A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information

Trio Exome Sequencing in VACTERL Association

INTRODUCTION

Currently, there is only limited data on monogenic causes of vertebral defects, anorectal malformations, cardiac defects, esophageal atresia or tracheoesophageal fistula, renal malformations, and limb defects (VACTERL) association. The aim of this study was to extend the spectrum of disease-causing variants in known genes, to determine the diagnostic yield of monogenic causes, and to identify candidate genes and rare variants by applying comprehensive genetic testing or rare variant burden.

METHODS

The total cohort comprised 101 affected individuals and their parents. Trio exome sequencing was only performed in 96 individuals and their parents because of DNA quality reasons and case-control gene and pathway burden tests were calculated and evaluated by quantile-quantile plots, principal component analysis plots and family-based association test (FBAT).

RESULTS

In 5 of 96 individuals, disease-causing variants in known genes or loci were identified to be associated with the following 4 disorders: Kabuki syndrome, Sotos syndrome, MELAS syndrome, and deletion syndrome encompassing TWIST1. In 91 individuals, no disease-causing variants were found. FBAT showed 14 significant variants, 2 significant genes (LOC645752 and ZNF417), and 8 significant pathways.

CONCLUSION

This study shows that most individuals with VACTERL association do not have known discrete genetic syndromes, implying that pathomechanisms or variants not identifiable by exome sequencing may exist requiring further investigation.

Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer

INTRODUCTION

Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex.

METHODS

In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa).

RESULTS

Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer.

CONCLUSION

Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification

Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.

Machine Learning Prediction of ADHD Severity: Association and Linkage to ADGRL3, DRD4, and SNAP25

OBJECTIVE

To investigate whether single nucleotide polymorphisms (SNPs) in the ADGRL3, DRD4, and SNAP25 genes are associated with and predict ADHD severity in families from a Caribbean community.

METHOD

ADHD severity was derived using latent class cluster analysis of DSM-IV symptomatology. Family-based association tests were conducted to detect associations between SNPs and ADHD severity latent phenotypes. Machine learning algorithms were used to build predictive models of ADHD severity based on demographic and genetic data.

RESULTS

Individuals with ADHD exhibited two seemingly independent latent class severity configurations. SNPs harbored in DRD4, SNAP25, and ADGRL3 showed evidence of linkage and association to symptoms severity and a potential pleiotropic effect on distinct domains of ADHD severity. Predictive models discriminate severe from non-severe ADHD in specific symptom domains.

CONCLUSION

This study supports the role of DRD4, SNAP25, and ADGRL3 genes in outlining ADHD severity, and a new prediction framework with potential clinical use.

A unifying framework for rare variant association testing in family-based designs, including higher criticism approaches, SKATs, and burden tests

MOTIVATION

Analysis of rare variants in family-based studies remains a challenge. Transmission-based approaches provide robustness against population stratification, but the evaluation of the significance of test statistics based on asymptotic theory can be imprecise. Also, power will depend heavily on the choice of the test statistic and on the underlying genetic architecture of the locus, which will be generally unknown.

RESULTS

In our proposed framework, we utilize the FBAT haplotype algorithm to obtain the conditional offspring genotype distribution under the null hypothesis given the sufficient statistic. Based on this conditional offspring genotype distribution, the significance of virtually any association test statistic can be evaluated based on simulations or exact computations, without the need for asymptotic approximations. Besides standard linear burden-type statistics, this enables our approach to also evaluate other test statistics such as variance components statistics, higher criticism approaches, and maximum-single-variant-statistics, where asymptotic theory might be involved or does not provide accurate approximations for rare variant data. Based on these P-values, combined test statistics such as the aggregated Cauchy association test (ACAT) can also be utilized. In simulation studies, we show that our framework outperforms existing approaches for family-based studies in several scenarios. We also applied our methodology to a TOPMed whole-genome sequencing dataset with 897 asthmatic trios from Costa Rica.

AVAILABILITY AND IMPLEMENTATION

FBAT software is available at https://sites.google.com/view/fbatwebpage. Simulation code is available at https://github.com/julianhecker/FBAT_rare_variant_test_simulations. Whole-genome sequencing data for 'NHLBI TOPMed: The Genetic Epidemiology of Asthma in Costa Rica' is available at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000988.v4.p1.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Innovative approach to identify multigenomic and environmental interactions associated with birth defects in family-based hybrid designs

Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between maternal and offspring genetic variants or their surrogate single nucleotide polymorphisms (SNPs) and environmental factors using family-based hybrid study design. The proposed approach can analyze diverse genetic and environmental factors and accommodate samples from a variety of family units, including case/control-parental triads, and case/control-parental dyads, while minimizing potential bias introduced by population admixture. Comprehensive simulations demonstrated that our innovative approach outperformed the log-linear approach, the best available method for case-control family data. The proposed approach had greater statistical power and was capable to unbiasedly estimate the maternal and child genetic effects and the effects of environmental factors, while controlling the Type I error rate against population stratification. Using our newly developed approach, we analyzed the associations between maternal and fetal SNPs and obstructive and conotruncal heart defects, with adjustment for demographic and lifestyle factors and dietary supplements. Fourteen and 11 fetal SNPs were associated with obstructive and conotruncal heart defects, respectively. Twenty-seven and 17 maternal SNPs were associated with obstructive and conotruncal heart defects, respectively. In addition, maternal body mass index was a significant risk factor for obstructive defects. The proposed approach is a powerful tool for interrogating the etiological mechanism underlying complex traits.

An evaluation of approaches for rare variant association analyses of binary traits in related samples

Recognizing that family data provide unique advantage of identifying rare risk variants in genetic association studies, many cohorts with related samples have gone through whole genome sequencing in large initiatives such as the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Analyzing rare variants poses challenges for binary traits in that some genotype categories may have few or no observed events, causing bias and inflation in commonly used methods. Several methods have recently been proposed to better handle rare variants while accounting for family relationship, but their performances have not been thoroughly evaluated together. Here we compare several existing approaches including SAIGE but not limited to related samples using simulations based on the Framingham Heart Study samples and genotype data from Illumina HumanExome BeadChip where rare variants are the majority. We found that logistic regression with likelihood ratio test applied to related samples was the only approach that did not have inflated type I error rates in both single variant test (SVT) and gene-based tests, followed by Firth logistic regression that had inflation in its direction insensitive gene-based test at prevalence 0.01 only, applied to either related or unrelated samples, though theoretically logistic regression and Firth logistic regression do not account for relatedness in samples. SAIGE had inflation in SVT at prevalence 0.1 or lower and the inflation was eliminated with a minor allele count filter of 5. As for power, there was no approach that outperformed others consistently among all single variant tests and gene-based tests.

Mixed logistic regression in genome-wide association studies

Background

Mixed linear models (MLM) have been widely used to account for population structure in case-control genome-wide association studies, the status being analyzed as a quantitative phenotype. Chen et al. proved in 2016 that this method is inappropriate in some situations and proposed GMMAT, a score test for the mixed logistic regression (MLR). However, this test does not produces an estimation of the variants’ effects. We propose two computationally efficient methods to estimate the variants’ effects. Their properties and those of other methods (MLM, logistic regression) are evaluated using both simulated and real genomic data from a recent GWAS in two geographically close population in West Africa.

