Case-control association testing with related individuals: a more powerful quasi-likelihood score test

JASPER: fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits and microbiome abundances. It allows for covariates, ascertainment and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, some of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

Exonic variants of the P2RX7 gene in familial multiple sclerosis

INTRODUCTION

Several studies have analysed the presence of P2RX7 variants in patients with MS, reporting diverging results.

METHODS

Our study analyses P2RX7 variants detected through whole-exome sequencing (WES).

RESULTS

We analysed P2RX7, P2RX4, and CAMKK2 gene variants detected by whole-exome sequencing in all living members (n = 127) of 21 families including at least 2 individuals with multiple sclerosis. P2RX7 gene polymorphisms previously associated with autoimmune disease. Although no differences were observed between individuals with and without multiple sclerosis, we found greater polymorphism of gain-of-function variants of P2RX7 in families with individuals with multiple sclerosis than in the general population. Copresence of gain-of-function and loss-of-function variants was not observed to reduce the risk of presenting the disease. Three families displayed heterozygous gain-of-function SNPs in patients with multiple sclerosis but not in healthy individuals. We were unable to determine the impact of copresence of P2RX4 and CAMKK2 variants with P2RX7 variants, or the potential effect of the different haplotypes described in the gene. No clinical correlations with other autoimmune diseases were observed in our cohort.

CONCLUSIONS

Our results support the hypothesis that the disease is polygenic and point to a previously unknown mechanism of genetic predisposition to familial forms of multiple sclerosis. P2RX7 gene activity can be modified, which suggests the possibility of preventive pharmacological treatments for families including patients with familial multiple sclerosis.

Exploiting family history in aggregation unit-based genetic association tests

The development of sequencing technology calls for new powerful methods to detect disease associations and lower the cost of sequencing studies. Family history (FH) contains information on disease status of relatives, adding valuable information about the probands' health problems and risk of diseases. Incorporating data from FH is a cost-effective way to improve statistical evidence in genetic studies, and moreover, overcomes limitations in study designs with insufficient cases or missing genotype information for association analysis. We proposed family history aggregation unit-based test (FHAT) and optimal FHAT (FHAT-O) to exploit available FH for rare variant association analysis. Moreover, we extended liability threshold model of case-control status and FH (LT-FH) method in aggregated unit-based methods and compared that with FHAT and FHAT-O. The computational efficiency and flexibility of the FHAT and FHAT-O were demonstrated through both simulations and applications. We showed that FHAT, FHAT-O, and LT-FH methods offer reasonable control of the type I error unless case/control ratio is unbalanced, in which case they result in smaller inflation than that observed with conventional methods excluding FH. We also demonstrated that FHAT and FHAT-O are more powerful than LT-FH and conventional methods in many scenarios. By applying FHAT and FHAT-O to the analysis of all cause dementia and hypertension using the exome sequencing data from the UK Biobank, we showed that our methods can improve significance for known regions. Furthermore, we replicated the previous associations in all cause dementia and hypertension and detected novel regions through the exome-wide analysis.

Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies

MOTIVATION

In the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution.

RESULTS

We propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case-control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant-phenotype associations.

AVAILABILITY AND IMPLEMENTATION

The summary statistics and code generated in this study are available at https://github.com/styvon/TAPE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Consequences of a Rare Complement Factor H Variant for Age-Related Macular Degeneration in the Amish

Purpose

Genetic variants in the complement factor H gene (CFH) have been consistently implicated in age-related macular degeneration (AMD) risk. However, their functional effects are not fully characterized. We previously identified a rare, AMD-associated variant in CFH (P503A, rs570523689) in 19 Amish individuals, but its functional consequences were not investigated.

Methods

We performed genotyping for CFH P503A in 1326 Amish individuals to identify additional risk allele carriers. We examined differences for age at AMD diagnosis between carriers and noncarriers. In blood samples from risk allele carriers and noncarriers, we quantified (i) CFH RNA expression, (ii) CFH protein expression, and (iii) C-reactive protein (CRP) expression. Potential changes to the CFH protein structure were interrogated computationally with Phyre² and Chimera software programs.

Results

We identified 39 additional carriers from Amish communities in Ohio and Indiana. On average, carriers were younger than noncarriers at AMD diagnosis, but this difference was not significant. CFH transcript and protein levels in blood samples from Amish carriers and noncarriers were also not significantly different. CRP levels were also comparable in plasma samples from carriers and noncarriers. Computational protein modeling showed slight changes in the CFH protein conformation that were predicted to alter interactions between the CFH 503 residue and other neighboring residues.

Conclusions

In total, we have identified 58 risk allele carriers for CFH P503A in the Ohio and Indiana Amish. Although we did not detect significant differences in age at AMD diagnosis or expression levels of CFH in blood samples from carriers and noncarriers, we observed modest structural changes to the CFH protein through in silico modeling. Based on our functional and computational observations, we hypothesize that CFH P503A may affect CFH binding or function rather than expression, which would require additional research to confirm.

Analysis of GWAS-nominated loci for lung cancer and COPD revealed a new asthma locus

BACKGROUND

Asthma, lung cancer (LC) and chronic obstructive pulmonary disease (COPD) are three respiratory diseases characterized by complex mechanisms underlying and genetic predispositions, with asthma having the highest calculated heritability. Despite efforts deployed in the last decades, only a small part of its heritability has been elucidated. It was hypothesized that shared genetic factors by these three diseases could help identify new asthma loci.

METHODS

GWAS-nominated LC and COPD loci were selected among studies performed in Caucasian cohorts using the GWAS Catalog. Genetic analyses were carried out for these loci in the Saguenay-Lac-Saint-Jean (SLSJ) asthma familial cohort and then replicated in two independent cohorts (the Canadian Cohort Obstructive Lung Disease [CanCOLD] and the Epidemiological Study of the Genetics and Environment of Asthma [EGEA]).

RESULTS

Analyses in the SLSJ cohort identified 2851 and 4702 genetic variants to be replicated in the CanCOLD and EGEA cohorts for LC and COPD loci respectively. Replication and meta-analyses allowed the association of one new locus with asthma, 2p24.3, from COPD studies. None was associated from LC studies reported.

CONCLUSIONS

The approach used in this study contributed to better understand the heritability of asthma with shared genetic backgrounds of respiratory diseases.

