Software for generating liability distributions for pedigrees conditional on their observed disease states and covariates

Dissociable impairments of verbal learning differentiate childhood risk profiles for schizophrenia

Poor verbal learning and memory function is well-documented among individuals with schizophrenia and those at clinical high-risk for psychosis. This study aimed to identify these impairments among children aged 9–12 years with different schizophrenia risk profiles (family history or antecedents of schizophrenia, each of higher^[H] or lower^[L] risk load) relative to typically developing peers. These three groups were recruited via community-screening, and differentiated for analysis into: typically developing children (TD = 45); children who had 1 first- or ≥2 second-degree affected relatives (FHx^H = 16) or one second-degree relative (FHx^L = 15); and children presenting multiple replicated antecedents of schizophrenia whose clinical symptoms persisted at 2- and/or 4-year follow-up (ASz^H = 16) or remitted during follow-up (ASz^L = 16). Verbal learning/memory measures assessed at baseline (age 9–12 years) included: (i) total recall; (ii) trial 1 recall; (iii) learning score; (iv) intrusions; (v) total words lost; and (vi) serial position patterns. Analyses of variance indicated that FHx^H and ASz^H youth demonstrated impaired total recall compared to TD and ASz^L children and lost significantly more words between trials than TD and FHx^L children. Learning score was impaired among both FHx^H and FHx^L relative to TD and ASz^L children. Thus, among putatively at-risk children, total words recalled and lost distinguished those with higher risk load (by family history or persistent antecedent symptomology), whereas learning score indexed familial vulnerability. Follow-up of the sample is needed to determine the capacity of verbal learning deficits to predict later illness and provide a potential avenue for early remediation to improve clinical or functional outcomes.

Multifactorial disease risk calculator: Risk prediction for multifactorial disease pedigrees

Construction of multifactorial disease models from epidemiological findings and their application to disease pedigrees for risk prediction is nontrivial for all but the simplest of cases. Multifactorial Disease Risk Calculator is a web tool facilitating this. It provides a user-friendly interface, extending a reported methodology based on a liability-threshold model. Multifactorial disease models incorporating all the following features in combination are handled: quantitative risk factors (including polygenic scores), categorical risk factors (including major genetic risk loci), stratified age of onset curves, and the partition of the population variance in disease liability into genetic, shared, and unique environment effects. It allows the application of such models to disease pedigrees. Pedigree-related outputs are (i) individual disease risk for pedigree members, (ii) n year risk for unaffected pedigree members, and (iii) the disease pedigree's joint liability distribution. Risk prediction for each pedigree member is based on using the constructed disease model to appropriately weigh evidence on disease risk available from personal attributes and family history. Evidence is used to construct the disease pedigree's joint liability distribution. From this, lifetime and n year risk can be predicted. Example disease models and pedigrees are provided at the website and are used in accompanying tutorials to illustrate the features available. The website is built on an R package which provides the functionality for pedigree validation, disease model construction, and risk prediction. Website: http://grass.cgs.hku.hk:3838/mdrc/current.

Case-control association mapping by proxy using family history of disease

Collecting cases for case-control genetic association studies can be time-consuming and expensive. In some situations (such as studies of late-onset or rapidly lethal diseases), it may be more practical to identify family members of cases. In randomly ascertained cohorts, replacing cases with their first-degree relatives enables studies of diseases that are absent (or nearly absent) in the cohort. We refer to this approach as genome-wide association study by proxy (GWAX) and apply it to 12 common diseases in 116,196 individuals from the UK Biobank. Meta-analysis with published genome-wide association study summary statistics replicated established risk loci and yielded four newly associated loci for Alzheimer's disease, eight for coronary artery disease and five for type 2 diabetes. In addition to informing disease biology, our results demonstrate the utility of association mapping without directly observing cases. We anticipate that GWAX will prove useful in future genetic studies of complex traits in large population cohorts.

The CCC2000 Birth Cohort Study of Register-Based Family History of Mental Disorders and Psychotic Experiences in Offspring

