Using the noninformative families in family-based association tests: a powerful new testing strategy

For genetic association studies with multiple phenotypes, we propose a new strategy for multiple testing with family-based association tests (FBATs). The strategy increases the power by both using all available family data and reducing the number of hypotheses tested while being robust against population admixture and stratification. By use of conditional power calculations, the approach screens all possible null hypotheses without biasing the nominal significance level, and it identifies the subset of phenotypes that has optimal power when tested for association by either univariate or multivariate FBATs. An application of our strategy to an asthma study shows the practical relevance of the proposed methodology. In simulation studies, we compare our testing strategy with standard methodology for family studies. Furthermore, the proposed principle of using all data without biasing the nominal significance in an analysis prior to the computation of the test statistic has broad and powerful applications in many areas of family-based association studies.     

Power and design considerations for a general class of family-based association tests: quantitative traits

In the present article, we address family-based association tests (FBATs) for quantitative traits. We propose an approach to analytical power and sample-size calculations for general FBATs; this approach can be applied to virtually any scenario (missing parental information, multiple offspring per family, etc.). The power calculations are used to discuss optimal choices of the phenotypes for the FBAT statistic and its power's dependence on ascertainment conditions, on study design, and on the correct specification of the distributional assumptions for the phenotypes. We also compare the general FBAT approach with PDT and QTDT. The practical relevance of our theoretical considerations is illustrated by their application to an asthma study.     

A review of family-based tests for linkage disequilibrium between a quantitative trait and a genetic marker

Quantitative trait transmission/disequilibrium tests (quantitative TDTs) are commonly used in family-based genetic association studies of quantitative traits. Despite the availability of various quantitative TDTs, some users are not aware of the properties of these tests and the relationships between them. This review aims at outlining the broad features of the various quantitative TDT procedures carried out in the frequently used QTDT and FBAT packages. Specifically, we discuss the “Rabinowitz” and the “Monks-Kaplan” procedures, as well as the various “Abecasis” and “Allison” regression-based procedures. We focus on the models assumed in these tests and the relationships between them. Moreover, we discuss what hypotheses are tested by the various quantitative TDTs, what testing procedures are best suited to various forms of data, and whether the regression-based tests overcome population stratification problems. Finally, we comment on power considerations in the choice of the test to be used. We hope this brief review will shed light on the similarities and differences of the various quantitative TDTs.     

Genomics and genome-wide association studies: an integrative approach to expression QTL mapping

Expression QTL mapping by integrating genome-wide gene expression and genotype data is a promising approach to identifying functional genetic variation, but is hampered by the large number of multiple comparisons inherent in such studies. A novel approach to addressing multiple testing problems in genome-wide family-based association studies is screening candidate markers using heritability or conditional power. We apply these methods in settings in which microarray gene expression data are used as phenotypes, screening for SNPs near the expressed genes. We perform association analyses for phenotypes using a univariate approach. We also perform simulations on trios with large numbers of causal SNPs to determine the optimal number of markers to use in a screen. We demonstrate that our family-based screening approach performs well in the analysis of integrative genomic datasets and that screening using either heritability or conditional power produces similar, though not identical, results.     

A genetic study of two calcium-independent cytosolic PLA2 genes in schizophrenia

The present study detected 9 single nucleotide polymorphisms (SNPs) at the PLA2G4C and PLA2G6 loci among 240 Chinese parent-offspring trios of Han descent. Of these 9 SNPs, 5 showed highly polymorphic in the Chinese population. They were then applied as genetic markers to test the genetic association of these two calcium-independent cytosolic PLA2 genes with schizophrenia. The transmission disequilibrium test (TDT) showed that rs1549637 at the PLA2G4C locus was the only SNP associated with the illness (chi(2) = 5.63, P = 0.018). The global P-value was 0.082 for 1000 permutations with the TDT analysis. Neither the conditional on allele test nor the conditional on genotype test showed a disease association for the combination of these two genes. Because the PLA2G4C association is so weak, this initial finding should be interpreted with caution.     

Maximizing the Power of Genome-Wide Association Studies: A Novel Class of Powerful Family-Based Association Tests

For genome-wide association studies in family-based designs, a new, universally applicable approach is proposed. Using a modified Liptak’s method, we combine the p-value of the family-based association test (FBAT) statistic with the p-value for the Van Steen-statistic. The Van Steen-statistic is independent of the FBAT-statistic and utilizes information that is ignored by traditional FBAT-approaches. The new test statistic takes advantages of all available information about the genetic association, while, by virtue of its design, it achieves complete robustness against confounding due to population stratification. The approach is suitable for the analysis of almost any trait type for which FBATs are available, e.g. binary, continuous, time-to-onset, multivariate, etc. The efficiency and the validity of the new approach depend on the specification of a nuisance/tuning parameter and the weight parameters in the modified Liptak’s method. For different trait types and ascertainment conditions, we discuss general guidelines for the optimal specification of the tuning parameter and the weight parameters. Our simulation experiments and an application to an Alzheimer study show the validity and the efficiency of the new method, which achieves power levels that are comparable to those of population-based approaches.     

