Family-based designs in the age of large-scale gene-association studies

Rare variant association studies: considerations, challenges and opportunities

Genome-wide association studies (GWASs) have successfully uncovered thousands of robust associations between common variants and complex traits and diseases. Despite these successes, much of the heritability of these traits remains unexplained. Because low-frequency and rare variants are not tagged by conventional genome-wide genotyping arrays, they may represent an important and understudied component of complex trait genetics. In contrast to common variant GWASs, there are many different types of study designs, assays and analytic techniques that can be utilized for rare variant association studies (RVASs). In this review, we briefly present the different technologies available to identify rare genetic variants, including novel exome arrays. We also compare the different study designs for RVASs and argue that the best design will likely be phenotype-dependent. We discuss the main analytical issues relevant to RVASs, including the different statistical methods that can be used to test genetic associations with rare variants and the various bioinformatic approaches to predicting in silico biological functions for variants. Finally, we describe recent rare variant association findings, highlighting the unexpected conclusion that most rare variants have modest-to-small effect sizes on phenotypic variation. This observation has major implications for our understanding of the genetic architecture of complex traits in the context of the unexplained heritability challenge.

A family-based joint test for mean and variance heterogeneity for quantitative traits

Traditional quantitative trait locus (QTL) analysis focuses on identifying loci associated with mean heterogeneity. Recent research has discovered loci associated with phenotype variance heterogeneity (vQTL), which is important in studying genetic association with complex traits, especially for identifying gene-gene and gene-environment interactions. While several tests have been proposed to detect vQTL for unrelated individuals, there are no tests for related individuals, commonly seen in family-based genetic studies. Here we introduce a likelihood ratio test (LRT) for identifying mean and variance heterogeneity simultaneously or for either effect alone, adjusting for covariates and family relatedness using a linear mixed effect model approach. The LRT test statistic for normally distributed quantitative traits approximately follows χ(2)-distributions. To correct for inflated Type I error for non-normally distributed quantitative traits, we propose a parametric bootstrap-based LRT that removes the best linear unbiased prediction (BLUP) of family random effect. Simulation studies show that our family-based test controls Type I error and has good power, while Type I error inflation is observed when family relatedness is ignored. We demonstrate the utility and efficiency gains of the proposed method using data from the Framingham Heart Study to detect loci associated with body mass index (BMI) variability.

Family-based association study of HLA class II with type 1 diabetes in Moroccans

BACKGROUND

The T1D is a multifactorial disease; with a strong genetic control. The human leukocyte antigen (HLA) system plays a crucial role in the autoimmune process leading to childhood diabetes. About 440,000 of the childhood population of the world (1.8 billion children under 14 years of age), have type 1 diabetes, and each year an additional 70,000 develop this disorder. The objective of this study was to investigate the distribution of HLA class II in Moroccan families of diabetic children to identify susceptibility alleles of the Moroccan population.

SUBJECTS AND METHODS

We included in this study, Moroccan families who have at least one child with T1D. The age of onset of diabetes was less than 15 years. HLA class II (DRB1* and DQB1*) was carried out by molecular biology techniques (PCR-SSP and PCR-SSO). The FBAT test (family-based association test) was used to highlight the association between T1D and the HLA-DRB1* and -DQB1* polymorphism.

RESULTS

The association of HLA class II (DRB1*, DQB1*) in type 1 diabetes was analyzed in fifty-one Moroccan families, including 90 diabetics. The results revealed that the most susceptible haplotypes are the DRB1*03:01-DQB1*02:01, DRB1*04:05-DQB1*03:02 (Z=3.674, P=0.000239; Z=2.828, P=0.004678, respectively). And the most protective haplotype is the DRB1*15-DQB1*06.

CONCLUSION

This is the first family-based association study searching for an association between HLA class II and T1D in a Moroccan population. Despite the different ethnic groups forming Morocco, Moroccan diabetics share the most susceptible and protective HLA haplotypes with other Caucasians populations, specifically the European and Mediterranean populations.

Comparing the power of family-based association tests for sequence data with applications in the GAW18 simulated data

We apply a family-based extension of the sequence kernel association test (SKAT) to 93 trios extracted from the 20 pedigrees in the Genetic Analysis Workshop 18 simulated data. Each extracted trio includes a unique set of parents to ensure conditionally independent trios are sampled. We compare the empirical type I error and power between the family-based SKAT and the burden test under varying percentages of causal single-nucleotide polymorphisms included in the analysis. Our investigation using simulated data suggests that, under the setting used for Genetic Analysis Workshop 18 data, both the family-based SKAT and the burden test have limited power, and that there is no substantial impact of percentage of signal on the power of either test. The low power is partially a result of the small sample size. However, we find that both the family-based SKAT and the burden test are more powerful when we use only rare variants, rather than common variants, to test the association.

Molecular genetic studies of ADHD and its candidate genes: a review

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset psychiatric disorder with high heritability. In recent years, numerous molecular genetic studies have been published to investigate susceptibility loci for ADHD. These results brought valuable candidates for further research, but they also presented great challenge for profound understanding of genetic data and general patterns of current molecular genetic studies of ADHD since they are scattered and heterogeneous. In this review, we presented a retrospective review of more than 300 molecular genetic studies for ADHD from two aspects: (1) the main achievements of various studies were summarized, including linkage studies, candidate-gene association studies, genome-wide association studies and genome-wide copy number variation studies, with a special focus on general patterns of study design and common sample features; (2) candidate genes for ADHD have been systematically evaluated in three ways for better utilization. The thorough summary of the achievements from various studies will provide an overview of the research status of molecular genetics studies for ADHD. Meanwhile, the analysis of general patterns and sample characteristics on the basis of these studies, as well as the integrative review of candidate ADHD genes, will propose new clues and directions for future experiment design.

