Defining genetic determinants of the Metabolic Syndrome in the Framingham Heart Study using association and structural equation modeling methods

Development of a Structural Equation Model to Examine the Relationships between Genetic Polymorphisms and Cardiovascular Risk Factors

Genome-wide association studies (GWASs) have been used to discover genetic polymorphisms that affect cardiovascular diseases (CVDs). Structural equation modelling (SEM) has been identified as a robust multivariate analysis tool. However, there is a paucity of research that has conducted SEM in African populations. The purpose of this study was to create a model that may be used to examine the relationships between genetic polymorphisms and their respective cardiovascular risk (CVR) factors. The procedure involved three steps. Firstly, the creation of latent variables and the hypothesis model. Next, confirmatory factor analysis (CFA) to examine the relationships between the latent variables, SNPs, dyslipidemia and metabolic syndrome, with their respective indicators. Then finally, model fitting using JASP statistical software v.0.16.4.0. The indicators for the SNPs and dyslipidemia all indicated significant factor loadings, -0.96 to 0.91 (p = <0.001) and 0.92 to 0.96 (p ≤ 0.001), respectively. The indicators for metabolic syndrome also had significant coefficients of 0.20 (p = 0.673), 0.36 (p = 0.645) and 0.15 (p = 0.576), but they were not statistically significant. There were no significant relationships observed between the SNPs, dyslipidemia and metabolic syndrome. The SEM produced an acceptable model according to the fit indices.

Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods

It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.

Structural Equation Modelling for Predicting the Relative Contribution of Each Component in the Metabolic Syndrome Status Change

Understanding the factor weighting in the development of metabolic syndrome (MetS) may help to predict the progression for cardiovascular and metabolic diseases. Thus, the aim of this study was to develop a confirmatory model to describe and explain the direct and indirect effect of each component in MetS status change. A total of 3581 individuals diagnosed with MetS, aged 18−102 years, were selected between January 2019 and December 2020 from a community-representative sample of Portuguese adults in a north-eastern Portuguese region to test the model’s goodness of fit. A structural equation modelling (SEM) approach and a two-way ANOVA (age × body composition) were performed to compare the relative contribution of each MetS component using joint interim statement (JIS). Waist circumference (β = 0.189−0.373, p < 0.001), fasting glucose (β = 0.168−0.199, p < 0.001) and systolic blood pressure (β = 0.140−0.162, p < 0.001) had the highest direct effect on the change in MetS status in the overall population and concerning both sexes. Moreover, diastolic blood pressure (DBP), triglycerides (TG) and high-density lipoprotein cholesterol (HDL-c) had a low or non-significant effect. Additionally, an indirect effect was reported for age and body composition involving the change in MetS status. The findings may suggest that other components with higher specificity and sensitivity should be considered to empirically validate the harmonised definition of MetS. Current research provides the first multivariate model for predicting the relative contribution of each component in the MetS status change, specifically in Portuguese adults.

Bayesian latent variable models for hierarchical clustered count outcomes with repeated measures in microbiome studies

Motivated by the multivariate nature of microbiome data with hierarchical taxonomic clusters, counts that are often skewed and zero inflated, and repeated measures, we propose a Bayesian latent variable methodology to jointly model multiple operational taxonomic units within a single taxonomic cluster. This novel method can incorporate both negative binomial and zero-inflated negative binomial responses, and can account for serial and familial correlations. We develop a Markov chain Monte Carlo algorithm that is built on a data augmentation scheme using Pólya-Gamma random variables. Hierarchical centering and parameter expansion techniques are also used to improve the convergence of the Markov chain. We evaluate the performance of our proposed method through extensive simulations. We also apply our method to a human microbiome study.

Structural Equation Modeling

Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

A Genome-Wide Association Study in isolated populations reveals new genes associated to common food likings

Food preferences are the first factor driving food choice and thus nutrition. They involve numerous different senses such as taste and olfaction as well as various other factors such as personal experiences and hedonistic aspects. Although it is clear that several of these have a genetic basis, up to now studies have focused mostly on the effects of polymorphisms of taste receptor genes. Therefore, we have carried out one of the first large scale (4611 individuals) GWAS on food likings assessed for 20 specific food likings belonging to 4 different categories (vegetables, fatty, dairy and bitter). A two-step meta-analysis using three different isolated populations from Italy for the discovery step and two populations from The Netherlands and Central Asia for replication, revealed 15 independent genome-wide significant loci (p < 5 × 10(-8)) for 12 different foods. None of the identified genes coded for either taste or olfactory receptors suggesting that genetics impacts in determining food likings in a much broader way than simple differences in taste perception. These results represent a further step in uncovering the genes that underlie liking of common foods that in the end will greatly help understanding the genetics of human nutrition in general.