Results

We show that, when the disease prevalence differs between population strata, MLM is inappropriate to analyze binary traits. MLR performs the best in all circumstances. The variants’ effects are well evaluated by our methods, with a moderate bias when the effect sizes are large. Additionally, we propose a stratified QQ-plot, enhancing the diagnosis of p values inflation or deflation when population strata are not clearly identified in the sample.

Conclusion

The two proposed methods are implemented in the R package milorGWAS available on the CRAN. Both methods scale up to at least 10,000 individuals. The same computational strategies could be applied to other models (e.g. mixed Cox model for survival analysis).

Causal inference in genetic trio studies

We introduce a method to draw causal inferences-inferences immune to all possible confounding-from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural randomness in meiosis can be viewed as a high-dimensional randomized experiment. We make this observation actionable by developing a conditional independence test that identifies regions of the genome containing distinct causal variants. The proposed digital twin test compares an observed offspring to carefully constructed synthetic offspring from the same parents to determine statistical significance, and it can leverage any black-box multivariate model and additional nontrio genetic data to increase power. Crucially, our inferences are based only on a well-established mathematical model of recombination and make no assumptions about the relationship between the genotypes and phenotypes. We compare our method to the widely used transmission disequilibrium test and demonstrate enhanced power and localization.

Statistically efficient association analysis of quantitative traits with haplotypes and untyped SNPs in family studies

BACKGROUND

Associations between haplotypes and quantitative traits provide valuable information about the genetic basis of complex human diseases. Haplotypes also provide an effective way to deal with untyped SNPs. Two major challenges arise in haplotype-based association analysis of family data. First, haplotypes may not be inferred with certainty from genotype data. Second, the trait values within a family tend to be correlated because of common genetic and environmental factors.

RESULTS

To address these challenges, we present an efficient likelihood-based approach to analyzing associations of quantitative traits with haplotypes or untyped SNPs. This approach properly accounts for within-family trait correlations and can handle general pedigrees with arbitrary patterns of missing genotypes. We characterize the genetic effects on the quantitative trait by a linear regression model with random effects and develop efficient likelihood-based inference procedures. Extensive simulation studies are conducted to examine the performance of the proposed methods. An application to family data from the Childhood Asthma Management Program Ancillary Genetic Study is provided. A computer program is freely available.

CONCLUSIONS

Results from extensive simulation studies show that the proposed methods for testing the haplotype effects on quantitative traits have correct type I error rates and are more powerful than some existing methods.

Maya gene variants related to the risk of type 2 diabetes in a family-based association study

Mexican Maya populations have a notably high prevalence of type 2 diabetes (T2D) as a consequence of the interaction between environmental factors and a genetic component. To assess the impact of 24 single nucleotide variants (SNVs) located in 18 T2D risk genes, we conducted a family-based association evaluation in samples from Maya communities with a high incidence of the disease. A total of four hundred individuals were recruited from three Maya communities with a high T2D incidence. Family pedigrees (100) and 49 nuclear families were included. Genotyping was performed by allelic discrimination with TaqMan probes. This study also included the family-based association test (FBAT) statistic U to assess the genetic associations with T2D, and the multivariate statistical and haplotype analyses. A positive association with TD2 risk was found for WFS1 rs6446482 (p = 0.046, Z = 1.994) under an additive model, and SIRT1 rs7896005 (p = 0.038, Z = 2.073) under the dominant model. Multivariate model analysis, including T2D status, age, and body mass index (BMI), displayed significant covariance in PPARGC-1α rs8192678; SIRT1 rs7896005; TCF7L2 rs7903146 and rs122243326; UCP3 rs3781907; and HHEX rs1111875 with a P < 0.05. This study revealed an association of SIRT1 and WFS1 with T2D risk.

Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma

BACKGROUND

Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma.

METHODS

WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician's diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes.

RESULTS

A genome-wide significant association was identified between baseline FEV1/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10-8 in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10-6). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV1 (P = 3.3 × 10-3), postbronchodilator (PB) FEV1 (7.3 × 10-3), and PB FEV1/FVC ratio (P = 2.7 × 10-3). The identified baseline FEV1/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR.

CONCLUSIONS

These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.

Genetic Variation Underpinning ADHD Risk in a Caribbean Community

Attention Deficit Hyperactivity Disorder (ADHD) is a highly heritable and prevalent neurodevelopmental disorder that frequently persists into adulthood. Strong evidence from genetic studies indicates that single nucleotide polymorphisms (SNPs) harboured in the ADGRL3 (LPHN3), SNAP25, FGF1, DRD4, and SLC6A2 genes are associated with ADHD. We genotyped 26 SNPs harboured in genes previously reported to be associated with ADHD and evaluated their potential association in 386 individuals belonging to 113 nuclear families from a Caribbean community in Barranquilla, Colombia, using family-based association tests. SNPs rs362990-SNAP25 (T allele; p = 2.46 × 10⁻⁴), rs2282794-FGF1 (A allele; p = 1.33 × 10⁻²), rs2122642-ADGRL3 (C allele, p = 3.5 × 10⁻²), and ADGRL3 haplotype CCC (markers rs1565902-rs10001410-rs2122642, OR = 1.74, P_permuted = 0.021) were significantly associated with ADHD. Our results confirm the susceptibility to ADHD conferred by SNAP25, FGF1, and ADGRL3 variants in a community with a significant African American component, and provide evidence supporting the existence of specific patterns of genetic stratification underpinning the susceptibility to ADHD. Knowledge of population genetics is crucial to define risk and predict susceptibility to disease.

Replication of previous GWAS hits suggests the association between rs4307059 near MSNP1AS and autism in a Chinese Han population

Autism is a complex neurodevelopmental disorder with high heritability. Previous genome-wide association studies (GWAS) demonstrated that some single-nucleotide polymorphisms (SNPs) were significantly associated with autism, while other studies focusing on these GWAS hits showed inconsistent results. Besides, the association between these variants and autism in the Chinese Han population remains unclear. Therefore, this family-based association study was performed in 640 Chinese Han autism trios to investigate the association between autism and 7 SNPs with genome-wide significance in previous GWAS (rs4307059 near MSNP1AS, rs4141463 in MACROD2, rs2535629 in ITIH3, rs11191454 in AS3MT, rs1625579 in MIR137HG, rs11191580 in NT5C2, and rs1409313 in CUEDC2). The results showed a nominal association between the T allele of rs4307059 and autism under both additive model (T>C, Z = 2.250, P = .024) and recessive model (T>C, Z = 2.109, P = .035). The findings provided evidence that rs4307059 near MSNP1AS might be a susceptibility variant for autism in the Chinese Han population.