Analytic power and sample size calculation for the genotypic transmission/disequilibrium test in case-parent trio studies

Case-parent trio studies considering genotype data from children affected by a disease and their parents are frequently used to detect single nucleotide polymorphisms (SNPs) associated with disease. The most popular statistical tests for this study design are transmission/disequilibrium tests (TDTs). Several types of these tests have been developed, for example, procedures based on alleles or genotypes. Therefore, it is of great interest to examine which of these tests have the highest statistical power to detect SNPs associated with disease. Comparisons of the allelic and the genotypic TDT for individual SNPs have so far been conducted based on simulation studies, since the test statistic of the genotypic TDT was determined numerically. Recently, however, it has been shown that this test statistic can be presented in closed form. In this article, we employ this analytic solution to derive equations for calculating the statistical power and the required sample size for different types of the genotypic TDT. The power of this test is then compared with the one of the corresponding score test assuming the same mode of inheritance as well as the allelic TDT based on a multiplicative mode of inheritance, which is equivalent to the score test assuming an additive mode of inheritance. This is, thus, the first time the power of these tests are compared based on equations, yielding instant results and omitting the need for time-consuming simulation studies. This comparison reveals that these tests have almost the same power, with the score test being slightly more powerful.

Complex pedigrees in the sequencing era: to track transmissions or decorrelate?

Next-generation sequencing (NGS) studies are becoming commonplace, and the NGS field is continuing to develop rapidly. Analytic methods aimed at testing for the various roles that genetic susceptibility plays in disease are also rapidly being developed and optimized. Studies that incorporate large, complex pedigrees are of particular importance because they provide detailed information about inheritance patterns and can be analyzed in a variety of complementary ways. The nine contributions from our Genetic Analysis Workshop 18 working group on family-based tests of association for rare variants using simulated data examined analytic methods for testing genetic association using whole-genome sequencing data from 20 large pedigrees with 200 phenotype simulation replicates. What distinguishes the approaches explored is how the complexities of analyzing familial genetic data were handled. Here, we explore the methods that either harness inheritance patterns and transmission information or attempt to adjust for the correlation between family members in order to utilize computationally and conceptually simpler statistical testing procedures. Although directly comparing these two classes of approaches across contributions is difficult, we note that the two classes balance robustness to population stratification and computational complexity (the transmission-based approaches) with simplicity and increased power, assuming no population stratification or proper adjustment for it (decorrelation approaches).

Is the gene-environment interaction paradigm relevant to genome-wide studies? The case of education and body mass index

This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses.

Detecting disease variants in case-parent trio studies using the bioconductor software package trio

Case-parent trio studies are commonly employed in genetics to detect variants underlying common complex disease risk. Both commercial and freely available software suites for genetic data analysis usually contain methods for case-parent trio designs. A user might, however, experience limitations with these packages, which can include missing functionality to extend the software if a desired analysis has not been implemented, and the inability to programmatically capture all the software versions used for low-level processing and high-level inference of genomic data, a critical consideration in particular for high-throughput experiments. Here, we present a software vignette (i.e., a manual with step by step instructions and examples to demonstrate software functionality) for reproducible genome-wide analyses of case-parent trio data using the open source Bioconductor package trio. The workflow for the practitioner uses data from previous genetic trio studies to illustrate functions for marginal association tests, assessment of parent-of-origin effects, power and sample size calculations, and functions to detect gene-gene and gene-environment interactions associated with disease.

A general efficient and flexible approach for genome-wide association analyses of imputed genotypes in family-based designs

Genotype imputation is a critical technique for following up genome-wide association studies. Efficient methods are available for dealing with the probabilistic nature of imputed single nucleotide polymorphisms (SNPs) in population-based designs, but not for family-based studies. We have developed a new analytical approach (FBATdosage), using imputed allele dosage in the general framework of family-based association tests to bridge this gap. Simulation studies showed that FBATdosage yielded highly consistent type I error rates, whatever the level of genotype uncertainty, and a much higher power than the best-guess genotype approach. FBATdosage allows fast linkage and association testing of several million of imputed variants with binary or quantitative phenotypes in nuclear families of arbitrary size with arbitrary missing data for the parents. The application of this approach to a family-based association study of leprosy susceptibility successfully refined the association signal at two candidate loci, C1orf141-IL23R on chromosome 1 and RAB32-C6orf103 on chromosome 6.

FamLBL: detecting rare haplotype disease association based on common SNPs using case-parent triads

MOTIVATION

In recent years, there has been an increasing interest in using common single-nucleotide polymorphisms (SNPs) amassed in genome-wide association studies to investigate rare haplotype effects on complex diseases. Evidence has suggested that rare haplotypes may tag rare causal single-nucleotide variants, making SNP-based rare haplotype analysis not only cost effective, but also more valuable for detecting causal variants. Although a number of methods for detecting rare haplotype association have been proposed in recent years, they are population based and thus susceptible to population stratification.

RESULTS

We propose family-triad-based logistic Bayesian Lasso (famLBL) for estimating effects of haplotypes on complex diseases using SNP data. By choosing appropriate prior distribution, effect sizes of unassociated haplotypes can be shrunk toward zero, allowing for more precise estimation of associated haplotypes, especially those that are rare, thereby achieving greater detection power. We evaluate famLBL using simulation to gauge its type I error and power. Compared with its population counterpart, LBL, highlights famLBL's robustness property in the presence of population substructure. Further investigation by comparing famLBL with Family-Based Association Test (FBAT) reveals its advantage for detecting rare haplotype association.

AVAILABILITY AND IMPLEMENTATION

famLBL is implemented as an R-package available at http://www.stat.osu.edu/∼statgen/SOFTWARE/LBL/.

Utilising family-based designs for detecting rare variant disease associations

Rare genetic variants are thought to be important components in the causality of many diseases but discovering these associations is challenging. We demonstrate how best to use family-based designs to improve the power to detect rare variant disease associations. We show that using genetic data from enriched families (those pedigrees with greater than one affected member) increases the power and sensitivity of existing case-control rare variant tests. However, we show that transmission- (or within-family-) based tests do not benefit from this enrichment. This means that, in studies where a limited amount of genotyping is available, choosing a single case from each of many pedigrees has greater power than selecting multiple cases from fewer pedigrees. Finally, we show how a pseudo-case-control design allows a greater range of statistical tests to be applied to family data.

Blocking approach for identification of rare variants in family-based association studies

With the advent of next-generation sequencing technology, rare variant association analysis is increasingly being conducted to identify genetic variants associated with complex traits. In recent years, significant effort has been devoted to develop powerful statistical methods to test such associations for population-based designs. However, there has been relatively little development for family-based designs although family data have been shown to be more powerful to detect rare variants. This study introduces a blocking approach that extends two popular family-based common variant association tests to rare variants association studies. Several options are considered to partition a genomic region (gene) into "independent" blocks by which information from SNVs is aggregated within a block and an overall test statistic for the entire genomic region is calculated by combining information across these blocks. The proposed methodology allows different variants to have different directions (risk or protective) and specification of minor allele frequency threshold is not needed. We carried out a simulation to verify the validity of the method by showing that type I error is well under control when the underlying null hypothesis and the assumption of independence across blocks are satisfied. Further, data from the Genetic Analysis Workshop [Formula: see text] are utilized to illustrate the feasibility and performance of the proposed methodology in a realistic setting.

