A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees

Statistical Genetic Approaches to Investigate Genotype-by-Environment Interaction: Review and Novel Extension of Models

Statistical genetic models of genotype-by-environment (G×E) interaction can be divided into two general classes, one on G×E interaction in response to dichotomous environments (e.g., sex, disease-affection status, or presence/absence of an exposure) and the other in response to continuous environments (e.g., physical activity, nutritional measurements, or continuous socioeconomic measures). Here we develop a novel model to jointly account for dichotomous and continuous environments. We develop the model in terms of a joint genotype-by-sex (for the dichotomous environment) and genotype-by-social determinants of health (SDoH; for the continuous environment). Using this model, we show how a depression variable, as measured by the Beck Depression Inventory-II survey instrument, is not only underlain by genetic effects (as has been reported elsewhere) but is also significantly determined by joint G×Sex and G×SDoH interaction effects. This model has numerous applications leading to potentially transformative research on the genetic and environmental determinants underlying complex diseases.

Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study

Background: Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS). Methods: We employed a maximum likelihood estimation of the decomposition of variance components to detect GxE interaction. After excluding individuals with diabetes and individuals on medication for diabetes, hypertension, or dyslipidemia, we analyzed 12 MS risk factors: fasting glucose (FG), fasting insulin (FI), 2-h glucose (2G), 2-h insulin (2I), body mass index (BMI), waist circumference (WC), leptin (LP), high-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), total serum cholesterol (TSC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our SES variable used a combined score of Duncan's socioeconomic index and education years. Heterogeneity in the additive genetic variance across the SES continuum and a departure from unity in the genetic correlation coefficient were taken as evidence of GxE interaction. Hypothesis tests were conducted using standard likelihood ratio tests. Results: We found evidence of GxE for fasting glucose, 2-h glucose, 2-h insulin, BMI, and triglycerides. The genetic effects underlying the insulin/glucose metabolism component of MS are upregulated at the lower end of the SES spectrum. We also determined that the household variance for systolic blood pressure decreased with increasing SES. Conclusion: These results show a significant change in the GxE interaction underlying the major components of MS in response to changes in socioeconomic status. Further mRNA sequencing studies will identify genes and canonical gene pathways to support our molecular-level hypotheses.

A Scan of Pleiotropic Immune Mediated Disease Genes Identifies Novel Determinants of Baseline FVIII Inhibitor Status in Hemophilia-A

Hemophilia-A (HA) is caused by heterogeneous loss-of-function factor (F)VIII gene (F8)-mutations and deficiencies in plasma-FVIII-activity that impair intrinsic-pathway-mediated coagulation-amplification. The standard-of-care for severe-HA-patients is regular infusions of therapeutic-FVIII-proteins (tFVIIIs) but ~30% develop neutralizing-tFVIII-antibodies called "FVIII-inhibitors (FEIs)" and become refractory. We used the PATH study and ImmunoChip to scan immune-mediated-disease (IMD)-genes for novel and/or replicated genomic-sequence-variations associated with baseline-FEI-status while accounting for non-independence of data due to genetic-relatedness and F8-mutational-heterogeneity. The baseline-FEI-status of 450 North American PATH subjects-206 with black-African-ancestry and 244 with white-European-ancestry-was the dependent variable. The F8-mutation-data and a genetic-relatedness matrix were incorporated into a binary linear-mixed model of genetic association with baseline-FEI-status. We adopted a gene-centric-association-strategy to scan, as candidates, pleiotropic-IMD-genes implicated in the development of either ³2 autoimmune-/autoinflammatory-disorders (AADs) or ³1 AAD and FEIs. Baseline-FEI-status was significantly associated with SNPs assigned to NOS2A (rs117382854; p=3.2E-6) and B3GNT2 (rs10176009; p=5.1E-6), which have functions in anti-microbial-/-tumoral-immunity. Among IMD-genes implicated in FEI-risk previously, we identified strong associations with CTLA4 assigned SNPs (p=2.2E-5). The F8-mutation-effect underlies ~15% of the total heritability for baseline-FEI-status. Additive genetic heritability and SNPs in IMD-genes account for >50% of the patient-specific variability in baseline-FEI-status. Race is a significant determinant independent of F8-mutation-effects and non-F8-genetics.

Genotype-by-socioeconomic status interaction influences heart disease risk scores and carotid artery thickness in Mexican Americans: the predominant role of education in comparison to household income and socioeconomic index

Background: Socioeconomic status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for cardiovascular disease (CVD). We analyzed Mexican American Family Studies (MAFS) data to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an important determinant of variation in CVD risk factors. Methods: We employed a linear mixed model to investigate GxE in Mexican American extended families. We studied two proxies for CVD [Pooled Cohort Equation Risk Scores/Framingham Risk Scores (FRS/PCRS) and carotid artery intima-media thickness (CA-IMT)] in relation to socioeconomic status as determined by Duncan's Socioeconomic Index (SEI), years of education, and household income. Results: We calculated heritability for FRS/PCRS and carotid artery intima-media thickness. There was evidence of GxE due to additive genetic variance heterogeneity and genetic correlation for FRS, PCRS, and CA-IMT measures for education (environment) but not for household income or SEI. Conclusion: The genetic effects underlying CVD are dynamically modulated at the lower end of the SES spectrum. There is a significant change in the genetic architecture underlying the major components of CVD in response to changes in education.

Gene-by-Environment Interaction in Non-Alcoholic Fatty Liver Disease and Depression: The Role of Hepatic Transaminases

Non-alcoholic fatty liver disease (NAFLD) encompasses a range of liver conditions, from benign fatty accumulation to severe fibrosis. The global prevalence of NAFLD has risen to 25-30%, with variations across ethnic groups. NAFLD may advance to hepatocellular carcinoma, increases cardiovascular risk, is associated with chronic kidney disease, and is an independent metabolic disease risk factor. Assessment methods for liver health include liver biopsy, magnetic resonance imaging, ultrasound, and vibration-controlled transient elastography (VCTE by FibroScan). Hepatic transaminases are cost-effective and minimally invasive liver health assessment methods options. This study focuses on the interaction between genetic factors underlying the traits (hepatic transaminases and the FibroScan results) on the one hand and the environment (depression) on the other. We examined 525 individuals at risk for metabolic disorders. We utilized variance components models and likelihood-based statistical inference to examine potential GxE interactions in markers of NAFLD, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and the AST/ALT ratio, and Vibration-Controlled Transient Elastography (VCTE by FibroScan). We calculated the Fibroscan-AST (FAST) score (a score that identifies the risk of progressive non-alcoholic steatohepatitis (NASH) and screened for depression using the Beck Depression Inventory-II (BDI-II). We identified significant G × E interactions for AST/ALT ratio × BDI-II, but not AST, ALT, or the FAST score. Our findings support that genetic factors play a role in hepatic transaminases, especially the AST/ALT ratio, with depression influencing this relationship. These insights contribute to understanding the complex interplay of genetics, environment, and liver health, potentially guiding future personalized interventions.