TET2 and CSMD1 genes affect SBP response to hydrochlorothiazide in never-treated essential hypertensives

BACKGROUND

Thiazide diuretics have been recommended as a first-line antihypertensive treatment, although the choice of 'the right drug in the individual essential hypertensive patient' remains still empirical. Essential hypertension is a complex, polygenic disease derived from the interaction of patient's genetic background with the environment. Pharmacogenomics could be a useful tool to pinpoint gene variants involved in antihypertensive drug response, thus optimizing therapeutic advantages and minimizing side effects.

METHODS AND RESULTS

We looked for variants associated with blood pressure response to hydrochlorothiazide over an 8-week follow-up by means of a genome-wide association analysis in two Italian cohorts of never-treated essential hypertensive patients: 343 samples from Sardinia and 142 from Milan. TET2 and CSMD1 as plausible candidate genes to affect SBP response to hydrochlorothiazide were identified. The specificity of our findings for hydrochlorothiazide was confirmed in an independent cohort of essential hypertensive patients treated with losartan. Our best findings were also tested for replication in four independent hypertensive samples of European Ancestry, such as GENetics of drug RESponsiveness in essential hypertension, Genetic Epidemiology of Responses to Antihypertensives, NORdic DILtiazem intervention, Pharmacogenomics Evaluation of Antihypertensive Responses, and Campania Salute Network-StayOnDiur. We validated a polymorphism in CSMD1 and UGGT2.

CONCLUSION

This exploratory study reports two plausible loci associated with SBP response to hydrochlorothiazide: TET2, an aldosterone-responsive mediator of αENaC gene transcription; and CSMD1, previously described as associated with hypertension in a case-control study.

Using a Bayesian latent variable approach to detect pleiotropy in the Genetic Analysis Workshop 18 data

Pleiotropy, which occurs when a single genetic factor influences multiple phenotypes, is present in many genetic studies of complex human traits. Longitudinal family data, such as the Genetic Analysis Workshop 18 data, combine the features of longitudinal studies in individuals and cross-sectional studies in families, thus providing richer information about the genetic and environmental factors associated with the trait of interest. We recently proposed a Bayesian latent variable methodology for the study of pleiotropy, in the presence of longitudinal and family correlation. The purpose of this work is to evaluate the Bayesian latent variable method in a real data setting using the Genetic Analysis Workshop 18 blood pressure phenotypes and sequenced genotype data. To detect single-nucleotide polymorphisms with pleiotropic effect on both diastolic and systolic blood pressure, we focused on a set of 6 single-nucleotide polymorphisms from chromosome 3 that was reported in the literature to be significantly associated with either diastolic blood pressure or the binary hypertension trait. Our analysis suggests that both diastolic blood pressure and systolic blood pressure are associated with the latent hypertension severity variable, but the analysis did not find any of the 6 single-nucleotide polymorphisms to have statistically significant pleiotropic effect on both diastolic blood pressure and systolic blood pressure.

Modeling metabolic syndrome through structural equations of metabolic traits, comorbid diseases, and GWAS variants

OBJECTIVE

To provide a quantitative map of relationships between metabolic traits, genome-wide association studies (GWAS) variants, metabolic syndrome (MetS), and metabolic diseases through factor analysis and structural equation modeling (SEM).

DESIGN AND METHODS

Cross-sectional data were collected on 1,300 individuals from an eastern Adriatic Croatian island, including 14 anthropometric and biochemical traits, and diagnoses of type 2 diabetes, coronary heart disease, gout, kidney disease, and stroke. MetS was defined based on Adult Treatment Panel III criteria. Forty widely replicated GWAS variants were genotyped. Correlated quantitative traits were reduced through factor analysis; relationships between factors, genetic variants, MetS, and metabolic diseases were determined through SEM.

RESULTS

MetS was associated with obesity (P < 0.0001), dyslipidemia (P < 0.0001), glycated hemoglobin (HbA1c; P = 0.0013), hypertension (P < 0.0001), and hyperuricemia (P < 0.0001). Of metabolic diseases, MetS was associated with gout (P = 0.024), coronary heart disease was associated with HbA1c (P < 0.0001), and type 2 diabetes was associated with HbA1c (P < 0.0001) and obesity (P = 0.008). Eleven GWAS variants predicted metabolic variables, MetS, and metabolic diseases. Notably, rs7100623 in HHEX/IDE was associated with HbA1c (β = 0.03; P < 0.0001) and type 2 diabetes (β = 0.326; P = 0.0002), underscoring substantial impact on glucose control.

