Using Alternative Definitions of Controls to Increase Statistical Power in GWAS.

Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.

Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models.

Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated assessments. The lagged effects in a CLPM depend on the time interval between assessments, eventually becoming undetectable at longer intervals. To address this limitation, we incorporate instrumental variables (IVs) into the CLPM with two study waves and two variables. Doing so enables estimation of both the lagged (i.e., "distal") effects and the bidirectional cross-sectional (i.e., "proximal") effects at each wave. The distal effects reflect Granger-causal influences across time, which decay with increasing time intervals. The proximal effects capture causal influences that accrue over time and can help infer causality when the distal effects become undetectable at longer intervals. Significant proximal effects, with a negligible distal effect, would imply that the time interval is too long to estimate a lagged effect at that time interval using the standard CLPM. Through simulations and an empirical application, we demonstrate the impact of time intervals on causal inference in the CLPM and present modeling strategies to detect causal influences regardless of the time interval in a study. Furthermore, to motivate empirical applications of the proposed model, we highlight the utility and limitations of using genetic variables as IVs in large-scale panel studies.

MR-DoC2: Bidirectional Causal Modeling with Instrumental Variables and Data from Relatives.

Establishing causality is an essential step towards developing interventions for psychiatric disorders, substance use and many other conditions. While randomized controlled trials (RCTs) are considered the gold standard for causal inference, they are unethical in many scenarios. Mendelian randomization (MR) can be used in such cases, but importantly both RCTs and MR assume unidirectional causality. In this paper, we developed a new model, MRDoC2, that can be used to identify bidirectional causation in the presence of confounding due to both familial and non-familial sources. Our model extends the MRDoC model (Minica et al. in Behav Genet 48:337-349,  https://doi.org/10.1007/s10519-018-9904-4 , 2018), by simultaneously including risk scores for each trait. Furthermore, the power to detect causal effects in MRDoC2 does not require the phenotypes to have different additive genetic or shared environmental sources of variance, as is the case in the direction of causation twin model (Heath et al. in Behav Genet 23:29-50,  https://doi.org/10.1007/BF01067552 , 1993).

Best Practices for Binary and Ordinal Data Analyses.

The measurement of many human traits, states, and disorders begins with a set of items on a questionnaire. The response format for these questions is often simply binary (e.g., yes/no) or ordered (e.g., high, medium or low). During data analysis, these items are frequently summed or used to estimate factor scores. In clinical applications, such assessments are often non-normally distributed in the general population because many respondents are unaffected, and therefore asymptomatic. As a result, in many cases these measures violate the statistical assumptions required for subsequent analyses. To reduce the influence of the non-normality and quasi-continuous assessment, variables are frequently recoded into binary (affected-unaffected) or ordinal (mild-moderate-severe) diagnoses. Ordinal data therefore present challenges at multiple levels of analysis. Categorizing continuous variables into ordered categories typically results in a loss of statistical power, which represents an incentive to the data analyst to assume that the data are normally distributed, even when they are not. Despite prior zeitgeists suggesting that, e.g., variables with more than 10 ordered categories may be regarded as continuous and analyzed as if they were, we show via simulation studies that this is not generally the case. In particular, using Pearson product-moment correlations instead of maximum likelihood estimates of polychoric correlations biases the estimated correlations towards zero. This bias is especially severe when a plurality of the observations fall into a single observed category, such as a score of zero. By contrast, estimating the ordinal correlation by maximum likelihood yields no estimation bias, although standard errors are (appropriately) larger. We also illustrate how odds ratios depend critically on the proportion or prevalence of affected individuals in the population, and therefore are sub-optimal for studies where comparisons of association metrics are needed. Finally, we extend these analyses to the classical twin model and demonstrate that treating binary data as continuous will underestimate genetic and common environmental variance components, and overestimate unique environment (residual) variance. These biases increase as prevalence declines. While modeling ordinal data appropriately may be more computationally intensive and time consuming, failing to do so will likely yield biased correlations and biased parameter estimates from modeling them.

Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data.

Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent's age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard "main effect" GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.

GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS.