A pedigree-based cohort to study the genetic risk factors for cardiometabolic diseases: study design, baseline characteristics and preliminary results

Background

We initiated the Fujian Tulou Pedigree-based Cohort (FTPC) as the integration of extended pedigrees and prospective cohort to clarify the genetic and environmental risk factors of cardiometabolic diseases.

Methods

FTPC was carried out in Nanjing County, Fujian Province, China from August 2015 to December 2017 to recruit probands with the same surnames and then enroll their first-degree and more distant relatives. The participants were asked to complete questionnaire interview, physical examination, and blood collection. According to the local genealogical booklets and family registry, we reconstructed extended pedigrees to estimate the heritability of cardiometabolic traits. The follow-up of FTPC is scheduled every 5 years in the future.

Results

The baseline survey interviewed 2,727 individuals in two clans. A total of 1,563 adult subjects who completed all baseline examinations were used to reconstruct pedigrees and 452 extended pedigrees were finally identified, including one seven-generation pedigree, two five-generation pedigrees, 23 four-generation pedigrees, 186 three-generation pedigrees, and 240 two-generation pedigrees. The average age of the participants was 57.4 years, with 43.6% being males. The prevalence of hypertension, diabetes and dyslipidemia in FTPC were 49.2, 10.0, and 45.2%, respectively. Based on the pedigree structure, the heritability of systolic blood pressure, diastolic blood pressure, fast blood glucose, total cholesterol, triglyceride, high-density lipoprotein, and low-density lipoprotein was estimated at 0.379, 0.306, 0.386, 0.452, 0.568, 0.852, and 0.387, respectively.

Conclusion

As an extended pedigree cohort in China, FTPC will provide an important source to study both genetic and environmental risk factors prospectively.

Non-alcoholic Fatty Liver Disease and Depression: Evidence for Genotype × Environment Interaction in Mexican Americans

This study examines the impact of G × E interaction effects on non-alcoholic fatty liver disease (NAFLD) among Mexican Americans in the Rio Grande Valley (RGV) of South Texas. We examined potential G × E interaction using variance components models and likelihood-based statistical inference in the phenotypic expression of NAFLD, including hepatic steatosis and hepatic fibrosis (identified using vibration controlled transient elastography and controlled attenuation parameter measured by the FibroScan Device). We screened for depression using the Beck Depression Inventory-II (BDI-II). We identified significant G × E interactions for hepatic fibrosis × BDI-II. These findings provide evidence that genetic factors interact with depression to influence the expression of hepatic fibrosis.

Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project

Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability – the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ~N^2–3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 10⁵ voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (10^2–4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63–0.76, p < 10⁻¹⁰). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.

Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop

Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.

Whole-exome sequencing in multiplex preeclampsia families identifies novel candidate susceptibility genes

OBJECTIVE

Preeclampsia is a common and serious heritable disorder of human pregnancy. Although there have been notable successes in identification of maternal susceptibility genes a large proportion of the heritability of preeclampsia remains unaccounted for. It is has been postulated that rare variation may account for some of this missing heritability. In this study, we performed whole-exome sequencing (WES) in multiplex families to identify rare exonic risk variants.

METHODS

We conducted WES in 244 individuals from 34 Australian/New Zealand multiplex preeclampsia families. Variants were tested for association with preeclampsia using a threshold model and logistic regression.

RESULTS

We found significant association for two moderately rare missense variants, rs145743393 (Padj = 0.0032, minor allele frequency = 0.016) in the chromosome 1 open reading frame 35 (C1orf35) gene, and rs34270076 (Padj = 0.0128, minor allele frequency = 0.024) in the pyroglutamylated RFamide peptide receptor (QRFPR) gene. To replicate these associations we performed imputation in our Australian genome wide association scan for preeclampsia and found no significant exonic variants in either C1orf35 or QRFPR. However, 11 variants demonstrating nominal significance (P < 0.05) in the genomic region between QRFPR and annexin A5 (ANXA5) were identified. We further leveraged publicly available genome-wide available summary data from the UK Biobank to investigate association of these two variants with the underlying clinical phenotypes of preeclampsia and detected nominal association of the QRFPR variant (rs34270076, P = 0.03) with protein levels in females.

CONCLUSION

The study represents the first to use WES in multiplex families for preeclampsia and identifies two novel genes (QRFPR and C1orf35) not previously associated with preeclampsia and find nominal association of rs34270076 with protein levels, a key clinical feature of preeclampsia. We find further support for ANXA5 previously associated with pregnancy complications, including preeclampsia.

Person-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features

The characterization of the functional significance of interindividual variation in brain morphometry is a core aim of cognitive neuroscience. Prior research has focused on interindividual variation at the level of regional brain measures thus overlooking the fact that each individual brain is a person-specific ensemble of interdependent regions. To expand this line of inquiry we introduce the person-based similarity index (PBSI) for brain morphometry. The conceptual unit of the PBSI is the individual person’s brain structural profile which considers all relevant morphometric measures as features of a single vector. In 2 independent cohorts (total of 1756 healthy participants), we demonstrate the foundational validity of this approach by affirming that the PBSI scores for subcortical volume and cortical thickness in healthy individuals differ between men and women, are heritable, and robust to variation in neuroimaging parameters, sample composition, and regional brain morphometry. Moreover, the PBSI scores correlate with age, body mass index, and fluid intelligence. Collectively, these results suggest that the person-based measures of brain morphometry are biologically and functionally meaningful and have the potential to advance the study of human variation in multivariate brain imaging phenotypes in healthy and clinical populations.