CONCLUSIONS

Although MetS was associated with obesity, dyslipidemia, glucose control, hypertension, and hyperuricemia, limited ability of MetS to indicate metabolic disease risk is suggested.

Neuropsychological deficits in mice depleted of the schizophrenia susceptibility gene CSMD1

Recent meta-analyses of schizophrenia genome-wide association studies (GWASs) have identified the CUB and SUSHI multiple domains 1 (CSMD1) gene as a statistically strong risk factor. CSMD1 is a complement control-related protein suggested to inhibit the classical complement pathway, being expressed in developing neurons. However, expression of CSMD1 is largely uncharacterized and relevance for behavioral phenotypes is not previously demonstrated. Here, we assess neuropsychological behaviors of a Csmd1 knockout (KO) mouse in a selection of standard behavioral tests. Deregulation of neuropsychological responses were observed in both the open field and the elevated plus maze tests, in which KO mice spent 55% and 33% less time than WT littermate mice in open areas, respectively. Altered behaviors were also observed in tail suspension and to higher acoustic stimuli, for which Csmd1 KO mice showed helplessness and moderate increase in startle amplitude, respectively. Furthermore, Csmd1 KO mice also displayed increased weight-gain and glucose tolerance, similar to a major phenotype of the metabolic syndrome that also has been associated to the human CSMD1 locus. Consistent with a role in the control of behaviors, Csmd1 was found highly expressed in the central nervous system (CNS), and with some expression in visceral fat and ovary, under tissue-specific control by a novel promoter-associated lncRNA. In summary, disruption of Csmd1 induces behaviors reminiscent of blunted emotional responses, anxiety and depression. These observations suggest an influence of the CSMD1 schizophrenia susceptibility gene on psychopathology and endophenotypes of the negative symptom spectra.

Assessment of behaviors modeling aspects of schizophrenia in Csmd1 mutant mice

Schizophrenia is a debilitating psychotic disorder that affects up to 1.5% of the population worldwide. Two recent studies in humans identified genome-wide significant associations between schizophrenia and single-nucleotide polymorphisms (SNPs) in an intron of CSMD1. The effect of deleting CSMD1 on mouse behavior is unknown. The present study utilized mice with a mutant Csmd1 allele in which the first exon had been ablated (KO mice). All Csmd1 transcripts that included the first exon were absent in the brains of KO mice, but there was persistent expression of at least one other transcript that does not include the first exon. Wild type (WT), heterozygous (HET), and KO mice were assessed using several well-established behavioral paradigms that model aspects of schizophrenia. Csmd1 KO mice did not differ from wild-type littermates for sensorimotor gating (measured as prepulse inhibition), social interaction, anhedonia (measured by sucrose preference), or sensitivity to the locomotor stimulant effects of the dopaminergic agent d-amphetamine. These data demonstrate that loss of Csmd1 transcripts that include the first exon does not alter multiple well-established behaviors that model aspects of schizophrenia. The SNP most strongly associated with schizophrenia in humans is between exons 3 and 4; therefore, ablation of exon 1 appeared to be a logical animal model. Nevertheless, future studies should consider alternative mouse models including gain-of-function mutations, and loss-of-function mutations that target alternative transcripts of Csmd1.

The metabolic syndrome and coronary artery disease: a structural equation modeling approach suggestive of a common underlying pathophysiology

OBJECTIVE

The purpose of this research was to utilize a structural equation modeling (SEM) approach to simultaneously examine the relationship among the Metabolic Syndrome (MetSyn) and coronary artery calcification (CAC), a surrogate marker for sub-clinical atherosclerosis and coronary artery disease (CAD).

MATERIALS/METHODS

Data were derived from the Spokane Heart Study (SHS), a prospective study designed to examine the role of traditional and non-traditional biological, psychological, and behavioral risk factors predictive of CAC. Study participants included 434 non-clinical healthy volunteers (54% female, 46% male; mean age of 56 years) who were asymptomatic for CAD at enrollment and had complete data for the primary variables of interest (MetSyn components and CAC) during the data collection period (i.e., 2002-2006). A confirmatory factor analysis (CFA) was conducted on the MetSyn factor with the following indicator variables: body mass index, fasting glucose, high-density lipoprotein cholesterol, fasting triglyceride levels, and systolic blood pressure. SEM was used to test the theoretical model that the MetSyn is associated with CAC.

RESULTS

This study demonstrated that body mass index, fasting glucose, fasting triglyceride levels, HDL cholesterol, and blood pressure do cluster together under a single latent factor, and that this latent factor is associated with CAC.

