Scott I. Vrieze

Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC).

This article reviews the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models, given their growing use in theory testing and construction. Theoretical statistical results in regression are discussed, and more important issues are illustrated with novel simulations involving latent variable models including factor analysis, latent profile analysis, and factor mixture models. Asymptotically, the BIC is consistent, in that it will select the true model if, among other assumptions, the true model is among the candidate models considered. The AIC is not consistent under these circumstances. When the true model is not in the candidate model set the AIC is efficient, in that it will asymptotically choose whichever model minimizes the mean squared error of prediction/estimation. The BIC is not efficient under these circumstances. Unlike the BIC, the AIC also has a minimax property, in that it can minimize the maximum possible risk in finite sample sizes. In sum, the AIC and BIC have quite different properties that require different assumptions, and applied researchers and methodologists alike will benefit from improved understanding of the asymptotic and finite-sample behavior of these criteria. The ultimate decision to use the AIC or BIC depends on many factors, including the loss function employed, the studys methodological design, the substantive research question, and the notion of a true model and its applicability to the study at hand.

Next-generation genotype imputation service and methods

Genotype imputation is a key component of genetic association studies, where it increases power, facilitates meta-analysis, and aids interpretation of signals. Genotype imputation is computationally demanding and, with current tools, typically requires access to a high-performance computing cluster and to a reference panel of sequenced genomes. Here we describe improvements to imputation machinery that reduce computational requirements by more than an order of magnitude with no loss of accuracy in comparison to standard imputation tools. We also describe a new web-based service for imputation that facilitates access to new reference panels and greatly improves user experience and productivity.

Knowns and unknowns for psychophysiological endophenotypes: Integration and response to commentaries

We review and summarize seven molecular genetic studies of 17 psychophysiological endophenotypes that comprise this special issue of Psychophysiology, address criticisms raised in accompanying Perspective and Commentary pieces, and offer suggestions for future research. Endophenotypes are polygenic, and possibly influenced by rare genetic variants. Because they are not simpler genetically than clinical phenotypes, they are unlikely to assist gene discovery for psychiatric disorder. Once genetic variants for clinical phenotypes are identified, associated endophenotypes are likely to provide valuable insights into the psychological and neural mechanisms important to disorder pathology. This special issue provides a foundation for informed future steps in endophenotype genetics, including the formation of large sample consortia capable of fleshing out the many genetic variants contributing to individual differences in psychophysiological measures.

Confluence of genes, environment, development, and behavior in a post Genome-Wide Association Study world

This article serves to outline a research paradigm to investigate main effects and interactions of genes, environment, and development on behavior and psychiatric illness. We provide a historical context for candidate gene studies and genome-wide association studies, including benefits, limitations, and expected payoffs. Using substance use and abuse as our driving example, we then turn to the importance of etiological psychological theory in guiding genetic, environmental, and developmental research, as well as the utility of refined phenotypic measures, such as endophenotypes, in the pursuit of etiological understanding and focused tests of genetic and environmental associations. Phenotypic measurement has received considerable attention in the history of psychology and is informed by psychometrics, whereas the environment remains relatively poorly measured and is often confounded with genetic effects (i.e., gene-environment correlation). Genetically informed designs, which are no longer limited to twin and adoption studies thanks to ever-cheaper genotyping, are required to understand environmental influences. Finally, we outline the vast amount of individual difference in structural genomic variation, most of which remains to be leveraged in genetic association tests. Although the genetic data can be massive and burdensome (tens of millions of variants per person), we argue that improved understanding of genomic structure and function will provide investigators with new tools to test specific a priori hypotheses derived from etiological psychological theory, much like current candidate gene research but with less confusion and more payoff than candidate gene research has to date.

Rare variant genotype imputation with thousands of study-specific whole-genome sequences: implications for cost-effective study designs

The utility of genotype imputation in genome-wide association studies is increasing as progressively larger reference panels are improved and expanded through whole-genome sequencing. Developing general guidelines for optimally cost-effective imputation, however, requires evaluation of performance issues that include the relative utility of study-specific compared with general/multipopulation reference panels; genotyping with various array scaffolds; effects of different ethnic backgrounds; and assessment of ranges of allele frequencies. Here we compared the effectiveness of study-specific reference panels to the commonly used 1000 Genomes Project (1000G) reference panels in the isolated Sardinian population and in cohorts of European ancestry including samples from Minnesota (USA). We also examined different combinations of genome-wide and custom arrays for baseline genotypes. In Sardinians, the study-specific reference panel provided better coverage and genotype imputation accuracy than the 1000G panels and other large European panels. In fact, even gene-centered custom arrays (interrogating ~200 000 variants) provided highly informative content across the entire genome. Gain in accuracy was also observed for Minnesotans using the study-specific reference panel, although the increase was smaller than in Sardinians, especially for rare variants. Notably, a combined panel including both study-specific and 1000G reference panels improved imputation accuracy only in the Minnesota sample, and only at rare sites. Finally, we found that when imputation is performed with a study-specific reference panel, cutoffs different from the standard thresholds of MACH-Rsq and IMPUTE-INFO metrics should be used to efficiently filter badly imputed rare variants. This study thus provides general guidelines for researchers planning large-scale genetic studies.