Genome-wide association study of bipolar disorder in Canadian and UK populations corroborates disease loci including SYNE1 and CSMD1

BACKGROUND

Recently, genome-wide association studies (GWAS) for cases versus controls using single nucleotide polymorphism microarray data have shown promising findings for complex neuropsychiatric disorders, including bipolar disorder (BD).

METHODS

Here we describe a comprehensive genome-wide study of bipolar disorder (BD), cross-referencing analysis from a family-based study of 229 small families with association analysis from over 950 cases and 950 ethnicity-matched controls from the UK and Canada. Further, loci identified in these analyses were supported by pathways identified through pathway analysis on the samples.

RESULTS

Although no genome-wide significant markers were identified, the combined GWAS findings have pointed to several genes of interest that support GWAS findings for BD from other groups or consortia, such as at SYNE1 on 6q25, PPP2R2C on 4p16.1, ZNF659 on 3p24.3, CNTNAP5 (2q14.3), and CDH13 (16q23.3). This apparent corroboration across multiple sites gives much confidence to the likelihood of genetic involvement in BD at these loci. In particular, our two-stage strategy found association in both our combined case/control analysis and the family-based analysis on 1q21.2 (closest gene: sphingosine-1-phosphate receptor 1 gene, S1PR1) and on 1q24.1 near the gene TMCO1, and at CSMD1 on 8p23.2, supporting several previous GWAS reports for BD and for schizophrenia. Pathway analysis suggests association of pathways involved in calcium signalling, neuropathic pain signalling, CREB signalling in neurons, glutamate receptor signalling and axonal guidance signalling.

CONCLUSIONS

The findings presented here show support for a number of genes previously implicated genes in the etiology of BD, including CSMD1 and SYNE1, as well as evidence for previously unreported genes such as the brain-expressed genes ADCY2, NCALD, WDR60, SCN7A and SPAG16.

Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data

Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.

The future for genetic studies in reproduction

Genetic factors contribute to risk of many common diseases affecting reproduction and fertility. In recent years, methods for genome-wide association studies (GWAS) have revolutionized gene discovery for common traits and diseases. Results of GWAS are documented in the Catalog of Published Genome-Wide Association Studies at the National Human Genome Research Institute and report over 70 publications for 32 traits and diseases associated with reproduction. These include endometriosis, uterine fibroids, age at menarche and age at menopause. Results that pass appropriate stringent levels of significance are generally well replicated in independent studies. Examples of genetic variation affecting twinning rate, infertility, endometriosis and age at menarche demonstrate that the spectrum of disease-related variants for reproductive traits is similar to most other common diseases. GWAS 'hits' provide novel insights into biological pathways and the translational value of these studies lies in discovery of novel gene targets for biomarkers, drug development and greater understanding of environmental factors contributing to disease risk. Results also show that genetic data can help define sub-types of disease and co-morbidity with other traits and diseases. To date, many studies on reproductive traits have used relatively small samples. Future genetic marker studies in large samples with detailed phenotypic and clinical information will yield new insights into disease risk, disease classification and co-morbidity for many diseases associated with reproduction and infertility.

GEE-based SNP set association test for continuous and discrete traits in family-based association studies

Family-based genetic association studies of related individuals provide opportunities to detect genetic variants that complement studies of unrelated individuals. Most statistical methods for family association studies for common variants are single marker based, which test one SNP a time. In this paper, we consider testing the effect of an SNP set, e.g., SNPs in a gene, in family studies, for both continuous and discrete traits. Specifically, we propose a generalized estimating equations (GEEs) based kernel association test, a variance component based testing method, to test for the association between a phenotype and multiple variants in an SNP set jointly using family samples. The proposed approach allows for both continuous and discrete traits, where the correlation among family members is taken into account through the use of an empirical covariance estimator. We derive the theoretical distribution of the proposed statistic under the null and develop analytical methods to calculate the P-values. We also propose an efficient resampling method for correcting for small sample size bias in family studies. The proposed method allows for easily incorporating covariates and SNP-SNP interactions. Simulation studies show that the proposed method properly controls for type I error rates under both random and ascertained sampling schemes in family studies. We demonstrate through simulation studies that our approach has superior performance for association mapping compared to the single marker based minimum P-value GEE test for an SNP-set effect over a range of scenarios. We illustrate the application of the proposed method using data from the Cleveland Family GWAS Study.

A systems biology approach using metabolomic data reveals genes and pathways interacting to modulate divergent growth in cattle

BACKGROUND

Systems biology enables the identification of gene networks that modulate complex traits. Comprehensive metabolomic analyses provide innovative phenotypes that are intermediate between the initiator of genetic variability, the genome, and raw phenotypes that are influenced by a large number of environmental effects. The present study combines two concepts, systems biology and metabolic analyses, in an approach without prior functional hypothesis in order to dissect genes and molecular pathways that modulate differential growth at the onset of puberty in male cattle. Furthermore, this integrative strategy was applied to specifically explore distinctive gene interactions of non-SMC condensin I complex, subunit G (NCAPG) and myostatin (GDF8), known modulators of pre- and postnatal growth that are only partially understood for their molecular pathways affecting differential body weight.

RESULTS

Our study successfully established gene networks and interacting partners affecting growth at the onset of puberty in cattle. We demonstrated the biological relevance of the created networks by comparison to randomly created networks. Our data showed that GnRH (Gonadotropin-releasing hormone) signaling is associated with divergent growth at the onset of puberty and revealed two highly connected hubs, BTC and DGKH, within the network. Both genes are known to directly interact with the GnRH signaling pathway. Furthermore, a gene interaction network for NCAPG containing 14 densely connected genes revealed novel information concerning the functional role of NCAPG in divergent growth.