Statistical Methods and Software for Substance Use and Dependence Genetic Research

BACKGROUND

Substantial substance use disorders and related health conditions emerged dur-ing the mid-20th century and continue to represent a remarkable 21st century global burden of disease. This burden is largely driven by the substance-dependence process, which is a complex process and is influenced by both genetic and environmental factors. During the past few decades, a great deal of pro-gress has been made in identifying genetic variants associated with Substance Use and Dependence (SUD) through linkage, candidate gene association, genome-wide association and sequencing studies.

METHODS

Various statistical methods and software have been employed in different types of SUD ge-netic studies, facilitating the identification of new SUD-related variants.

CONCLUSION

In this article, we review statistical methods and software that are currently available for SUD genetic studies, and discuss their strengths and limitations.

Extensive Ethnic Variation and Linkage Disequilibrium at the FCGR2/3 Locus: Different Genetic Associations Revealed in Kawasaki Disease

The human Fc-gamma receptors (FcγRs) link adaptive and innate immunity by binding immunoglobulin G (IgG). All human low-affinity FcγRs are encoded by the FCGR2/3 locus containing functional single nucleotide polymorphisms (SNPs) and gene copy number variants. This locus is notoriously difficult to genotype and high-throughput methods commonly used focus on only a few SNPs. We performed multiplex ligation-dependent probe amplification for all relevant genetic variations at the FCGR2/3 locus in >4,000 individuals to define linkage disequilibrium (LD) and allele frequencies in different populations. Strong LD and extensive ethnic variation in allele frequencies was found across the locus. LD was strongest for the FCGR2C-ORF haplotype (rs759550223+rs76277413), which leads to expression of FcγRIIc. In Europeans, the FCGR2C-ORF haplotype showed strong LD with, among others, rs201218628 (FCGR2A-Q27W, r² = 0.63). LD between these two variants was weaker (r² = 0.17) in Africans, whereas the FCGR2C-ORF haplotype was nearly absent in Asians (minor allele frequency <0.005%). The FCGR2C-ORF haplotype and rs1801274 (FCGR2A-H131R) were in weak LD (r² = 0.08) in Europeans. We evaluated the importance of ethnic variation and LD in Kawasaki Disease (KD), an acute vasculitis in children with increased incidence in Asians. An association of rs1801274 with KD was previously shown in ethnically diverse genome-wide association studies. Now, we show in 1,028 European KD patients that the FCGR2C-ORF haplotype, although nearly absent in Asians, was more strongly associated with susceptibility to KD than rs1801274 in Europeans. Our data illustrate the importance of interpreting findings of association studies concerning the FCGR2/3 locus with knowledge of LD and ethnic variation.

A Family-Based Rare Haplotype Association Method for Quantitative Traits

BACKGROUND

The variants identified in genome-wide association studies account for only a small fraction of disease heritability. A key to this "missing heritability" is believed to be rare variants. Specifically, we focus on rare haplotype variant (rHTV). The existing methods for detecting rHTV are mostly population-based, and as such, are susceptible to population stratification and admixture, leading to an inflated false-positive rate. Family-based methods are more robust in this respect.

METHODS

We propose a method for detecting rHTVs associated with quantitative traits called family-based quantitative Bayesian LASSO (famQBL). FamQBL can analyze any type of pedigree and is based on a mixed model framework. We regularize the haplotype effects using Bayesian LASSO and estimate the posterior distributions using Markov chain Monte Carlo methods.

RESULTS

We conduct simulation studies, including analyses of Genetic Analysis Workshop 18 simulated data, to study the properties of famQBL and compare with a standard family-based haplotype association test implemented in FBAT (family-based association test) software. We find famQBL to be more powerful than FBAT with well-controlled false-positive rates. We also apply famQBL to the Framingham Heart Study data and detect an rHTV associated with diastolic blood pressure.

CONCLUSION

FamQBL can help uncover rHTVs associated with quantitative traits.

Haplotype and Haplotype-Environment Interaction Analysis Revealed Roles of SPRY2 for NSCL/P among Chinese Populations

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is one of common birth defects in China, with genetic and environmental components contributing to the etiology. Genome wide association studies (GWASs) have identified SPRY1 and SPRY2 to be associated with NSCL/P among Chinese populations. This study aimed to further explore potential genetic effect and gene—environment interaction among SPRY genes based on haplotype analysis, using 806 Chinese case—parent NSCL/P trios drawn from an international consortium which conducted a genome-wide association study. After the process of quality control, 190 single nucleotide polymorphisms (SNPs) of SPRY genes were included for analyses. Haplotype and haplotype—environment interaction analyses were conducted in Population-Based Association Test (PBAT) software. A 2-SNP haplotype and three 3-SNP haplotypes showed a significant association with the risk of NSCL/P after Bonferroni correction (corrected significance level = 2.6 × 10⁻⁴). Moreover, haplotype—environment interaction analysis identified these haplotypes respectively showing statistically significant interactions with maternal multivitamin supplementation or maternal environmental tobacco smoke. This study showed SPRY2 to be associated with NSCL/P among the Chinese population through not only gene effects, but also a gene—environment interaction, highlighting the importance of considering environmental exposures in the genetic etiological study of NSCL/P.

A comparison of popular TDT-generalizations for family-based association analysis

The transmission disequilibrium test (TDT) is the gold standard for testing the association between a genetic variant and disease in samples consisting of affected individuals and their parents. In practice, more complex pedigree structures, that is siblings with no parents, or three-generational pedigrees with possibly missing genotypes, are common. There are several generalizations of the TDT that are suitable for use with arbitrary pedigree structures. We consider three such frequently used generalizations, family-based association test, pedigree disequilibrium test, and generalized disequilibrium test, that have accompanying software and compare them regarding validity and power in the single variant setting. We use simulations to study the effects of population admixture, populations whose genotypes are not in Hardy-Weinberg equilibrium (HWE), different pedigree structures, and the presence of linkage. Whereas our results show that some TDT generalizations can have a substantially increased Type 1 error, these tests are often used in substantive research without caveats about the validity of their Type 1 error. For the association analysis of rare variants in sequencing studies, region-based extensions of the TDT generalizations, that rely on the postulated robustness of the single variant tests, have been proposed. We discuss the implications of our results for these region-based extensions.

Powerful and robust cross-phenotype association test for case-parent trios

There has been increasing interest in identifying genes within the human genome that influence multiple diverse phenotypes. In the presence of pleiotropy, joint testing of these phenotypes is not only biologically meaningful but also statistically more powerful than univariate analysis of each separate phenotype accounting for multiple testing. Although many cross-phenotype association tests exist, the majority of such methods assume samples composed of unrelated subjects and therefore are not applicable to family-based designs, including the valuable case-parent trio design. In this paper, we describe a robust gene-based association test of multiple phenotypes collected in a case-parent trio study. Our method is based on the kernel distance covariance (KDC) method, where we first construct a similarity matrix for multiple phenotypes and a similarity matrix for genetic variants in a gene; we then test the dependency between the two similarity matrices. The method is applicable to either common variants or rare variants in a gene, and resulting tests from the method are by design robust to confounding due to population stratification. We evaluated our method through simulation studies and observed that the method is substantially more powerful than standard univariate testing of each separate phenotype. We also applied our method to phenotypic and genotypic data collected in case-parent trios as part of the Genetics of Kidneys in Diabetes (GoKinD) study and identified a genome-wide significant gene demonstrating cross-phenotype effects that was not identified using standard univariate approaches.