Family history aggregation unit-based tests to detect rare genetic variant associations with application to the Framingham Heart Study

A challenge in standard genetic studies is maintaining good power to detect associations, especially for low prevalent diseases and rare variants. The traditional methods are most powerful when evaluating the association between variants in balanced study designs. Without accounting for family correlation and unbalanced case-control ratio, these analyses could result in inflated type I error. One cost-effective solution to increase statistical power is exploitation of available family history (FH) that contains valuable information about disease heritability. Here, we develop methods to address the aforementioned type I error issues while providing optimal power to analyze aggregates of rare variants by incorporating additional information from FH. With enhanced power in these methods exploiting FH and accounting for relatedness and unbalanced designs, we successfully detect genes with suggestive associations with Alzheimer disease, dementia, and type 2 diabetes by using the exome chip data from the Framingham Heart Study.

An Efficient Score Test Integrated with Empirical Bayes for Genome-Wide Association Studies

Many methods used in multi-locus genome-wide association studies (GWAS) have been developed to improve statistical power. However, most existing multi-locus methods are not quicker than single-locus methods. To address this concern, we proposed a fast score test integrated with Empirical Bayes (ScoreEB) for multi-locus GWAS. Firstly, a score test was conducted for each single nucleotide polymorphism (SNP) under a linear mixed model (LMM) framework, taking into account the genetic relatedness and population structure. Then, all of the potentially associated SNPs were selected with a less stringent criterion. Finally, Empirical Bayes in a multi-locus model was performed for all of the selected SNPs to identify the true quantitative trait nucleotide (QTN). Our new method ScoreEB adopts the similar strategy of multi-locus random-SNP-effect mixed linear model (mrMLM) and fast multi-locus random-SNP-effect EMMA (FASTmrEMMA), and the only difference is that we use the score test to select all the potentially associated markers. Monte Carlo simulation studies demonstrate that ScoreEB significantly improved the computational efficiency compared with the popular methods mrMLM, FASTmrEMMA, iterative modified-sure independence screening EM-Bayesian lasso (ISIS EM-BLASSO), hybrid of restricted and penalized maximum likelihood (HRePML) and genome-wide efficient mixed model association (GEMMA). In addition, ScoreEB remained accurate in QTN effect estimation and effectively controlled false positive rate. Subsequently, ScoreEB was applied to re-analyze quantitative traits in plants and animals. The results show that ScoreEB not only can detect previously reported genes, but also can mine new genes.

Whole-genome sequencing identifies functional noncoding variation in SEMA3C that cosegregates with dyslexia in a multigenerational family

Dyslexia is a common heritable developmental disorder involving impaired reading abilities. Its genetic underpinnings are thought to be complex and heterogeneous, involving common and rare genetic variation. Multigenerational families segregating apparent monogenic forms of language-related disorders can provide useful entrypoints into biological pathways. In the present study, we performed a genome-wide linkage scan in a three-generational family in which dyslexia affects 14 of its 30 members and seems to be transmitted with an autosomal dominant pattern of inheritance. We identified a locus on chromosome 7q21.11 which cosegregated with dyslexia status, with the exception of two cases of phenocopy (LOD = 2.83). Whole-genome sequencing of key individuals enabled the assessment of coding and noncoding variation in the family. Two rare single-nucleotide variants (rs144517871 and rs143835534) within the first intron of the SEMA3C gene cosegregated with the 7q21.11 risk haplotype. In silico characterization of these two variants predicted effects on gene regulation, which we functionally validated for rs144517871 in human cell lines using luciferase reporter assays. SEMA3C encodes a secreted protein that acts as a guidance cue in several processes, including cortical neuronal migration and cellular polarization. We hypothesize that these intronic variants could have a cis-regulatory effect on SEMA3C expression, making a contribution to dyslexia susceptibility in this family.

A rare variant in EZH2 is associated with prostate cancer risk

Prostate cancer (PrCa) is highly heritable, and although rare variants contribute significantly to PrCa risk, few have been identified to date. Herein, whole-genome sequencing was performed in a large PrCa family featuring multiple affected relatives spanning several generations. A rare, predicted splice site EZH2 variant, rs78589034 (G > A), was identified as segregating with disease in all but two individuals in the family, one of whom was affected with lymphoma and bowel cancer and a female relative. This variant was significantly associated with disease risk in combined familial and sporadic PrCa datasets (n = 1551; odds ratio [OR] = 3.55, P = 1.20 × 10^-5 ). Transcriptome analysis was performed on prostate tumour needle biopsies available for two rare variant carriers and two wild-type cases. Although no allele-dependent differences were detected in EZH2 transcripts, a distinct differential gene expression signature was observed when comparing prostate tissue from the rare variant carriers with the wild-type samples. The gene expression signature comprised known downstream targets of EZH2 and included the top-ranked genes, DUSP1, FOS, JUNB and EGR1, which were subsequently validated by qPCR. These data provide evidence that rs78589034 is associated with increased PrCa risk in Tasmanian men and further, that this variant may be associated with perturbed EZH2 function in prostate tissue. Disrupted EZH2 function is a driver of tumourigenesis in several cancers, including prostate, and is of significant interest as a therapeutic target.

A generalized robust allele-based genetic association test

The allele-based association test, comparing allele frequency difference between case and control groups, is locally most powerful. However, application of the classical allelic test is limited in practice, because the method is sensitive to the Hardy-Weinberg equilibrium (HWE) assumption, not applicable to continuous traits, and not easy to account for covariate effect or sample correlation. To develop a generalized robust allelic test, we propose a new allele-based regression model with individual allele as the response variable. We show that the score test statistic derived from this robust and unifying regression framework contains a correction factor that explicitly adjusts for potential departure from HWE and encompasses the classical allelic test as a special case. When the trait of interest is continuous, the corresponding allelic test evaluates a weighted difference between individual-level allele frequency estimate and sample estimate where the weight is proportional to an individual's trait value, and the test remains valid under Y-dependent sampling. Finally, the proposed allele-based method can analyze multiple (continuous or binary) phenotypes simultaneously and multiallelic genetic markers, while accounting for covariate effect, sample correlation, and population heterogeneity. To support our analytical findings, we provide empirical evidence from both simulation and application studies.