CONCLUSIONS

Merging both concepts, systems biology and metabolomic analyses, successfully yielded new insights into gene networks and interacting partners affecting growth at the onset of puberty in cattle. Genetic modulation in GnRH signaling was identified as key modifier of differential cattle growth at the onset of puberty. In addition, the benefit of our innovative concept without prior functional hypothesis was demonstrated by data suggesting that NCAPG might contribute to vascular smooth muscle contraction by indirect effects on the NO pathway via modulation of arginine metabolism. Our study shows for the first time in cattle that integration of genetic, physiological and metabolomics data in a systems biology approach will enable (or contribute to) an improved understanding of metabolic and gene networks and genotype-phenotype relationships.

Twin studies in inherited eye disease

Eye diseases represent a significant source of health impairment in humans. Twin studies offer an excellent model to dissect the genetic basis of human diseases. In this review, we discuss the potential advantages of using twin-based studies in investigating the genetics of eye diseases--from heritability estimation to identifying underlying genetic and epigenetic changes. We also discuss some of the notable findings of twin studies exploring the genetics of eye diseases. Finally, we suggest other novel approaches that can be utilized to tap the potential of twin studies to provide a more complete understanding of genetic factors underlying ocular diseases.

Combining genetic association study designs: a GWAS case study

Genome-wide association studies (GWAS) explore the relationship between genome variability and disease susceptibility with either population- or family-based data. Here, we have evaluated the utility of combining population- and family-based statistical association tests and have proposed a method for reducing the burden of multiple testing. Unrelated singleton and parent-offspring trio cases and controls from the Genetics of Kidneys in Diabetes (GoKinD) study were analyzed for genetic association with diabetic nephropathy (DN) in type 1 diabetics (T1D). The Cochran-Armitage test for trend and the family-based association test were employed using either unrelated cases and controls or trios, respectively. In addition to combining single nucleotide polymorphism (SNP) p-values across these tests via Fisher's method, we employed a novel screening approach to rank SNPs based on conditional power for more efficient testing. Using either the population-based or family-based subset alone predictably limited resolution to detect DN SNPs. For 384,197 SNPs passing quality control (QC), none achieved strict genome-wide significance (1.4 × 10⁻⁷) using 1171 singletons (577/594 cases/controls) or 1738 pooled singletons and offspring probands (841/897). Similarly, none of the 352,004 SNPs passing QC in 567 family trios (264/303 case/control proband trios) reached genome-wide significance. Testing the top 10 SNPs ranked using aggregated conditional power resulted in two SNPs reaching genome-wide significance, rs11645147 on chromosome 16 (p = 1.74 × 10⁻⁴ < 0.05/10 = 0.005) and rs7866522 on chromosome 9 (p = 0.0033). Efficient usage of mixed designs incorporating both unrelated and family-based data may help to uncover associations otherwise difficult to detect in the presence of massive multiple testing corrections. Capitalizing on the strengths of both types while using screening approaches may be useful especially in light of large-scale, next-generation sequencing and rare variant studies.

A genome-wide association study of chronic otitis media with effusion and recurrent otitis media identifies a novel susceptibility locus on chromosome 2

Chronic otitis media with effusion (COME) and recurrent otitis media (ROM) have been shown to be heritable, but candidate gene and linkage studies to date have been equivocal. Our aim was to identify genetic susceptibility factors using a genome-wide association study (GWAS). We genotyped 602 subjects from 143 families with 373 COME/ROM subjects using the Illumina Human CNV370-Duo DNA Bead Chip (324,748 SNPs). We carried out the GWAS scan and imputed SNPs at the regions with the most significant associations. Replication genotyping in an independent family-based sample was conducted for 53 SNPs: the 41 most significant SNPs with P < 10(-4) and 12 imputed SNPs with P < 10(-4) on chromosome 15 (near the strongest signal). We replicated the association of rs10497394 (GWAS discovery P = 1.30 × 10(-5)) on chromosome 2 in the independent otitis media population (P = 4.7 × 10(-5); meta-analysis P = 1.52 × 10(-8)). Three additional SNPs had replication P values < 0.10. Two were on chromosome 15q26.1 including rs1110060, the strongest association with COME/ROM in the primary GWAS (P = 3.4 ×10(-7)) in KIF7 intron 7 (P = 0.072), and rs10775247, a non-synonymous SNP in TICRR exon 2 (P = 0.075). The third SNP rs386057 was on chromosome 5 in TPPP intron 1 (P = 0.045). We have performed the first GWAS of COME/ROM and have identified a SNP rs10497394 on chromosome 2 is significantly associated with COME/ROM susceptibility. This SNP is within a 537 kb intergenic region, bordered by CDCA7 and SP3. The genomic and functional significance of this newly identified locus in COME/ROM pathogenesis requires additional investigation.

Association study between genes in Reelin signaling pathway and autism identifies DAB1 as a susceptibility gene in a Chinese Han population

Autism is a pervasive neurodevelopmental disorder diagnosed in early childhood. The genetic factors might play an important role in its pathogenesis. Previous studies revealed that Reelin (RELN) polymorphisms were associated with autism. However, the roles of genes in Reelin signaling pathway for autism are largely unknown. As several knockout mice models in which the Reelin pathway genes (i.e. DAB1, VLDLR/APOER2, FYN/SRC and CRK/CRKL) are deficient have the similar phenotype as the reeler mice (Reelin(-/-)), we hypothesized that the Reelin signaling pathway genes might play roles in the etiology of autism. Therefore, we conducted a family-based association study. Sixty-two tagged single nucleotide polymorphisms (SNPs) covering 15 genes in Reelin pathway were genotyped in 239 trios, and 14 significant SNPs were further investigated in the additional 188 trios. In the total 427 trios, we found significant genetic association between autism and four SNPs in DAB1 (rs12035887 G: p=0.0006; rs3738556 G: p=0.0044; rs1202773 A: p=0.0048; rs12740765 T: p=0.0196). After the Bonferroni correction, SNP rs12035887 remained significant. Furthermore, the haplotype constructed with rs1202773 and rs12023109 in DAB1 showed significant excess transmission in both individual and global haplotype analyses (p=0.0052 and 0.0279, respectively). Our findings suggested that variations in DAB1 involved in the Reelin signaling pathway might contribute to genetic susceptibility to autism with Chinese Han decent, supporting the defect in the Reelin signaling pathway as a predisposition factor for autism.

