Genetic investigation of quantitative traits related to autism: use of multivariate polygenic models with ascertainment adjustment

Autism is a severe developmental disorder of unknown etiology but with evidence for genetic influences. Here, we provide evidence for a genetic basis of several quantitative traits that are related to autism. These traits, from the Broader Phenotype Autism Symptom Scale (BPASS), were measured in nuclear families, each ascertained through two probands affected by autism spectrum disorder. The BPASS traits capture the continuum of severity of impairments and may be more informative for genetic studies than are the discrete diagnoses of autism that have been used by others. Using a sample of 201 nuclear families consisting of a total of 694 individuals, we implemented multivariate polygenic models with ascertainment adjustment to estimate heritabilities and genetic and environmental correlations between these traits. Our ascertainment adjustment uses conditioning on the phenotypes of probands, requires no modeling of the ascertainment process, and is applicable to multiplex ascertainment and multivariate traits. This appears to be the first such implementation for multivariate quantitative traits. The marked difference between heritability estimates of the trait for language onset with and without an ascertainment adjustment (0.08 and 0.22, respectively) shows that conclusions are sensitive to whether or not an ascertainment adjustment is used. Among the five BPASS traits that were analyzed, the traits for social motivation and range of interest/flexibility show the highest heritability (0.19 and 0.16, respectively) and also have the highest genetic correlation (0.92). This finding suggests a shared genetic basis of these two traits and that they may be most promising for future gene mapping and for extending pedigrees by phenotyping additional relatives.     

Human pedigree-based quantitative-trait-locus mapping: localization of two genes influencing HDL-cholesterol metabolism.

Common disorders with genetic susceptibilities involve the action of multiple genes interacting with each other and with environmental factors, making it difficult to localize the specific genetic loci responsible. An important route to the disentangling of this complex inheritance is through the study of normal physiological variation in quantitative risk factors that may underlie liability to disease. We present an analysis of HDL-cholesterol (HDL-C), which is inversely correlated with risk of heart disease. A variety of HDL subphenotypes were analyzed, including HDL particle-size classes and the concentrations and proportions of esterified and unesterified HDL-C. Results of a complete genomic screen in large, randomly ascertained pedigrees implicated two loci, one on chromosome 8 and the other on chromosome 15, that influence a component of HDL-C-namely, unesterified HDL2a-C. Multivariate analyses of multiple HDL phenotypes and simultaneous multilocus analysis of the quantitative-trait loci identified permit further characterization of the genetic effects on HDL-C. These analyses suggest that the action of the chromosome 8 locus is specific to unesterified cholesterol levels, whereas the chromosome 15 locus appears to influence both HDL-C concentration and distribution of cholesterol among HDL particle sizes.     

Genetic determinants of risk factors for cardiovascular disease in a population from rural Brazil

We investigate the heritability of and pleiotropic relationships among triglycerides and cholesterol lipoproteins that have long been considered traditional risk factors for cardiovascular disease. Quantitative lipid and lipoprotein phenotypes were determined for a cross-sectional sample of a community in Jequitinhonha valley in northern Minas Gerais state, Brazil. The sample consisted primarily of subsistence farmers. Two hundred sixty-nine individuals (128 males and 141 females), ages 18-88 years, were sampled. Eighty-eight percent (n = 252) of the individuals belonged to a single pedigree, which was highly informative for genetic analysis. Data on anthropometrics, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol, and triglycerides were available for each study participant. Extended pedigrees were constructed using the pedigree-based data management software PedSys. Univariate and bivariate variance-components analyses, adjusted by sex and age, were performed using the SOLAR software package. Heritability estimates of lipids and lipoproteins ranged from 29% to 45% (p < 0.008). The highest heritability estimated was for HDL-C (h2 = 44.8%, p < 0.0001), and this was the only trait that exhibited a significant household effect (c2 = 25%). Strong positive genetic correlations were found between triglycerides and very low density lipoprotein (VLDL) (rhog = 0.998) and between total cholesterol and LDL-C (rhog = 0.948). Significant genetic correlations were also found between triglycerides and LDL-C, between total cholesterol and VLDL, and between total cholesterol and LDL-C and VLDL, and finally between LDL and VLDL. There was a significant negative environmental correlation between triglycerides and HDL-C (rhoe = -0.406).     

