Covariance component models for multivariate binary traits in family data analysis

Variance Components Models for Analysis of Big Family Data of Health Outcomes in the Lifelines Cohort Study

Large multigenerational cohort studies offer powerful ways to study the hereditary effects on various health outcomes. However, accounting for complex kinship relations in big data structures can be methodologically challenging. The traditional kinship model is computationally infeasible when considering thousands of individuals. In this article, we propose a computationally efficient alternative that employs fractional relatedness of family members through a series of founding members. The primary goal of this study is to investigate whether the effect of determinants on health outcome variables differs with and without accounting for family structure. We compare a fixed-effects model without familial effects with several variance components models that account for heritability and shared environment structure. Our secondary goal is to apply the fractional relatedness model in a realistic setting. Lifelines is a three-generation cohort study investigating the biological, behavioral, and environmental determinants of healthy aging. We analyzed a sample of 89,353 participants from 32,452 reconstructed families. Our primary conclusion is that the effect of determinants on health outcome variables does not differ with and without accounting for family structure. However, accounting for family structure through fractional relatedness allows for estimating heritability in a computationally efficient way, showing some interesting differences between physical and mental quality of life heritability. We have shown through simulations that the proposed fractional relatedness model performs better than the standard kinship model, not only in terms of computational time and convenience of fitting using standard functions in R, but also in terms of bias of heritability estimates and coverage.

Heritable Variation, With Little or No Maternal Effect, Accounts for Recurrence Risk to Autism Spectrum Disorder in Sweden

BACKGROUND

Autism spectrum disorder (ASD) has both genetic and environmental origins, including potentially maternal effects. Maternal effects describe the association of one or more maternal phenotypes with liability to ASD in progeny that are independent of maternally transmitted risk alleles. While maternal effects could play an important role, consistent with association to maternal traits such as immune status, no study has estimated maternal, additive genetic, and environmental effects in ASD.

METHODS

Using a population-based sample consisting of all children born in Sweden from 1998 to 2007 and their relatives, we fitted statistical models to family data to estimate the variance in ASD liability originating from maternal, additive genetic, and shared environmental effects. We calculated sibling and cousin family recurrence risk ratio as a direct measure of familial, genetic, and environmental risk factors and repeated the calculations on diagnostic subgroups, specifically autistic disorder (AD) and spectrum disorder (SD), which included Asperger's syndrome and/or pervasive developmental disorder not otherwise specified.

RESULTS

The sample consisted of 776,212 children of whom 11,231 had a diagnosis of ASD: 4554 with AD, 6677 with SD. We found support for large additive genetic contribution to liability; heritability (95% confidence interval [CI]) was estimated to 84.8% (95% CI: 73.1-87.3) for ASD, 79.6% (95% CI: 61.2-85.1) for AD, and 76.4% (95% CI: 63.0-82.5) for SD.

CONCLUSIONS

There was modest, if any, contribution of maternal effects to liability for ASD, including subtypes AD and SD, and there was no support for shared environmental effects. These results show liability to ASD arises largely from additive genetic variation.

Using summary data from the danish national registers to estimate heritabilities for schizophrenia, bipolar disorder, and major depressive disorder

Estimates of heritability of psychiatric disorders quantify the genetic contribution to their etiology. Estimation of these parameters requires affected status on probands and their family members. Traditionally, heritabilities have been estimated from families ascertained from specific hospital registers, but accumulating sufficient numbers of families can be difficult. Larger sample sizes are achievable from national registries, but calculation of heritability from individual level data from these data sets is accompanied by other problems. Here, we use published summary data from a national population-based cohort of >2.6 million persons in Denmark to estimate heritabilities of schizophrenia, bipolar disorder, and major depressive disorder (MDD). The summary data comprised cumulative incidences up to 52 years of age for schizophrenia and bipolar disorder and up to 51 years for MDD in offspring where either one or both parents were diagnosed with one of these disorders. Estimates of the heritabilities of the liability to developing schizophrenia, bipolar disorder, and MDD are 0.67 (95% confidence interval (CI) 0.64–0.71), 0.62 (95% CI 0.58–0.65), and 0.32 (95% CI 0.30–0.34) respectively. The estimates may be inflated by common environmental effects, but despite this, they are somewhat lower for schizophrenia and bipolar disorder than those estimated from contemporary twin samples. The lower estimates may reflect the diverse environments (including diagnostic interpretation) that contribute to national data, compared to twin/family studies. Our estimates are similar to those estimated previously from national data of Sweden, and they may be more representative of the international samples brought together for large-scale genome-wide association studies. We investigated the estimation of genetic correlations from these data. We used simulation to conclude that estimates may not be interpretable and so report them only in the Section “Appendix.”

Identifying comorbidity patterns of health conditions via cluster analysis of pairwise concordance statistics

Identification of comorbidity patterns of health conditions is critical for evidence-based practice to improve the prevention, treatment and health care of relevant diseases. Existing approaches focus mainly on either using descriptive measures of comorbidity in terms of the prevalence of coexisting conditions, or addressing the prevalence of comorbidity based on a particular disease (e.g. psychosis) or a specific population (e.g. hospital patients). As coincidental comorbidity by chance increases with the prevalence rates of the conditions, which in turn depend heavily on the population under study, research findings on comorbidity patterns using those approaches may provide unreliable results. In this paper, we propose an asymmetric version of Somers' D statistic to provide a quantitative measure of comorbidity that accounts for co-occurrence of conditions by chance, and develop a unified clustering algorithm to identify comorbidity patterns with adjustment for multiple testing and control for the false discovery rate. We assess the applicability of the proposed comorbidity measure and investigate the performance of the proposed procedure for the adjustment of multiple testing by conducting a comparative study and a sensitivity analysis, respectively. The proposed method is illustrated using a national survey data set of mental health and wellbeing and a national health survey data set in Australia.