CONCLUSIONS

Our results highlight the possibility that there is a common pathophysiological pathway that could explain the relationship between the MetSyn and CAD. Future studies should examine these relationships in a prospective fashion for early detection and prevention of CAD and to identify ideal time points for clinical intervention.

MicroRNA-125b induces metastasis by targeting STARD13 in MCF-7 and MDA-MB-231 breast cancer cells

MicroRNAs (miRNAs) are a class of small noncoding RNAs that regulate gene expression by targeting mRNAs to trigger either translation repression or mRNA degradation. miR-125b is down-regulated in human breast cancer cells compared with the normal ones except highly metastatic tumor cells MDA-MB-231. However, few functional studies were designed to investigate metastatic potential of miR-125b. In this study, the effects of miR-125b on metastasis in human breast cancer cells were studied, and the targets of miR-125b were also explored. Transwell migration assay, cell wound healing assay, adhesion assay and nude mice model of metastasis were utilized to investigate the effects of miR-125b on metastasis potential in vitro and in vivo. In addition, it was implied STARD13 (DLC2) was a direct target of miR-125b by Target-Scan analysis, luciferase reporter assay and western blot. Furthermore, activation of STARD13 was identified responsible for metastasis induced by miR-125b through a siRNA targeting STARD13. qRT-PCR, immunofluorescent assay and western blot was used to observe the variation of Vimentin and α-SMA in breast cancer cells. In summary, our study provided new insights into the function of miR-125b during the metastasis of breat cancer cells and also suggested the role of miR-125b in pro-metastasis by targeting STARD13.

Structural equation modeling

Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of general pedigrees. Here, we review the theory of SEM for both unrelated and family data, the available software for SEM, and provide an example of SEM analysis.

Evaluating aggregate effects of rare and common variants in the 1000 Genomes Project exon sequencing data using latent variable structural equation modeling

Methods that can evaluate aggregate effects of rare and common variants are limited. Therefore, we applied a two-stage approach to evaluate aggregate gene effects in the 1000 Genomes Project data, which contain 24,487 single-nucleotide polymorphisms (SNPs) in 697 unrelated individuals from 7 populations. In stage 1, we identified potentially interesting genes (PIGs) as those having at least one SNP meeting Bonferroni correction using univariate, multiple regression models. In stage 2, we evaluate aggregate PIG effects on trait, Q1, by modeling each gene as a latent construct, which is defined by multiple common and rare variants, using the multivariate statistical framework of structural equation modeling (SEM). In stage 1, we found that PIGs varied markedly between a randomly selected replicate (replicate 137) and 100 other replicates, with the exception of FLT1. In stage 1, collapsing rare variants decreased false positives but increased false negatives. In stage 2, we developed a good-fitting SEM model that included all nine genes simulated to affect Q1 (FLT1, KDR, ARNT, ELAV4, FLT4, HIF1A, HIF3A, VEGFA, VEGFC) and found that FLT1 had the largest effect on Q1 (β_std = 0.33 ± 0.05). Using replicate 137 estimates as population values, we found that the mean relative bias in the parameters (loadings, paths, residuals) and their standard errors across 100 replicates was on average, less than 5%. Our latent variable SEM approach provides a viable framework for modeling aggregate effects of rare and common variants in multiple genes, but more elegant methods are needed in stage 1 to minimize type I and type II error.

Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas

AIMS/HYPOTHESIS

We report a genome-wide association study of type 2 diabetes in an admixed sample from Mexico City and describe the results of a meta-analysis of this study and another genome-wide scan in a Mexican-American sample from Starr County, TX, USA. The top signals observed in this meta-analysis were followed up in the Diabetes Genetics Replication and Meta-analysis Consortium (DIAGRAM) and DIAGRAM+ datasets.

METHODS

We analysed 967 cases and 343 normoglycaemic controls. The samples were genotyped with the Affymetrix Genome-wide Human SNP array 5.0. Associations of genotyped and imputed markers with type 2 diabetes were tested using a missing data likelihood score test. A fixed-effects meta-analysis including 1,804 cases and 780 normoglycaemic controls was carried out by weighting the effect estimates by their inverse variances.

RESULTS

In the meta-analysis of the two Hispanic studies, markers showing suggestive associations (p < 10(-5)) were identified in two known diabetes genes, HNF1A and KCNQ1, as well as in several additional regions. Meta-analysis of the two Hispanic studies and the recent DIAGRAM+ dataset identified genome-wide significant signals (p < 5 × 10(-8)) within or near the genes HNF1A and CDKN2A/CDKN2B, as well as suggestive associations in three additional regions, IGF2BP2, KCNQ1 and the previously unreported C14orf70.