Multivariate linear mixed model enhanced the power of identifying genome-wide association to poplar tree heights in a randomized complete block design

With the advances in high-throughput sequencing technologies, it is not difficult to extract tens of thousands of single-nucleotide polymorphisms (SNPs) across many individuals in a fast and cheap way, making it possible to perform genome-wide association studies (GWAS) of quantitative traits in outbred forest trees. It is very valuable to apply traditional breeding experiments in GWAS for identifying genome variants associated with ecologically and economically important traits in Populus. Here, we reported a GWAS of tree height measured at multiple time points from a randomized complete block design (RCBD), which was established with clones from an F₁ hybrid population of Populus deltoides and Populus simonii. A total of 22,670 SNPs across 172 clones in the RCBD were obtained with restriction site-associated DNA sequencing (RADseq) technology. The multivariate mixed linear model was applied by incorporating the pedigree relationship matrix of individuals to test the association of each SNP to the tree heights over 8 time points. Consequently, 41 SNPs were identified significantly associated with the tree height under the P-value threshold determined by Bonferroni correction at the significant level of 0.01. These SNPs were distributed on all but two chromosomes (Chr02 and Chr18) and explained the phenotypic variance ranged from 0.26% to 2.64%, amounting to 63.68% in total. Comparison with previous mapping studies for poplar height as well as the candidate genes of these detected SNPs were also investigated. We therefore showed that the application of multivariate linear mixed model to the longitudinal phenotypic data from the traditional breeding experimental design facilitated to identify far more genome-wide variants for tree height in poplar. The significant SNPs identified in this study would enhance understanding of molecular mechanism for growth traits and would accelerate marker-assisted breeding programs in Populus.

Variants of genes encoding TNF receptors and ligands and proteins regulating TNF activation in familial multiple sclerosis

INTRODUCTION

Numerous genetic variants have been associated with susceptibility to multiple sclerosis (MS). Variants located in genes involved in specific pathways, such as those affecting TNF-α, can contribute to the risk of MS. The purpose of this study was to determine whether variants of these genes are associated with greater risk of MS.

METHODS

We used whole-exome sequencing to study genes coding for TNF-α receptors and ligands, and proteins promoting TNF-α expression in 116 individuals from 19 families including at least two MS patients. We compared patients with MS, patients with other autoimmune diseases, and healthy individuals.

RESULTS

Greater polymorphism was observed in several genes in families with familial MS compared to the general population; this may reflect greater susceptibility to autoimmune diseases. Pedigree analysis also revealed that LT-α variants rs1041981 and rs2229094 and LT-β variant rs4647197 were associated with MS and that LT-β variant rs4647183 was associated with other autoimmune diseases. The association between autoimmune disease and TNFAIP2 variant rs1132339 is particularly noteworthy, as is the fact that TNFAIP6 variant rs1046668 appears to follow a recessive inheritance pattern.

CONCLUSIONS

Our findings support the idea that the risk of familial MS is associated with variants of signaling pathways, including those involving TNF-α.

Principals about principal components in statistical genetics

Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration

Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Detecting rare haplotypes associated with complex diseases using both population and family data: Combined logistic Bayesian Lasso

Haplotype-based association methods have been developed to understand the genetic architecture of complex diseases. Compared to single-variant-based methods, haplotype methods are thought to be more biologically relevant, since there are typically multiple non-independent genetic variants involved in complex diseases, and the use of haplotypes implicitly accounts for non-independence caused by linkage disequilibrium. In recent years, with the focus moving from common to rare variants, haplotype-based methods have also evolved accordingly to uncover the roles of rare haplotypes. One particular approach is regularization-based, with the use of Bayesian least absolute shrinkage and selection operator (Lasso) as an example. This type of methods has been developed for either case-control population data (the logistic Bayesian Lasso (LBL)) or family data (family-triad-based logistic Bayesian Lasso (famLBL)). In some situations, both family data and case-control data are available; therefore, it would be a waste of resources if only one of them could be analyzed. To make full usage of available data to increase power, we propose a unified approach that can combine both case-control and family data (combined logistic Bayesian Lasso (cLBL)). Through simulations, we characterized the performance of cLBL and showed the advantage of cLBL over existing methods. We further applied cLBL to the Framingham Heart Study data to demonstrate its utility in real data applications.

Efficient computation of the kinship coefficients

MOTIVATION

For families, kinship coefficients are quantifications of the amount of genetic sharing between a pair of individuals. These coefficients are critical for understanding the breeding habits and genetic diversity of diploid populations. Historically, computations of the inbreeding coefficient were used to prohibit inbred marriages and prohibit breeding of some pairs of pedigree animals. Such prohibitions foster genetic diversity and help prevent recessive Mendelian disease at a population level.

RESULTS

This paper gives the fastest known algorithms for computing the kinship coefficient of a set of individuals with a known pedigree, especially for large pedigrees. These algorithms outperform existing methods. In addition, the algorithms given here consider the possibility that the founders of the known pedigree may themselves be inbred and compute the appropriate inbreeding-adjusted kinship coefficients, which has not been addressed in literature. The exact kinship algorithm has running-time O(n2) for an n-individual pedigree. The recursive-cut exact kinship algorithm has running time O(s2m) where s is the number of individuals in the largest segment of the pedigree and m is the number of cuts. The approximate algorithm has running-time O(nd) for an n-individual pedigree on which to estimate the kinship coefficients of n individuals of interest from n founder kinship coefficients and d is the number of samples.

AVAILABILITY AND IMPLEMENTATION

The above polynomial-time exact algorithm and the linear-time approximation algorithms are implemented as PedKin in C++ and are available under the GNU GPL v2.0 open source license. The PedKin source code is available at: http://www.intrepidnetcomputing.com/research/code/.

metaFARVAT: An Efficient Tool for Meta-Analysis of Family-Based, Case-Control, and Population-Based Rare Variant Association Studies

Family-based designs have been shown to be powerful in detecting the significant rare variants associated with human diseases. However, very few significant results have been found owing to relatively small sample sizes and the fact that statistical analyses often suffer from high false-negative error rates. These limitations can be avoided by combining results from multiple studies via meta-analysis. However, statistical methods for meta-analysis with rare variants are limited for family-based samples. In this report, we propose a tool for the meta-analysis of family-based rare variant associations, metaFARVAT. metaFARVAT is based on a quasi-likelihood score for each variant. These scores are combined to generate burden test, variable-threshold test, sequence kernel association test (SKAT), and optimal SKAT statistics. The proposed method tests homogeneous and heterogeneous effects of variants among different studies and can be applied to both quantitative and dichotomous phenotypes. Simulation results demonstrated the robustness and efficiency of the proposed method in different scenarios. By applying metaFARVAT to data from a family-based study and a case-control study, we identified a few promising candidate genes, including DLEC1, which is associated with chronic obstructive pulmonary disease.

Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data

Genome-wide association studies on alcohol dependence, by themselves, have yet to account for the estimated heritability of the disorder and provide incomplete mechanistic understanding of this complex trait. Integrating brain ethanol-responsive gene expression networks from model organisms with human genetic data on alcohol dependence could aid in identifying dependence-associated genes and functional networks in which they are involved. This study used a modification of the Edge-Weighted Dense Module Searching for genome-wide association studies (EW-dmGWAS) approach to co-analyze whole-genome gene expression data from ethanol-exposed mouse brain tissue, human protein-protein interaction databases and alcohol dependence-related genome-wide association studies. Results revealed novel ethanol-responsive and alcohol dependence-associated gene networks in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Three of these networks were overrepresented with genome-wide association signals from an independent dataset. These networks were significantly overrepresented for gene ontology categories involving several mechanisms, including actin filament-based activity, transcript regulation, Wnt and Syndecan-mediated signaling, and ubiquitination. Together, these studies provide novel insight for brain mechanisms contributing to alcohol dependence.

Exonic variants of genes related to the vitamin D signaling pathway in the families of familial multiple sclerosis using whole-exome next generation sequencing

INTRODUCTION

Vitamin D (VD) deficiency has been associated with multiple sclerosis (MS) and other autoimmune diseases (AIDs). However, the effect of the genetics of VD on the risk of MS is subject to debate. This study focuses on genes linked to the VD signaling pathway in families with MS. The evaluation of gene variants in all the members of families could contribute to an additional knowledge on the information obtained from case-control studies that use nonrelated healthy people.

MATERIAL AND METHODS

We studied 94 individuals from 15 families including at least two patients with MS. We performed whole-exome next generation sequencing on all individuals and analyzed variants of the DHCR7, CYP2R1, CYP3A4, CYP27A1, GC, CYP27B1, LRP2, CUBN, DAB2, FCGR, RXR, VDR, CYP24A1, and PDIA3 genes. We also studied PTH, FGF23, METTL1, METTL21B, and the role of the linkage disequilibrium block on the long arm of chromosome 12, through analysis of the CDK4, TSFM, AGAP2, and AVIL genes. We compared patients with MS, other AIDs and unaffected members from different family types.

RESULTS

The study described the variants in the VD signaling pathway that appear in families with at least two patients with MS. Some infrequent variants were detected in these families, but no significant difference was observed between patients with MS and/or other AIDs and unaffected family members in the frequency of these variants. Variants previously associated with MS in the literature were not observed in these families or were distributed similarly in patients and unaffected family members.

CONCLUSION

The study of genes involved in the VD signaling pathway in families that include more than one patient with MS did not identify any variants that could explain the presence of the disease, suggesting that VD metabolism could probably play a role in MS more as an environmental factor rather than as a genetic factor. Our study also supports the analysis of cases and unaffected individuals within families in order to determine the influence of genetic factors.

Replication, reanalysis, and gene expression: ME2 and genetic generalized epilepsy

OBJECTIVE

Genetic generalized epilepsy (GGE) consists of epileptic syndromes with overlapping symptoms and is considered to be largely genetic. Previous cosegregation and association studies have pointed to malic enzyme 2 (ME2) as a candidate susceptibility gene for adolescent-onset GGE. In this article, we present new evidence supporting ME2's involvement in GGE.

METHODS

To definitively test ME2's influence on GGE, we used 3 different approaches. First, we compared a newly recruited GGE cohort with an ethnically matched reference sample from 1000 Genomes Project, using an efficient test of association (POPFAM+). Second, we used POPFAM+ to reanalyze a previously collected data set, wherein the original controls were replaced with ethnically matched reference samples to minimize the confounding effect of population stratification. Third, in a post hoc analysis of expression data from healthy human prefrontal cortex, we identified single nucleotide polymorphisms (SNPs) influencing ME2 messenger RNA (mRNA) expression; and then we tested those same SNPs for association with GGE in a large case-control cohort.

RESULTS

First, in the analysis of our newly recruited GGE Cohort, we found a strong association between an ME2 SNP and GGE (P = 0.0006 at rs608781). Second, in the reanalysis of previously collected data, we confirmed the Greenberg et al (2005) finding of a GGE-associated ME2 risk haplotype. Third, in the post hoc ME2 expression analysis, we found evidence for a possible link between GGE and ME2 gene expression in human brain.

SIGNIFICANCE

Overall, our research, and the research of others, provides compelling evidence that ME2 influences susceptibility to adolescent-onset GGE.

Association score testing for rare variants and binary traits in family data with shared controls

Genome-wide association studies have been an important approach used to localize trait loci, with primary focus on common variants. The multiple rare variant-common disease hypothesis may explain the missing heritability remaining after accounting for identified common variants. Advances of sequencing technologies with their decreasing costs, coupled with methodological advances in the context of association studies in large samples, now make the study of rare variants at a genome-wide scale feasible. The resurgence of family-based association designs because of their advantage in studying rare variants has also stimulated more methods development, mainly based on linear mixed models (LMMs). Other tests such as score tests can have advantages over the LMMs, but to date have mainly been proposed for single-marker association tests. In this article, we extend several score tests (χcorrected2, WQLS, and SKAT) to the multiple variant association framework. We evaluate and compare their statistical performances relative with the LMM. Moreover, we show that three tests can be cast as the difference between marker allele frequencies (AFs) estimated in each of the group of affected and unaffected subjects. We show that these tests are flexible, as they can be based on related, unrelated or both related and unrelated subjects. They also make feasible an increasingly common design that only sequences a subset of affected subjects (related or unrelated) and uses for comparison publicly available AFs estimated in a group of healthy subjects. Finally, we show the great impact of linkage disequilibrium on the performance of all these tests.

Cross-species molecular dissection across alcohol behavioral domains

This review summarizes the proceedings of a symposium presented at the "Alcoholism and Stress: A Framework for Future Treatment Strategies" conference held in Volterra, Italy on May 9-12, 2017. Psychiatric diseases, including alcohol-use disorders (AUDs), are influenced through complex interactions of genes, neurobiological pathways, and environmental influences. A better understanding of the common neurobiological mechanisms underlying an AUD necessitates an integrative approach, involving a systematic assessment of diverse species and phenotype measures. As part of the World Congress on Stress and Alcoholism, this symposium provided a detailed account of current strategies to identify mechanisms underlying the development and progression of AUDs. Dr. Sean Farris discussed the integration and organization of transcriptome and postmortem human brain data to identify brain regional- and cell type-specific differences related to excessive alcohol consumption that are conserved across species. Dr. Brien Riley presented the results of a genome-wide association study of DSM-IV alcohol dependence; although replication of genetic associations with alcohol phenotypes in humans remains challenging, model organism studies show that COL6A3, KLF12, and RYR3 affect behavioral responses to ethanol, and provide substantial evidence for their role in human alcohol-related traits. Dr. Rob Williams expanded upon the systematic characterization of extensive genetic-genomic resources for quantifying and clarifying phenotypes across species that are relevant to precision medicine in human disease. The symposium concluded with Dr. Robert Hitzemann's description of transcriptome studies in a mouse model selectively bred for high alcohol ("binge-like") consumption and a non-human primate model of long-term alcohol consumption. Together, the different components of this session provided an overview of systems-based approaches that are pioneering the experimental prioritization and validation of novel genes and gene networks linked with a range of behavioral phenotypes associated with stress and AUDs.