Missing heritability of complex diseases: Enlightenment by genetic variants from intermediate phenotypes

Diseases of complex origin have a component of quantitative genetics that contributes to their susceptibility and phenotypic variability. However, after several studies, a major part of the genetic component of complex phenotypes has still not been found, a situation known as “missing heritability.” Although there have been many hypotheses put forward to explain the reasons for the missing heritability, its definitive causes remain unknown. Complex diseases are caused by multiple intermediate phenotypes involved in their pathogenesis and, very often, each one of these intermediate phenotypes also has a component of quantitative inheritance. Here we propose that at least part of the missing heritability can be explained by the genetic component of intermediate phenotypes that is not detectable at the level of the main complex trait. At the same time, the identification of the genetic component of intermediate phenotypes provides an opportunity to identify part of the missing heritability of complex diseases.     

Genetic risk factors of venous thrombosis

Venous thrombosis, whose main clinical presentations include deep vein thrombosis and pulmonary embolism, represents a major health problem worldwide. Numerous conditions are known to predispose to venous thrombosis and these conditions are commonly referred to as risk indicators or risk factors. Generally accepted or "classically" acquired risk factors for venous thromboembolism include advanced age, prolonged immobilisation, surgery, fractures, use of oral contraceptives and hormone replacement therapy, pregnancy, puerperium, cancer and antiphospholipid syndrome. In addition to these well-established risk factors for venous thrombosis, several lines of evidence that have emerged over the past few decades indicate a role of novel genetic risk factors, mainly related to the haemostatic system, in influencing thrombotic risk. The most significant breakthrough has been the confirmation of the concept that inherited hypercoagulable conditions are present in a large proportion of patients with venous thromboembolic disease. These include mutations in the genes that encode antithrombin, protein C and protein S, and the factor V Leiden and factor II G20210 A mutations. Moreover, plasmatic risk indicators, such as hyperhomocysteinemia and elevated concentrations of factors II, VIII, IX, XI and fibrinogen, have also been documented. This extensive list of genetic and acquired factors serves to illustrate that a single cause of venous thrombosis does not exist and that this condition should be considered as a complex or multifactorial trait. Complex traits can be understood by assuming an interaction between different mutations in candidate susceptibility genes. The risk that is associated with each genetic defect may be relatively low in isolation but the simultaneous presence of several mutations may dramatically increase disease susceptibility. Moreover, environmental factors may interact with one or more genetic variations to add further to the risk. The analysis of genetic risk factors and plasmatic factors, together with private life style and environmental factors, has contributed significantly to our understanding of the genetic predisposition to venous thrombosis.     

Heritability of cardiovascular and personality traits in 6,148 Sardinians

In family studies, phenotypic similarities between relatives yield information on the overall contribution of genes to trait variation. Large samples are important for these family studies, especially when comparing heritability between subgroups such as young and old, or males and females. We recruited a cohort of 6,148 participants, aged 14–102 y, from four clustered towns in Sardinia. The cohort includes 34,469 relative pairs. To extract genetic information, we implemented software for variance components heritability analysis, designed to handle large pedigrees, analyze multiple traits simultaneously, and model heterogeneity. Here, we report heritability analyses for 98 quantitative traits, focusing on facets of personality and cardiovascular function. We also summarize results of bivariate analyses for all pairs of traits and of heterogeneity analyses for each trait. We found a significant genetic component for every trait. On average, genetic effects explained 40% of the variance for 38 blood tests, 51% for five anthropometric measures, 25% for 20 measures of cardiovascular function, and 19% for 35 personality traits. Four traits showed significant evidence for an X-linked component. Bivariate analyses suggested overlapping genetic determinants for many traits, including multiple personality facets and several traits related to the metabolic syndrome; but we found no evidence for shared genetic determinants that might underlie the reported association of some personality traits and cardiovascular risk factors. Models allowing for heterogeneity suggested that, in this cohort, the genetic variance was typically larger in females and in younger individuals, but interesting exceptions were observed. For example, narrow heritability of blood pressure was approximately 26% in individuals more than 42 y old, but only approximately 8% in younger individuals. Despite the heterogeneity in effect sizes, the same loci appear to contribute to variance in young and old, and in males and females. In summary, we find significant evidence for heritability of many medically important traits, including cardiovascular function and personality. Evidence for heterogeneity by age and sex suggests that models allowing for these differences will be important in mapping quantitative traits.     