Psychotic experiences (PE) in individuals of the general population are hypothesized to mark the early expression of the pathology underlying psychosis. This notion of PE as an intermediate phenotype is based on the premise that PE share genetic liability with psychosis. We examined whether PE in childhood was predicted by a family history of mental disorder with psychosis rather than a family history of nonpsychotic mental disorder and whether this association differed by severity of PE. The study examined data on 1632 children from a general population birth cohort assessed at age 11-12 years by use of a semistructured interview covering 22 psychotic symptoms. The Danish national registers were linked to describe the complete family history of hospital-based psychiatric diagnoses. Uni- and multivariable logistic regressions were used to test whether a family history of any mental disorder with psychosis, or of nonpsychotic mental disorder, vs no diagnoses was associated with increased risk of PE in offspring (hierarchical exposure variable). The occurrence of PE in offspring was significantly associated with a history of psychosis among the first-degree relatives (adjusted relative risk [RR] = 3.29, 95% CI: 1.82-5.93). The risk increased for combined hallucinations and delusions (adjusted RR = 5.90, 95% CI: 2.64-13.16). A history of nonpsychotic mental disorders in first-degree relatives did not contribute to the risk of PE in offspring nor did any mental disorder among second-degree relatives. Our findings support the notion of PE as a vulnerability marker of transdiagnostic psychosis. The effect of psychosis in first-degree relatives may operate through shared genetic and environmental factors.

Mixed model with correction for case-control ascertainment increases association power

We introduce a liability-threshold mixed linear model (LTMLM) association statistic for case-control studies and show that it has a well-controlled false-positive rate and more power than existing mixed-model methods for diseases with low prevalence. Existing mixed-model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem by using a χ(2) score statistic computed from posterior mean liabilities (PMLs) under the liability-threshold model. Each individual's PML is conditional not only on that individual's case-control status but also on every individual's case-control status and the genetic relationship matrix (GRM) obtained from the data. The PMLs are estimated with a multivariate Gibbs sampler; the liability-scale phenotypic covariance matrix is based on the GRM, and a heritability parameter is estimated via Haseman-Elston regression on case-control phenotypes and then transformed to the liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed-model methods for diseases with low prevalence, and the magnitude of the improvement depended on sample size and severity of case-control ascertainment. In a Wellcome Trust Case Control Consortium 2 multiple sclerosis dataset with >10,000 samples, LTMLM was correctly calibrated and attained a 4.3% improvement (p = 0.005) in χ(2) statistics over existing mixed-model methods at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, case-control studies of diseases with low prevalence can achieve power higher than that in existing mixed-model methods.

Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.

Effective genetic-risk prediction using mixed models

For predicting genetic risk, we propose a statistical approach that is specifically adapted to dealing with the challenges imposed by disease phenotypes and case-control sampling. Our approach (termed Genetic Risk Scores Inference [GeRSI]), combines the power of fixed-effects models (which estimate and aggregate the effects of single SNPs) and random-effects models (which rely primarily on whole-genome similarities between individuals) within the framework of the widely used liability-threshold model. We demonstrate in extensive simulation that GeRSI produces predictions that are consistently superior to current state-of-the-art approaches. When applying GeRSI to seven phenotypes from the Wellcome Trust Case Control Consortium (WTCCC) study, we confirm that the use of random effects is most beneficial for diseases that are known to be highly polygenic: hypertension (HT) and bipolar disorder (BD). For HT, there are no significant associations in the WTCCC data. The fixed-effects model yields an area under the ROC curve (AUC) of 54%, whereas GeRSI improves it to 59%. For BD, using GeRSI improves the AUC from 55% to 62%. For individuals ranked at the top 10% of BD risk predictions, using GeRSI substantially increases the BD relative risk from 1.4 to 2.5.

Cognitive impairment among children at-risk for schizophrenia

Adults with schizophrenia present cognitive impairments, as do individuals at ultra-high risk for the disorder, youth with relatives with schizophrenia spectrum disorders, and children with antecedents of schizophrenia. The present study aimed to determine if impairments in childhood differed depending on the definition of risk and/or on the degree of relatedness to an affected individual, and if impairments were explained by IQ. Four groups of children aged 9-12 years were studied: (1) 13 children with ≥1 first-degree or ≥2 second-degree affected relatives (high familial loading: FHx(H)); (2) 14 with ≥1 affected second-degree relative (lower familial loading: FHx(L)); (3) 32 with well-replicated antecedents of schizophrenia (ASz); and (4) 45 typically-developing (TD) children with neither a positive family history nor antecedents. Compared to TD children, both FHx(H) and ASz children exhibited significantly poorer verbal comprehension, scholastic achievement, and verbal working memory, while FHx(H) children additionally displayed significantly lower full-scale IQ, and verbal memory and executive function impairments. After adjusting statistical analyses for IQ, group differences were attenuated. Relative to TD children, FHx(L) children showed no significant differences in performance. The results imply that impairments in verbal comprehension, scholastic achievement, and verbal working memory may index vulnerability for schizophrenia among children with affected relatives with the disorder and among those with multiple antecedents of the disorder who have no affected relatives. More accurate identification of children at-risk for schizophrenia and the specific deficits that they present provides opportunities for interventions such as cognitive remediation that may impact the development of the illness.