Outcomes of 92 patient-driven family studies for reclassification of variants of uncertain significance

PURPOSE

Family studies are an important but underreported source of information for reclassification of variants of uncertain significance (VUS). We evaluated outcomes of a patient-driven framework that offered familial VUS reclassification analysis to any adult with any clinically ascertained VUS from any laboratory in the United States.

METHODS

With guidance from FindMyVariant.org, participants recruited their own relatives for study participation. We genotyped relatives, calculated quantitative cosegregation likelihood ratios, and evaluated variant classifications using Tavtigian's unified framework for Bayesian analysis with American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) criteria. We report participation and VUS reclassification rates from the 50 families enrolled for at least one year and reclassification results for 112 variants from the larger 92-family cohort.

RESULTS

For the 50-family cohort, 6.7 relatives per family were invited to participate and 67% of relatives returned samples for genotyping. Sixty-one percent of VUS were reclassified, 84% of which were classified as benign or likely benign. Genotyping relatives identified a de novo variant, phase variants, and relatives with phenotypes highly specific for or incompatible with specific classifications.

CONCLUSIONS

Motivated families can contribute to successful VUS reclassification at substantially higher rates than those previously published. Clinical laboratories could consider offering family studies to all patients with VUS.

ADAPTIVE-WEIGHT BURDEN TEST FOR ASSOCIATIONS BETWEEN QUANTITATIVE TRAITS AND GENOTYPE DATA WITH COMPLEX CORRELATIONS

High-throughput sequencing has often been used to screen samples from pedigrees or with population structure, producing genotype data with complex correlations rendered from both familial relation and linkage disequilibrium. With such data, it is critical to account for these genotypic correlations when assessing the contribution of variants by gene or pathway. Recognizing the limitations of existing association testing methods, we propose Adaptive-weight Burden Test (ABT), a retrospective, mixed-model test for genetic association of quantitative traits on genotype data with complex correlations. This method makes full use of genotypic correlations across both samples and variants, and adopts "data-driven" weights to improve power. We derive the ABT statistic and its explicit distribution under the null hypothesis, and demonstrate through simulation studies that it is generally more powerful than the fixed-weight burden test and family-based SKAT in various scenarios, controlling for the type I error rate. Further investigation reveals the connection of ABT with kernel tests, as well as the adaptability of its weights to the direction of genetic effects. The application of ABT is illustrated by a whole genome analysis of genes with common and rare variants associated with fasting glucose from the NHLBI "Grand Opportunity" Exome Sequencing Project.

Variants regulating ZBTB4 are associated with age-at-onset of Alzheimer's disease

The identification of novel genetic modifiers of age-at-onset (AAO) of Alzheimer's disease (AD) could advance our understanding of AD and provide novel therapeutic targets. A previous genome scan for modifiers of AAO among families affected by early-onset AD caused by the PSEN2 N141I variant identified 2 loci with significant evidence for linkage: 1q23.3 and 17p13.2. Here, we describe the fine-mapping of these 2 linkage regions, and test for replication in 6 independent datasets. By fine-mapping these linkage signals in a single large family, we reduced the linkage regions to 11% their original size and nominated 54 candidate variants. Among the 11 variants associated with AAO of AD in a larger sample of Germans from Russia, the strongest evidence implicated promoter variants influencing NCSTN on 1q23.3 and ZBTB4 on 17p13.2. The association between ZBTB4 and AAO of AD was replicated by multiple variants in independent, trans-ethnic datasets. Our results show association between AAO of AD and both ZBTB4 and NCSTN. ZBTB4 is a transcriptional repressor that regulates the cell cycle, including the apoptotic response to amyloid beta, while NCSTN is part of the gamma secretase complex, known to influence amyloid beta production. These genes therefore suggest important roles for amyloid beta and cell cycle pathways in AAO of AD.

WISARD: workbench for integrated superfast association studies for related datasets

BACKGROUND

A Mendelian transmission produces phenotypic and genetic relatedness between family members, giving family-based analytical methods an important role in genetic epidemiological studies-from heritability estimations to genetic association analyses. With the advance in genotyping technologies, whole-genome sequence data can be utilized for genetic epidemiological studies, and family-based samples may become more useful for detecting de novo mutations. However, genetic analyses employing family-based samples usually suffer from the complexity of the computational/statistical algorithms, and certain types of family designs, such as incorporating data from extended families, have rarely been used.

RESULTS

We present a Workbench for Integrated Superfast Association studies for Related Data (WISARD) programmed in C/C++. WISARD enables the fast and a comprehensive analysis of SNP-chip and next-generation sequencing data on extended families, with applications from designing genetic studies to summarizing analysis results. In addition, WISARD can automatically be run in a fully multithreaded manner, and the integration of R software for visualization makes it more accessible to non-experts.

CONCLUSIONS

Comparison with existing toolsets showed that WISARD is computationally suitable for integrated analysis of related subjects, and demonstrated that WISARD outperforms existing toolsets. WISARD has also been successfully utilized to analyze the large-scale massive sequencing dataset of chronic obstructive pulmonary disease data (COPD), and we identified multiple genes associated with COPD, which demonstrates its practical value.