Heritability and Sex-Specific Genetic Effects of Self-Reported Physical Activity in a Brazilian Highly Admixed Population

INTRODUCTION

The engagement in sports or habitual physical activity (PA) has shown an extensive protective role against multiple diseases such as cancer, obesity, and many others. Additionally, PA has also a significant impact on life quality, since it aids with managing stress, preserving cognitive function and memory, and preventing fractures in the elderly.

OBJECTIVE

Considering there has been multiple evidence showing that genetic variation underpins variation of PA-related traits, we aimed to estimate the heritability (h2) of these phenotypes in a sample from the Brazilian population and assess whether males and females differ in relation to those estimates.

METHODS

2,027 participants from a highly admixed population from Baependi, MG, Brazil, had information regarding their PA and sedentary behavior (SB) phenotypes collected through a questionnaire (IPAQ-SF). After data cleaning and transformation procedures, we obtained four variables to be evaluated: total PA (TPA MET), walking time, (WK MET), moderate-vigorous PA (MVPA MET), and SB. A model selection procedure was performed using a single-step covariate inclusion approach. We tested for BMI, waist, hip and neck circumferences, smoking, and depression separately, and performed correlation tests among covariates. Linear mixed models, selection procedure, and the variance components approach to estimate h2 were implemented using SOLAR-Eclipse 8.3.1.

RESULTS

We obtained estimates of 0.221, 0.109, 0.226, and 0 for TPA MET, WK MET, MVPA MET, and SB, respectively. We found evidence for gene-sex interactions, with males having higher sex-specific heritabilities than females for TPA MET and MVPA MET. In addition, we found higher estimates of the genetic variance component in males than females for most phenotypes.

DISCUSSION/CONCLUSION

The heritability estimates presented in this work show a moderate heritable set of genetic factors affecting PA in a sample from the Brazilian population. The evaluation of the genetic variance component suggests segregating genetic factors in male individuals are more heterogeneous, which can explain why men globally tend to need to practice more intense PA than women to achieve similar health benefits. Hence, these findings have significant implications for the understanding of the genetic architecture of PA and might aid to promote health in the future.

Correlations between relatives: From Mendelian theory to complete genome sequence

It is 100 years since R. A. Fisher proposed that a Mendelian model of genetic variant effects, additive over loci, could explain the patterns of observed phenotypic correlations between relatives. His loci were hypothetical and his model theoretical. It is only about 50 years since the first genetic markers allowed the detection of even variants with major effects on phenotype, and only 20 years since the development of single-nucleotide polymorphism technology provided dense markers over the genome. Then both mappings in defined pedigrees and population-based genome-wide association studies samples allowed the detection of multiple contributing variants of smaller effect. Finally, with methods based on genotypic correlations between individuals, or on allelic associations between loci, the additive heritability contributions of the genome can be estimated from large population samples. In this review we trace, from 1918 to 2018, the analysis of observed phenotypic correlations between relatives to estimate underlying genetic components of traits in human populations. As with studies from 1918 onward, we use height as the example trait where not only data are readily available, but where Fisher's model of large numbers of variants of infinitesimal effect appears to provide a good approximation to reality. However, we also trace the use of phenotypic and genotypic correlations between relatives in mapping causal variants and resolving genetic contributions to more complex human traits. With the availability of DNA sequence data, we can hope to not only estimate the total genetic contribution to a trait, but to resolve effects of individual genetic variants on biological function.

Genetic influences on the onset of obstructive sleep apnoea and daytime sleepiness: a twin study

Background

Obstructive sleep apnoea (OSA) is one of the major sources of the excessive daily sleepiness, cognitive dysfunction, and it increases cardiovascular morbidity and mortality. Previous studies suggested a possible genetic influence, based on questionnaires but no objective genetic study was conducted to understand the exact variance underpinned by genetic factors.

Methods

Seventy-one Hungarian twin pairs involved from the Hungarian Twin Registry (48 monozygotic, MZ and 23 dizygotic, DZ pairs, mean age 51 ± 15 years) underwent overnight polysomnography (Somnoscreen Plus Tele PSG, Somnomedics GMBH, Germany). Apnoea hypopnea index (AHI), respiratory disturbance index (RDI) and oxygen desaturation index (ODI) were registered. Daytime sleepiness was measured with the Epworth Sleepiness Scale (ESS). Bivariate heritability analysis was applied.

Results

The prevalence of OSA was 41% in our study population. The heritability of the AHI, ODI and RDI ranged between 69% and 83%, while the OSA, defined by an AHI ≥5/h, was itself 73% heritable. The unshared environmental component explained the rest of the variance between 17% and 31%. Daytime sleepiness was mostly determined by the environment, and the variance was influenced in 34% by the additive genetic factors. These associations were present after additional adjustment for body mass index.

Conclusion

OSA and the indices of OSA severity are heritable, while daytime sleepiness is mostly influenced by environmental factors. Further studies should elucidate whether close relatives of patients with OSA may benefit from early family risk based screening.

Rediscovering the value of families for psychiatric genetics research

As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.

Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data

Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.

Does thinner right entorhinal cortex underlie genetic liability to cannabis use?

BACKGROUND

Although alterations in medial temporal lobe structures have been previously associated with use of cannabis, one of the most widely used illicit drugs, whether such alterations are a cause or effect of cannabis use has been unclear.

METHODS

In this cross-sectional observational study involving 404 twins/siblings, we have compared cortical thickness and surface area between groups of gender-matched sibling-pairs (concordant cannabis unexposed, concordant exposed and discordant for cannabis exposure) using permutation tests after controlling for potential confounds. Bi-variate polygenic model was used to assess the genetic and environmental contributions underlying cortical morphological phenotypes and frequency of cannabis use.

RESULTS

Cortical thickness of the right entorhinal cortex was significantly lower in the concordant exposed siblings compared to both discordant unexposed and discordant exposed groups [false discovery rate (FDR)-corrected, q < 0.05]. The association between the right entorhinal cortex thickness and frequency of cannabis use is due to the contribution of significant shared additive genetic (ρg = -0.19 ± 0.08; p = 0.02) factors but not unique environment (ρe = 0.05 ± 0.09; p = 0.53). Significantly lower surface area of the right entorhinal cortex in discordant exposed group compared with the discordant unexposed group furnishes preliminary evidence in support of causal effect of cannabis use (FDR-corrected, q < 0.05). However, bi-variate polygenic model-based analysis did not show any significant effect.