Most genome-wide association study (GWAS) analyses test the association between single-nucleotide polymorphisms (SNPs) and a single trait or outcome. While valuable second-step analyses of these associations (e.g., calculating genetic correlations between traits) are common, single-step multivariate analyses of GWAS data are rarely performed. This is unfortunate because multivariate analyses can reveal information which is irrevocably obscured in multi-step analysis. One simple example is the distinction between variance common to a set of measures, and variance specific to each. Neither GWAS of sum- or factor-scores, nor GWAS of the individual measures will deliver a clean picture of loci associated with each measure's specific variance. While multivariate GWAS opens up a broad new landscape of feasible and informative analyses, its adoption has been slow, likely due to the heavy computational demands and difficulties specifying models it requires. Here we describe GW-SEM 2.0, which is designed to simplify model specification and overcome the inherent computational challenges associated with multivariate GWAS. In addition, GW-SEM 2.0 allows users to accurately model ordinal items, which are common in behavioral and psychological research, within a GWAS context. This new release enhances computational efficiency, allows users to select the fit function that is appropriate for their analyses, expands compatibility with standard genomic data formats, and outputs results for seamless reading into other standard post-GWAS processing software. To demonstrate GW-SEM's utility, we conducted (1) a series of GWAS using three substance use frequency items from data in the UK Biobank, (2) a timing study for several predefined GWAS functions, and (3) a Type I Error rate study. Our multivariate GWAS analyses emphasize the utility of GW-SEM for identifying novel patterns of associations that vary considerably between genomic loci for specific substances, highlighting the importance of differentiating between substance-specific use behaviors and polysubstance use. The timing studies demonstrate that the analyses take a reasonable amount of time and show the cost of including additional items. The Type I Error rate study demonstrates that hypothesis tests for genetic associations with latent variable models follow the hypothesized uniform distribution. Taken together, we suggest that GW-SEM may provide substantially deeper insights into the underlying genomic architecture for multivariate behavioral and psychological systems than is currently possible with standard GWAS methods. The current release of GW-SEM 2.0 is available on CRAN (stable release) and GitHub (beta release), and tutorials are available on our github wiki ( https://jpritikin.github.io/gwsem/ ).

Clarifying the Genetic Influences on Nicotine Dependence and Quantity of Use in Cigarette Smokers.

Nicotine dependence and smoking quantity are both robustly associated with the CHRNA5-A3-B4 gene cluster in the 15q25 region, and SNP rs16969968 in particular. The purpose of this paper is to use structural equation modeling techniques (SEM) to disentangle the complex pattern of relationships between rs16969968, nicotine quantity (as measured by the number of cigarettes an individual smokes per day; CPD) and nicotine dependence (as measured by the Fagerström Test for Nicotine Dependence; FTND). CPD is an indicator, but also a potential cause, of FTND, complicating the interpretation of associations between these constructs and requires a more detailed investigation than standard GWAS or general linear regression models can provide. FTND items and genotypes were collected in four samples, with a combined sample size of 5,373 respondents. A mega-analysis was conducted using a multiple group SEM approach to test competing hypotheses regarding the relationships between the SNP rs16969968, FTND and CPD. In the best fitting model, the FTND items loaded onto two correlated factors. The first, labeled "maintenance," assesses the motivation to maintain constant levels of nicotine through out the day. The second was labeled "urgency" as its items concern the urgency to restore nicotine levels after abstinence. We focus our attention on the "maintenance" factor, of which CPD was an indicator. The best fitting model included a negative feedback loop between the Maintenance factor and CPD. Accordingly, the motivation to maintain higher levels of nicotine increased the quantity of nicotine consumed, which subsequently decreases the maintenance motivation. The fact that the Maintenance-CPD feedback model fits the data best implies that there are at least two biological pathways that lead from rs16969968 to smoking behaviors. The model is consistent with a supply and demand system, which allows individuals to achieve a homeostatic equilibrium for their nicotine concentration.

Genetic and environmental risk structure of internalizing psychopathology in youth.

BACKGROUND: Internalizing disorders (IDs), consisting of syndromes of anxiety and depression, are common, debilitating conditions often beginning early in life. Various trait-like psychological constructs are associated with IDs. Our prior analysis identified a tripartite model of Fear/Anxiety, Dysphoria, and Positive Affect among symptoms of anxiety and depression and the following constructs in youth: anxiety sensitivity, fearfulness, behavioral activation and inhibition, irritability, neuroticism, and extraversion. The current study sought to elucidate their overarching latent genetic and environmental risk structure. METHODS: The sample consisted of 768 juvenile twin subjects ages 9-14 assessed for the nine, abovementioned measures. We compared two multivariate twin models of this broad array of phenotypes. RESULTS: A hypothesis-driven, common pathway twin model reflecting the tripartite structure of the measures were fit to these data. However, an alternative independent pathway model provided both a better fit and more nuanced insights into their underlying genetic and environmental risk factors. CONCLUSIONS: Our findings suggest a complex latent genetic and environmental structure to ID phenotypes in youth. This structure, which incorporates both clinical symptoms and various psychological traits, informs future phenotypic approaches for identifying specific genetic and pathophysiological mechanisms underlying ID risk.

