Family-based Designs Research Articles

DIRECT ESTIMATION OF GENETIC LIABILITY FOR MENTAL ILLNESS USING ENDOPHENOTYPES

The concept of continuous liability underlies most inferential procedures that are used in complex disease genetics. Statistical genetic methods often attempt to take dichotomous affection status information and project it onto the unobservable liability scale. For substance abuse or any other disorder, this inference about individual genetic liability is often coarse unless additional information can be incorporated. In this talk, I propose a model that leads to improved estimation of individual genetic liability for substance abuse. It incorporates quantitative endophenotypes that are both heritable and genetically correlated with the diagnosis of substance abuse. Family-based designs are optimal for inferences regarding genetic liability since substantial additional information can be drawn from correlated information known about the liability distribution in related individuals. After reviewing the statistical framework required for the multivariate polygenic architecture of affection status and endophenotypes, I develop a best linear unbiased prediction scheme for estimating the genetic liability underlying each endophenotype. Based upon the observed genetic covariance matrix, a multivariate informed single index of genetic liability can be estimated. This index is theoretically largely free of environmental influence and should better represent the true genetic liability for each individual. The resulting liability index can be used as the core phenotype for subsequent analyses searching for specific causal genes and functional genetic variants of relevance to psychiatric disease. As an example, I utilize data from 1,628 Mexican American individuals who are members of large extended pedigrees from the Genetics of Brain Structure and Function Study. Estimates of individual genetic liability for major depressive disorder will be used as examples employing endophenotypes derived from cognitive performance and MRI-based measures of neuroanatomy and neurophysiology. Once estimated, I show how the liability estimates can then be used to search for likely functional non-synonymous deleterious variants that influence addictive behavior by exploiting the extensive whole genome sequence information that we have for this study. The proposed estimation of disease liability using additional information from endophenotypes represents a novel way to explicitly incorporate endophenotypic information into analyses while retaining a direct focus on the genetic basis of the disease itself.

Single Marker Family-Based Association Analysis Conditional on Parental Information.

Family-based designs have been commonly used in association studies. Different family structures such as extended pedigrees and nuclear families, including parent-offspring triads and families with multiple affected siblings (multiplex families), can be ascertained for family-based association analysis. Flexible association tests that can accommodate different family structures have been proposed. The pedigree disequilibrium test (PDT) (Martin et al., Am J Hum Genet 67:146-154, 2000) can use full genotype information from general (possibly extended) pedigrees with one or multiple affected siblings but requires parental genotypes or genotypes of unaffected siblings. On the other hand, the association in the presence of linkage (APL) test (Martin et al., Am J Hum Genet 73:1016-1026, 2003) is restricted to nuclear families with one or more affected siblings but can infer missing parental genotypes properly by accounting for identity-by-descent (IBD) parameters. Both the PDT and APL test are powerful association tests in the presence of linkage and can be used as complementary tools for association analysis. This chapter introduces these two tests and compares their properties. Recommendations and notes for performing the tests in practice are provided.

A meta-analysis of FZD3 gene polymorphisms and their association with schizophrenia.

The aim of this study was to investigate the potential association of FZD3 polymorphisms with schizophrenia. A systematic review and a meta-analysis were carried out comprising of nine genetic association studies, with both a population-based and a family-based design, and three genome-wide association studies. A total of 1601 family trios, 39 922 schizophrenic patients, and 61 287 healthy individuals were involved in the analysis and six polymorphisms were examined: rs2241802, rs2323019, rs352203, rs3757888, rs880481, and rs960914. A summary-based method for pooling genetic association studies under both family-based and population-based designs was used. Odds ratios along with their 95% confidence intervals were computed to compare the contrast of alleles in patients and controls. The results indicate a potentially weaker effect of FZD3 polymorphisms on schizophrenia than that suggested originally and possibly limited to Chinese populations. No relationship was identified between all examined polymorphisms and schizophrenia, except for rs352203, which plays a protective role against schizophrenia. However, this effect was mainly attributed to studies including Chinese patients. In the Chinese population, evidence for an elevated risk for schizophrenia linked to the rs2323019 polymorphism was also identified. Given the different linkage disequilibrium patterns observed in Chinese populations, schizophrenia may be related to some other polymorphisms of gene FZD3 that are in stronger linkage disequilibrium to Chinese than to the other populations studied.

