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Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.
Sequence-based association studies are at a critical inflexion point with the increasing availability of exome-sequencing data. A popular test of association is the sequence kernel association test (SKAT). Weights are embedded within SKAT to reflect the hypothesized contribution of the variants to the trait variance. Because the true weights are generally unknown, and so are subject to misspecification, we examined the efficiency of a data-driven weighting scheme. We propose the use of a set of theoretically defensible weighting schemes, of which, we assume, the one that gives the largest test statistic is likely to capture best the allele frequency–functional effect relationship. We show that the use of alternative weights obviates the need to impose arbitrary frequency thresholds. As both the score test and the likelihood ratio test (LRT) may be used in this context, and may differ in power, we characterize the behavior of both tests. The two tests have equal power, if the weights in the set included weights resembling the correct ones. However, if the weights are badly specified, the LRT shows superior power (due to its robustness to misspecification). With this data-driven weighting procedure the LRT detected significant signal in genes located in regions already confirmed as associated with schizophrenia — the PRRC2A (p = 1.020e-06) and the VARS2 (p = 2.383e-06) — in the Swedish schizophrenia case-control cohort of 11,040 individuals with exome-sequencing data. The score test is currently preferred for its computational efficiency and power. Indeed, assuming correct specification, in some circumstances, the score test is the most powerful test. However, LRT has the advantageous properties of being generally more robust and more powerful under weight misspecification. This is an important result given that, arguably, misspecified models are likely to be the rule rather than the exception in weighting-based approaches.
With the dramatic technological developments of genome-wide association single-nucleotide polymorphism (SNP) chips and next generation sequencing, human geneticists now have the ability to assay genetic variation at ever-rarer allele frequencies. To fully understand the impact of these rare variants on common, complex diseases, we must be able to accurately assess their statistical significance. However, it is well established that classical association tests are not appropriate for the analysis of low-frequency variation, giving spurious findings when observed counts are too few. To further our understanding of the asymptotic properties of traditional association tests, we conducted a range of simulations of a typical rare variant (~1%) under the null hypothesis and tested the allelic χ2, Cochran–Armitage trend, Wald, and Fisher's exact tests. We demonstrate that rare variation shows marked deviation from the expected distributional behavior for each test, with fewer minor alleles corresponding to a greater degree of test statistics deflation. The effect becomes more pronounced at progressively smaller α levels. We also show that the Wald test is particularly deflated at α levels consistent with genome-wide association significance, much more so than the other association tests considered. In general, these classical association tests are inappropriate for the analysis of variants for which the minor allele is observed fewer than 80 times, largely irrespective of sample size.
This chapter discusses the methodological considerations surrounding linkage and association studies as well as results of both approaches as they relate to sleep and sleep disorders. The initial study of familial advanced sleep phase syndrome (FASPS) that showed it to be inherited in an autosomal dominant fashion was a linkage study on a large family with over 20 affected individuals. For the most part, the risk of narcolepsy to relatives of an affected individual is low (1-2%), albeit higher than the average population risk. Restless leg syndrome (RLS) is fairly common, with the prevalence estimated to be between 1.2 and 15% depending on the population. Complex phenotypes are influenced by multiple genetic and non-genetic factors. These phenotypes cluster in families do not follow any clear mode of inheritance. Complex phenotypes are divided into two classes: continuous and categorical. Genome-wide association study (GWAS) has been recently employed in studying sleep phenotypes.
The Eating Inventory (EI) is commonly used to measure a range of eating behaviors. It includes three subscales: Cognitive Restraint, Hunger, and Disinhibition. In this study, we decomposed the variance of the three subscales, and evaluated the genetic, common environment and specific environmental effects on each in a sample of female-female twin pairs. Multivariate models were also used to examine whether the EI represented three individual factors, or whether there was extensive covariance among subscales. Heritabilities were estimated at 45% (CI of 32–57%) for Disinhibition, 8% (CI of 0–38%) for Hunger, and 0% (CI of 0–30%) for Restraint. Common environmental influences were estimated at 0% (CI of 0–23%) for Disinhibition, 16% (CI of 0–34%) for Hunger, and 31% (4–42%) for Restraint. Specific environmental influences accounted for the rest of the variance of the subscales. However, multivariate modeling indicated that Disinhibition and Hunger covaried significantly, indicating that these two subscales are influenced by the same set of genetic factors. Furthermore, Restraint appeared to be empirically distinct from Hunger or Disinhibition.
Questionnaire-based dimensional measures are often employed in epidemiological studies to predict the presence of psychiatric disorders. The present study sought to determine how accurately 4 dimensional mental health measures, the 12-item General Health Questionnaire (GHQ-12), Neuroticism (EPQ-N), the high positive affect and anxious arousal scales from the Mood and Anxiety Symptoms Questionnaire (MASQ-HPA and MASQ-AA) and a composite of all 4, predicted psychiatric caseness as diagnosed by the University of Michigan Composite International Diagnostic Interview (UM-CIDI). Community subjects were recruited through general practitioners; those who agreed to participate were sent a questionnaire containing the above measures. Subsequently, the UM-CIDI was administered by telephone to 469 subjects consisting of sibling pairs who scored most discordantly or concordantly on a composite index of the 4 measures. Logistic Regression and Receiver Operating Characteristic (ROC) curve analyses were carried out to assess the predictive accuracy of the dimensional measures on UM-CIDI diagnosis. A total of 179 subjects, 62 men and 117 women with an average age of 42 years, were diagnosed with at least one of the following psychiatric disorders: depression, dysthymia, generalized anxiety disorder (GAD), social phobia, agoraphobia and panic attack. The six disorders showed high comorbidity. EPQ-N and the Composite Index were found to be very strong and accurate predictors of psychiatric caseness; they were however unable to differentiate between specific disorders. The results from the present study therefore validated the four mental health measures as being predictive of psychiatric caseness.
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