CONCLUSIONS

Shared genetic liability may underlie the association between cannabis exposure and thinner right entorhinal cortex. Prospective longitudinal studies are necessary to definitively disentangle the cause-effect relationships of cannabis use.

Heritability and genetic associations of triglyceride and HDL-C levels using pedigree-based and empirical kinships

The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributable to additive genetic factors. Heritability is optimally estimated in family-based sample populations. Traditionally, this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationships between family members. An alternative, when dense genotype data are available, is to directly measure the empirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20 data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR (Sequential Oligogenic Linkage Analysis Routines), pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK) were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted using each kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation. The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall, the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generated using IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated using IBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknown relatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was not expected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic data are available, empirical kinship estimation is a practical alternative to using pedigree-based kinships.

Modeling methylation data as an additional genetic variance component

High-throughput platforms allow the characterization of thousands of previously known methylation sites. These platforms have great potential for investigating the epigenetic effects that are partially responsible for gene expression control. Methylation sites provide a bridge for the investigation of real-time environmental contributions on genomic events by the alteration of methylation status of those sites. Using the data provided by GAW20's organization committee, we calculated the heritability estimates of each cytosine-phosphate-guanine (CpG) island before and after the use of fenofibrate, a lipid-control drug. Surprisingly, we detected substantially high heritability estimates before drug usage. This somewhat unexpected high sample correlation was corrected by the use of principal components and the distributions of heritability estimates before and after fenofibrate treatment, which made the distributions comparable. The methylation sites located near a gene were collected and a genetic relationship matrix estimated to represent the overall correlation between samples. We implemented a random-effect association test to screen genes whose methylation patterns partially explain the observable high-density lipoprotein (HDL) heritability. Our leading association was observed for the TMEM52 gene that encodes a transmembrane protein, and is largely expressed in the liver, had not been previously associated with HDL until this manuscript. Using a variance component decomposition framework with the linear mixed model allows the integration of data from different sources, such as methylation, gene expression, metabolomics, and proteomics. The decomposition of the genetic variance component decomposition provides a flexible analytical approach for the challenges of this new omics era.

Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes

Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present a method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals

BACKGROUND

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.

RESULTS

To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.

CONCLUSIONS

Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

Genetic and environmental (physical fitness and sedentary activity) interaction effects on cardiometabolic risk factors in Mexican American children and adolescents

Knowledge on genetic and environmental (G × E) interaction effects on cardiometabolic risk factors (CMRFs) in children is limited.  The purpose of this study was to examine the impact of G × E interaction effects on CMRFs in Mexican American (MA) children (n = 617, ages 6-17 years). The environments examined were sedentary activity (SA), assessed by recalls from "yesterday" (SAy) and "usually" (SAu) and physical fitness (PF) assessed by Harvard PF scores (HPFS). CMRF data included body mass index (BMI), waist circumference (WC), fat mass (FM), fasting insulin (FI), homeostasis model of assessment-insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), systolic (SBP) and diastolic (DBP) blood pressure, and number of metabolic syndrome components (MSC). We examined potential G × E interaction in the phenotypic expression of CMRFs using variance component models and likelihood-based statistical inference. Significant G × SA interactions were identified for six CMRFs: BMI, WC, FI, HOMA-IR, MSC, and HDL, and significant G × HPFS interactions were observed for four CMRFs: BMI, WC, FM, and HOMA-IR. However, after correcting for multiple hypothesis testing, only WC × SAy, FM × SAy, and FI × SAu interactions became marginally significant. After correcting for multiple testing, most of CMRFs exhibited significant G × E interactions (Reduced G × E model vs. Constrained model). These findings provide evidence that genetic factors interact with SA and PF to influence variation in CMRFs, and underscore the need for better understanding of these relationships to develop strategies and interventions to effectively reduce or prevent cardiometabolic risk in children.

Adjusting for Familial Relatedness in the Analysis of GWAS Data

Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.

Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

Significant Linkage Evidence for Interstitial Cystitis/Painful Bladder Syndrome on Chromosome 3

PURPOSE

Interstitial cystitis/painful bladder syndrome is a chronic pelvic pain condition of unknown etiology. We hypothesized that related interstitial cystitis/painful bladder syndrome cases were more likely to have a genetic etiology. The purpose of this study was to perform a genetic linkage analysis.

MATERIALS AND METHODS

We identified interstitial cystitis/painful bladder syndrome cases using diagnostic codes linked to the Utah Population Database genealogy resource and to electronic medical records. For this analysis we used 13 high risk pedigrees, defined as having a statistical excess number of interstitial cystitis/painful bladder syndrome cases among descendants compared to matched hospital population rates. Case status was confirmed in medical records using natural language processing. DNA was obtained from stored, nonneoplastic, formalin fixed, paraffin embedded tissue blocks. Each pedigree had at least 2 cases with DNA available. Parametric linkage analysis was performed.

RESULTS

Pedigrees ranged in size from 2 to 12 genotyped cases for a total of 48 cases. Significant genome wide linkage evidence was found under a dominant model on chromosome 3p13-p12.3 (maximum heterogeneity θ logarithm of odds 3.56). Two pedigrees showed at least nominal linkage evidence in this region (logarithm of odds greater than 0.59). The most informative pedigree included 12 interstitial cystitis/painful bladder syndrome cases (pedigree θ logarithm of odds 2.1). Other regions with suggestive linkage evidence included 1p21-q25, 3p21.1-p14.3, 4q12-q13, 9p24-p22 and 14q24-q31, all under a dominant model.

CONCLUSIONS

While the etiology of interstitial cystitis/painful bladder syndrome is unknown, this study provides evidence that a genetic variant(s) on chromosome 3 and possibly on chromosomes 1, 4, 9 and 14 contribute to an interstitial cystitis/painful bladder syndrome predisposition. Sequence analysis of affected cases in identified pedigrees may provide insight into genes contributing to interstitial cystitis/painful bladder syndrome.