Novel Methods for Family-Based Genetic Studies

The recent development of microarray and sequencing technology allows identification of disease susceptibility genes. Although the genome-wide association studies (GWAS) have successfully identified many genetic markers related to human diseases, the traditional statistical methods are not powerful to detect rare genetic markers. The rare genetic markers are usually grouped together and tested at the set level. One of such methods is the sequence kernel association test (SKAT), which has been commonly used in the rare genetic marker analysis. In recent publications, SKAT has been extended to be applicable for family-based rare variant analysis. Here, I present three published statistical approaches for family-based rare variant analysis for: 1. continuous traits, 2. binary traits, and 3. multiple correlated traits.

Association of WNT9B Gene Polymorphisms With Nonsyndromic Cleft Lip With or Without Cleft Palate in Brazilian Nuclear Families

OBJECTIVE

Nonsyndromic cleft lip with or without cleft palate (NSCL±P) is a common craniofacial anomaly of complex etiology in people. WNT pathway genes have important roles during craniofacial development, and an association of WNT genes with NSCL±P has been demonstrated in different populations. The aim of this study was to evaluate the association between polymorphisms in WNT3 and WNT9B genes and CL/P in Brazilian families.

PATIENTS

Seventy nuclear families composed of an affected child and the child's unaffected parents were examined clinically. Saliva samples were collected for molecular analyses.

DESIGN

Three single nucleotide polymorphisms (SNPs) in the WNT3 gene and two in WNT9B were investigated in real-time polymerase chain reaction using TaqMan chemistry. The Family-Based Association Test and the transmission disequilibrium test were used to verify the association between each marker allele and NSCL±P. The level of significance was established at P ≤ .01 after Bonferroni correction.

RESULTS

A positive association was detected between NSCL±P and SNP rs1530364 in the WNT9B gene. Haplotype analysis showed an association of WNT3 and WNT9B haplotypes. No association was detected between NSCL±P and individual SNPs in WNT3.

CONCLUSION

Our study further supports the involvement of WNT9B as a cleft susceptibility gene in Brazilian families experiencing NSCL±P. Although additional studies are still necessary to unveil the exact mechanism by which WNT genes would contribute to NSCL±P, allelic polymorphisms in these genes and their interactions may partly explain the variance of individual susceptibility to NSCL±P.

Netrin G1: its downregulation in the nucleus accumbens of cocaine-conditioned mice and genetic association in human cocaine dependence

Netrin G1 is a presynaptic ligand involved in axonal projection. Although molecular mechanisms underlying cocaine addiction are still poorly understood, Netrin G1 might have a role as a regulator of anxiety, fear and spatial memory, behavioural traits impaired in the context of cocaine exposure. In this study, the Netrin G1 (Ntng1) expression was investigated in the nucleus accumbens of mice primarily conditioned to cocaine using a place preference paradigm. A genetic association study was then conducted on 146 multiplex families of the Collaborative study on Genetics of Alcoholism, in which seven single nucleotide polymorphisms located in the NTNG1 gene were genotyped. NTNG1 expression levels were also quantified in BA10, BA46 and the cerebellum of healthy controls (with no Axis 1 psychopathology). Decreased Ntng1 expression was initially observed in the nucleus accumbens of mice conditioned to cocaine. Significant genetic family-based associations were detected between NTNG1 polymorphisms and cocaine dependence. NTNG1 expression in BA10, BA46 and the cerebellum, however, were not significantly associated with any allele or haplotype of this gene. These results confirm that Ntng1 expression is disturbed in the nucleus accumbens of mice, after cocaine conditioning. A haplotype of NTNG1 was found to constitute a vulnerability factor for cocaine use disorder in patients, although none of its single nucleotide polymorphisms were associated with a differential expression pattern in healthy controls. The data suggest that change in the Ntng1 expression is a consequence of cocaine exposure, and that some of its genetic markers are associated with a greater risk for cocaine use disorder.

TNFSF10/TRAIL regulates human T4 effector memory lymphocyte radiosensitivity and predicts radiation-induced acute and subacute dermatitis

Sensitivity of T4 effector-memory (T4EM) lymphocytes to radiation-induced apoptosis shows heritability compatible with a Mendelian mode of transmission. Using gene expression studies and flow cytometry, we show a higher TNF-Related Apoptosis Inducing Ligand (TRAIL/TNFSF10) mRNA level and a higher level of membrane bound TRAIL (mTRAIL) on radiosensitive compared to radioresistant T4EM lymphocytes. Functionally, we show that mTRAIL mediates a pro-apoptotic autocrine signaling after irradiation of T4EM lymphocytes linking mTRAIL expression to T4EM radiosensitivity. Using single marker and multimarker Family-Based Association Testing, we identified 3 SNPs in the TRAIL gene that are significantly associated with T4EM lymphocytes radiosensitivity. Among these 3 SNPs, two are also associated with acute and subacute dermatitis after radiotherapy in breast cancer indicating that T4EM lymphocytes radiosensitivity may be used to predict response to radiotherapy. Altogether, these results show that mTRAIL level regulates the response of T4EM lymphocytes to ionizing radiation and suggest that TRAIL/TNFSF10 genetic variants hold promise as markers of individual radiosensitivity.

Family-based tests for associating haplotypes with general phenotype data: Improving the FBAT-haplotype algorithm

For family-based association studies, Horvath et al. proposed an algorithm for the association analysis between haplotypes and arbitrary phenotypes when the phase of the haplotypes is unknown, that is, genotype data is given. Their approach to haplotype analysis maintains the original features of the TDT/FBAT-approach, that is, complete robustness against genetic confounding and misspecification of the phenotype. The algorithm has been implemented in the FBAT and PBAT software package and has been used in numerous substantive manuscripts. Here, we propose a simplification of the original algorithm that maintains the original approach but reduces the computational burden of the approach substantially and gives valuable insights regarding the conditional distribution. With the modified algorithm, the application to whole-genome sequencing (WGS) studies becomes feasible; for example, in sliding window approaches or spatial-clustering approaches. The reduction of the computational burden that our modification provides is especially dramatic when both parental genotypes are missing. For example, for eight variants and 441 nuclear families with mostly offspring-only families, in a WGS study at the APOE locus, the running time decreased from approximately 21 hr for the original algorithm to 0.11 sec after our modification.

