A multiclass likelihood ratio approach for genetic risk prediction allowing for phenotypic heterogeneity

Association between MSX1 rs12532 polymorphism with nonsyndromic unilateral complete cleft lip and palate and tooth agenesis

OBJECTIVES

To investigate the association of MSX1 rs12532 polymorphism with the risk of nonsyndromic unilateral complete cleft lip and palate (NSCLP) and tooth agenesis.

MATERIALS AND METHODS

The study is comprised of 384 individuals divided into 4 groups: group 1, patients with unilateral complete NSCLP and premolar agenesis (n = 57); group 2, patients with unilateral NSCLP without tooth agenesis (n = 117); group 3, patients with premolar agenesis without oral cleft (n = 53) and group 4 (n = 157), a control group with individuals without tooth agenesis and oral cleft. Genotyping of rs12532 was carried out with Taqman chemistry, and associations were investigated using logistic regression analyses.

RESULTS

Overall rs12532 allele and genotype distributions revealed no significant differences between the groups of NSCLP or tooth agenesis.

CONCLUSION

Although our results are consistent with a lack of association of MSX1 rs12532 and the risk of unilateral NSCLP and tooth agenesis, further studies with additional SNPs and a more diverse ethnic cohort are warranted.

Risk Prediction Modeling on Family-Based Sequencing Data Using a Random Field Method

Family-based design is one of the most popular designs in genetic studies and has many unique features for risk-prediction research. It is robust against genetic heterogeneity, and the relatedness among family members can be informative for predicting an individual's risk for disease with polygenic and shared environmental components of risk. Despite these strengths, family-based designs have been used infrequently in current risk-prediction studies, and their related statistical methods have not been well developed. In this article, we developed a generalized random field (GRF) method for family-based risk-prediction modeling on sequencing data. In GRF, subjects' phenotypes are viewed as stochastic realizations of a random field in a space, and a subject's phenotype is predicted by adjacent subjects, where adjacencies between subjects are determined by their genetic and within-family similarities. Different from existing methods that adjust for familial correlations, the GRF uses this information to form surrogates to further improve prediction accuracy. It also uses within-family information to capture predictors (e.g., rare mutations) that are homogeneous in families. Through simulations, we have demonstrated that the GRF method attained better performance than an existing method by considering additional information from family members and accounting for genetic heterogeneity. We further provided practical recommendations for designing family-based risk prediction studies. Finally, we illustrated the GRF method with an application to a whole-genome exome data set from the Michigan State University Twin Registry study.

Mental disorders of known aetiology and precision medicine in psychiatry: a promising but neglected alliance

Personalized or precision medicine is predicated on the assumption that the average response to treatment is not necessarily representative of the response of each individual. A commitment to personalized medicine demands an effort to bring evidence-based medicine and personalized medicine closer together. The use of relatively homogeneous groups, defined using a priori criteria, may constitute a promising initial step for developing more accurate risk-prediction models with which to advance the development of personalized evidence-based medicine approaches to heterogeneous syndromes such as schizophrenia. However, this can lead to a paradoxical situation in the field of psychiatry. Since there has been a tendency to loosely define psychiatric disorders as ones without a known aetiology, the discovery of an aetiology for psychiatric syndromes (e.g. 22q11.2 deletion syndrome in some cases of schizophrenia), while offering a path toward more precise treatments, may also lead to their reclassification away from psychiatry. We contend that psychiatric disorders with a known aetiology should not be removed from the field of psychiatry. This knowledge should be used instead to guide treatment, inasmuch as psychotherapies, pharmacotherapies and other treatments can all be valid approaches to mental disorders. The translation of the personalized clinical approach inherent to psychiatry into evidence-based precision medicine can lead to the development of novel treatment options for mental disorders and improve outcomes.

A method for identifying genetic heterogeneity within phenotypically defined disease subgroups

Many common diseases show wide phenotypic variation. We present a statistical method for determining whether phenotypically defined subgroups of disease cases represent different genetic architectures, in which disease-associated variants have different effect sizes in two subgroups. Our method models the genome-wide distributions of genetic association statistics with mixture Gaussians. We apply a global test without requiring explicit identification of disease-associated variants, thus maximizing power in comparison to standard variant-by-variant subgroup analysis. Where evidence for genetic subgrouping is found, we present methods for post hoc identification of the contributing genetic variants. We demonstrate the method on a range of simulated and test data sets, for which expected results are already known. We investigate subgroups of individuals with type 1 diabetes (T1D) defined by autoantibody positivity, establishing evidence for differential genetic architecture with positivity for thyroid-peroxidase-specific antibody, driven generally by variants in known T1D-associated genomic regions.

Risk prediction models for oral clefts allowing for phenotypic heterogeneity

Oral clefts are common birth defects that have a major impact on the affected individual, their family and society. World-wide, the incidence of oral clefts is 1/700 live births, making them the most common craniofacial birth defects. The successful prediction of oral clefts may help identify sub-population at high risk, and promote new diagnostic and therapeutic strategies. Nevertheless, developing a clinically useful oral clefts risk prediction model remains a great challenge. Compelling evidences suggest the etiologies of oral clefts are highly heterogeneous, and the development of a risk prediction model with consideration of phenotypic heterogeneity may potentially improve the accuracy of a risk prediction model. In this study, we applied a previously developed statistical method to investigate the risk prediction on sub-phenotypes of oral clefts. Our results suggested subtypes of cleft lip (CL) and palate have similar genetic etiologies (AUC = 0.572) with subtypes of CL only (AUC = 0.589), while the subtypes of cleft palate only (CPO) have heterogeneous underlying mechanisms (AUCs for soft CPO and hard CPO are 0.617 and 0.623, respectively). This highlighted the potential that the hard and soft forms of CPO have their own mechanisms despite sharing some of the genetic risk factors. Comparing with conventional methods for risk prediction modeling, our method considers phenotypic heterogeneity of a disease, which potentially improves the accuracy for predicting each sub-phenotype of oral clefts.