The genetic determination of plasma apolipoprotein A-I levels measured by radioimmunoassay: a study of high-risk pedigrees.

Apolipoprotein A-I (apo A-I) is the major protein component of the high-density lipoprotein (HDL) found in all primates. Using radioimmunoassay, we measured plasma apo A-I levels in 97 individuals from 23 pedigrees ascertained through cases of hypertension or early coronary artery disease (CAD). Using complex segregation analysis, we found that a genetic model with both a single locus with a major effect and polygenic loci gave the best explanation for the distribution of apo A-I levels in these pedigrees. There was no evidence for a major locus effect on HDL cholesterol in these pedigrees. This is the first study to show evidence of a major effect of a single genetic locus on the quantitative variation of plasma apo A-I in a sample of pedigrees enriched for individuals at risk for CAD.     

Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings

The study of continuously varying, quantitative traits is important in evolutionary biology, agriculture, and medicine. Variation in such traits is attributable to many, possibly interacting, genes whose expression may be sensitive to the environment, which makes their dissection into underlying causative factors difficult. An important population parameter for quantitative traits is heritability, the proportion of total variance that is due to genetic factors. Response to artificial and natural selection and the degree of resemblance between relatives are all a function of this parameter. Following the classic paper by R. A. Fisher in 1918, the estimation of additive and dominance genetic variance and heritability in populations is based upon the expected proportion of genes shared between different types of relatives, and explicit, often controversial and untestable models of genetic and non-genetic causes of family resemblance. With genome-wide coverage of genetic markers it is now possible to estimate such parameters solely within families using the actual degree of identity-by-descent sharing between relatives. Using genome scans on 4,401 quasi-independent sib pairs of which 3,375 pairs had phenotypes, we estimated the heritability of height from empirical genome-wide identity-by-descent sharing, which varied from 0.374 to 0.617 (mean 0.498, standard deviation 0.036). The variance in identity-by-descent sharing per chromosome and per genome was consistent with theory. The maximum likelihood estimate of the heritability for height was 0.80 with no evidence for non-genetic causes of sib resemblance, consistent with results from independent twin and family studies but using an entirely separate source of information. Our application shows that it is feasible to estimate genetic variance solely from within-family segregation and provides an independent validation of previously untestable assumptions. Given sufficient data, our new paradigm will allow the estimation of genetic variation for disease susceptibility and quantitative traits that is free from confounding with non-genetic factors and will allow partitioning of genetic variation into additive and non-additive components.     

The role of phenotype in gene discovery in the whole genome sequencing era

As whole genome sequence becomes a routine component of gene discovery studies in humans, we will have an exhaustive catalog of genetic variation and the challenge becomes understanding the phenotypic consequences of these variants. Statistical genetic methods and analytical approaches that are concerned with optimizing phenotypes for gene discovery for complex traits offer two general categories of advantages. They may increase power to localize genes of interest and also aid in interpreting associations between genetic variants and disease outcomes by suggesting potential mechanisms and pathways through which genes may affect outcomes. Such phenotype optimization approaches include use of allied phenotypes such as symptoms or ages of onset to reduce genetic heterogeneity within a set of cases, study of quantitative risk factors or endophenotypes, joint analyses of related phenotypes, and derivation of new phenotypes designed to extract independent measures underlying the correlations among a set of related phenotypes through approaches such as principal components. New opportunities are also presented by technological advances that permit efficient collection of hundreds or thousands of phenotypes on an individual, including phenotypes more proximal to the level of gene action such as levels of gene expression, microRNAs, or metabolic and proteomic profiles.     