Sociopolitical Attitudes Through the Lens of Behavioral Genetics: Contributions from Dr Nicholas Martin.

Professor Nicholas (Nick) Martin spearheaded initial investigations into the genetic basis of political attitudes and behaviors, demonstrating that behaviors that are perceived as socially constructed could have a biological basis. As he showed, the typical mode of inheritance for political attitudes consists of approximately equal proportions of variance from additive genetic, shared environmental and unique environmental sources. This differs from other psychological variables, such as personality traits, which tend to be characterized by genetic and unique environmental sources of variation. By treating political attitudes as a model phenotype, Nick Martin was able to leverage the unique pattern of observed intergenerational transmission for political attitudes to reexamine the quintessential assumptions of the classical twin model. Specifically, by creatively leveraging the nuances of the genetic architecture of political attitudes, he was able to demonstrate the robustness of the equal environments assumption and suggest corrections to account for assortative mating. These advances have had a substantial impact on both the fields of political science, as well as behavioral and quantitative genetics.

Genome-wide association study of shared liability to anxiety disorders in Army STARRS.

Anxiety disorders (ANX), namely generalized anxiety, panic disorder, and phobias, are common, etiologically complex syndromes that show increasing prevalence and comorbidity throughout adolescence and beyond. Few genome-wide association studies (GWAS) examining ANX risk have been published and almost exclusively in individuals of European ancestry. In this study, we phenotyped participants from the Army Study To Assess Risk and Resilience in Servicemembers (STARRS) to approximate DSM-based ANX diagnoses. We factor-analyzed those to create a single dimensional anxiety score for each subject. GWAS were conducted using that score within each of three ancestral groups (EUR, AFR, LAT) and then meta-analyzed across ancestries (NTotal = 16,510). We sought to (a) replicate prior ANX GWAS findings in ANGST; (b) determine whether results extended to other ancestry groups; and (c) meta-analyze with ANGST for increased power to identify novel susceptibility loci. No reliable genome-wide significant SNP associations were detected in STARRS. However, SNPs within the CAMKMT gene located in region 2p21 associated with shared ANX risk in ANGST were replicated in EUR soldiers but not other ancestry groups. Combining EUR STARRS and ANGST (N = 28,950) yielded a more robust 2p21 association signal (p = 9.08x10-11 ). Gene-based analyses supported three genes within 2p21 and LBX1 on chromosome 10. More powerful ANX genetic studies will be required to identify further loci.

Type I Error Rates and Parameter Bias in Multivariate Behavioral Genetic Models.

For many multivariate twin models, the numerical Type I error rates are lower than theoretically expected rates using a likelihood ratio test (LRT), which implies that the significance threshold for statistical hypothesis tests is more conservative than most twin researchers realize. This makes the numerical Type II error rates higher than theoretically expected. Furthermore, the discrepancy between the observed and expected error rates increases as more variables are included in the analysis and can have profound implications for hypothesis testing and statistical inference. In two simulation studies, we examine the Type I error rates for the Cholesky decomposition and Correlated Factors models. Both show markedly lower than nominal Type I error rates under the null hypothesis, a discrepancy that increases with the number of variables in the model. In addition, we observe slightly biased parameter estimates for the Cholesky decomposition and Correlated Factors models. By contrast, if the variance-covariance matrices for variance components are estimated directly (without constraints), the numerical Type I error rates are consistent with theoretical expectations and there is no bias in the parameter estimates regardless of the number of variables analyzed. We call this the direct symmetric approach. It appears that each model-implied boundary, whether explicit or implicit, increases the discrepancy between the numerical and theoretical Type I error rates by truncating the sampling distributions of the variance components and inducing bias in the parameters. The direct symmetric approach has several advantages over other multivariate twin models as it corrects the Type I error rate and parameter bias issues, is easy to implement in current software, and has fewer optimization problems. Implications for past and future research, and potential limitations associated with direct estimation of genetic and environmental covariance matrices are discussed.

The Genetic and Environmental Relationship Between Childhood Behavioral Inhibition and Preadolescent Anxiety.

This study uses novel approaches to examine genetic and environmental influences shared between childhood behavioral inhibition (BI) and symptoms of preadolescent anxiety disorders. Three hundred and fifty-two twin pairs aged 9-13 and their mothers completed questionnaires about BI and anxiety symptoms. Biometrical twin modeling, including a direction-of-causation design, investigated genetic and environmental risk factors shared between BI and social, generalized, panic and separation anxiety. Social anxiety shared the greatest proportion of genetic (20%) and environmental (16%) variance with BI with tentative evidence for causality. Etiological factors underlying BI explained little of the risk associated with the other anxiety domains. Findings further clarify etiologic pathways between BI and anxiety disorder domains in children.