Whole exome association of rare deletions in multiplex oral cleft families.

By sequencing the exomes of distantly related individuals in multiplex families, rare mutational and structural changes to coding DNA can be characterized and their relationship to disease risk can be assessed. Recently, several rare single nucleotide variants (SNVs) were associated with an increased risk of nonsyndromic oral cleft, highlighting the importance of rare sequence variants in oral clefts and illustrating the strength of family-based study designs. However, the extent to which rare deletions in coding regions of the genome occur and contribute to risk of nonsyndromic clefts is not well understood. To identify putative structural variants underlying risk, we developed a pipeline for rare hemizygous deletions in families from whole exome sequencing and statistical inference based on rare variant sharing. Among 56 multiplex families with 115 individuals, we identified 53 regions with one or more rare hemizygous deletions. We found 45 of the 53 regions contained rare deletions occurring in only one family member. Members of the same family shared a rare deletion in only eight regions. We also devised a scalable global test for enrichment of shared rare deletions.

Previous GWAS hits in relation to young-onset breast cancer.

Genome-wide association studies (GWAS) have identified dozens of single-nucleotide polymorphisms (SNPs) associated with breast cancer. Few studies focused on young-onset breast cancer, which exhibits etiologic and tumor-type differences from older-onset disease. Possible confounding by prenatal effects of the maternal genome has also not been considered. Using a family-based design for breast cancer before age 50, we assessed the relationship between breast cancer and 77 GWAS-identified breast cancer risk SNPs. We estimated relative risks (RR) for inherited and maternally mediated genetic effects. We also used published RR estimates to calculate genetic risk scores and model joint effects. Seventeen of the candidate SNPs were nominally associated with young-onset breast cancer in our 1296 non-Hispanic white affected families (uncorrected p value <0.05). Top-ranked SNPs included rs3803662-A (TOX3, RR=1.39; p=7.0×10-6), rs12662670-G (ESR1, RR=1.56; p=5.7×10-4), rs2981579-A (FGFR2, RR=1.24; p=0.002), and rs999737-G (RAD51B, RR=1.37; p=0.003). No maternally mediated effects were found. A risk score based on all 77 SNPs indicated that their overall relationship to young-onset breast cancer risk was more than additive (additive-fit p=2.2×10-7) and consistent with a multiplicative joint effect (multiplicative-fit p=0.27). With the multiplicative formulation, the case sister's genetic risk score exceeded that of her unaffected sister in 59% of families. The results of this family-based study indicate that no effects of previously identified risk SNPs were explained by prenatal effects of maternal variants. Many of the known breast cancer risk variants were associated with young-onset breast cancer, with evidence that TOX3, ESR1, FGFR2, and RAD51B are important for young-onset disease.

Joint association analysis of a binary and a quantitative trait in family samples.

In recent years, improved genotyping and sequencing technologies have enabled the discovery of new loci associated with various diseases or traits. For instance, by testing the association with each single-nucleotide variant (SNV) separately, genome-wide association studies (GWAS) have achieved tremendous success in identifying SNVs associated with specific traits. However, little is known about the common genetic basis of multiple traits owing to lack of efficient methods. With the use of extended quasi-likelihood, a Wald test has been proposed to perform a bivariate analysis of a continuous and a binary trait in unrelated samples. However, owing to its low computational efficiency, it has not been implemented in real applications to large-scale genetic studies. In this paper, we propose an efficient bivariate robust score test for two traits, one continuous and one binary, based on extended generalized estimating equations. Our approach is applicable to both family-based and unrelated study designs and can be extended to test the association of multiple traits. Our simulation studies demonstrate the type-I error rate of our approach is well controlled in all minor allele frequency (MAF) scenarios, with MAF ranging from 1 to 30%, and the method is more powerful in certain MAF scenarios than univariate testing with correction for multiple testing. Because of the computational advantage of score tests, our approach is readily applicable to GWAS or sequencing studies. Finally, we present a real application to uncover genetic variants associated with body mass index and type-2 diabetes in the Framingham Heart Study.