SNPs in the neural cell adhesion molecule 1 gene (NCAM1) may be associated with human neural tube defects

Neural tube defects (NTDs) are common birth defects, occurring in approximately 1/1,000 births; both genetic and environmental factors are implicated. To date, no major genetic risk factors have been identified. Throughout development, cell adhesion molecules are strongly implicated in cell–cell interactions, and may play a role in the formation and closure of the neural tube. To evaluate the role of neural cell adhesion molecule 1 (NCAM1) in risk of human NTDs, we screened for novel single-nucleotide polymorphisms (SNPs) within the gene. Eleven SNPs across NCAM1 were genotyped using TaqMan. We utilized a family-based approach to evaluate evidence for association and/or linkage disequilibrium. We evaluated American Caucasian simplex lumbosacral myelomeningocele families (n=132 families) using the family based association test (FBAT) and the pedigree disequilibrium test (PDT). Association analysis revealed a significant association between risk for NTDs and intronic SNP rs2298526 using both the FBAT test (P=0.0018) and the PDT (P=0.0025). Using the HBAT version of the FBAT to look for haplotype association, all pairwise comparisons with SNP rs2298526 were also significant. A replication study set, consisting of 72 additional families showed no significant association; however, the overall trend for overtransmission of the less common allele of SNP rs2298526 remained significant in the combined sample set. In addition, we analyzed the expression pattern of the NCAM1 protein in human embryos, and while NCAM1 is not expressed within the neural tube at the time of closure, it is expressed in the surrounding and later in differentiated neurons of the CNS. These results suggest variations in NCAM1 may influence risk for human NTDs.Other members of NTD Collaborative Group involved in this study are listed in the appendix     

Glutamate Receptor 6 Gene (<Emphasis Type="Italic">GluR6</Emphasis> or <Emphasis Type="Italic">GRIK2</Emphasis>) Polymorphisms in the Indian Population: A Genetic Association Study on Autism Spectrum Disorder

Autism is a neurodevelopmental disorder with early manifestation. It is a multifactorial disorder and several susceptible chromosomal regions for autism are identified through genome scan studies. The gene coding for glutamate receptor 6 (GluR6 or GRIK2) has been suggested as a candidate gene for autism based on its localization in the autism specific region on chromosome 6q21 and the involvement of receptor protein in cognitive functions like learning and memory. Despite its importance, so far no studies have been carried out on possible involvement of GluR6 with autism in the Indian population. Therefore in the present study, we have performed genetic analysis of three markers of GluR6 (SNP1: rs2227281, SNP2: rs2227283, SNP3: rs2235076) for possible association with autism through population, and family-based (TDT and HHRR) approaches. DSM-IV criteria and CARS/ADI-R have been utilized for diagnosis. Genotyping analysis for the SNPs has been carried out in 101 probands with autism spectrum disorder, 180 parents and 152 controls from different regions of India. Since the minor allele frequency of SNP3 was too low, the association studies have been carried out only for SNP1 and SNP2. Even though two earlier studies have shown association of these markers with autism, the present case–control and TDT, as well as HHRR analyses have not demonstrated any biased transmission of alleles or haplotypes to the affected offspring. Thus our results suggest that these markers of GluR6 are unlikely to be associated with autism in the Indian population.     

Multi-locus test conditional on confirmed effects leads to increased power in genome-wide association studies

Complex diseases or phenotypes may involve multiple genetic variants and interactions between genetic, environmental and other factors. Current genome-wide association studies (GWAS) mostly used single-locus analysis and had identified genetic effects with multiple confirmations. Such confirmed single-nucleotide polymorphism (SNP) effects were likely to be true genetic effects and ignoring this information in testing new effects of the same phenotype results in decreased statistical power due to increased residual variance that has a component of the omitted effects. In this study, a multi-locus association test (MLT) was proposed for GWAS analysis conditional on SNPs with confirmed effects to improve statistical power. Analytical formulae for statistical power were derived and were verified by simulation for MLT accounting for confirmed SNPs and for single-locus test (SLT) without accounting for confirmed SNPs. Statistical power of the two methods was compared by case studies with simulated and the Framingham Heart Study (FHS) GWAS data. Results showed that the MLT method had increased statistical power over SLT. In the GWAS case study on four cholesterol phenotypes and serum metabolites, the MLT method improved statistical power by 5% to 38% depending on the number and effect sizes of the conditional SNPs. For the analysis of HDL cholesterol (HDL-C) and total cholesterol (TC) of the FHS data, the MLT method conditional on confirmed SNPs from GWAS catalog and NCBI had considerably more significant results than SLT.     