Novel genomic approaches unravel genetic architecture of complex traits in apple

Background

Understanding the genetic architecture of quantitative traits is important for developing genome-based crop improvement methods. Genome-wide association study (GWAS) is a powerful technique for mining novel functional variants. Using a family-based design involving 1,200 apple (Malus × domestica Borkh.) seedlings genotyped for an 8K SNP array, we report the first systematic evaluation of the relative contributions of different genomic regions to various traits related to eating quality and susceptibility to some physiological disorders. Single-SNP analyses models that accounted for population structure, or not, were compared with models fitting all markers simultaneously. The patterns of linkage disequilibrium (LD) were also investigated.

Results

A high degree of LD even at longer distances between markers was observed, and the patterns of LD decay were similar across successive generations. Genomic regions were identified, some of which coincided with known candidate genes, with significant effects on various traits. Phenotypic variation explained by the loci identified through a whole-genome scan ranged from 3% to 25% across different traits, while fitting all markers simultaneously generally provided heritability estimates close to those from pedigree-based analysis. Results from ‘Q+K’ and ‘K’ models were very similar, suggesting that the SNP-based kinship matrix captures most of the underlying population structure. Correlations between allele substitution effects obtained from single-marker and all-marker analyses were about 0.90 for all traits. Use of SNP-derived realized relationships in linear mixed models provided a better goodness-of-fit than pedigree-based expected relationships. Genomic regions with probable pleiotropic effects were supported by the corresponding higher linkage group (LG) level estimated genetic correlations.

Conclusions

The accuracy of artificial selection in plants species can be increased by using more precise marker-derived estimates of realized coefficients of relationships. All-marker analyses that indirectly account for population- and pedigree structure will be a credible alternative to single-SNP analyses in GWAS. This study revealed large differences in the genetic architecture of apple fruit traits, and the marker-trait associations identified here will help develop genome-based breeding methods for apple cultivar development.

Family-based association mapping in crop species

Identification of allelic variants associated with complex traits provides molecular genetic information associated with variability upon which both artificial and natural selections are based. Family-based association mapping (FBAM) takes advantage of linkage disequilibrium among segregating progeny within crosses and among parents to provide greater power than association mapping and greater resolution than linkage mapping. Herein, we discuss the potential adaption of human family-based association tests and quantitative transmission disequilibrium tests for use in crop species. The rapid technological advancement of next generation sequencing will enable sequencing of all parents in a planned crossing design, with subsequent imputation of genotypes for all segregating progeny. These technical advancements are easily adapted to mating designs routinely used by plant breeders. Thus, FBAM has the potential to be widely adopted for discovering alleles, common and rare, underlying complex traits in crop species.

Dosage transmission disequilibrium test (dTDT) for linkage and association detection

Both linkage and association studies have been successfully applied to identify disease susceptibility genes with genetic markers such as microsatellites and Single Nucleotide Polymorphisms (SNPs). As one of the traditional family-based studies, the Transmission/Disequilibrium Test (TDT) measures the over-transmission of an allele in a trio from its heterozygous parents to the affected offspring and can be potentially useful to identify genetic determinants for complex disorders. However, there is reduced information when complete trio information is unavailable. In this study, we developed a novel approach to "infer" the transmission of SNPs by combining both the linkage and association data, which uses microsatellite markers from families informative for linkage together with SNP markers from the offspring who are genotyped for both linkage and a Genome-Wide Association Study (GWAS). We generalized the traditional TDT to process these inferred dosage probabilities, which we name as the dosage-TDT (dTDT). For evaluation purpose, we developed a simulation procedure to assess its operating characteristics. We applied the dTDT to the simulated data and documented the power of the dTDT under a number of different realistic scenarios. Finally, we applied our methods to a family study of alcohol dependence (COGA) and performed individual genotyping on complete families for the top signals. One SNP (rs4903712 on chromosome 14) remained significant after correcting for multiple testing Methods developed in this study can be adapted to other platforms and will have widespread applicability in genomic research when case-control GWAS data are collected in families with existing linkage data.

Efficient simulation of epistatic interactions in case-parent trios

Statistical approaches to evaluate interactions between single nucleotide polymorphisms (SNPs) and SNP-environment interactions are of great importance in genetic association studies, as susceptibility to complex disease might be related to the interaction of multiple SNPs and/or environmental factors. With these methods under active development, algorithms to simulate genomic data sets are needed to ensure proper type I error control of newly proposed methods and to compare power with existing methods. In this paper we propose an efficient method for a haplotype-based simulation of case-parent trios when the disease risk is thought to depend on possibly higher-order epistatic interactions or gene-environment interactions with binary exposures.

X-linked markers in the Duchenne muscular dystrophy gene associated with oral clefts

As part of an international consortium, case-parent trios were collected for a genome-wide association study of isolated, non-syndromic oral clefts, including cleft lip (CL), cleft palate (CP), and cleft lip and palate (CLP). Non-syndromic oral clefts have a complex and heterogeneous etiology. Risk is influenced by genes and environmental factors, and differs markedly by gender. Family-based association tests (FBAT) were used on 14,486 single nucleotide polymorphisms (SNPs) spanning the X chromosome, stratified by type of cleft and racial group. Significant results, even after multiple-comparisons correction, were obtained for the Duchenne muscular dystrophy (DMD) gene, the largest single gene in the human genome, among CL/P (i.e., both CL and CLP combined) trios. When stratified into groups of European and Asian ancestry, stronger signals were obtained for Asian subjects. Although conventional sliding-window haplotype analysis showed no increase in significance, selected combinations of the 25 most significant SNPs in the DMD gene identified four SNPs together that attained genome-wide significance among Asian CL/P trios, raising the possibility of interaction between distant SNPs within the DMD gene.