CONCLUSIONS/INTERPRETATION

We observed numerous regions with suggestive associations with type 2 diabetes. Some of these signals correspond to regions described in previous studies. However, many of these regions could not be replicated in the DIAGRAM datasets. It is critical to carry out additional studies in Hispanic and American Indian populations, which have a high prevalence of type 2 diabetes.

A framework for structural equation models in general pedigrees

BACKGROUND/AIMS

Structural Equation Modeling (SEM) is an analysis approach that accounts for both the causal relationships between variables and the errors associated with the measurement of these variables. In this paper, a framework for implementing structural equation models (SEMs) in family data is proposed.

METHODS

This framework includes both a latent measurement model and a structural model with covariates. It allows for a wide variety of models, including latent growth curve models. Environmental, polygenic and other genetic variance components can be included in the SEM. Kronecker notation makes it easy to separate the SEM process from a familial correlation model. A limited information method of model fitting is discussed. We show how missing data and ascertainment may be handled. We give several examples of how the framework may be used.

RESULTS

A simulation study shows that our method is computationally feasible, and has good statistical properties.

CONCLUSION

Our framework may be used to build and compare causal models using family data without any genetic marker data. It also allows for a nearly endless array of genetic association and/or linkage tests. A preliminary Matlab program is available, and we are currently implementing a more complete and user-friendly R package.

Structural equation modeling of gene-environment interactions in coronary heart disease

Coronary heart disease (CHD) is a complex disease, which is influenced not only by genetic and environmental factors but also by gene-environment (GE) interactions in interconnected biological pathways or networks. The classical methods are inadequate for identifying GE interactions due to the complex relationships among risk factors, mediating risk factors (e.g., hypertension, blood lipids, and glucose), and CHD. Our aim was to develop a two-level structural equation model (SEM) to identify genes and GE interactions in the progress of CHD to take into account the causal structure among mediating risk factors and CHD (Level 1), and hierarchical family structure (Level 2). The method was applied to the Framingham Heart Study (FHS) Offspring Cohort data. Our approach has several advantages over classical methods: (1) it provides important insight into how genes and contributing factors affect CHD by investigating the direct, indirect, and total effects; and (2) it aids the development of biological models that more realistically reflect the complex biological pathways or networks. Using our method, we are able to detect GE interaction of SERPINE1 and body mass index (BMI) on CHD, which has not been reported. We conclude that SEM modeling of GE interaction can be applied in the analysis of complex epidemiological data sets.

Identification of evidence suggestive of an association with peripheral arterial disease at the OSBPL10 locus by genome-wide investigation in the Japanese population

AIM

Peripheral arterial disease (PAD) is a common cause of cardiovascular morbidity and an independent predictor of cardiovascular mortality. However, little is known about the genetic basis of PAD. To elucidate this, we performed a two-staged genome-wide association study in Japanese individuals.

METHODS

We initially tested 222,285 single-nucleotide polymorphisms (SNPs). After the first screening in a panel of 195 PAD cases and 1,358 controls, 2,696 SNPs (1.2%) were further genotyped in the second screening using another panel of 699 PAD cases and 1540 controls. In both screenings, controls were subjects affected with some diseases other than PAD.

RESULTS

When analyzed in the combined panel, the strongest signal of PAD association was observed at rs1902341 in the intron of OSBPL10 (p=4.7E-7 for trend test; OR=1.31, 95% CI 1.18-1.46). Also, PAD was modestly associated at several other loci such as rs2554503 in CSMD1 (p=5.7E-5; OR=1.32, 95% CI 1.15-1.51) or rs235243 in VSP13D (p=0.04; OR=1.18, 95% CI 1.01-1.37).

CONCLUSION

Our genome-wide exploration identified suggestive evidence of PAD association at the OSBPL10 locus. Because the association has not reached a genome-wide significant level, further replication study is warranted for verification in the Japanese population.

Phenotype definition and development--contributions from Group 7

The papers in Genetic Analysis Workshop 16 Group 7 covered a wide range of topics. The effects of confounder misclassification and selection bias on association results were examined by one group. Another focused on bias introduced by various methods of accounting for treatment effects. Two groups used related methods to derive phenotypic traits. They used different analytic strategies for genetic associations with non-overlapping results (but because they used different sets of single-nucleotide polymorphisms (SNPs) and significance criteria, this is not surprising). Another group relied on the well-characterized definition of type 2 diabetes to show benefits of a novel predictive test. Transmission-ratio distortion was the focus of another paper. The results were extended to show a potential secondary benefit of the test to identify potentially mis-called SNPs.