Potassium channel gene associations with joint processing speed and white matter impairments in schizophrenia

Patients with schizophrenia show decreased processing speed on neuropsychological testing and decreased white matter integrity as measured by diffusion tensor imaging, two traits shown to be both heritable and genetically associated indicating that there may be genes that influence both traits as well as schizophrenia disease risk. The potassium channel gene family is a reasonable candidate to harbor such a gene given the prominent role potassium channels play in the central nervous system in signal transduction, particularly in myelinated axons. We genotyped members of the large potassium channel gene family focusing on putatively functional single nucleotide polymorphisms (SNPs) in a population of 363 controls, 194 patients with schizophrenia spectrum disorder (SSD) and 28 patients with affective disorders with psychotic features who completed imaging and neuropsychological testing. We then performed three association analyses using three phenotypes - processing speed, whole-brain white matter fractional anisotropy (FA) and schizophrenia spectrum diagnosis. We extracted SNPs showing an association at a nominal P value of <0.05 with all three phenotypes in the expected direction: decreased processing speed, decreased FA and increased risk of SSD. A single SNP, rs8234, in the 3' untranslated region of voltage-gated potassium channel subfamily Q member 1 (KCNQ1) was identified. Rs8234 has been shown to affect KCNQ1 expression levels, and KCNQ1 levels have been shown to affect neuronal action potentials. This exploratory analysis provides preliminary data suggesting that KCNQ1 may contribute to the shared risk for diminished processing speed, diminished white mater integrity and increased risk of schizophrenia.

The heritable basis of gene-environment interactions in cardiometabolic traits

AIMS/HYPOTHESIS

Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions.

METHODS

Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software.

RESULTS

All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h ²) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction.

CONCLUSIONS/INTERPRETATION

Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.

Human Birth Weight and Reproductive Immunology: Testing for Interactions between Maternal and Offspring KIR and HLA-C Genes

BACKGROUND/AIMS

Maternal and offspring cell contact at the site of placentation presents a plausible setting for maternal-fetal genotype (MFG) interactions affecting fetal growth. We test hypotheses regarding killer cell immunoglobulin-like receptor (KIR) and HLA-C MFG effects on human birth weight by extending the quantitative MFG (QMFG) test.

METHODS

Until recently, association testing for MFG interactions had limited applications. To improve the ability to test for these interactions, we developed the extended QMFG test, a linear mixed-effect model that can use multi-locus genotype data from families.

RESULTS

We demonstrate the extended QMFG test's statistical properties. We also show that if an offspring-only model is fit when MFG effects exist, associations can be missed or misattributed. Furthermore, imprecisely modeling the effects of both KIR and HLA-C could result in a failure to replicate if these loci's allele frequencies differ among populations. To further illustrate the extended QMFG test's advantages, we apply the extended QMFG test to a UK cohort study and the Norwegian Mother and Child Cohort (MoBa) study.

CONCLUSION

We find a significant KIR-HLA-C interaction effect on birth weight. More generally, the QMFG test can detect genetic associations that may be missed by standard genome-wide association studies for quantitative traits.

Gray matter heritability in family-based and population-based studies using voxel-based morphometry

BACKGROUND

The combination of genetics and imaging has improved their understanding of the brain through studies of aggregate measures obtained from high-resolution structural imaging. Voxel-wise analyses have the potential to provide more detailed information of genetic influences on the brain. Here they report a large-scale study of the heritability of gray matter at voxel resolution (1 × 1 × 1 mm).

METHODS

Validated voxel-based morphometry (VBM) protocols were applied to process magnetic resonance imaging data of 3,239 unrelated subjects from a population-based study and 491 subjects from two family-based studies. Genome-wide genetic data was used to estimate voxel-wise gray matter heritability of the unrelated subjects and pedigree-structure was used to estimate heritability in families. They subsequently associated two genetic variants, known to be linked with subcortical brain volume, with most heritable voxels to determine if this would enhance their association signals.

RESULTS

Voxels significantly heritable in both estimates mapped to subcortical structures, but also voxels in the language areas of the left hemisphere were found significantly heritable. When comparing regional patterns of heritability, family-based estimates were higher than population-based estimates. However, regional consistency of the heritability measures across study designs was high (Pearson's correlation coefficient = 0.73, P = 2.6 × 10^-13 ). They further show enhancement of the association signal of two previously discovered genetic loci with subcortical volume by using only the most heritable voxels.

CONCLUSION

Gray matter voxel-wise heritability can be reliably estimated with different methods. Combining heritability estimates from multiple studies is feasible to construct reliable heritability maps of gray matter voxels. Hum Brain Mapp 38:2408-2423, 2017. © 2017 Wiley Periodicals, Inc.

Genetic complexity at expression quantitative trait loci

BACKGROUND

Identifying variants that regulate gene expression and delineating their genetic architecture is a critical next step in our endeavors to better understand the genetic etiology of complex diseases. The appropriate genomic tools are in place, and preliminary analytic strategies have been developed.

METHODS

Here we used Genetic Analysis Workshop (GAW) 19 data to investigate the genetic complexity of expression quantitative trait loci (eQTL), chromosomal regions likely to harbor regulatory elements responsible for gene expression. For this investigation, we analyzed the lymphocyte expression profiles of 653 individuals in 20 pedigrees who were also genotyped by single nucleotide polymorphism (SNP) arrays, followed by sequencing and imputation. We used these data to examine the degree of allelic heterogeneity, a contributor to genetic complexity at eQTL, by sequentially conditioning on the most significantly associated SNPs.

RESULT

SOLAR (Sequential Oligogenic Linkage Analysis Routines)-MGA (measured genotype approach) and FaST-LMM (Factored Spectrally Transformed Linear Mixed Model) software allowed us to analyze pedigree data. The power and Type 1 error rates for single SNP association testing and multiple SNP sequential association testing were consistent for these programs. Sequential conditioning of the real expression data revealed substantial levels of allelic heterogeneity at the 2 eQTL examined, illustrating this feature of genetic complexity.

CONCLUSIONS

eQTL exhibit substantial genetic complexity among and within pedigrees.