Genetic variants in the transcription regulatory region of MEGF10 are associated with autism in Chinese Han population

Multiple epidermal growth factor-like-domains 10 (MEGF10), a critical member of the apoptotic engulfment pathway, mediates axon pruning and synapse elimination during brain development. Previous studies indicated that synaptic pruning deficit was associated with autism-related phenotypes. However, the relationship between MEGF10 and autism remains poorly understood. Disease-associated variants are significantly enriched in the transcription regulatory regions. These include the transcription start site (TSS) and its cis-regulatory elements. To investigate the role of MEGF10 variants with putative transcription regulatory function in the etiology of autism, we performed a family-based association study in 410 Chinese Han trios. Our results indicate that three single nucleotide polymorphisms (SNPs), rs4836316, rs2194079 and rs4836317 near the TSS are significantly associated with autism following Bonferroni correction (p = 0.0011, p = 0.0088, and p = 0.0023, respectively). Haplotype T-A-G (rs4836316-rs2194079-rs4836317) was preferentially transmitted from parents to affected offspring (p _permutation = 0.0055). Consistently, functional exploration further verified that the risk allele and haplotype might influence its binding with transcription factors, resulting in decreased transcriptional activity of MEGF10. Our findings indicated that the risk alleles and haplotype near the MEGF10 TSS might modulate transcriptional activity and increase the susceptibility to autism.

ASD and Genetic Associations with Receptors for Oxytocin and Vasopressin-AVPR1A, AVPR1B, and OXTR

Background: There are limited treatments available for autism spectrum disorder (ASD). Studies have reported significant associations between the receptor genes of oxytocin (OT) and vasopressin (AVP) and ASD diagnosis, as well as ASD-related phenotypes. Researchers have also found the manipulation of these systems affects social and repetitive behaviors, core characteristics of ASD. Consequently, research involving the oxytocin/vasopressin pathways as intervention targets has increased. Therefore, further examination into the relationship between these neuropeptides and ASD was undertaken. In this study, we examined associations between variants in the receptor genes of vasopressin (AVPR1A, AVPR1B), oxytocin (OXTR), and ASD diagnosis along with related subphenotypes.

Methods: Probands were assessed using Autism Diagnostic Interview-Revised, Autism Diagnostic Observation Schedule, and clinical DSM-IV-TR criteria. Single nucleotide polymorphisms (SNPs) in AVPR1B and OXTR, and microsatellites in AVPR1A were genotyped in ~200 families with a proband with ASD. Family-based association testing (FBAT) was utilized to determine associations between variants and ASD. Haplotypes composed of OXTR SNPs (i.e., rs53576-rs2254298-rs2268493) were also analyzed due to previously published associations.

Results: Using the additive inheritance model in FBAT we found associations between AVPR1B SNPs (rs28632197, p = 0.005, rs35369693, p = 0.025) and diagnosis. As in other studies, OXTR rs2268493 (p = 0.050) was associated with diagnosis. rs2268493 was also associated with ASD subphenotypes of social withdrawal (p = 0.013) and Insistence on Sameness (p = 0.039). Further analyses demonstrated that the haplotype, rs2254298–rs2268493 was found to be significantly associated with diagnosis (A-T; p = 0.026). FBAT was also used to analyze AVPR1A microsatellites (RS1 and RS3). Both length variants were found to be associated with restrictive, repetitive behaviors, but not overall diagnosis. Correction for multiple comparisons was performed for SNPs tested in each gene region, only AVPR1B SNPs remained significantly associated with ASD diagnosis.

Conclusions: Autism is a heterogeneous disorder with many genes and pathways that contribute to its development. SNPs and microsatellites in the receptor genes of OT and AVP are associated with ASD diagnosis and measures of social behavior as well as restricted repetitive behaviors. We reported a novel association with ASD and AVPR1B SNPs. Understanding of genotype-phenotype relationships may be helpful in the development of pharmacological interventions for the OT/AVP system.

Detecting multiple variants associated with disease based on sequencing data of case-parent trios

With the advance of next-generation sequencing technology, the rare variants join the common ones in explaining more proportions of heritability. The coexistence of variants of common with rare, causal with neutral and deleterious with protective is a norm and should be appropriately addressed. Some existing methods suffer from low power when one or more forms of coexistence present, impeding their applications in practice. In this paper, for case-parent trios, pseudocontrols are constructed using the nontransmitted alleles of the parents. The Kullback-Leibler divergence is utilized to measure the difference between the distributions of variants in a genetic region for the affected children and pseudocontrols, and two nonparametric test statistics KLTT and cKLTT are proposed. Extensive simulations show that they are robust to the opposite directions of the causal variants and the amount of neutral variants, and have superiority over the existing methods when both rare and common variants are involved. Furthermore, their efficiency is demonstrated in the application to the data from Framingham Heart Study.

Assessing transmission ratio distortion in extended families: a comparison of analysis methods

A statistical departure from Mendel's law of segregation is known as transmission ratio distortion. Although well documented in many other organisms, the extent of transmission ratio distortion and its influence in the human genome remains incomplete. Using Genetic Analysis Workshop 19 whole genome sequence data from 20 large Mexican American pedigrees, our goal was to identify potentially distorted regions in the genome using family-based association methods such as the transmission disequilibrium test, the pedigree disequilibrium test, and the family-based association test. Preliminary results showed an unusually high number of transmission ratio distortion signals identified by the transmission disequilibrium test, but this phenomenon could not be replicated by the pedigree disequilibrium test or family-based association test. Applying these tests to different subsets of the data, we found the transmission disequilibrium test to be very sensitive to imputed genotypes. Regression analysis of transmission ratio distortion test p values controlling for minor allele frequency and quality control checks showed that Hardy Weinberg p values are associated with this inflation. Although the transmission disequilibrium test appears confounded by imputation of single nucleotide polymorphisms, the pedigree disequilibrium test and family-based association test seem to offer more robust alternatives when searching for transmission ratio distortion loci in whole genome sequence data from extended families.

Test for association of common variants in GRM7 with alcohol consumption

Recent work using a mouse model has identified the glutamate metabotropic receptor 7 (Grm7) gene as a strong candidate gene for alcohol consumption. Although there has been some work examining the effect of human glutamate metabotropic receptor 7 (GRM7) polymorphisms on human substance use disorders, the majority of the work has focused on other psychiatric disorders such as ADHD, major depressive disorder, schizophrenia, bipolar disorder, panic disorder, and autism spectrum disorders. The current study aimed to evaluate evidence for association between GRM7 and alcohol behaviors in humans using a single nucleotide polymorphism (SNP) approach, as well as a gene-based approach. Using 1803 non-Hispanic European Americans (EAs) (source: the Colorado Center on Antisocial Drug Dependence [CADD]) and 1049 EA subjects from an independent replication sample (source: the Genetics of Antisocial Drug Dependence [GADD]), two SNPs in GRM7 were examined for possible association with alcohol consumption using two family-based association tests implemented in FBAT and QTDT. Rs3749380 was suggestively associated with alcohol consumption in the CADD sample (p = 0.010) with the minor T allele conferring risk. There was no evidence for association in the GADD sample. A gene-based test using four Genome-Wide Association Studies (GWAS) revealed no association between variation in GRM7 and alcohol consumption. This study had several limitations: the SNPs chosen likely do not tag expression quantitative trait loci; a human alcohol consumption phenotype was used, complicating the interpretation with respect to rodent studies that found evidence for a cis-regulatory link between alcohol preference and Grm7; and only common SNPs imputed in all four datasets were included in the gene-based test. These limitations highlight the fact that rare variants, some potentially important common signals in the gene, and regions farther upstream were not examined.