Association analysis of 94 candidate genes and schizophrenia-related endophenotypes

While it is clear that schizophrenia is highly heritable, the genetic basis of this heritability is complex. Human genetic, brain imaging, and model organism studies have met with only modest gains. A complementary research tactic is to evaluate the genetic substrates of quantitative endophenotypes with demonstrated deficits in schizophrenia patients. We used an Illumina custom 1,536-SNP array to interrogate 94 functionally relevant candidate genes for schizophrenia and evaluate association with both the qualitative diagnosis of schizophrenia and quantitative endophenotypes for schizophrenia. Subjects included 219 schizophrenia patients and normal comparison subjects of European ancestry and 76 schizophrenia patients and normal comparison subjects of African ancestry, all ascertained by the UCSD Schizophrenia Research Program. Six neurophysiological and neurocognitive endophenotype test paradigms were assessed: prepulse inhibition (PPI), P50 suppression, the antisaccade oculomotor task, the Letter-Number Span Test, the California Verbal Learning Test-II, and the Wisconsin Card Sorting Test-64 Card Version. These endophenotype test paradigms yielded six primary endophenotypes with prior evidence of heritability and demonstrated schizophrenia-related impairments, as well as eight secondary measures investigated as candidate endophenotypes. Schizophrenia patients showed significant deficits on ten of the endophenotypic measures, replicating prior studies and facilitating genetic analyses of these phenotypes. A total of 38 genes were found to be associated with at least one endophenotypic measure or schizophrenia with an empirical p-value<0.01. Many of these genes have been shown to interact on a molecular level, and eleven genes displayed evidence for pleiotropy, revealing associations with three or more endophenotypic measures. Among these genes were ERBB4 and NRG1, providing further support for a role of these genes in schizophrenia susceptibility. The observation of extensive pleiotropy for some genes and singular associations for others in our data may suggest both converging and independent genetic (and neural) pathways mediating schizophrenia risk and pathogenesis.     

Genetic architecture of knee radiographic joint space in healthy young adults

Evidence of a significant genetic component to the age-related degenerative joint disease osteoarthritis has been established, but the nature of genetic influences on normal joint morphology in healthy individuals remains unclear. Following up on our previous findings on the influence of body habitus on phenotypic variation in knee joint space [Duren et al., Human Biology 78:353-364 (2006)], the objective of the current study was to estimate the heritability of radiographic joint space in the knees of healthy young adults from a community-based sample of families. A sample of 253 subjects (mean age = 18.02 years) from 87 randomly ascertained nuclear and extended families was examined. Joint width (JW) and minimum joint space in the medial (MJS) and lateral (LJS) knee compartments were measured. A maximum-likelihood variance components method was used to estimate the heritability of MJS, LJS, and JW. Covariate effects of age, sex, age-by-sex interactions, stature, weight, and BMI were simultaneously estimated. Genetic correlation analyses were then conducted to examine relationships between trait pairs. MJS, LJS, and JW were each significantly heritable (p < 0.001), with heritabilities of 0.52, 0.53, and 0.63, respectively. The genetic correlation between MJS and LJS was not significantly different from 1. Genetic correlations between each joint space measure and JW were not significantly different from 0. This study demonstrates a significant genetic component to radiographic knee joint space during young adulthood in healthy subjects. This suggests that there are specific but as yet unidentified genes that influence the morphology of healthy articular cartilage, the target tissue of osteoarthritis. Genetic correlation analyses indicate complete pleiotropy between MJS and LJS but genetic independence of joint space and JW.     