A novel statistical method for rare-variant association studies in general pedigrees.

Both population-based and family-based designs are commonly used in genetic association studies to identify rare variants that underlie complex diseases. For any type of study design, the statistical power will be improved if rare variants can be enriched in the samples. Family-based designs, with ascertainment based on phenotype, may enrich the sample for causal rare variants and thus can be more powerful than population-based designs. Therefore, it is important to develop family-based statistical methods that can account for ascertainment. In this paper, we develop a novel statistical method for rare-variant association studies in general pedigrees for quantitative traits. This method uses a retrospective view that treats the traits as fixed and the genotypes as random, which allows us to account for complex and undefined ascertainment of families. We then apply the newly developed method to the Genetic Analysis Workshop 19 data set and compare the power of the new method with two other methods for general pedigrees. The results show that the newly proposed method increases power in most of the cases we consider, more than the other two methods.

On combining family- and population-based sequencing data.

Several statistical group-based approaches have been proposed to detect effects of variation within a gene for each of the population- and family-based designs. However, unified tests to combine gene-phenotype associations obtained from these 2 study designs are not yet well established. In this study, we investigated the efficient combination of population-based and family-based sequencing data to evaluate best practices using the Genetic Analysis Workshop 19 (GAW19) data set. Because one design employed whole genome sequencing and the other whole exome sequencing, we examined variants overlapping both data sets. We used the family-based sequence kernel association test (famSKAT) to analyze the family- and population-based data sets separately as well as with a combined data set. These were compared against meta-analysis. Using the combined data, we showed that famSKAT has high power to detect associations between diastolic and/or systolic blood pressures and the genes that have causal variants with large effect sizes, such as MAP4, TNN, and CGN. However, when there was a considerable difference in the powers between family- and population-based data, famSKAT with the combined data had lower power than that from the population-based data alone. The famSKAT test statistic for the combined data can be influenced by sample imbalance from the 2 designs. This underscores the importance of foresight in study design as, in this situation, the greatly lower sample size in the family-based data essentially serves to dilute signal. We observed inflated type I errors in our simulation study, largely when using population-based data, which might be a result of principal components failing to completely account for population admixture in this cohort.

Gene coexpression network analysis for family studies based on a meta-analytic approach.

For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between genes are commonly used. In case of a family-based design, it can be problematic when there is a large between-family variation in expression levels. We propose here a gene coexpression network analysis for family studies. We build a coexpression network for each family and then combine the results. We applied our approach to data provided for analysis in the Genetic Analysis Workshop 19 and compared it to 2 naïve approaches—ignoring correlations among the expressions and decorrelating the gene expression by using the residuals of a mixed model—and a single-probe analysis. Our approach seemed to better deal with heterogeneity with regard to the naïve approaches. The naïve approaches did not provide any significant results, while our approach detected genes via indirect effects. It also detected more genes than the single-probe analysis.

Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data.

BackgroundIn the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis.MethodsWe compared the performance of several family- and population-based imputation methods in large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the performance of a new IBD mapping approach that we propose, which combines IBD information from known pedigrees with information from unrelated individuals.ResultsDifferent combinations of the imputation methods have varied imputation accuracies. Moreover, we showed gains from the use of both known pedigrees and unrelated individuals with our IBD mapping approach over the use of known pedigrees only.ConclusionsOur results represent accuracies of different combinations of imputation methods that may be useful for data sets similar to the GAW19 pedigree data. Our IBD mapping approach, which uses both known pedigree and unrelated individuals, performed better than classical linkage analysis.