Fetal polymorphisms at the ABCB1-transporter gene locus are associated with susceptibility to non-syndromic oral cleft malformations

ATP-binding cassette (ABC) proteins in the placenta regulate fetal exposure to xenobiotics. We hypothesized that functional polymorphisms in ABC genes influence risk for non-syndromic oral clefts (NSOC). Both family-based and case-control studies were undertaken to evaluate the association of nine potentially functional single-nucleotide polymorphisms within four ABC genes with risk of NSOC. Peripheral blood DNA from a total of 150 NSOC case-parent trios from Singapore and Taiwan were genotyped, as was cord blood DNA from 189 normal Chinese neonates used as controls. In trios, significant association was observed between the ABCB1 single-nucleotide polymorphisms and NSOC (P<0.05). Only ABCB1 rs1128503 retained significant association after Bonferroni correction (odds ratio (OR)=2.04; 95% confidence interval (CI)=1.42-2.98), while rs2032582 and rs1045642 showed nominal significance. Association with rs1128503 was replicated in a case-control analysis comparing NSOC probands with controls (OR=1.58; 95% CI=1.12-2.23). A comparison between the mothers of probands and controls showed no evidence of association, suggesting NSOC risk is determined by fetal and not maternal ABCB1 genotype. The two studies produced a combined OR of 1.79 (95% CI=1.38-2.30). The T-allele at rs1128503 was associated with higher risk. This study thus provides evidence that potentially functional polymorphisms in fetal ABCB1 modulate risk for NSOC, presumably through suboptimal exclusion of xenobiotics at the fetal-maternal interface.

Association study of 25 type 2 diabetes related Loci with measures of obesity in Indian sib pairs

Obesity is an established risk factor for type 2 diabetes (T2D) and they are metabolically related through the mechanism of insulin resistance. In order to explore how common genetic variants associated with T2D correlate with body mass index (BMI), we examined the influence of 25 T2D associated loci on obesity risk. We used 5056 individuals (2528 sib-pairs) recruited in Indian Migration Study and conducted within sib-pair analysis for six obesity phenotypes. We found associations of variants in CXCR4 (rs932206) and HHEX (rs5015480) with higher body mass index (BMI) (β=0.13, p=0.001) and (β=0.09, p=0.002), respectively and weight (β=0.13, p=0.001) and (β=0.09, p=0.001), respectively. CXCR4 variant was also strongly associated with body fat (β=0.10, p=0.0004). In addition, we demonstrated associations of CXCR4 and HHEX with overweight/obesity (OR=1.6, p=0.003) and (OR=1.4, p=0.002), respectively, in 1333 sib-pairs (2666 individuals). We observed marginal evidence of associations between variants at six loci (TCF7L2, NGN3, FOXA2, LOC646279, FLJ39370 and THADA) and waist hip ratio (WHR), BMI and/or overweight which needs to be validated in larger set of samples. All the above findings were independent of daily energy consumption and physical activity level. The risk score estimates based on eight significant loci (including nominal associations) showed associations with WHR and body fat which were independent of BMI. In summary, we establish the role of T2D associated loci in influencing the measures of obesity in Indian population, suggesting common underlying pathophysiology across populations.

Rare variant analysis for family-based design

Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.

Joint associations of 61 genetic variants in the nicotinic acetylcholine receptor genes with subclinical atherosclerosis in American Indians: a gene-family analysis

BACKGROUND

Atherosclerosis is the underlying cause of cardiovascular disease, the leading cause of morbidity and mortality in all American populations, including American Indians. Genetic factors play an important role in the pathogenesis of atherosclerosis. Although a single-nucleotide polymorphism (SNP) may explain only a small portion of variability in disease, the joint effect of multiple variants in a pathway on disease susceptibility could be large.

METHODS AND RESULTS

Using a gene-family analysis, we investigated the joint associations of 61 tag SNPs in 7 nicotinic acetylcholine receptor genes with subclinical atherosclerosis, as measured by carotid intima-media thickness and plaque score, in 3665 American Indians from 94 families recruited by the Strong Heart Family Study (SHFS). Although multiple SNPs showed marginal association with intima-media thickness and plaque score individually, only a few survived adjustments for multiple testing. However, simultaneously modeling of the joint effect of all 61 SNPs in 7 nicotinic acetylcholine receptor genes revealed significant association of the nicotinic acetylcholine receptor gene family with both intima-media thickness and plaque score independent of known coronary risk factors.

CONCLUSIONS

Genetic variants in the nicotinic acetylcholine receptor gene family jointly contribute to subclinical atherosclerosis in American Indians who participated in the SHFS. These variants may influence the susceptibility of atherosclerosis through pathways other than cigarette smoking per se.

A family-based association study after genome-wide linkage analysis identified two genetic loci for renal function in a Mongolian population

The estimated glomerular filtration rate is a well-known measure of renal function and is widely used to follow the course of disease. Although there have been several investigations establishing the genetic background contributing to renal function, Asian populations have rarely been used in these genome-wide studies. Here, we aimed to find candidate genetic determinants of renal function in 1007 individuals from 73 extended families of Mongolian origin. Linkage analysis found two suggestive regions near 9q21 (logarithm of odds (LOD) 2.82) and 15q15 (LOD 2.70). The subsequent family-based association study found 2 and 10 significant single-nucleotide polymorphisms (SNPs) in each region, respectively. The strongest SNPs on chromosome 9 and 15 were rs17400257 and rs1153831 with P-values of 7.21 × 10(-9) and 2.47 × 10(-11), respectively. Genes located near these SNPs are considered candidates for determining renal function and include FRMD3, GATM, and SPATA5L1. Thus, we identified possible loci that determine renal function in an isolated Asian population. Consistent with previous reports, our study found genes linked and associated with renal function in other populations.

Statistical distributions of test statistics used for quantitative trait association mapping in structured populations

Background

Spurious associations between single nucleotide polymorphisms and phenotypes are a major issue in genome-wide association studies and have led to underestimation of type 1 error rate and overestimation of the number of quantitative trait loci found. Many authors have investigated the influence of population structure on the robustness of methods by simulation. This paper is aimed at developing further the algebraic formalization of power and type 1 error rate for some of the classical statistical methods used: simple regression, two approximate methods of mixed models involving the effect of a single nucleotide polymorphism (SNP) and a random polygenic effect (GRAMMAR and FASTA) and the transmission/disequilibrium test for quantitative traits and nuclear families. Analytical formulae were derived using matrix algebra for the first and second moments of the statistical tests, assuming a true mixed model with a polygenic effect and SNP effects.

Results

The expectation and variance of the test statistics and their marginal expectations and variances according to the distribution of genotypes and estimators of variance components are given as a function of the relationship matrix and of the heritability of the polygenic effect. These formulae were used to compute type 1 error rate and power for any kind of relationship matrix between phenotyped and genotyped individuals for any level of heritability. For the regression method, type 1 error rate increased with the variability of relationships and with heritability, but decreased with the GRAMMAR method and was not affected with the FASTA and quantitative transmission/disequilibrium test methods.