Lipidomic analyses in epidemiology

Clinical lipid measurements have been the mainstay of risk assessment for chronic disease since the Framingham study commenced over 60 years ago. Thousands of subsequent epidemiological studies have provided much insight into the relationship between plasma lipid profiles, health and disease. However, the human lipidome consists of thousands of individual lipid species, and current lipidomic technology presents us with an unprecedented opportunity to measure lipid phenotypes, representing genomic, metabolic, diet and lifestyle-related exposures, in large epidemiological studies. The number of epidemiological studies using lipidomic profiling is increasing and has the potential to provide improved biological and clinical insight into human disease. In this review, we discuss current lipidomic technologies, epidemiological studies using these technologies and the statistical approaches used in the analysis of the resulting data. We highlight the potential of integrating genomic and lipidomic datasets and discuss the future opportunities and challenges in this emerging field.

Intergenerational Neuroimaging of Human Brain Circuitry

Neuroscientists are increasingly using advanced neuroimaging methods to elucidate the intergenerational transmission of human brain circuitry. This new line of work promises to shed light on the ontogeny of complex behavioral traits, including psychiatric disorders, and possible mechanisms of transmission. Here we highlight recent intergenerational neuroimaging studies and provide recommendations for future work.

Reflections on the Field of Human Genetics: A Call for Increased Disease Genetics Theory

Development of human genetics theoretical models and the integration of those models with experiment and statistical evaluation are critical for scientific progress. This perspective argues that increased effort in disease genetics theory, complementing experimental, and statistical efforts, will escalate the unraveling of molecular etiologies of complex diseases. In particular, the development of new, realistic disease genetics models will help elucidate complex disease pathogenesis, and the predicted patterns in genetic data made by these models will enable the concurrent, more comprehensive statistical testing of multiple aspects of disease genetics predictions, thereby better identifying disease loci. By theoretical human genetics, I intend to encompass all investigations devoted to modeling the heritable architecture underlying disease traits and studies of the resulting principles and dynamics of such models. Hence, the scope of theoretical disease genetics work includes construction and analysis of models describing how disease-predisposing alleles (1) arise, (2) are transmitted across families and populations, and (3) interact with other risk and protective alleles across both the genome and environmental factors to produce disease states. Theoretical work improves insight into viable genetic models of diseases consistent with empirical results from linkage, transmission, and association studies as well as population genetics. Furthermore, understanding the patterns of genetic data expected under realistic disease models will enable more powerful approaches to discover disease-predisposing alleles and additional heritable factors important in common diseases. In spite of the pivotal role of disease genetics theory, such investigation is not particularly vibrant.

Power and Effective Study Size in Heritability Studies

Correlation between study units in quantitative genetics studies often makes it difficult to compare important inferential aspects of studies. Describing the relatedness between study units is critical to capture features of pedigree studies involving heritability, including power and precision of heritability estimates. Blangero et al (2012) showed that in pedigree studies the power to detect heritability is a function of the true heritability and the eigenvalues of the kinship matrix. We extend this to a more general setting which allows statements about expected precision of heritability estimates. Using two different Taylor series approximations, we summarize the relatedness in a study design by one or two parameters. These relatedness summary parameters (RSPs) are functions of the eigenvalues or log-eigenvalues of the kinship matrix. Using the RSPs based on the log-eigenvalues, we accurately approximate the expectation of the likelihood ratio test and expected confidence interval widths. We define an effective sample size of a target study as one which has the equivalent power and precision to a reference design. Using unrelated sibpairs as the reference design provides very accurate assessments of power. RSPs and effective sample sizes provide new tools for comparing studies and communicating information about relatedness in heritability studies.

Sex-specific genetic effects in physical activity: results from a quantitative genetic analysis

BACKGROUND

The objective of this study is to present a model to estimate sex-specific genetic effects on physical activity (PA) levels and sedentary behaviour (SB) using three generation families.

METHODS

The sample consisted of 100 families covering three generations from Portugal. PA and SB were assessed via the International Physical Activity Questionnaire short form (IPAQ-SF). Sex-specific effects were assessed by genotype-by-sex interaction (GSI) models and sex-specific heritabilities. GSI effects and heterogeneity were tested in the residual environmental variance. SPSS 17 and SOLAR v. 4.1 were used in all computations.

RESULTS

The genetic component for PA and SB domains varied from low to moderate (11% to 46%), when analyzing both genders combined. We found GSI effects for vigorous PA (p = 0.02) and time spent watching television (WT) (p < 0.001) that showed significantly higher additive genetic variance estimates in males. The heterogeneity in the residual environmental variance was significant for moderate PA (p = 0.02), vigorous PA (p = 0.006) and total PA (p = 0.001). Sex-specific heritability estimates were significantly higher in males only for WT, with a male-to-female difference in heritability of 42.5 (95% confidence interval: 6.4, 70.4).

CONCLUSIONS

Low to moderate genetic effects on PA and SB traits were found. Results from the GSI model show that there are sex-specific effects in two phenotypes, VPA and WT with a stronger genetic influence in males.

Fast and powerful heritability inference for family-based neuroimaging studies

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods. In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero et al., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study.

A general unified framework to assess the sampling variance of heritability estimates using pedigree or marker-based relationships

Heritability is a population parameter of importance in evolution, plant and animal breeding, and human medical genetics. It can be estimated using pedigree designs and, more recently, using relationships estimated from markers. We derive the sampling variance of the estimate of heritability for a wide range of experimental designs, assuming that estimation is by maximum likelihood and that the resemblance between relatives is solely due to additive genetic variation. We show that well-known results for balanced designs are special cases of a more general unified framework. For pedigree designs, the sampling variance is inversely proportional to the variance of relationship in the pedigree and it is proportional to 1/N, whereas for population samples it is approximately proportional to 1/N(2), where N is the sample size. Variation in relatedness is a key parameter in the quantification of the sampling variance of heritability. Consequently, the sampling variance is high for populations with large recent effective population size (e.g., humans) because this causes low variation in relationship. However, even using human population samples, low sampling variance is possible with high N.