Effects of measured susceptibility genes on cancer risk in family studies

Numerous family studies have been performed to assess the associations between cancer incidence and genetic and non-genetic risk factors and to quantitatively evaluate the cancer risk attributable to these factors. However, mathematical models that account for a measured hereditary susceptibility gene have not been fully explored in family studies. In this report, we proposed statistical approaches to precisely model a measured susceptibility gene fitted to family data and simultaneously determine the combined effects of individual risk factors and their interactions. Our approaches are structured for age-specific risk models based on Cox proportional hazards regression methods. They are useful for analyses of families and extended pedigrees in which measured risk genotypes are segregated within the family and are robust even when the genotypes are available only in some members of a family. We exemplified these methods by analyzing six extended pedigrees ascertained through soft-tissue sarcoma patients with p53 germ-line mutations. Our analyses showed that germ-line p53 mutations and sex had significant interaction effects on cancer risk. Our proposed methods in family studies are accurate and robust for assessing age-specific cancer risk attributable to a measured hereditary susceptibility gene, providing valuable inferences for genetic counseling and clinical management.     

Heritability and genetic correlations of metabolic disease-related phenotypes in Mexico: preliminary report from the GEMM Family Study

Cardiovascular disease (CVD) is a major cause of mortality in the Republic of Mexico, and metabolic syndrome, a complex of CVD risk factors, is increasingly prevalent. To date, however, there have been few studies of the genetic epidemiology of metabolic syndrome in Mexico. As a first step in implementing the GEMM Family Study, a large, multicenter collaborative study, we recruited 375 individuals in 21 extended families, without ascertainment on disease, at 9 medical institutions across Mexico. Participants were measured for anthropometric (stature, weight, waist circumference) and hemodynamic (blood pressure, heart rate) phenotypes; glucose, cholesterol, and triglyceride levels were measured in fasting blood. Variance components-based quantitative genetic analyses were performed using SOLAR. All phenotypes except diastolic blood pressure were significantly heritable. Consistent with the definition of metabolic syndrome, many phenotypes exhibited significant environmental correlation, and significant genetic correlations were found between measures of adiposity and fasting glucose and fasting triglyceride levels. These preliminary data represent the first heritability estimates for many of these phenotypes in the Republic of Mexico and indicate that this study design offers excellent power for future gene discovery relative to metabolic disease.     

Genetic and environmental influences on skeletal muscle phenotypes as a function of age and sex in large, multigenerational families of African heritage.

The aim of this study was to estimate the heritability of and environmental contributions to skeletal muscle phenotypes (appendicular lean mass and calf muscle cross-sectional area) in subjects of African descent and to determine whether heritability estimates are impacted by sex or age. Body composition was measured by dual-energy X-ray absorptiometry and computed tomography in 444 men and women aged 18 yr and older (mean: 43 yr) from eight large, multigenerational Afro-Caribbean families (family size range: 21-112). Using quantitative genetic methods, we estimated heritability and the association of anthropometric, lifestyle, and medical variables with skeletal muscle phenotypes. In the overall group, we estimated the heritability of lean mass and calf muscle cross-sectional area (h(2) = 0.18-0.23, P < 0.01) and contribution of environmental factors to these phenotypes (r(2) = 0.27-0.55, P < 0.05). In our age-specific analysis, the heritability of leg lean mass was lower in older vs. younger individuals (h(2) = 0.05 vs. 0.23, respectively, P = 0.1). Sex was a significant covariate in our models (P < 0.001), although sex-specific differences in heritability varied depending on the lean mass phenotype analyzed. High genetic correlations (rho(G) = 0.69-0.81; P < 0.01) between different lean mass measures suggest these traits share a large proportion of genetic components. Our results demonstrate the heritability of skeletal muscle traits in individuals of African heritage and that heritability may differ as a function of sex and age. As the loss of skeletal muscle mass is related to metabolic abnormalities, disability, and mortality in older individuals, further research is warranted to identify specific genetic loci that contribute to these traits in general and in a sex- and age-specific manner.     