A multi-SNP association test for complex diseases incorporating an optimal P-value threshold algorithm in nuclear families

Background

Genome-wide association studies (GWAS) have become a common approach to identifying single nucleotide polymorphisms (SNPs) associated with complex diseases. As complex diseases are caused by the joint effects of multiple genes, while the effect of individual gene or SNP is modest, a method considering the joint effects of multiple SNPs can be more powerful than testing individual SNPs. The multi-SNP analysis aims to test association based on a SNP set, usually defined based on biological knowledge such as gene or pathway, which may contain only a portion of SNPs with effects on the disease. Therefore, a challenge for the multi-SNP analysis is how to effectively select a subset of SNPs with promising association signals from the SNP set.

Results

We developed the Optimal P-value Threshold Pedigree Disequilibrium Test (OPTPDT). The OPTPDT uses general nuclear families. A variable p-value threshold algorithm is used to determine an optimal p-value threshold for selecting a subset of SNPs. A permutation procedure is used to assess the significance of the test. We used simulations to verify that the OPTPDT has correct type I error rates. Our power studies showed that the OPTPDT can be more powerful than the set-based test in PLINK, the multi-SNP FBAT test, and the p-value based test GATES. We applied the OPTPDT to a family-based autism GWAS dataset for gene-based association analysis and identified MACROD2-AS1 with genome-wide significance (p-value= 2.5 × 10^− 6).

Conclusions

Our simulation results suggested that the OPTPDT is a valid and powerful test. The OPTPDT will be helpful for gene-based or pathway association analysis. The method is ideal for the secondary analysis of existing GWAS datasets, which may identify a set of SNPs with joint effects on the disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1620-3) contains supplementary material, which is available to authorized users.     

Association and linkage analysis of RGS4 polymorphisms with schizophrenia and bipolar disorder in Brazil

Linkage and association studies in five independently ascertained samples have suggested that polymorphisms of the regulator of G-protein signaling 4 (RGS4) may confer risk for schizophrenia (SCZ). Suggestive evidence for association with bipolar disorder (BD) has also been presented. However, the associated alleles and haplotypes have differed among the samples. Data from other independent samples may clarify the putative associations. Hence, we investigated an independent, ethnically diverse Brazilian population comprising patients with SCZ (n=271) or BD1 (n=306), who were contrasted with 576 community-based controls. Parents of 49 SCZ cases and 44 BD cases were available for transmission disequilibrium tests (TDTs). Four RGS4 single-nucleotide polymorphisms (SNPs) 1, 4, 7 and 18 putatively associated with SCZ were investigated. In the SCZ samples, significant case-control differences were not observed for individual SNPs or haplotypes, though the TDT suggested transmission distortion similar to that observed in the initial report. For the BD sample, case-control comparisons revealed no significant differences for individual SNPs, but an omnibus test suggested differences in the overall distribution of haplotypes bearing all four SNPs (SNP-EM Omnibus likelihood ratio test; P=0.003). The TDT revealed over-transmission of allele A at SNP7 (P=0.016), as well as haplotypes incorporating this allele. However, global tests incorporating all haplotypes yielded only suggestive trends for association (P=0.19). In conclusion, association with SCZ was not detected in the present analyses. The failure to detect an association may be related to inadequate power or to confounds related to ethnic admixture. Suggestive associations with BD detected here require further investigation in a larger sample.     

A common haplotype of the nicotine acetylcholine receptor alpha 4 subunit gene is associated with vulnerability to nicotine addiction in men