L-GATOR: Genetic Association Testing for a Longitudinally Measured Quantitative Trait in Samples with Related Individuals

In complex-trait mapping, when each subject has multiple measurements of a quantitative trait over time, power for detecting genetic association can be gained by the inclusion of all measurements and not just single time points or averages in the analysis. To increase power and control type 1 error, one should account for dependence among observations for a single individual as well as dependence between observations of related individuals if they are present in the sample. We propose L-GATOR, a retrospective, mixed-effects method for association mapping of longitudinally measured traits in samples with related individuals. L-GATOR allows arbitrary time points for different individuals, incorporates both time-varying and static covariates, and properly addresses various types of dependence. In simulations, we show that L-GATOR outperforms existing prospective methods in terms of both type 1 error and power when there is phenotype model misspecification or missing data. Compared with the previously proposed longGWAS method, L-GATOR was more than ten times faster for association testing in our simulations and almost 100 times faster for parameter estimation. L-GATOR is applicable to essentially arbitrary combinations of related and unrelated individuals, including small families as well as large, complex pedigrees. We apply the method to data from the Framingham Heart Study to identify association between longitudinal systolic blood pressure measurements and genome-wide SNPs. Of the smallest p values, one-third occur in or near genes that have been previously identified as associated with pulse pressure (such as PIK3CG) and systolic and diastolic blood pressure (such as C10orf107), showing that L-GATOR is able to prioritize relevant loci in a genome screen.

Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease

Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families (N = 1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, and MAS1L) as candidate genes for familial LOAD.

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.

Impact of the G84E variant on HOXB13 gene and protein expression in formalin-fixed, paraffin-embedded prostate tumours

The HOXB13 G84E variant is associated with risk of prostate cancer (PCa), however the role this variant plays in PCa development is unknown. This study examined 751 cases, 450 relatives and 355 controls to determine the contribution of this variant to PCa risk in Tasmania and investigated HOXB13 gene and protein expression in tumours from nine G84E heterozygote variant and 13 wild-type carriers. Quantitative PCR and immunohistochemistry showed that HOXB13 gene and protein expression did not differ between tumour samples from variant and wild-type carriers. Allele-specific transcription revealed that two of seven G84E carriers transcribed both the variant and wild-type allele, while five carriers transcribed the wild-type allele. Methylation of surrounding CpG sites was lower in the variant compared to the wild-type allele, however overall methylation across the region was very low. Notably, tumour characteristics were less aggressive in the two variant carriers that transcribed the variant allele compared to the five that did not. This study has shown that HOXB13 expression does not differ between tumour tissue of G84E variant carriers and non-carriers. Intriguingly, the G84E variant allele was rarely transcribed in carriers, suggesting that HOXB13 expression may be driven by the wild-type allele in the majority of carriers.

Efficient Statistical Method for Association Analysis of X-Linked Variants

BACKGROUND/AIMS

Unlike the gene-poor Y chromosome, the X chromosome contains over 1,000 genes that are essential for viability of cells. Females have 2 X chromosomes, and thus female X-linked gene expression would be expected to be twice that of males. To adjust this imbalance, one of the 2 X-linked genes is often inactivated, and this is known as X-chromosome inactivation (XCI). However, recent studies described that a gene can be nonrandomly selected for inactivation from 2 X-linked genes and that XCI is not observed in some X-linked genes. Since this complex biological process has prevented efficient statistical association analyses, we propose a new statistical method against this uncertain biological process.

METHODS

The proposed method consists of 2 steps. First, p values for various biological processes are calculated and then combined into a single p value with the modified Fisher method and a minimum p value.

RESULTS

Our simulation results show that the proposed method is generally the most statistically efficient and is not sensitive to the unknown biological model.

CONCLUSION

Therefore, we can conclude that the proposed approaches are robust against the various XCI processes for testing the association of X-linked single nucleotide polymorphisms with the disease of interest and the proposed method is a practical solution.

A Pragmatic Test for Detecting Association between a Dichotomous Trait and the Genotypes of Affected Families, Controls and Independent Cases

The efficient analysis of hybrid designs [e.g., affected families, controls, and (optionally) independent cases] is attractive because it should have increased power to detect associations between genetic variants and disease. However, the computational complexity of such an analysis is not trivial, especially when the data contain pedigrees of arbitrary size and structure. To address this concern, we developed a pragmatic test of association that summarizes all of the available evidence in certain hybrid designs, irrespective of pedigree size or structure. Under the null hypothesis of no association, our proposed test statistic (POPFAM+) is the quadratic form of two correlated tests: a population-based test (e.g., wQLS), and a family-based test (e.g., PDT). We use the parametric bootstrap in conjunction with an estimate of the correlation to compute p-values, and we illustrate the potential for increased power when (1) the heritability of the trait is high; and, (2) the marker-specific association is driven by the over-representation of risk alleles in cases, and by the preferential transmission of risk alleles from heterozygous parents to their affected offspring. Based on simulation, we show that type I error is controlled, and that POPFAM+ is more powerful than wQLS or PDT alone. In a real data application, we used POPFAM+ to analyze 43 genes of a hybrid epilepsy study containing 85 affected families, 80 independent cases, 234 controls, and 118 reference samples from the International HapMap Project. The results of our analysis identified a promising epilepsy candidate gene for follow-up sequencing: malic enzyme 2 (ME2; min p < 0.0084).

Genomewide Association Study of Alcohol Dependence Identifies Risk Loci Altering Ethanol-Response Behaviors in Model Organisms

BACKGROUND

Alcohol dependence (AD) shows evidence for genetic liability, but genes influencing risk remain largely unidentified.

METHODS

We conducted a genomewide association study in 706 related AD cases and 1,748 unscreened population controls from Ireland. We sought replication in 15,496 samples of European descent. We used model organisms (MOs) to assess the role of orthologous genes in ethanol (EtOH)-response behaviors. We tested 1 primate-specific gene for expression differences in case/control postmortem brain tissue.

RESULTS

We detected significant association in COL6A3 and suggestive association in 2 previously implicated loci, KLF12 and RYR3. None of these signals are significant in replication. A suggestive signal in the long noncoding RNA LOC339975 is significant in case:control meta-analysis, but not in a population sample. Knockdown of a COL6A3 ortholog in Caenorhabditis elegans reduced EtOH sensitivity. Col6a3 expression correlated with handling-induced convulsions in mice. Loss of function of the KLF12 ortholog in C. elegans impaired development of acute functional tolerance (AFT). Klf12 expression correlated with locomotor activation following EtOH injection in mice. Loss of function of the RYR3 ortholog reduced EtOH sensitivity in C. elegans and rapid tolerance in Drosophila. The ryanodine receptor antagonist dantrolene reduced motivation to self-administer EtOH in rats. Expression of LOC339975 does not differ between cases and controls but is reduced in carriers of the associated rs11726136 allele in nucleus accumbens (NAc).