Familial Resemblance in Eating Behaviors in Men and Women from the Quebec Family Study

Objective: It is commonly recognized that genetic, environmental, behavioral, and social factors are involved in the development of obesity. The family environment may play a key role in shaping children's eating behaviors. The purpose of this study was to estimate the degree of familial resemblance in eating behavioral traits (cognitive dietary restraint, disinhibition, and susceptibility to hunger). Research Methods and Procedures: Eating behavioral traits were assessed with the Three‐Factor Eating Questionnaire in 282 men and 402 women (202 families) from the Quebec Family Study. Familial resemblance for each trait (adjusted for age, sex, and BMI) was investigated using a familial correlation model. Results: The pattern of familial correlation showed significant spouse correlation for the three eating behavior phenotypes, as well as significant parent‐offspring and sibling correlations for disinhibition and susceptibility to hunger. According to the most parsimonious model, generalized heritability estimates (including genetic and shared familial environmental effects) reached 6%, 18%, and 28% for cognitive dietary restraint, disinhibition, and susceptibility to hunger, respectively. Discussion: These results suggest that there is a significant familial component to eating behavioral traits but that the additive genetic component appears to be small, with generalized heritability estimates ranging from 6% to 28%. Thus, non‐familial environmental factors and gene‐gene and gene‐environmental interactions seem to be the major determinants of the eating/behavioral traits.     

DNA markers in primate models of human disease

Nonhuman primates are particularly useful as animal models for common human diseases in which both genetic and environmental factors play important roles. The recent development of DNA markers (restriction fragment length polymorphisms, RFLPs) greatly increases the power of linkage analysis to detect major genes that affect quantitative phenotypes, including those related to diseases. This paper summarizes a strategy for using RFLPs in linkage analysis of baboon pedigrees to identify genes that control lipoprotein phenotype, which in turn is predictive of susceptibility to atherosclerosis. This strategy also can be applied to other common human diseases for which nonhuman primate models exist.     

The contribution of pleiotropy to blood pressure and body-mass index variation: the Gubbio Study.

Blood pressure (BP), body-mass index (BMI), and quantitative phenotypes thought to influence BP (e.g., lithium-sodium countertransport activity) were studied in 2,184 households comprising 5,376 people in Gubbio, Italy. Variance-components models were used to partition the variation of these phenotypes into components characterizing the effects of age-related, measured environmental, additive genetic, pleiotropic, unmeasured shared-household, and individual-specific (or random) factors. The goal of the investigation was to estimate the contribution of pleiotropy to variation in BP and BMI in population-based samples. Although our results suggest that numerous significant bivariate genetic correlations exist between BP and some of the traits investigated, they ultimately lead us to reject a prominent role for any individual bivariate pleiotropic system influencing the natural variation of BP. However, because we found evidence that many traits enter into small-impact pleiotropic relationships with BP, we cannot rule out the possibility that pleiotropic genes, when considered collectively, may contribute to BP variation at the population level. Similar results were obtained when BMI was taken as the primary variable of interest. We argue that the small but significant portion of BP variation explained by individual genes displaying bivariate pleiotropic effects is intuitive, in light of the relatively low heritabilities associated with quantitative cardiovascular phenotypes and the low phenotypic correlations between BP, BMI, and many other physiologically linked measures of cardiovascular function. Our results not only bear directly on both the nature of the multifactorial determinants of BP and the maintenance of BP variation in the population at large, but also emphasize the utility of variance-components models in epidemiologic and population genetics research. We discuss the implications of our results for genetic epidemiologists and medical researchers studying hypertension, as well as the limitations of our study and areas for future research.     

An Effective Method to Identify Heritable Components from Multivariate Phenotypes

Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.     

Progress in Multiple Sclerosis Genetics

A genetic component in the susceptibility to multiple sclerosis (MS) has long been known, and the first and major genetic risk factor, the HLA region, was identified in the 1970’s. However, only with the advent of genome-wide association studies in the past five years did the list of risk factors for MS grow from 1 to over 50. In this review, we summarize the search for MS risk genes and the latest results. Comparison with data from other autoimmune and neurological diseases and from animal models indicates parallels and differences between diseases. We discuss how these translate into an improved understanding of disease mechanisms, and address current challenges such as genotype-phenotype correlations, functional mechanisms of risk variants and the missing heritability.