Nicotine is the major addictive substance in cigarettes, and genes involved in sensing nicotine are logical candidates for vulnerability to nicotine addiction. We studied six single-nucleotide polymorphisms (SNPs) in the CHRNA4 gene and four SNPs in the CHRNB2 gene with respect to nicotine dependence in a collection of 901 subjects (815 siblings and 86 parents) from 222 nuclear families with multiple nicotine-addicted siblings. The subjects were assessed for addiction by both the Fagerstrom Test for Nicotine Dependence (FTND) and the Revised Tolerance Questionnaire (RTQ). Because only 5.8% of female offspring were smokers, only male subjects were included in the final analyses (621 men from 206 families). Univariate (single-marker) family-based association tests (FBATs) demonstrated that variant alleles at two SNPs, rs1044396 and rs1044397, in exon 5 of the CHRNA4 gene were significantly associated with a protective effect against nicotine addiction as either a dichotomized trait or a quantitative phenotype (i.e., age-adjusted FTND and RTQ scores), which was consistent with the results of the global haplotype FBAT. Furthermore, the haplotype-specific FBAT showed a common (22.5%) CHRNA4 haplotype, GCTATA, which was significantly associated with both a protective effect against nicotine addiction as a dichotomized trait (Z=-3.04, P<.005) and significant decreases of age-adjusted FTND (Z=-3.31, P<.005) or RTQ scores (Z=-2.73, P=.006). Our findings provide strong evidence suggesting a common CHRNA4 haplotype might be protective against vulnerability to nicotine addiction in men.     

Association analysis of 9,560 prostate cancer cases from the International Consortium of Prostate Cancer Genetics confirms the role of reported prostate cancer associated SNPs for familial disease

Previous GWAS studies have reported significant associations between various common SNPs and prostate cancer risk using cases unselected for family history. How these variants influence risk in familial prostate cancer is not well studied. Here, we analyzed 25 previously reported SNPs across 14 loci from prior prostate cancer GWAS. The International Consortium for Prostate Cancer Genetics (ICPCG) previously validated some of these using a family-based association method (FBAT). However, this approach suffered reduced power due to the conditional statistics implemented in FBAT. Here, we use a case–control design with an empirical analysis strategy to analyze the ICPCG resource for association between these 25 SNPs and familial prostate cancer risk. Fourteen sites contributed 12,506 samples (9,560 prostate cancer cases, 3,368 with aggressive disease, and 2,946 controls from 2,283 pedigrees). We performed association analysis with Genie software which accounts for relationships. We analyzed all familial prostate cancer cases and the subset of aggressive cases. For the familial prostate cancer phenotype, 20 of the 25 SNPs were at least nominally associated with prostate cancer and 16 remained significant after multiple testing correction (p ≤ 1E ^?3) occurring on chromosomal bands 6q25, 7p15, 8q24, 10q11, 11q13, 17q12, 17q24, and Xp11. For aggressive disease, 16 of the SNPs had at least nominal evidence and 8 were statistically significant including 2p15. The results indicate that the majority of common, low-risk alleles identified in GWAS studies for all prostate cancer also contribute risk for familial prostate cancer, and that some may contribute risk to aggressive disease.     

Family-based genome-wide association study of frontal theta oscillations identifies potassium channel gene KCNJ6

Event-related oscillations (EROs) represent highly heritable neuroelectric correlates of cognitive processes that manifest deficits in alcoholics and in offspring at high risk to develop alcoholism. Theta ERO to targets in the visual oddball task has been shown to be an endophenotype for alcoholism. A family-based genome-wide association study was performed for the frontal theta ERO phenotype using 634 583 autosomal single nucleotide polymorphisms (SNPs) genotyped in 1560 family members from 117 families densely affected by alcohol use disorders, recruited in the Collaborative Study on the Genetics of Alcoholism. Genome-wide significant association was found with several SNPs on chromosome 21 in KCNJ6 (a potassium inward rectifier channel; KIR3.2/GIRK2), with the most significant SNP at P = 4.7 × 10(-10)). The same SNPs were also associated with EROs from central and parietal electrodes, but with less significance, suggesting that the association is frontally focused. One imputed synonymous SNP in exon four, highly correlated with our top three SNPs, was significantly associated with the frontal theta ERO phenotype. These results suggest KCNJ6 or its product GIRK2 account for some of the variations in frontal theta band oscillations. GIRK2 receptor activation contributes to slow inhibitory postsynaptic potentials that modulate neuronal excitability, and therefore influence neuronal networks.     

Whole-genome association studies on alcoholism comparing different phenotypes using single-nucleotide polymorphisms and microsatellites

Alcoholism is a complex disease. As with other common diseases, genetic variants underlying alcoholism have been illusive, possibly due to the small effect from each individual susceptible variant, gene x environment and gene x gene interactions and complications in phenotype definition. We conducted association tests, the family-based association tests (FBAT) and the backward haplotype transmission association (BHTA), on the Collaborative Study of the Genetics of Alcoholism (COGA) data provided by Genetic Analysis Workshop (GAW) 14. Efron's local false discovery rate method was applied to control the proportion of false discoveries. For FBAT, we compared the results based on different types of genetic markers (single-nucleotide polymorphisms (SNPs) versus microsatellites) and different phenotype definitions (clinical diagnoses versus electrophysiological phenotypes). Significant association results were found only between SNPs and clinical diagnoses. In contrast, significant results were found only between microsatellites and electrophysiological phenotypes. In addition, we obtained the association results for SNPs and microsatellites using COGA diagnosis as phenotype based on BHTA. In this case, the results for SNPs and microsatellites are more consistent. Compared to FBAT, more significant markers are detected with BHTA.     