Cross-Lagged Analysis of Interplay Between Differential Traits in Sibling Pairs: Validation and Application to Parenting Behavior and ADHD Symptomatology.

Understanding the factors that contribute to behavioral traits is a complex task, and partitioning variance into latent genetic and environmental components is a useful beginning, but it should not also be the end. Many constructs are influenced by their contextual milieu, and accounting for background effects (such as gene-environment correlation) is necessary to avoid bias. This study introduces a method for examining the interplay between traits, in a longitudinal design using differential items in sibling pairs. The model is validated via simulation and power analysis, and we conclude with an application to paternal praise and ADHD symptoms in a twin sample. The model can help identify what type of genetic and environmental interplay may contribute to the dynamic relationship between traits using a cross-lagged panel framework. Overall, it presents a way to estimate and explicate the developmental interplay between a set of traits, free from many common sources of bias.

Age-Related Differences in the Structure of Genetic and Environmental Contributions to Types of Peer Victimization.

The goal of the present investigation was to clarify and compare the structure of genetic and environmental influences on different types (e.g., physical, verbal) of peer victimization experienced by youth in pre-/early adolescence and mid-/late adolescence. Physical, verbal, social, and property-related peer victimization experiences were assessed in two twin samples (306 pairs, ages 9-14 and 294 pairs, ages 15-20). Cholesky decompositions of individual differences in victimization were conducted, and independent pathway (IP) and common pathway (CP) twin models were tested in each sample. In the younger sample, a Cholesky decomposition best described the structure of genetic and environmental contributors to peer victimization, with no evidence that common additive genetic or environmental factors influence different types of peer victimization. In the older sample, common environmental factors influenced peer victimization types via a general latent liability for peer victimization (i.e., a CP model). Whereas the pre-/early adolescent sample demonstrated no evidence of a shared genetic and environmental structure for different types of peer victimization, the mid-/late adolescent sample demonstrates the emergence of an environmentally-driven latent liability for peer victimization across peer victimization types.

Genetic and environmental influences on the codevelopment among borderline personality disorder traits, major depression symptoms, and substance use disorder symptoms from adolescence to young adulthood.

Although borderline personality disorder (BPD) traits decline from adolescence to adulthood, comorbid psychopathology such as symptoms of major depressive disorder (MDD), alcohol use disorder (AUD), and drug use disorders (DUDs) likely disrupt this normative decline. Using a longitudinal sample of female twins (N = 1,763), we examined if levels of BPD traits were correlated with changes in MDD, AUD, and DUD symptoms from ages 14 to 24. A parallel process biometric latent growth model examined the contributions of genetic and environmental factors to the relationships between developmental components of these phenotypes. Higher BPD trait levels predicted a greater rate of increase in AUD and DUD symptoms, and higher AUD and DUD symptoms predicted a slower rate of decline of BPD traits from ages 14 to 24. Common genetic influences accounted for the associations between BPD traits and each disorder, as well as the interrelationships of AUD and DUD symptoms. Both genetic and nonshared environmental influences accounted for the correlated levels between BPD traits and MDD symptoms, but solely environmental influences accounted for the correlated changes between the two over time. Results indicate that higher levels of BPD traits may contribute to an earlier onset and faster escalation of AUD and DUD symptoms, and substance use problems slow the normative decline in BPD traits. Overall, our data suggests that primarily genetic influences contribute to the comorbidity between BPD features and substance use disorder symptoms. We discuss our data in the context of two major theories of developmental psychopathology and comorbidity.

Extended Twin Study of Alcohol Use in Virginia and Australia.

Drinking alcohol is a normal behavior in many societies, and prior studies have demonstrated it has both genetic and environmental sources of variation. Using two very large samples of twins and their first-degree relatives (Australia ≈ 20,000 individuals from 8,019 families; Virginia ≈ 23,000 from 6,042 families), we examine whether there are differences: (1) in the genetic and environmental factors that influence four interrelated drinking behaviors (quantity, frequency, age of initiation, and number of drinks in the last week), (2) between the twin-only design and the extended twin design, and (3) the Australian and Virginia samples. We find that while drinking behaviors are interrelated, there are substantial differences in the genetic and environmental architectures across phenotypes. Specifically, drinking quantity, frequency, and number of drinks in the past week have large broad genetic variance components, and smaller but significant environmental variance components, while age of onset is driven exclusively by environmental factors. Further, the twin-only design and the extended twin design come to similar conclusions regarding broad-sense heritability and environmental transmission, but the extended twin models provide a more nuanced perspective. Finally, we find a high level of similarity between the Australian and Virginian samples, especially for the genetic factors. The observed differences, when present, tend to be at the environmental level. Implications for the extended twin model and future directions are discussed.