CONCLUSIONS

We detect association between AD and COL6A3, KLF12, RYR3, and LOC339975. Despite nonreplication of COL6A3, KLF12, and RYR3 signals, orthologs of these genes influence behavioral response to EtOH in MOs, suggesting potential involvement in human EtOH response and AD liability. The associated LOC339975 allele may influence gene expression in human NAc. Although the functions of long noncoding RNAs are poorly understood, there is mounting evidence implicating these genes in multiple brain functions and disorders.

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel.

Combining omics data to identify genes associated with allergic rhinitis

Allergic rhinitis is a common chronic disorder characterized by immunoglobulin E-mediated inflammation. To identify new genes associated with this trait, we performed genome- and epigenome-wide association studies and linked marginally significant CpGs located in genes or its promoter and SNPs located 1 Mb from the CpGs, by identifying cis methylation quantitative trait loci (mQTL). This approach relies on functional cellular aspects rather than stringent statistical correction. We were able to identify one gene with significant cis-mQTL for allergic rhinitis, caudal-type homeobox 1 (CDX1). We also identified 11 genes with marginally significant cis-mQTLs (p < 0.05) including one with both allergic rhinitis with or without asthma (RNF39). Moreover, most SNPs identified were not located closest to the gene they were linked to through cis-mQTLs counting the one linked to CDX1 located in a gene previously associated with asthma and atopic dermatitis. By combining omics data, we were able to identify new genes associated with allergic rhinitis and better assess the genes linked to associated SNPs.

Case-control association mapping by proxy using family history of disease

Collecting cases for case-control genetic association studies can be time-consuming and expensive. In some situations (such as studies of late-onset or rapidly lethal diseases), it may be more practical to identify family members of cases. In randomly ascertained cohorts, replacing cases with their first-degree relatives enables studies of diseases that are absent (or nearly absent) in the cohort. We refer to this approach as genome-wide association study by proxy (GWAX) and apply it to 12 common diseases in 116,196 individuals from the UK Biobank. Meta-analysis with published genome-wide association study summary statistics replicated established risk loci and yielded four newly associated loci for Alzheimer's disease, eight for coronary artery disease and five for type 2 diabetes. In addition to informing disease biology, our results demonstrate the utility of association mapping without directly observing cases. We anticipate that GWAX will prove useful in future genetic studies of complex traits in large population cohorts.

Mixed Model Association with Family-Biased Case-Control Ascertainment

Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ² = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ² = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples.

Single Marker Family-Based Association Analysis Not Conditional on Parental Information

Family-based association analysis unconditional on parental genotypes models the effects of observed genotypes. This approach has been shown to have greater power than conditional methods. In this chapter, we review popular association analysis methods accounting for familial correlations: the marginal model using generalized estimating equations (GEE), the mixed model with a polygenic random component, and genome-wide association analyses. The marginal approach does not explicitly model familial correlations but uses the information to improve the efficiency of parameter estimates. This model, using GEE, is useful when the correlation structure is not of interest; the correlations are treated as nuisance parameters. In the mixed model, familial correlations are modeled as random effects, e.g., the polygenic inheritance model accounts for correlations originating from shared genomic components within a family. These unconditional methods provide a flexible modeling framework for general pedigree data to accommodate traits with various distributions and many types of covariate effects. Genome-wide association studies usually test more than 10,000 SNPs and thus traditional statistical methods accounting for the familial correlations often suffer from a computational burden. Multiple approaches that have been recently proposed to avoid this computational issue are reviewed. The single-marker analysis procedures are demonstrated using the R package gee and the ASSOC program in the S.A.G.E. package, including how to prepare input data, conduct the analysis, and interpret the output. ASSOC allows models to include random components of additional familial correlations that may be not sufficiently explained by a polygenic effect and addresses nonnormality of response variables by transformation methods. With its ease of use, ASSOC provides a useful tool for association analysis of large pedigree data.

Association of CACNA1C and SYNE1 in offspring of patients with psychiatric disorders

Schizophrenia (SZ) and bipolar disorder (BD) are severe mental diseases associated with cognitive impairment, mood disturbance, and psychosis. Both disorders are highly heritable and share a common genetic background. The present study assesses, for the first time, differences in genotype frequencies of polymorphisms located in genes involved in neurodevelopment and synaptic plasticity between genetic high-risk individuals (offspring of patients with SZ or BD; N=100: 31 and 69, respectively) and control subjects (offspring of community controls; N=96). Individuals from both groups had similar ages, around 12 years. A higher percentage of men were included in the genetic high-risk group (58%) compared with the control group (40.6%). A total of 244 validated SNPs located in 35 candidate gene regions were analyzed in 196 participants. Multivariate methods based on logistic regression analysis were performed to assess differences in genotype frequencies. Bonferroni correction was applied for the multiple comparisons performed. Two polymorphisms, CACNA1C rs10848683 and SYNE1 rs214950, showed significant differences. The frequency of heterozygotes for CACNA1C rs10848683 in genetic high-risk individuals was double that in controls (OR=3.15; P=0.00016). For SYNE1 rs214950, higher frequencies of heterozygotes (OR=1.97) and homozygotes for the minor allele (OR=17.89; P=0.00020) were found in the genetic high-risk group than in the control group. In conclusion, polymorphisms in CACNA1C and SYNE1 could confer a greater risk of developing SZ and BD in individuals who are already at high risk because of their family history. This could help identify subjects with a very high genetic risk, in whom early detection and early intervention could lead to better prognosis.

When Is an Endophenotype Useful to Detect Association to a Disease? Exploring the Relationships between Disease Status, Endophenotype and Genetic Polymorphisms

OBJECTIVES

To investigate the conditions and analysis strategies required so that endophenotypes related to a disease help discover genetic variants involved in the disease.

METHODS

The association with disease susceptibility variants is examined as a function of the relationships between disease status, endophenotype values and the genotype at another disease or endophenotype susceptibility locus assumed to be previously known, using approximate linear models of allele frequencies as a function of these variables and simulations in the context of family studies when the endophenotype is dichotomous.

RESULTS

Under genetic mechanisms where the risk allele of the tested locus has an effect exclusively in subjects with the endophenotype, the risk allele frequency differences between affected and unaffected subjects are much greater in the subset of subjects with an endophenotype impairment than in those without such an impairment, and power gains are obtained when testing the association under a joint disease-endophenotype model, both with two-locus or single-locus tests. However, with moderate main effect on the risk of disease or endophenotype impairment, testing directly the association between risk allele and disease or endophenotype is more powerful than testing under a joint disease-endophenotype model.

CONCLUSIONS

Joint modeling of disease and endophenotype should be used only in parallel with standard disease association testing.