Sample reproducibility of genetic association using different multimarker TDTs in genome-wide association studies: characterization and a new approach

Multimarker Transmission/Disequilibrium Tests (TDTs) are very robust association tests to population admixture and structure which may be used to identify susceptibility loci in genome-wide association studies. Multimarker TDTs using several markers may increase power by capturing high-degree associations. However, there is also a risk of spurious associations and power reduction due to the increase in degrees of freedom. In this study we show that associations found by tests built on simple null hypotheses are highly reproducible in a second independent data set regardless the number of markers. As a test exhibiting this feature to its maximum, we introduce the multimarker 2-Groups TDT (mTDT(2G)), a test which under the hypothesis of no linkage, asymptotically follows a χ2 distribution with 1 degree of freedom regardless the number of markers. The statistic requires the division of parental haplotypes into two groups: disease susceptibility and disease protective haplotype groups. We assessed the test behavior by performing an extensive simulation study as well as a real-data study using several data sets of two complex diseases. We show that mTDT(2G) test is highly efficient and it achieves the highest power among all the tests used, even when the null hypothesis is tested in a second independent data set. Therefore, mTDT(2G) turns out to be a very promising multimarker TDT to perform genome-wide searches for disease susceptibility loci that may be used as a preprocessing step in the construction of more accurate genetic models to predict individual susceptibility to complex diseases.     

Genome-wide SNP-based linkage scan identifies a locus on 8q24 for an age-related hearing impairment trait

Age-related hearing impairment (ARHI), or presbycusis, is a very common multifactorial disorder. Despite the knowledge that genetics play an important role in the etiology of human ARHI as revealed by heritability studies, to date, its precise genetic determinants remain elusive. Here we report the results of a cross-sectional family-based genetic study employing audiometric data. By using principal component analysis, we were able to reduce the dimensionality of this multivariate phenotype while capturing most of the variation and retaining biologically important features of the audiograms. We conducted a genome-wide association as well as a linkage scan with high-density SNP microarrays. Because of the presence of genetic population substructure, association testing was stratified after which evidence was combined by meta-analysis. No association signals reaching genome-wide significance were detected. Linkage analysis identified a linkage peak on 8q24.13-q24.22 for a trait correlated to audiogram shape. The signal reached genome-wide significance, as assessed by simulations. This finding represents the first locus for an ARHI trait.     

Family-Based Bivariate Association Tests for Quantitative Traits

The availability of a large number of dense SNPs, high-throughput genotyping and computation methods promotes the application of family-based association tests. While most of the current family-based analyses focus only on individual traits, joint analyses of correlated traits can extract more information and potentially improve the statistical power. However, current TDT-based methods are low-powered. Here, we develop a method for tests of association for bivariate quantitative traits in families. In particular, we correct for population stratification by the use of an integration of principal component analysis and TDT. A score test statistic in the variance-components model is proposed. Extensive simulation studies indicate that the proposed method not only outperforms approaches limited to individual traits when pleiotropic effect is present, but also surpasses the power of two popular bivariate association tests termed FBAT-GEE and FBAT-PC, respectively, while correcting for population stratification. When applied to the GAW16 datasets, the proposed method successfully identifies at the genome-wide level the two SNPs that present pleiotropic effects to HDL and TG traits.     

P2BAT: a massive parallel implementation of PBAT for genome-wide association studies in R

The software tool P2BAT provides a massive parallel and user friendly implementation of the PBAT-analysis tools for family-based association tests (FBATs) in large-scale studies, including genome-wide association studies with several thousand subjects. Built on the original PBAT-implementation of the Lange-Van Steen algorithm to bypass the multiple testing problem in family-based association studies, P2BAT integrates all PBAT-analysis tools for binary and complex traits into R and makes them accessible through a user-friendly GUI. The genome-wide analysis tools are fully automated and can be ran massively parallel directly through the GUI. P2BAT is fully documented and contains graphical output tools for time-to-onset analysis. P2BAT also features the ability to test for gene and environment/drug interaction. AVAILABILITY: The P2BAT package is available as the R package 'pbatR' which can be downloaded from http://cran.r-project.org/. The PBAT-software is available at http://www.biostat.harvard.edu/~clange/.