Conclusions

The formulae can be easily used to provide the correct threshold of type 1 error rate and to calculate the power when designing experiments or data collection protocols. The results concerning the efficacy of each method agree with simulation results in the literature but were generalized in this work. The power of the GRAMMAR method was equal to the power of the FASTA method at the same type 1 error rate. The power of the quantitative transmission/disequilibrium test was low. In conclusion, the FASTA method, which is very close to the full mixed model, is recommended in association mapping studies.

Effect of angiotensin II type I receptor A1166C polymorphism on benazepril action in hypertensive patients: a family-based association test study

BACKGROUND

Few studies have examined the effect of the angiotensin II type I receptor (AT1R) A1166C polymorphism on the antihypertensive effect of the angiotensin-converting-enzyme inhibitor benazepril in patients with hypertension, and no such studies have performed analysis using the Family-Based Association Test (FBAT), The aim of our study was to examine the association between AT1R A1166C gene polymorphism and the antihypertensive effect of benazepril using the FBAT.

METHODS AND RESULTS

A total of 864 patients (aged, 26-62 years) with essential hypertension were identified in an epidemiological survey and enrolled in this study. Blood pressure (BP) was measured before and after 16 days of treatment with benazepril (10 mg/day). The association between the A1166C gene polymorphism and the antihypertensive effect of benazepril was assessed by FBAT. The frequencies of alleles A and C were 95.1% and 4.9%, respectively. FBAT analysis revealed that the C allele was significantly associated with high baseline diastolic BP (Z = 2.041, p = 0.041), decreased systolic BP after treatment (Z = 2.549, p = 0.011), and decreased diastolic BP after treatment (Z = 2.320, p = 0.020).

CONCLUSION

Our results, determined using the FBAT, are the first evidence that the AT1R A1166C polymorphism may increase the antihypertensive effect of benazepril in patients with hypertension.

Population-based case-control association studies

This unit provides an overview of the design and analysis of population-based case-control studies of genetic risk factors for complex disease. Considerations specific to genetic studies are emphasized. The unit reviews basic study designs differentiating case-control studies from others, presents different genetic association strategies (candidate gene, genome-wide association, and high-throughput sequencing), introduces basic methods of statistical analysis for case-control data and approaches to combining case-control studies, and discusses measures of association and impact. Admixed populations, controlling for confounding (including population stratification), consideration of multiple loci and environmental risk factors, and complementary analyses of haplotypes, genes, and pathways are briefly discussed. Readers are referred to basic texts on epidemiology for more details on general conduct of case-control studies.

Comprehensive genomic analyses associate UGT8 variants with musical ability in a Mongolian population

Background

Musical abilities such as recognising music and singing performance serve as means for communication and are instruments in sexual selection. Specific regions of the brain have been found to be activated by musical stimuli, but these have rarely been extended to the discovery of genes and molecules associated with musical ability.

Methods

A total of 1008 individuals from 73 families were enrolled and a pitch-production accuracy test was applied to determine musical ability. To identify genetic loci and variants that contribute to musical ability, we conducted family-based linkage and association analyses, and incorporated the results with data from exome sequencing and array comparative genomic hybridisation analyses.

Results

We found significant evidence of linkage at 4q23 with the nearest marker D4S2986 (LOD=3.1), whose supporting interval overlaps a previous study in Finnish families, and identified an intergenic single nucleotide polymorphism (SNP) (rs1251078, p=8.4×10⁻¹⁷) near UGT8, a gene highly expressed in the central nervous system and known to act in brain organisation. In addition, a non-synonymous SNP in UGT8 was revealed to be highly associated with musical ability (rs4148254, p=8.0×10⁻¹⁷), and a 6.2 kb copy number loss near UGT8 showed a plausible association with musical ability (p=2.9×10⁻⁶).

Conclusions

This study provides new insight into the genetics of musical ability, exemplifying a methodology to assign functional significance to synonymous and non-coding alleles by integrating multiple experimental methods.

Causation and causal inference for genetic effects

Over the past three decades, substantial developments have been made on how to infer the causal effect of an exposure on an outcome, using data from observational studies, with the randomized experiment as the golden standard. These developments have reshaped the paradigm of how to build statistical models, how to adjust for confounding, how to assess direct effects, mediated effects and interactions, and even how to analyze data from randomized experiments. The congruence of random transmission of alleles during meiosis and the randomization in controlled experiments/trials, suggests that genetic studies may lend themselves naturally to a causal analysis. In this contribution, we will reflect on this and motivate, through illustrative examples, where insights from the causal inference literature may help to understand and correct for typical biases in genetic effect estimates.

Gene mapping study for constitutive skin color in an isolated Mongolian population

To elucidate the genes responsible for constitutive human skin color, we measured the extent of skin pigmentation in the buttock, representative of lifelong non-sun-exposed skin, and conducted a gene mapping study on skin color in an isolated Mongolian population composed of 344 individuals from 59 families who lived in Dashbalbar, Mongolia. The heritability of constitutive skin color was 0.82, indicating significant genetic association on this trait. Through the linkage analysis using 1,039 short tandem repeat (STR) microsatellite markers, we identified a novel genomic region regulating constitutive skin color on 11q24.2 with an logarithm of odds (LOD) score of 3.39. In addition, we also found other candidate regions on 17q23.2, 6q25.1, and 13q33.2 (LOD ≥ 2). Family-based association tests on these regions with suggestive linkage peaks revealed ten and two significant single nucleotide polymorphisms (SNPs) on the linkage regions of chromosome 11 and 17, respectively. We were able to discover four possible candidate genes that would be implicated to regulate human skin color: ETS1, UBASH3B, ASAM, and CLTC.

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

Strategies for genetic association analyses combining unrelated case-control individuals and family trios

In genetic association studies, analyses integrating data or estimates from unrelated case-control individuals and case trios (case offspring and their parents) can increase statistical power to identify disease susceptibility loci. Data on control trios may also be available, but how and when their use is advantageous is less familiar and is described here. In addition, the authors examine assumptions and properties of hybrid analyses combining association estimates from unrelated case-control individuals together with case and control family trios, focusing on low-prevalence disease. One such assumption is absence of population stratification bias (PSB), a potential source of confounding in case-control analyses. For detection of PSB, the authors discuss 4 possible tests that assess equality between individual-level and family-based estimates. Furthermore, a weighted framework is presented, in which estimates from analyses combining unrelated individuals and families (most powerful but subject to PSB) and family-based analyses (robust to PSB) are weighted according to the observed PSB test P value. In contrast to existing hybrid designs that combine individuals and families only if no significant PSB is detected, the weighted framework does not require specification of an arbitrary PSB testing level to establish significance. The statistical methods are evaluated using simulations and applied to a candidate gene study of childhood leukemia (Quebec Childhood Leukemia Study, 1980-2000).