Genotype by sex and genotype by age interactions with sedentary behavior: the Portuguese Healthy Family Study

Sedentary behavior (SB) expression and its underlying causal factors have been progressively studied, as it is a major determinant of decreased health quality. In the present study we applied Genotype x Age (GxAge) and Genotype x Sex (GxSex) interaction methods to determine if the phenotypic expression of different SB traits is influenced by an interaction between genetic architecture and both age and sex. A total of 1345 subjects, comprising 249 fathers, 327 mothers, 334 sons and 325 daughters, from 339 families of The Portuguese Healthy Family Study were included in the analysis. SB traits were assessed by means of a 3-d physical activity recall, the Baecke and IPAQ questionnaires. GxAge and GxSex interactions were analyzed using SOLAR 4.0 software. Sedentary behaviour heritability estimates were not always statistically significant (p>0.05) and ranged from 3% to 27%. The GxSex and GxAge interaction models were significantly better than the single polygenic models for TV (min/day), EEsed (kcal/day), personal computer (PC) usage and physical activty (PA) tertiles. The GxAge model is also significantly better than the polygenic model for Sed (min/day). For EEsed, PA tertiles, PC and Sed, the GxAge interaction was significant because the genetic correlation between SB environments was significantly different from 1. Further, PC and Sed variance heterogeneity among distinct ages were observed. The GxSex interaction was significant for EEsed due to genetic variance heterogeneity between genders and for PC due to a genetic correlation less than 1 across both sexes. Our results suggest that SB expression may be influenced by the interactions between genotype with both sex and age. Further, different sedentary behaviors seem to have distinct genetic architectures and are differentially affected by age and sex.

Genetic prediction in the Genetic Analysis Workshop 18 sequencing data

High-throughput sequencing data can be used to predict phenotypes from genotypes, and this corresponds to establishing a prognostic model. In extended pedigrees the relatedness of subjects provides additional information so that genetic values, fixed or random genetic components, and heritability can be estimated. At the Genetic Analysis Workshop 18, the working group on genetic prediction dealt with both establishing a prognostic model and, in one contribution, comparing standard logistic regression with robust logistic regression in a sample of unrelated affected or unaffected individuals. Results of both logistic regression approaches were similar. All other contributions to this group used extended family data, in general using the quantitative trait blood pressure. The individual contributions varied in several important aspects, such as the estimation of the kinship matrix and the estimation method. Contributors chose various approaches for model validation, including different versions of cross-validation or within-family validation. Within-family validation included model building in the upper generations and validation in later generations. The choice of the statistical model and the computational algorithm had substantial effects on computation time. If decorrelation approaches were applied, the computational burden was substantially reduced. Some software packages estimated negative eigenvalues, although eigenvalues of correlation matrices should be non-negative. Most statistical models and software packages have been developed for experimental crosses and planned breeding programs. With their specialized pedigree structures, they are not sufficiently flexible to accommodate the variability of human pedigrees in general, and improved implementations are required.

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

Pedigree-based random effect tests to screen gene pathways

The new generation of sequencing platforms opens new horizons in the genetics field. It is possible to exhaustively assay all genetic variants in an individual and search for phenotypic associations. The whole genome sequencing approach, when applied to a large human sample like the San Antonio Family Study, detects a very large number (>25 million) of single nucleotide variants along with other more complex variants. The analytical challenges imposed by this number of variants are formidable, suggesting that methods are needed to reduce the overall number of statistical tests. In this study, we develop a single degree-of-freedom test of variants in a gene pathway employing a random effect model that uses an empirical pathway-specific genetic relationship matrix as the focal covariance kernel. The empirical pathway-specific genetic relationship uses all variants (or a chosen subset) from gene members of a given biological pathway. Using SOLAR's pedigree-based variance components modeling, which also allows for arbitrary fixed effects, such as principal components, to deal with latent population structure, we employ a likelihood ratio test of the pathway-specific genetic relationship matrix model. We examine all gene pathways in KEGG database gene pathways using our method in the first replicate of the Genetic Analysis Workshop 18 simulation of systolic blood pressure. Our random effect approach was able to detect true association signals in causal gene pathways. Those pathways could be easily be further dissected by the independent analysis of all markers.

Increased waist circumference is independently associated with hypothyroidism in Mexican Americans: replicative evidence from two large, population-based studies

BACKGROUND

Mexican Americans are at an increased risk of both thyroid dysfunction and metabolic syndrome (MS). Thus it is conceivable that some components of the MS may be associated with the risk of thyroid dysfunction in these individuals. Our objective was to investigate and replicate the potential association of MS traits with thyroid dysfunction in Mexican Americans.

METHODS

We conducted association testing for 18 MS traits in two large studies on Mexican Americans - the San Antonio Family Heart Study (SAFHS) and the National Health and Nutrition Examination Survey (NHANES) 2007-10. A total of 907 participants from 42 families in SAFHS and 1633 unrelated participants from NHANES 2007-10 were included in this study. The outcome measures were prevalence of clinical and subclinical hypothyroidism and thyroid function index (TFI) - a measure of thyroid function. For the SAFHS, we used polygenic regression analyses with multiple covariates to test associations in setting of family studies. For the NHANES 2007-10, we corrected for the survey design variables as needed for association analyses in survey data. In both datasets, we corrected for age, sex and their linear and quadratic interactions.

RESULTS

TFI was an accurate indicator of clinical thyroid status (area under the receiver-operating-characteristic curve to detect clinical hypothyroidism, 0.98) in both SAFHS and NHANES 2007-10. Of the 18 MS traits, waist circumference (WC) showed the most consistent association with TFI in both studies independently of age, sex and body mass index (BMI). In the SAFHS and NHANES 2007-10 datasets, each standard deviation increase in WC was associated with 0.13 (p < 0.001) and 0.11 (p < 0.001) unit increase in the TFI, respectively. In a series of polygenic and linear regression models, central obesity (defined as WC ≥ 102 cm in men and ≥88 cm in women) was associated with clinical and subclinical hypothyroidism independent of age, sex, BMI and type 2 diabetes in both datasets. Estimated prevalence of hypothyroidism was consistently high in those with central obesity, especially below 45y of age.

CONCLUSIONS

WC independently associates with increased risk of thyroid dysfunction. Use of WC to identify Mexican American subjects at high risk of thyroid dysfunction should be investigated in future studies.