Analysis of Case-Parent Trios Using a Loglinear Model with Adjustment for Transmission Ratio Distortion

Transmission of the two parental alleles to offspring deviating from the Mendelian ratio is termed Transmission Ratio Distortion (TRD), occurs throughout gametic and embryonic development. TRD has been well-studied in animals, but remains largely unknown in humans. The Transmission Disequilibrium Test (TDT) was first proposed to test for association and linkage in case-trios (affected offspring and parents); adjusting for TRD using control-trios was recommended. However, the TDT does not provide risk parameter estimates for different genetic models. A loglinear model was later proposed to provide child and maternal relative risk (RR) estimates of disease, assuming Mendelian transmission. Results from our simulation study showed that case-trios RR estimates using this model are biased in the presence of TRD; power and Type 1 error are compromised. We propose an extended loglinear model adjusting for TRD. Under this extended model, RR estimates, power and Type 1 error are correctly restored. We applied this model to an intrauterine growth restriction dataset, and showed consistent results with a previous approach that adjusted for TRD using control-trios. Our findings suggested the need to adjust for TRD in avoiding spurious results. Documenting TRD in the population is therefore essential for the correct interpretation of genetic association studies.

GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits

Identification of multifactor gene-gene (G×G) and gene-environment (G×E) interactions underlying complex traits poses one of the great challenges to today’s genetic study. Development of the generalized multifactor dimensionality reduction (GMDR) method provides a practicable solution to problems in detection of interactions. To exploit the opportunities brought by the availability of diverse data, it is in high demand to develop the corresponding GMDR software that can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates. We developed a versatile GMDR package to implement this serial of GMDR analyses for various scenarios (e.g., unified analysis of unrelated and family samples) and large-scale (e.g., genome-wide) data. This package includes other desirable features such as data management and preprocessing. Permutation testing strategies are also built in to evaluate the threshold or empirical p values. In addition, its performance is scalable to the computational resources. The software is available at http://www.soph.uab.edu/ssg/software or http://ibi.zju.edu.cn/software.

Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples

OBJECTIVE

The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples.

METHODS

This approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM).

RESULTS

In our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18).

CONCLUSION

We propose a method for rare-variant association testing in population and family samples using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.

Testing Allele Transmission of an SNP Set Using a Family-Based Generalized Genetic Random Field Method

Family-based association studies are commonly used in genetic research because they can be robust to population stratification (PS). Recent advances in high-throughput genotyping technologies have produced a massive amount of genomic data in family-based studies. However, current family-based association tests are mainly focused on evaluating individual variants one at a time. In this article, we introduce a family-based generalized genetic random field (FB-GGRF) method to test the joint association between a set of autosomal SNPs (i.e., single-nucleotide polymorphisms) and disease phenotypes. The proposed method is a natural extension of a recently developed GGRF method for population-based case-control studies. It models offspring genotypes conditional on parental genotypes, and, thus, is robust to PS. Through simulations, we presented that under various disease scenarios the FB-GGRF has improved power over a commonly used family-based sequence kernel association test (FB-SKAT). Further, similar to GGRF, the proposed FB-GGRF method is asymptotically well-behaved, and does not require empirical adjustment of the type I error rates. We illustrate the proposed method using a study of congenital heart defects with family trios from the National Birth Defects Prevention Study (NBDPS).

D5S351 and D5S1414 located at the spinal muscular atrophy critical region represent novel informative markers in the Iranian population

Spinal muscular atrophy (SMA) is a degenerative neuromuscular disease associated with progressive symmetric weakness and atrophy of the limb muscles. In view of the involvement of numerous point mutations and deletions associated with the disease, the application of polymorphic markers flanking the SMA critical region could be valuable in molecular diagnosis of the disease. In the present study, D5S351 and D5S1414 polymorphic markers located at the SMA critical region in the Iranian populations were characterized. Genotyping of the markers indicated the presence of six and nine different alleles for D5S351 and D5S1414, respectively. Haplotype frequency estimation in 25 trios families and 75 unrelated individuals indicated the presence of six informative haplotypes with frequency higher than 0.05 in the studied population. Furthermore, the D′ coefficient and the χ² value for D5S351 and D5S1414 markers revealed the presence of linkage disequilibrium between the two markers in the Iranians. These data suggested that D5S351 and D5S1414 could be suggested as informative markers for linkage analysis and molecular diagnosis of SMA in the Iranian population.

Genome-wide association study of antibody responses to Plasmodium falciparum candidate vaccine antigens

We conducted a genome-wide association study (GWAS) of antibody responses directed to three Plasmodium falciparum vaccine candidate antigens (MSP1, MSP2 and GLURP) previously associated with different patterns of protection against malaria infection in Senegalese children. A total of 174 950 single-nucleotide polymorphisms (SNPs) were tested for association with immunoglobulin G1 (IgG1) responses directed to MSP1 and to GLURP and with IgG3 responses to MSP2 FC27 and to MSP2 3D7. We first performed a single-trait analysis with each antibody response and then a multiple-trait analysis in which we analyzed simultaneously the three immune responses associated with the control of clinical malaria episodes. Suggestive associations (P<1 × 10(-4)) were observed for 25 SNPs in MSP1 antibody response analysis or in multiple-trait analysis. According to the strength of their observed associations and their functional role, the following genes are of particular interest: RASGRP3 (2p22.3, P=7.6 × 10(-6)), RIMS1 (6q13, P=2.0 × 10(-5)), MVB12B (9q33.3, P=8.9 × 10(-5)) and GNPTAB (12q23.2, P=7.4 × 10(-5)). Future studies will be required to replicate these findings in other African populations. This work will contribute to the elucidation of the host genetic factors underlying variable immune responses to P. falciparum.

Synaptic P-Rex1 signaling regulates hippocampal long-term depression and autism-like social behavior

Autism spectrum disorders (ASDs) are a group of highly inheritable mental disorders associated with synaptic dysfunction, but the underlying cellular and molecular mechanisms remain to be clarified. Here we report that autism in Chinese Han population is associated with genetic variations and copy number deletion of P-Rex1 (phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 1). Genetic deletion or knockdown of P-Rex1 in the CA1 region of the hippocampus in mice resulted in autism-like social behavior that was specifically linked to the defect of long-term depression (LTD) in the CA1 region through alteration of AMPA receptor endocytosis mediated by the postsynaptic PP1α (protein phosphase 1α)-P-Rex1-Rac1 (Ras-related C3 botulinum toxin substrate 1) signaling pathway. Rescue of the LTD in the CA1 region markedly alleviated autism-like social behavior. Together, our findings suggest a vital role of P-Rex1 signaling in CA1 LTD that is critical for social behavior and cognitive function and offer new insight into the etiology of ASDs.