Gene-by-environment effect of house dust mite on purinergic receptor P2Y12 (P2RY12) and lung function in children with asthma

BACKGROUND

Distinct receptors likely exist for leukotriene (LT)E(4), a potent mediator of airway inflammation. Purinergic receptor P2Y12 is needed for LTE(4)-induced airways inflammation, and P2Y12 antagonism attenuates house dust mite-induced pulmonary eosinophilia in mice. Although experimental data support a role for P2Y12 in airway inflammation, its role in human asthma has never been studied.

OBJECTIVE

To test for association between variants in the P2Y12 gene (P2RY12) and lung function in human subjects with asthma, and to examine for gene-by-environment interaction with house dust mite exposure.

METHODS

Nineteen single nucleotide polymorphisms (SNPs) in P2RY12 were genotyped in 422 children with asthma and their parents (n = 1266). Using family based methods, we tested for associations between these SNPs and five lung function measures. We performed haplotype association analyses and tested for gene-by-environment interactions using house dust mite exposure. We used the false discovery rate to account for multiple comparisons.

RESULTS

Five SNPs in P2RY12 were associated with multiple lung function measures (P-values 0.006–0.025). Haplotypes in P2RY12 were also associated with lung function (P-values 0.0055–0.046). House dust mite exposure modulated associations between P2RY12 and lung function, with minor allele homozygotes exposed to house dust mite demonstrating worse lung function than those unexposed (significant interaction P-values 0.0028–0.040).

CONCLUSIONS AND CLINICAL RELEVANCE

The P2RY12 variants were associated with lung function in a large family-based asthma cohort. House dust mite exposure caused significant gene-by-environment effects. Our findings add the first human evidence to experimental data supporting a role for P2Y12 in lung function. P2Y12 could represent a novel target for asthma treatment.

DOCK4 and CEACAM21 as novel schizophrenia candidate genes in the Jewish population

It is well accepted that schizophrenia has a strong genetic component. Several genome-wide association studies (GWASs) of schizophrenia have been published in recent years; most of them population based with a case-control design. Nevertheless, identifying the specific genetic variants which contribute to susceptibility to the disorder remains a challenging task. A family-based GWAS strategy may be helpful in the identification of schizophrenia susceptibility genes since it is protected against population stratification, enables better accounting for genotyping errors and is more sensitive for identification of rare variants which have a very low frequency in the general population. In this project we implemented a family-based GWAS of schizophrenia in a sample of 107 Jewish-Israeli families. We found one genome-wide significant association in the intron of the DOCK4 gene (rs2074127, p value=1.134×10⁻⁷) and six additional nominally significant association signals with p<1×10⁻⁵. One of the top single nucleotide polymorphisms (p<1×10⁻⁵) which is located in the predicted intron of the CEACAM21 gene was significantly replicated in independent family-based sample of Arab-Israeli origin (rs4803480: p value=0.002; combined p value=9.61×10⁻⁸), surviving correction for multiple testing. Both DOCK4 and CEACAM21 are biologically reasonable candidate genes for schizophrenia although generalizability of the association of DOCK4 with schizophrenia should be investigated in further studies. In addition, gene-wide significant associations were found within three schizophrenia candidate genes: PGBD1, RELN and PRODH, replicating previously reported associations. By application of a family-based strategy to GWAS, our study revealed new schizophrenia susceptibility loci in the Jewish-Israeli population.

Externalizing behaviors are associated with SNPs in the CHRNA5/CHRNA3/CHRNB4 gene cluster

There is strong evidence for shared genetic factors contributing to childhood externalizing disorders and substance abuse. Externalizing disorders often precede early substance experimentation, leading to the idea that individuals inherit a genetic vulnerability to generalized disinhibitory psychopathology. Genetic variation in the CHRNA5/CHRNA3/CHRNB4 gene cluster has been associated with early substance experimentation, nicotine dependence, and other drug behaviors. This study examines whether the CHRNA5/CHRNA3/CHRNB4 locus is correlated also with externalizing behaviors in three independent longitudinally assessed adolescent samples. We developed a common externalizing behavior phenotype from the available measures in the three samples, and tested for association with 10 SNPs in the gene cluster. Significant results were detected in two of the samples, including rs8040868, which remained significant after controlling for smoking quantity. These results expand on previous work focused mainly on drug behaviors, and support the hypothesis that variation in the CHRNA5/CHRNA3/CHRNB4 locus is associated with early externalizing behaviors.

Family-based association study of early growth response gene 3 with child bipolar I disorder

BACKGROUND

The risk for relapse of child bipolar I disorder (BP-I) is highly correlated with environmental factors. Immediate early genes of the early growth response (EGR) gene family are activated at high levels in the brain in response to environmental events, including stress, and mediate numerous neurobiological processes that have been associated with mental illness risk. The objective of this study is to evaluate whether single nucleotide polymorphisms (SNPs) in EGR genes are associated with the risk to develop child bipolar I disorder.

METHODS

To investigate whether EGR genes may influence susceptibility to child bipolar I disorder (BP-I), we used Family Based Association Tests to examine whether SNPs in each of the EGR genes were associated with illness in 49 families.

RESULTS

Two SNPs in EGR3 displayed nominally significant associations with child BP-I (p=0.027 and p=0.028); though neither was statistically significant following correction for multiple comparisons. Haplotype association analysis indicated that these SNPs are in linkage disequilibrium (LD). None of the SNPs tested in EGR1, EGR2, or EGR4 was associated with child BP-I.

LIMITATIONS

This study was limited by small sample size, which resulted in it being underpowered to detect a significant association after correction for multiple comparisons.

CONCLUSIONS

Our study revealed a preliminary finding suggesting that EGR3, a gene that translates environmental stimuli into long-term changes in the brain, warrants further investigation for association with risk for child BP-I disorder in a larger sample. Such studies may help reveal mechanisms by which environment can interact with genetic predisposition to influence this severe mental illness.