Common genetic variants and gene expression associated with white matter microstructure in the human brain

Identifying genes that contribute to white matter microstructure should provide insights into the neurobiological processes that regulate white matter development, plasticity and pathology. We detected five significant SNPs using genome-wide association analysis on a global measure of fractional anisotropy in 776 individuals from large extended pedigrees. Genetic correlations and genome-wide association results indicated that the genetic signal was largely homogeneous across white matter regions. Using RNA transcripts derived from lymphocytes in the same individuals, we identified two genes (GNA13 and CCDC91) that are likely to be cis-regulated by top SNPs, and whose expression levels were also genetically correlated with fractional anisotropy. A transcript of HTR7 was phenotypically associated with FA, and was associated with an intronic genome-wide significant SNP. These results encourage further research in the mechanisms by which GNA13, HTR7 and CCDC91 influence brain structure, and emphasize a role for g-protein signaling in the development and maintenance of white matter microstructure in health and disease.

Genotype by energy expenditure interaction and body composition traits: The Portuguese Healthy Family Study

Background and Aims. Energy expenditure has been negatively correlated with fat accumulation. However, this association is highly variable. In the present study we applied a genotype by environment interaction method to examine the presence of Genotype x by Total Daily Energy Expenditure and Genotype x by Daily Energy Expenditure interactions in the expression of different body composition traits. Methods and Results. A total of 958 subjects from 294 families of The Portuguese Healthy Family Study were included in the analysis. TDEE and DEE were assessed using a physical activity recall. Body fat percentages were measured with a bioelectrical impedance scale. GxTDEE and GxDEE examinations were performed using SOLAR 4.0 software. All BC traits were significantly heritable, with heritabilities ranging from 21% to 34%. The GxTDEE and GxDEE interaction models fitted the data better than the polygenic model for all traits. For all traits, a significant GxTDEE and GxDEE interaction was due to variance heterogeneity among distinct levels of TDEE and DEE. For WC, GxTDEE was also significant due to the genetic correlation function. Conclusions. TDEE and DEE are environmental constraints associated with the expression of individuals' BC genotypes, leading to variability in the phenotypic expression of BC traits.

Plasma lipidome is independently associated with variability in metabolic syndrome in Mexican American families

Plasma lipidome is now increasingly recognized as a potentially important marker of chronic diseases, but the exact extent of its contribution to the interindividual phenotypic variability in family studies is unknown. Here, we used the rich data from the ongoing San Antonio Family Heart Study (SAFHS) and developed a novel statistical approach to quantify the independent and additive value of the plasma lipidome in explaining metabolic syndrome (MS) variability in Mexican American families recruited in the SAFHS. Our analytical approach included two preprocessing steps: principal components analysis of the high-resolution plasma lipidomics data and construction of a subject-subject lipidomic similarity matrix. We then used the Sequential Oligogenic Linkage Analysis Routines software to model the complex family relationships, lipidomic similarities, and other important covariates in a variance components framework. Our results suggested that even after accounting for the shared genetic influences, indicators of lipemic status (total serum cholesterol, TGs, and HDL cholesterol), and obesity, the plasma lipidome independently explained 22% of variability in the homeostatic model of assessment-insulin resistance trait and 16% to 22% variability in glucose, insulin, and waist circumference. Our results demonstrate that plasma lipidomic studies can additively contribute to an understanding of the interindividual variability in MS.

Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics

Endophenotypes are measurable biomarkers that are correlated with an illness, at least in part, because of shared underlying genetic influences. Endophenotypes may improve our power to detect genes influencing risk of illness by being genetically simpler, closer to the level of gene action, and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. Furthermore, they also provide insight into the mechanisms underlying illness and will be valuable in developing biologically-based nosologies, through efforts such as RDoC, that seek to explain both the heterogeneity within current diagnostic categories and the overlapping clinical features between them. While neuroimaging, electrophysiological, and cognitive measures are currently most used in psychiatric genetic studies, researchers currently are attempting to identify candidate endophenotypes that are less genetically complex and potentially closer to the level of gene action, such as transcriptomic and proteomic phenotypes. Sifting through tens of thousands of such measures requires automated, high-throughput ways of assessing, and ranking potential endophenotypes, such as the Endophenotype Ranking Value. However, despite the potential utility of endophenotypes for gene characterization and discovery, there is considerable resistance to endophenotypic approaches in psychiatry. In this review, we address and clarify some of the common issues associated with the usage of endophenotypes in the psychiatric genetics community.

Genome wide association and linkage analyses identified three loci-4q25, 17q23.2, and 10q11.21-associated with variation in leukocyte telomere length: the Long Life Family Study

Leukocyte telomere length is believed to measure cellular aging in humans, and short leukocyte telomere length is associated with increased risks of late onset diseases, including cardiovascular disease, dementia, etc. Many studies have shown that leukocyte telomere length is a heritable trait, and several candidate genes have been identified, including TERT, TERC, OBFC1, and CTC1. Unlike most studies that have focused on genetic causes of chronic diseases such as heart disease and diabetes in relation to leukocyte telomere length, the present study examined the genome to identify variants that may contribute to variation in leukocyte telomere length among families with exceptional longevity. From the genome wide association analysis in 4,289 LLFS participants, we identified a novel intergenic SNP rs7680468 located near PAPSS1 and DKK2 on 4q25 (p = 4.7E-8). From our linkage analysis, we identified two additional novel loci with HLOD scores exceeding three, including 4.77 for 17q23.2, and 4.36 for 10q11.21. These two loci harbor a number of novel candidate genes with SNPs, and our gene-wise association analysis identified multiple genes, including DCAF7, POLG2, CEP95, and SMURF2 at 17q23.2; and RASGEF1A, HNRNPF, ANF487, CSTF2T, and PRKG1 at 10q11.21. Among these genes, multiple SNPs were associated with leukocyte telomere length, but the strongest association was observed with one contiguous haplotype in CEP95 and SMURF2. We also show that three previously reported genes-TERC, MYNN, and OBFC1-were significantly associated with leukocyte telomere length at p empirical < 0.05.

Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data

Statistical genetics is undergoing the same transition to big data that all branches of applied statistics are experiencing. With the advent of inexpensive DNA sequencing, the transition is only accelerating. This brief review highlights some modern techniques with recent successes in statistical genetics. These include: (a) lasso penalized regression and association mapping, (b) ethnic admixture estimation, (c) matrix completion for genotype and sequence data, (d) the fused lasso and copy number variation, (e) haplotyping, (f) estimation of relatedness, (g) variance components models, and (h) rare variant testing. For more than a century, genetics has been both a driver and beneficiary of statistical theory and practice. This symbiotic relationship will persist for the foreseeable future.