Assessing a Novel Method to Reduce Anesthesia Machine Contamination: A Prospective, Observational Trial.

BACKGROUND: Anesthesia machines are known reservoirs of bacterial species, potentially contributing to healthcare associated infections (HAIs). An inexpensive, disposable, nonpermeable, transparent anesthesia machine wrap (AMW) may reduce microbial contamination of the anesthesia machine. This study quantified the density and diversity of bacterial species found on anesthesia machines after terminal cleaning and between cases during actual anesthesia care to assess the impact of the AMW. We hypothesized reduced bioburden with the use of the AMW. METHODS: In a prospective, experimental research design, the AMW was used in 11 surgical cases (intervention group) and not used in 11 control surgical cases. Cases were consecutively assigned to general surgical operating rooms. Seven frequently touched and difficult to disinfect "hot spots" were cultured on each machine preceding and following each case. The density and diversity of cultured colony forming units (CFUs) between the covered and uncovered machines were compared using Wilcoxon signed-rank test and Student's t-tests. RESULTS: There was a statistically significant reduction in CFU density and diversity when the AMW was employed. CONCLUSION: The protective effect of the AMW during regular anesthetic care provides a reliable and low-cost method to minimize the transmission of pathogens across patients and potentially reduces HAIs.

A Power Calculator for the Classical Twin Design.

Power is a ubiquitous, though often overlooked, component of any statistical analyses. Almost every funding agency and institutional review board requires that some sort of power analysis is conducted prior to data collection. While there are several excellent on line power calculators for independent observations, twin studies pose unique challenges that are not incorporated into these algorithms. The goal of the current manuscript is to outline a general method for calculating power in twin studies, and to provide functions to allow researchers to easily conduct power analyses for a range of common twin models. Several scenarios are discussed to demonstrate the importance of various factors that influence the power within the classical twin design and to serve as examples for the provided functions.

GW-SEM: A Statistical Package to Conduct Genome-Wide Structural Equation Modeling.

Improving the accuracy of phenotyping through the use of advanced psychometric tools will increase the power to find significant associations with genetic variants and expand the range of possible hypotheses that can be tested on a genome-wide scale. Multivariate methods, such as structural equation modeling (SEM), are valuable in the phenotypic analysis of psychiatric and substance use phenotypes, but these methods have not been integrated into standard genome-wide association analyses because fitting a SEM at each single nucleotide polymorphism (SNP) along the genome was hitherto considered to be too computationally demanding. By developing a method that can efficiently fit SEMs, it is possible to expand the set of models that can be tested. This is particularly necessary in psychiatric and behavioral genetics, where the statistical methods are often handicapped by phenotypes with large components of stochastic variance. Due to the enormous amount of data that genome-wide scans produce, the statistical methods used to analyze the data are relatively elementary and do not directly correspond with the rich theoretical development, and lack the potential to test more complex hypotheses about the measurement of, and interaction between, comorbid traits. In this paper, we present a method to test the association of a SNP with multiple phenotypes or a latent construct on a genome-wide basis using a diagonally weighted least squares (DWLS) estimator for four common SEMs: a one-factor model, a one-factor residuals model, a two-factor model, and a latent growth model. We demonstrate that the DWLS parameters and p-values strongly correspond with the more traditional full information maximum likelihood parameters and p-values. We also present the timing of simulations and power analyses and a comparison with and existing multivariate GWAS software package.

Minor Allele Frequency Changes the Nature of Genotype by Environment Interactions.

In the classical twin study, phenotypic variation is often partitioned into additive genetic (A), common (C) and specific environment (E) components. From genetical theory, the outcome of genotype by environment interaction is expected to inflate A when the interacting factor is shared (i.e., C) between the members of a twin pair. We show that estimates of both A and C can be inflated. When the shared interacting factor changes the size of the difference between homozygotes' means, the expected sibling or DZ twin correlation is .5 if and only if the minor allele frequency (MAF) is .5; otherwise the expected DZ correlation is greater than this value, consistent (and confounded) with some additional effect of C. This result is considered in the light of the distribution of minor allele frequencies for polygenic traits. Also discussed is whether such interactions take place at the locus level or affect an aggregated biological structure or system. Interactions with structures or endophenotypes that result from the aggregated effects of many loci will generally emerge as part of the A estimate.