Genetic Evidence for Possible Involvement of the Calcium Channel Gene CACNA1A in Autism Pathogenesis in Chinese Han Population

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders. Recent studies suggested that calcium channel genes might be involved in the genetic etiology of ASD. CACNA1A, encoding an alpha-1 subunit of voltage-gated calcium channel, has been reported to play an important role in neural development. Previous study detected that a single nucleotide polymorphism (SNP) in CACNA1A confers risk to ASD in Central European population. However, the genetic relationship between autism and CACNA1A in Chinese Han population remains unclear. To explore the association of CACNA1A with autism, we performed a family-based association study. First, we carried out a family-based association test between twelve tagged SNPs and autism in 239 trios. To further confirm the association, the sample size was expanded to 553 trios by recruiting 314 additional trios. In a total of 553 trios, we identified association of rs7249246 and rs12609735 with autism though this would not survive after Bonferroni correction. Our findings suggest that CACNA1A might play a role in the etiology of autism.

Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method

The recent development of sequencing technology allows identification of association between the whole spectrum of genetic variants and complex diseases. Over the past few years, a number of association tests for rare variants have been developed. Jointly testing for association between genetic variants and multiple correlated phenotypes may increase the power to detect causal genes in family-based studies, but familial correlation needs to be appropriately handled to avoid an inflated type I error rate. Here we propose a novel approach for multivariate family data using kernel machine regression (denoted as MF-KM) that is based on a linear mixed-model framework and can be applied to a large range of studies with different types of traits. In our simulation studies, the usual kernel machine test has inflated type I error rates when applied directly to familial data, while our proposed MF-KM method preserves the expected type I error rates. Moreover, the MF-KM method has increased power compared to methods that either analyze each phenotype separately while considering family structure or use only unrelated founders from the families. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.

Association of single nucleotide polymorphism rs3792876 in SLC22A4 gene with autoimmune thyroid disease in a Chinese Han population

Background

The autoimmune thyroid diseases (AITD), including Graves’ disease (GD) and Hashimoto’s thyroiditis (HT), are caused by interactions between susceptibility genes and environmental triggers. Single nucleotide polymorphisms (SNPs) of Solute carrier family 22, member 4 (SLC22A4) have been shown to be associated with several autoimmune diseases, including Crohn’s disease (CD) and rheumatoid arthritis (RA). The aim of this study is to investigate whether SNP rs3792876 in the SLC22A4 gene is associated with GD, HT and AITD in a Chinese Han population.

Methods

In this study, we collected specimens from 553 Chinese Han individuals of 92 AITD pedigrees in 10 cities in Liaoning province, China (80 GD pedigrees, 478 members; 12 HT pedigrees, 75 members). SNP rs3792876 was genotyped using the TaqMan allelic discrimination assay. Hardy-Weinberg Equilibrium tests were performed among founders of the pedigrees using Haploview software. Family-based association tests performed using FBAT software.

Results

No deviation from Hardy-Weinberg equilibrium was observed (p > 0.05). There were not significant association between the SLC22A4 gene polymorphism (rs3792876) and GD, HT and AITD was found.

Conclusions

These results suggest a lack of association between the SLC22A4 gene polymorphism rs3792876 and susceptibility to GD, HT and AITD in a Chinese Han population.

A New Role for LOC101928437 in Non-Syndromic Intellectual Disability: Findings from a Family-Based Association Test

Non-syndromic intellectual disability (NSID) is mental retardation in persons of normal physical appearance who have no recognisable features apart from obvious deficits in intellectual functioning and adaptive ability; however, its genetic etiology of most patients has remained unknown. The main purpose of this study was to fine map and identify specific causal gene(s) by genotyping a NSID family cohort using a panel of markers encompassing a target region reported in a previous work. A total of 139 families including probands, parents and relatives were included in the household survey, clinical examinations and intelligence tests, recruited from the Qinba mountain region of Shannxi province, western China. A collection of 34 tagged single nucleotide polymorphisms (tSNPs) spanning five microsatellite marker (STR) loci were genotyped using an iPLEX Gold assay. The association between tSNPs and patients was analyzed by family-based association testing (FBAT) and haplotype analysis (HBAT). Four markers (rs5974392, rs12164331, rs5929554 and rs3116911) in a block that showed strong linkage disequilibrium within the first three introns of the LOC101928437 locus were found to be significantly associated with NSID (all P<0.01) by the FBAT method for a single marker in additive, dominant and recessive models. The results of haplotype tests of this block also revealed a significant association with NSID (all P<0.05) using 2-window and larger HBAT analyses. These results suggest that LOC101928437 is a novel candidate gene for NSID in Han Chinese individuals of the Qinba region of China. Although the biological function of the gene has not been well studied, knowledge about this gene will provide insights that will increase our understanding of NSID development.

Schizophrenia Related Variants in CACNA1C also Confer Risk of Autism

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with a strong genetic component. Many lines of evidence indicated that ASD shares common genetic variants with other psychiatric disorders (for example, schizophrenia). Previous studies detected that calcium channels are involved in the etiology of many psychiatric disorders including schizophrenia and autism. Significant association between CACNA1C (calcium channel, voltage-dependent, L type, alpha 1C subunit) and schizophrenia was detected. Furthermore, rare mutation in CACNA1C is suggested to cause Timothy syndrome, a multisystem disorder including autism-associated phenotype. However, there is no evidence for association between CACNA1C and autism in Chinese Han population. To investigate the association between single nucleotide polymorphisms (SNP) in CACNA1C and autism, we first performed a family-based association study between eighteen SNPs in CACNA1C and autism in 239 trios. All SNPs were genotyped by using Sequenom genotyping platform. Two SNPs (rs1006737 and rs4765905) have a trend of association with autism. To further confirm the association between these two SNPs with autism, we expanded the sample size to 553 trios by adding 314 trios. Association analyses for SNPs and haplotype were performed by using family-based association test (FBAT) and Haploview software. Permutation tests were used for multiple testing corrections of the haplotype analyses (n=10,000). The significance level for all statistical tests was two-tailed (p<0.05). The results demonstrated that G allele of rs1006737 and G allele of rs4765905 showed a preferential transmission to affected offspring in 553 trios (p=0.035). Haplotype analyses showed that two haplotypes constructed from rs1006737 and rs4765905 were significantly associated with autism (p=0.030, 0.023, respectively; Global p=0.046). These results were still significant after permutation correction (n=10,000, p=0.027). Our research suggests that CACNA1C might play a role in the genetic etiology of autism in Chinese Han population.

Sequencing of the TBX6 Gene in Families with Familial Idiopathic Scoliosis

STUDY DESIGN

A hypothesis-driven study was conducted in a familial cohort to determine the potential association between variants within the TBX6 gene and Familial Idiopathic Scoliosis (FIS).

OBJECTIVE

To determine if variants within exons of the TBX6 gene segregate with the FIS phenotype within a sample of families with FIS.