Rare variant association test in family-based sequencing studies.

The objective of this article is to introduce valid and robust methods for the analysis of rare variants for family-based exome chips, whole-exome sequencing or whole-genome sequencing data. Family-based designs provide unique opportunities to detect genetic variants that complement studies of unrelated individuals. Currently, limited methods and software tools have been developed to assist family-based association studies with rare variants, especially for analyzing binary traits. In this article, we address this gap by extending existing burden and kernel-based gene set association tests for population data to related samples, with a particular emphasis on binary phenotypes. The proposed approach blends the strengths of kernel machine methods and generalized estimating equations. Importantly, the efficient generalized kernel score test can be applied as a mega-analysis framework to combine studies with different designs. We illustrate the application of the proposed method using data from an exome sequencing study of autism. Methods discussed in this article are implemented in an R package 'gskat', which is available on CRAN and GitHub.

Serum metabolomics profile of type 2 diabetes mellitus in a Brazilian rural population

The development of common forms of diabetes comes from the interaction between environmental and genetic factors, and the consequences of poor glycemic control in these patients could result in several complications. Metabolomic studies for type 2 diabetes mellitus in serum/plasma have reported changes in numerous metabolites, which might be considered possible targets for future mechanistic research. However, the specific role of a particular metabolite as cause or consequence of diabetes derangements is difficult to establish. As type 2 diabetes is a disease that changes the metabolic profile in several levels, this work aimed to compare the metabolomic profiles of type 2 diabetes mellitus and non-diabetic participants. In addition, we exploited our family-based study design to bring a better understanding of the causal relationship of identified metabolites and diabetes. In the current study, population based metabolomics was applied in 939 subjects from the Baependi Heart Study. Participants were separated into two groups: diabetic (77 individuals) and non-diabetic (862 individuals), and the metabolic profile was performed by GC/MS technique. We have identified differentially concentrated metabolites in serum of diabetic and non-diabetic individuals. We identified 72 metabolites up-regulated in type 2 diabetes mellitus compared to non-diabetics. It was possible to recapitulate the main pathways that the literature shows as changed in diabetes. Also, based on metabolomic profile, we separated pre-diabetic individuals (with glucose concentration between 100–125 mg/dL) from non-diabetics and diabetics. Finally, using heritability analysis, we were able to suggest metabolites in which altered levels may precede diabetic development. Our data can be used to derive a better understanding of the causal relationship of the observed associations and help to prioritize diabetes-associated metabolites for further work.

GCORE-sib: An efficient gene-gene interaction tool for genome-wide association studies based on discordant sib pairs.

BackgroundA computationally efficient tool is required for a genome-wide gene-gene interaction analysis that tests an extremely large number of single-nucleotide polymorphism (SNP) interaction pairs in genome-wide association studies (GWAS). Current tools for GWAS interaction analysis are mainly developed for unrelated case-control samples. Relatively fewer tools for interaction analysis are available for complex disease studies with family-based design, and these tools tend to be computationally expensive.ResultsWe developed a fast gene-gene interaction test, GCORE-sib, for discordant sib pairs and implemented the test into an efficient tool. We used simulations to demonstrate that the GCORE-sib has correct type I error rates and has comparable power to that of the regression-based interaction test. We also showed that the GCORE-sib can run more than 10 times faster than the regression-based test. Finally, the GCORE-sib was applied to a GWAS dataset with approximately 2,000 discordant sib pairs, and the GCORE-sib finished testing 19,368,078,382 pairs of SNPs within 6 days.ConclusionsAn efficient gene-gene interaction tool for discordant sib pairs was developed. It will be very useful for genome-wide gene-gene interaction analysis in GWAS using discordant sib pairs. The tool can be downloaded for free at http://gcore-sib.sourceforge.net.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1145-z) contains supplementary material, which is available to authorized users.

Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.

Parental perceptions on childrens out-of-school physical activity and family-based physical activity

ABSTRACTThis study explored parents’ physical activity (PA) knowledge and perceptions of children’s out-of-school PA to formatively contribute to a family-based intervention design. Eleven telephone interviews were conducted with parents of children aged 10–11 years. Child and parent data were triangulated and family case studies were written. Most parents were unaware of the UK child PA guidelines and whether their child achieved the guidelines daily. PA for many parents was attributed to healthy weight status, and the neighbourhood environment was perceived as unconducive to children’s outdoor play which consequently increased the attractiveness of adult supervised organized activities. Consulting with parents in a formative sense prior to familial PA intervention facilitates intervention content to be aligned with family-specific perceptions and needs, and offers opportunities to communicate the relevance of programs to parents. This may aid subsequent intervention recruitment and engagement.

FamPipe: An Automatic Analysis Pipeline for Analyzing Sequencing Data in Families for Disease Studies.

In disease studies, family-based designs have become an attractive approach to analyzing next-generation sequencing (NGS) data for the identification of rare mutations enriched in families. Substantial research effort has been devoted to developing pipelines for automating sequence alignment, variant calling, and annotation. However, fewer pipelines have been designed specifically for disease studies. Most of the current analysis pipelines for family-based disease studies using NGS data focus on a specific function, such as identifying variants with Mendelian inheritance or identifying shared chromosomal regions among affected family members. Consequently, some other useful family-based analysis tools, such as imputation, linkage, and association tools, have yet to be integrated and automated. We developed FamPipe, a comprehensive analysis pipeline, which includes several family-specific analysis modules, including the identification of shared chromosomal regions among affected family members, prioritizing variants assuming a disease model, imputation of untyped variants, and linkage and association tests. We used simulation studies to compare properties of some modules implemented in FamPipe, and based on the results, we provided suggestions for the selection of modules to achieve an optimal analysis strategy. The pipeline is under the GNU GPL License and can be downloaded for free at http://fampipe.sourceforge.net.

Cognitive ability and risk for substance misuse in men: genetic and environmental correlations in a longitudinal nation-wide family study.

To investigate the association in males between cognitive ability in late adolescence and subsequent substance misuse-related events, and to study the underlying genetic and environmental correlations. A population-based longitudinal study with three different family-based designs. Cox proportional hazards models were conducted to investigate the association at the individual level. Bivariate quantitative genetic modelling in (1) full brothers and maternal half-brothers, (2) full brothers reared together and apart and (3) monozygotic and dizygotic twin brothers was used to estimate genetic and environmental correlations. Register-based study in Sweden. The full sample included 1 402 333 Swedish men born 1958-91 and conscripted at mean age 18.2 [standard deviation (SD) = 0.5] years. A total of 1 361 066 men who had no substance misuse events before cognitive assessment at mandatory military conscription were included in the Cox regression models, with a follow-up time of up to 35.6 years. Cognitive ability was assessed at conscription with the Swedish Enlistment Battery. Substance misuse events included alcohol- and drug-related court convictions, medical treatments and deaths, available from governmental registries. Lower cognitive ability in late adolescence predicted an increased risk for substance misuse events [hazard ratio (HR) for a 1-stanine unit decrease in cognitive ability: 1.29, 95% confidence interval (CI) = 1.29-1.30]. The association was somewhat attenuated within clusters of full brothers (HR = 1.21, 95% CI = 1.20-1.23). Quantitative genetic analyses indicated that the association was due primarily to genetic influences; the genetic correlations ranged between -0.39 (95% CI = -0.45, -0.34) and -0.52 (95% CI -0.55, -0.48) in the three different designs. Shared genetic influences appear to underlie the association between low cognitive ability and subsequent risk for substance misuse events among Swedish men.