Phenotypically Enriched Genotypic Imputation in Genetic Association Tests

BACKGROUND

In longitudinal epidemiological studies there may be individuals with rich phenotype data who die or are lost to follow-up before providing DNA for genetic studies. Often, the genotypic and phenotypic data of the relatives are available. Two strategies for analyzing the incomplete data are to exclude ungenotyped subjects from analysis (the complete-case method, CC) and to include phenotyped but ungenotyped individuals in analysis by using relatives' genotypes for genotype imputation (GI). In both strategies, the information in the phenotypic data was not used to handle the missing-genotype problem.

METHODS

We propose a phenotypically enriched genotypic imputation (PEGI) method that uses the EM (expectation-maximization)-based maximum likelihood method to incorporate observed phenotypes into genotype imputation.

RESULTS

Our simulations with genotypes missing completely at random show that, for a single-nucleotide polymorphism (SNP) with moderate to strong effect on a phenotype, PEGI improves power more than GI without excess type I errors. Using the Framingham Heart Study data set, we compare the ability of the PEGI, GI, and CC to detect the associations between 5 SNPs and age at natural menopause.

CONCLUSION

The PEGI method may improve power to detect an association over both CC and GI under many circumstances.

CERAMIC: Case-Control Association Testing in Samples with Related Individuals, Based on Retrospective Mixed Model Analysis with Adjustment for Covariates

We consider the problem of genetic association testing of a binary trait in a sample that contains related individuals, where we adjust for relevant covariates and allow for missing data. We propose CERAMIC, an estimating equation approach that can be viewed as a hybrid of logistic regression and linear mixed-effects model (LMM) approaches. CERAMIC extends the recently proposed CARAT method to allow samples with related individuals and to incorporate partially missing data. In simulations, we show that CERAMIC outperforms existing LMM and generalized LMM approaches, maintaining high power and correct type 1 error across a wider range of scenarios. CERAMIC results in a particularly large power increase over existing methods when the sample includes related individuals with some missing data (e.g., when some individuals with phenotype and covariate information have missing genotype), because CERAMIC is able to make use of the relationship information to incorporate partially missing data in the analysis while correcting for dependence. Because CERAMIC is based on a retrospective analysis, it is robust to misspecification of the phenotype model, resulting in better control of type 1 error and higher power than that of prospective methods, such as GMMAT, when the phenotype model is misspecified. CERAMIC is computationally efficient for genomewide analysis in samples of related individuals of almost any configuration, including small families, unrelated individuals and even large, complex pedigrees. We apply CERAMIC to data on type 2 diabetes (T2D) from the Framingham Heart Study. In a genome scan, 9 of the 10 smallest CERAMIC p-values occur in or near either known T2D susceptibility loci or plausible candidates, verifying that CERAMIC is able to home in on the important loci in a genome scan.

Retrospective Association Analysis of Binary Traits: Overcoming Some Limitations of the Additive Polygenic Model

Case-control genetic association analysis is an extremely common tool in human complex trait mapping. From a statistical point of view, the analysis of binary traits poses somewhat different challenges from the analysis of quantitative traits. Desirable features of a binary trait mapping approach would include (1) phenotype modeled as binary, with appropriate dependence between the mean and variance; (2) appropriate correction for relevant covariates; (3) appropriate correction for sample structure of various types, including related individuals, admixture and other types of population structure; (4) both fast and accurate computations; (5) robustness to ascertainment and other types of phenotype model misspecification, and (6) ability to leverage partially missing data to increase power. We review these challenges and argue, both theoretically and in simulations, for the value of retrospective association analysis as a way to overcome some of the limitations of the phenotype model, including model misspecification due to ascertainment. We give an overview of two recent retrospective methods, CARAT and CERAMIC, that are designed to meet criteria 1-6.

FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.

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.

G-STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies

In a large-scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study's sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed-size subset of individuals to sequence in such a way that the power to detect association is maximized. When the phenotyped sample includes related individuals, power for association can be gained by including partial information, such as phenotype data of ungenotyped relatives, in the analysis, and this should be taken into account when assessing whom to sequence. We propose G-STRATEGY, which uses simulated annealing to choose a subset of individuals for sequencing that maximizes the expected power for association. In simulations, G-STRATEGY performs extremely well for a range of complex disease models and outperforms other strategies with, in many cases, relative power increases of 20-40% over the next best strategy, while maintaining correct type 1 error. G-STRATEGY is computationally feasible even for large datasets and complex pedigrees. We apply G-STRATEGY to data on high-density lipoprotein and low-density lipoprotein from the AGES-Reykjavik and REFINE-Reykjavik studies, in which G-STRATEGY is able to closely approximate the power of sequencing the full sample by selecting for sequencing a only small subset of the individuals.

Whole-genome association analysis of treatment response in obsessive-compulsive disorder

Up to 30% of patients with obsessive-compulsive disorder (OCD) exhibit an inadequate response to serotonin reuptake inhibitors (SRIs). To date, genetic predictors of OCD treatment response have not been systematically investigated using genome-wide association study (GWAS). To identify specific genetic variations potentially influencing SRI response, we conducted a GWAS study in 804 OCD patients with information on SRI response. SRI response was classified as 'response' (n=514) or 'non-response' (n=290), based on self-report. We used the more powerful Quasi-Likelihood Score Test (the MQLS test) to conduct a genome-wide association test correcting for relatedness, and then used an adjusted logistic model to evaluate the effect size of the variants in probands. The top single-nucleotide polymorphism (SNP) was rs17162912 (P=1.76 × 10(-8)), which is near the DISP1 gene on 1q41-q42, a microdeletion region implicated in neurological development. The other six SNPs showing suggestive evidence of association (P<10(-5)) were rs9303380, rs12437601, rs16988159, rs7676822, rs1911877 and rs723815. Among them, two SNPs in strong linkage disequilibrium, rs7676822 and rs1911877, located near the PCDH10 gene, gave P-values of 2.86 × 10(-6) and 8.41 × 10(-6), respectively. The other 35 variations with signals of potential significance (P<10(-4)) involve multiple genes expressed in the brain, including GRIN2B, PCDH10 and GPC6. Our enrichment analysis indicated suggestive roles of genes in the glutamatergic neurotransmission system (false discovery rate (FDR)=0.0097) and the serotonergic system (FDR=0.0213). Although the results presented may provide new insights into genetic mechanisms underlying treatment response in OCD, studies with larger sample sizes and detailed information on drug dosage and treatment duration are needed.