similar to or surpassing diabetes. Lp(a) measurement is ready for clinical use and should be an essential part of all CAD research in Indians.     

Brick tunnel randomization for unequal allocation to two or more treatment groups

Studies with unequal allocation to two or more treatment groups often require a large block size for permuted block allocation. This could present a problem in small studies, multi-center studies, or adaptive design dose-finding studies. In this paper, an allocation procedure, which generalizes the maximal procedure by Berger, Ivanova, and Knoll to the case of K≥2 treatment groups and any allocation ratio, is offered. Brick tunnel (BT) randomization requires the allocation path drawn in the k-dimensional space to stay close to the allocation ray that corresponds to the targeted allocation ratio. Specifically, it requires the allocation path to be confined to the set of the k-dimensional unitary cubes that are pierced by the allocation ray (the 'brick tunnel'). The important property of the BT randomization is that the transition probabilities at each node within the tunnel are defined in such a way that the unconditional allocation ratio is the same for every allocation step. This property is not necessarily met by other allocation procedures that implement unequal allocation.     

A Bayesian response-adaptive covariate-balanced randomization design with application to a leukemia clinical trial

We propose a Bayesian response-adaptive covariate-balanced (RC) randomization design for multiple-arm comparative clinical trials. The goal of the design is to skew the allocation probability to more efficacious treatment arms, while also balancing the distribution of the covariates across the arms. In particular, we first propose a new covariate-adaptive randomization (CA) method based on a prognostic score that naturally accommodates continuous and categorical prognostic factors and automatically assigns imbalance weights to covariates according to their importance in response prediction. We then incorporate this CA design into a group sequential response-adaptive randomization (RA) scheme. The resulting RC randomization design combines the advantages of both CA and RA randomizations and meets the design goal. We illustrate the proposed design through its application to a phase II leukemia clinical trial, and evaluate its operating characteristics through simulation studies.