Detecting multi-way epistasis in family-based association studies.

The era of genome-wide association studies (GWAS) has led to the discovery of numerous genetic variants associated with disease. Better understanding of whether these or other variants interact leading to differential risk compared with individual marker effects will increase our understanding of the genetic architecture of disease, which may be investigated using the family-based study design. We present M-TDT (the multi-locus transmission disequilibrium test), a tool for detecting family-based multi-locus multi-allelic effects for qualitative or quantitative traits, extended from the original transmission disequilibrium test (TDT). Tests to handle the comparison between additive and epistatic models, lack of independence between markers and multiple offspring are described. Performance of M-TDT is compared with a multifactor dimensionality reduction (MDR) approach designed for investigating families in the hypothesis-free genome-wide setting (the multifactor dimensionality reduction pedigree disequilibrium test, MDR-PDT). Other methods derived from the TDT or MDR to investigate genetic interaction in the family-based design are also discussed. The case of three independent biallelic loci is illustrated using simulations for one- to three-locus alternative hypotheses. M-TDT identified joint-locus effects and distinguished effectively between additive and epistatic models. We showed a practical example of M-TDT based on three genes already known to be implicated in malaria susceptibility. Our findings demonstrate the value of M-TDT in a hypothesis-driven context to test for multi-way epistasis underlying common disease etiology, whereas MDR-PDT-based methods are more appropriate in a hypothesis-free genome-wide setting.

Survival analysis with delayed entry in selected families with application to human longevity.

In the field of aging research, family-based sampling study designs are commonly used to study the lifespans of long-lived family members. However, the specific sampling procedure should be carefully taken into account in order to avoid biases. This work is motivated by the Leiden Longevity Study, a family-based cohort of long-lived siblings. Families were invited to participate in the study if at least two siblings were 'long-lived', where 'long-lived' meant being older than 89 years for men or older than 91 years for women. As a result, more than 400 families were included in the study and followed for around 10 years. For estimation of marker-specific survival probabilities and correlations among life times of family members, delayed entry due to outcome-dependent sampling mechanisms has to be taken into account. We consider shared frailty models to model left-truncated correlated survival data. The treatment of left truncation in shared frailty models is still an open issue and the literature on this topic is scarce. We show that the current approaches provide, in general, biased estimates and we propose a new method to tackle this selection problem by applying a correction on the likelihood estimation by means of inverse probability weighting at the family level.

Oxytocin and vasopressin hormone genes in children's externalizing problems: A cognitive endophenotype approach

Externalizing problems are among the most common mental health problems of children. Research suggests that these problems are heritable, yet little is known about the specific genes involved in their pathophysiology. The current study examined a genotype-endophenotype-phenotype model of externalizing problems in 320 preschool-aged children. Markers of the oxytocin (OXT) and arginine vasopressin (AVP) hormone genes were selected as candidates owing to their known association with psychopathology in other domains. We tested whether OXT and AVP variants were related to children's externalizing problems, as well as two cognitive endophenotypes presumed to underlie these problems: theory of mind (ToM) and executive functioning (EF). Externalizing problems were assessed at age 4.5 using a previously-validated rating scale. ToM and EF were measured with age-appropriate tasks. Using a family-based association design and controlling for non-genomic confounds, support was found for an association between a two-marker OXT haplotype (rs2740210–rs2770378) and a two-marker AVP haplotype (rs1887854–rs3761249) and externalizing problems. Specific associations of these haplotypes with ToM and EF were also observed. Further, ToM and EF were shown to independently and jointly predict externalizing problems, and to partially mediate the effects of OXT and AVP on externalizing problems. This study provides the first evidence that genetic variation in OXT and AVP may contribute to individual differences in childhood externalizing problems, and that these effects may operate through emerging neurocognitive abilities in the preschool period.