SUMMARY OF BACKGROUND DATA

Idiopathic Scoliosis (IS) is a structural curvature of the spine whose underlying genetic etiology has not been established. IS has been reported to occur at a higher rate than expected in family members of individuals with congenital scoliosis (CS), suggesting that the two diseases might have a shared etiology. The TBX6 gene on chromosome 16p, essential to somite development, has been associated with CS in a Chinese population. Previous studies have identified linkage to this locus in families with FIS, and specifically with rs8060511, located in an intron of the TBX6 gene.

METHODS

Parent-offspring trios from 11 families (13 trios, 42 individuals) with FIS were selected for Sanger sequencing of the TBX6 gene. Trios were selected from a large population of families with FIS in which a genome-wide scan had resulted in linkage to 16p.

RESULTS

Sequencing analyses of the subset of families resulted in the identification of five coding variants. Three of the five variants were novel; the remaining two variants were previously characterized and account for 90% of the observed variants in these trios. In all cases, there was no correlation between transmission of the TBX6 variant allele and FIS phenotype. However, an analysis of regulatory markers in osteoblasts showed that rs8060511 is in a putative enhancer element.

CONCLUSIONS

Although this study did not identify any TBX6 coding variants that segregate with FIS, we identified a variant that is located in a potential TBX6 enhancer element. Therefore, further investigation of the region is needed.

Genetic association of key Th1/Th2 pathway candidate genes, IRF2, IL6, IFNGR2, STAT4 and IL4RA, with atopic asthma in the Indian population

Asthma is a complex, multifactorial disease resulting due to dysregulated immune responses. Genetic factors contribute significantly to asthma pathogenesis, and identification of these factors is one of the major goals in understanding the disease. Th1/Th2 helper differentiation has a critical role in modulating the phenotypes associated with atopic asthma. This study was aimed at identifying genetic modifiers of asthma in selected genes involved in T helper differentiation. A total of 354 single-nucleotide polymorphisms (SNPs) in 33 candidate genes were genotyped in a case-control cohort (cases=147, controls=199) and families (n=247) using Illumina's Golden Gate Assay. Five SNPs, rs3733475A/C (IRF2), rs2069832A/G (IL6), rs2012075G/A (IFNGR2) and rs1400656G/A (STAT4) and rs1805011C/A (IL4RA) were found to be associated with asthma in family based as well as in case-control analyses (P=0.002, P=0.001, P=0.004, P=0.003 and P=0.001, respectively). Interestingly, the minor alleles at these loci showed a protective effect. A five loci haplotype, TAACG, in IRF2 gene, was significantly associated with asthma in families (P=1.1 × 10(-6)) and in case-control cohort (P=0.01). In conclusion, our studies led to identification of some key candidate genes, namely IRF2, IL6, IFNGR2, STAT4 and IL4RA that modulate genetic susceptibility to asthma in the Indian population. Also, this is the first report of independent association of IL6 gene polymorphism with atopic asthma.

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.

Genetic Effect of Transforming Growth Factor Alpha Gene Variants on the Risk of Nonsyndromic Cleft Lip with or without Palate in Korean Populations

Objective

To identify the contribution of TGFA gene variants to the risk of nonsyndromic cleft lip with or without palate (NS-CL±P).

Design

The samples were from 142 Korean NS-CL±P families and 119 control parents having nonaffected children. Minor allele frequency, heterozygosity, and χ2 test for Hardy-Weinberg equilibrium were calculated for each of 10 selected single-nucleotide polymorphisms (SNPs). Ten SNPs were used to examine the association of case-parent trios with the transmission disequilibrium test (TDT) and conditional logistic regression models (CLRMs). Both allelic and genotypic TDTs for individual SNPs and sliding windows of haplotypes consisting of two to five SNPs were tested using family- and haplotype-based association test programs. Genotypic odd ratios (GORs) were obtained from CLRMs using STATA software. The parent-of-origin effect was evaluated for 10 SNPs, and a comparison between 218 case parents and 119 control parents was performed to investigate paternal and maternal ORs.

Results

Family-based TDT and haplotype analysis exhibited no statistical significance, but a relatively meaningful association was shown with rs3771497 (all P < .05; two SNPs, rs3771497 and rs3755377; five SNPs, rs3771497, rs3755377, rs3771485, rs11466212, and rs3771475). G/G homozygotes at rs3771497 have a significant decreased risk of NS-CL±P (GOR = 0.30, P < .01). No SNPs showed parent-of-origin effects. However, in the comparison between case parents and control parents, a single-marker analysis of maternal line showed a significant association with NS-CL±P in rs3771497 ( P < .001, recessive model).

Conclusion

The association of the TGFA gene with NS-CL±P in Korean populations was not clearly found. However, the etiologic effect of the TGFA gene on NS-CL±P patients should be investigated in terms of maternal genotype influence.

ACN9 and alcohol dependence: family-based association analysis in multiplex alcohol dependence families

A previous genome-wide linkage study of alcohol dependence (AD) in the Pittsburgh-based multiplex family study found suggestive evidence for linkage on Chromosome 7q, a region in which the ACN9 gene is located. Using the same two generation Pittsburgh family data in which linkage was found, data for a third generation was added. The expanded sample included 133 pedigrees with 995 individuals. Finer mapping was undertaken using six SNPs extending from rs1917939 to rs13475 with minor allele frequency (MAF) ≥0.15 and pair-wise linkage disequilibrium (LD) of r(2) <0.8 using the HapMap CEU population. Binary affection status, visual, and auditory P300 data were tested for family-based association. Family-based analyses found all six SNPs associated with affected status. Three SNPs are located upstream of the gene, two SNPs are within intron 1 and one is in Exon 4. FBAT P-values for the six SNPs ranged between 0.05 and 0.0005. Haplotype analysis revealed one four-SNP block formed by rs10499934, rs7794886, rs12056091, and rs13475 with an overall significant association at P = 0.0008. Analysis of visual P300 amplitude data, a known endophenotype of alcohol dependence risk, revealed a significant association for SNPs within intron 1 and exon 4 under a dominant model of transmission. Family-based association analysis shows the ACN9 gene significantly associated with alcohol dependence and P300 amplitude variation. The potential importance of the ACN9 gene for AD risk may be related to its role in gluconeogenesis which may be involved in the regulation of alcohol metabolism.

A re-formulation of generalized linear mixed models to fit family data in genetic association studies

The generalized linear mixed model (GLMM) is a useful tool for modeling genetic correlation among family data in genetic association studies. However, when dealing with families of varied sizes and diverse genetic relatedness, the GLMM has a special correlation structure which often makes it difficult to be specified using standard statistical software. In this study, we propose a Cholesky decomposition based re-formulation of the GLMM so that the re-formulated GLMM can be specified conveniently via “proc nlmixed” and “proc glimmix” in SAS, or OpenBUGS via R package BRugs. Performances of these procedures in fitting the re-formulated GLMM are examined through simulation studies. We also apply this re-formulated GLMM to analyze a real data set from Type 1 Diabetes Genetics Consortium (T1DGC).