In search of rare variants: Preliminary results from whole genome sequencing of 1,325 individuals with psychophysiological endophenotypes

Whole genome sequencing was completed on 1,325 individuals from 602 families, identifying 27 million autosomal variants. Genetic association tests were conducted for those individuals who had been assessed for one or more of 17 endophenotypes (N range = 802-1,185). No significant associations were found. These 27 million variants were then imputed into the full sample of individuals with psychophysiological data (N range = 3,088-4,469) and again tested for associations with the 17 endophenotypes. No association was significant. Using a gene-based variable threshold burden test of nonsynonymous variants, we obtained five significant associations. These findings are preliminary and call for additional analysis of this rich sample. We argue that larger samples, alternative study designs, and additional bioinformatics approaches will be necessary to discover associations between these endophenotypes and genomic variation.

Genome‐wide scans of genetic variants for psychophysiological endophenotypes: A methodological overview

This article provides an introductory overview of the investigative strategy employed to evaluate the genetic basis of 17 endophenotypes examined as part of a 20-year data collection effort from the Minnesota Center for Twin and Family Research. Included are characterization of the study samples, descriptive statistics for key properties of the psychophysiological measures, and rationale behind the steps taken in the molecular genetic study design. The statistical approach included (a) biometric analysis of twin and family data, (b) heritability analysis using 527,829 single nucleotide polymorphisms (SNPs), (c) genome-wide association analysis of these SNPs and 17,601 autosomal genes, (d) follow-up analyses of candidate SNPs and genes hypothesized to have an association with each endophenotype, (e) rare variant analysis of nonsynonymous SNPs in the exome, and (f) whole genome sequencing association analysis using 27 million genetic variants. These methods were used in the accompanying empirical articles comprising this special issue, Genome-Wide Scans of Genetic Variants for Psychophysiological Endophenotypes.

In search of rare variants

Whole genome sequencing was completed on 1,325 individuals from 602 families, identifying 27 million autosomal variants. Genetic association tests were conducted for those individuals who had been assessed for one or more of 17 endophenotypes (N range = 802-1,185). No significant associations were found. These 27 million variants were then imputed into the full sample of individuals with psychophysiological data (N range = 3,088-4,469) and again tested for associations with the 17 endophenotypes. No association was significant. Using a gene-based variable threshold burden test of nonsynonymous variants, we obtained five significant associations. These findings are preliminary and call for additional analysis of this rich sample. We argue that larger samples, alternative study designs, and additional bioinformatics approaches will be necessary to discover associations between these endophenotypes and genomic variation.

Genetic associations of nonsynonymous exonic variants with psychophysiological endophenotypes

We mapped ∼85,000 rare nonsynonymous exonic single nucleotide polymorphisms (SNPs) to 17 psychophysiological endophenotypes in 4,905 individuals, including antisaccade eye movements, resting EEG, P300 amplitude, electrodermal activity, affect-modulated startle eye blink. Nonsynonymous SNPs are predicted to directly change or disrupt proteins encoded by genes and are expected to have significant biological consequences. Most such variants are rare, and new technologies can efficiently assay them on a large scale. We assayed 247,870 mostly rare SNPs on an Illumina exome array. Approximately 85,000 of the SNPs were polymorphic, rare (MAF < .05), and nonsynonymous. Single variant association tests identified a SNP in the PARD3 gene associated with theta resting EEG power. The sequence kernel association test, a gene-based test, identified a gene PNPLA7 associated with pleasant difference startle, the difference in startle magnitude between pleasant and neutral images. No other single nonsynonymous variant, or gene-based group of variants, was strongly associated with any endophenotype.

Is the continuity of externalizing psychopathology the same in adolescents and middle–aged adults? A test of the externalizing spectrum’s developmental coherence

Externalizing psychopathology (EXT) is a framework for understanding diagnostic comorbidity and etiology of antisocial and substance-use behaviors. EXT indicates continuity in adulthood but the structure of adolescent EXT is less clear. This report examines whether adolescent EXT is trait-like, as has been found with adults, or categorical. We use tests of measurement invariance to determine how diagnostic indicators of EXT differ in adolescents compared to adults. The EXT measures employed were DSM-IIIR diagnoses of adult antisocial behavior, conduct disorder, and alcohol, marijuana, and drug dependence. Latent trait, latent class, and hybrid models were fit to two separate data sets: 2,769 seventeen-year-old adolescents and 2,619 adults from the Minnesota Twin Family Study. The best model in both samples was a single-trait LT model. Parameters from the adolescent and adult models were equivalent for all disorders except alcohol dependence. It appears that EXT in adolescence can be accurately represented by a single-trait model, and the measurement properties of EXT are similar during these time periods with the exception of alcohol dependence.