Matched ascertainment of informative families for complex genetic modelling

Family data are used extensively in quantitative genetic studies to disentangle the genetic and environmental contributions to various diseases. Many family studies based their analysis on population-based registers containing a large number of individuals composed of small family units. For binary trait analyses, exact marginal likelihood is a common approach, but, due to the computational demand of the enormous data sets, it allows only a limited number of effects in the model. This makes it particularly difficult to perform joint estimation of variance components for a binary trait and the potential confounders. We have developed a data-reduction method of ascertaining informative families from population-based family registers. We propose a scheme where the ascertained families match the full cohort with respect to some relevant statistics, such as the risk to relatives of an affected individual. The ascertainment-adjusted analysis, which we implement using a pseudo-likelihood approach, is shown to be efficient relative to the analysis of the whole cohort and robust to mis-specification of the random effect distribution.

The tryptophan hydroxylase 1 (TPH1) gene, schizophrenia susceptibility, and suicidal behavior: a multi-centre case-control study and meta-analysis

Serotonin (5-hydroxytryptamin; 5-HT) alternations has since long been suspected in the pathophysiology of schizophrenia. Tryptophan hydroxylase (tryptophan 5-monooxygenase; TPH) is the rate-limiting enzyme in the biosynthesis of 5-HT, and sequence variation in intron 6 of the TPH1 gene has been associated with schizophrenia. The minor allele (A) of this polymorphism (A218C) is also more frequent in patients who have attempted suicide and individuals who died by suicide, than in healthy control individuals. In an attempt to replicate previous findings, five single nucleotide polymorphisms (SNPs) were genotyped in 837 Scandinavian schizophrenia patients and 1,473 controls. Three SNPs spanning intron 6 and 7, including the A218C and A779C polymorphisms, were associated with schizophrenia susceptibility (P = 0.019). However there were no differences in allele frequencies of these loci between affected individuals having attempted suicide at least once and patients with no history of suicide attempts (P = 0.84). A systematic literature review and meta-analysis support the A218C polymorphism as a susceptibility locus for schizophrenia (odds ratio 1.17, 95% confidence interval 1.07-1.29). Association studies on suicide attempts are however conflicting (heterogeneity index I(2) = 0.54) and do not support the A218C/A779C polymorphisms being a susceptibility locus for suicidal behavior among individuals diagnosed with a psychiatric disorder (OR = 0.96 [0.80-1.16]). We conclude that the TPH1 A218/A779 locus increases the susceptibility of schizophrenia in Caucasian and Asian populations. In addition, the data at hand suggest that the locus contributes to the liability of psychiatric disorders characterized by elevated suicidal rates, rather than affecting suicidal behavior of individuals suffering from a psychiatric disorder.

Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study

BACKGROUND

Whether schizophrenia and bipolar disorder are the clinical outcomes of discrete or shared causative processes is much debated in psychiatry. We aimed to assess genetic and environmental contributions to liability for schizophrenia, bipolar disorder, and their comorbidity.

METHODS

We linked the multi-generation register, which contains information about all children and their parents in Sweden, and the hospital discharge register, which includes all public psychiatric inpatient admissions in Sweden. We identified 9 009 202 unique individuals in more than 2 million nuclear families between 1973 and 2004. Risks for schizophrenia, bipolar disorder, and their comorbidity were assessed for biological and adoptive parents, offspring, full-siblings and half-siblings of probands with one of the diseases. We used a multivariate generalised linear mixed model for analysis of genetic and environmental contributions to liability for schizophrenia, bipolar disorder, and the comorbidity.

FINDINGS

First-degree relatives of probands with either schizophrenia (n=35 985) or bipolar disorder (n=40 487) were at increased risk of these disorders. Half-siblings had a significantly increased risk (schizophrenia: relative risk [RR] 3.6, 95% CI 2.3-5.5 for maternal half-siblings, and 2.7, 1.9-3.8 for paternal half-siblings; bipolar disorder: 4.5, 2.7-7.4 for maternal half-siblings, and 2.4, 1.4-4.1 for paternal half-siblings), but substantially lower than that of the full-siblings (schizophrenia: 9.0, 8.5-11.6; bipolar disorder: 7.9, 7.1-8.8). When relatives of probands with bipolar disorder were analysed, increased risks for schizophrenia existed for all relationships, including adopted children to biological parents with bipolar disorder. Heritability for schizophrenia and bipolar disorder was 64% and 59%, respectively. Shared environmental effects were small but substantial (schizophrenia: 4.5%, 4.4%-7.4%; bipolar disorder: 3.4%, 2.3%-6.2%) for both disorders. The comorbidity between disorders was mainly (63%) due to additive genetic effects common to both disorders.

INTERPRETATION

Similar to molecular genetic studies, we showed evidence that schizophrenia and bipolar disorder partly share a common genetic cause. These results challenge the current nosological dichotomy between schizophrenia and bipolar disorder, and are consistent with a reappraisal of these disorders as distinct diagnostic entities.

Approaches for unraveling the joint genetic determinants of schizophrenia and bipolar disorder

Since Emil Kraepelin proposed in 1919 that dementia praecox (schizophrenia) be differentiated from manic depression (bipolar disorder), the concept of nosological dichotomy has greatly influenced the diagnosis, treatment, and research of pathogenesis of these 2 disorders. However, this concept has recently been challenged by increasing evidence showing biological overlap between schizophrenia and bipolar disorder. This article reviews some of the previous evidence for phenomenological and molecular overlaps between these 2 disorders. We then discuss approaches for examining shared etiological mechanisms with a concentration on genetic factors. We have put a particular emphasis on incorporating the concept of endophenotypes in research of shared genetic liability for these 2 disorders.