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

Genetic variants of HvCbf14 are statistically associated with frost tolerance in a European germplasm collection of Hordeum vulgare

Two quantitative trait loci (Fr-H1 and Fr-H2) for frost tolerance (FT) have been discovered on the long arm of chromosome 5H in barley. Two tightly linked groups of CBF genes, known to play a key role in the FT regulatory network in A. thaliana, have been found to co- segregate with Fr-H2. Here, we investigate the allelic variations of four barley CBF genes (HvCbf3, HvCbf6, HvCbf9 and HvCbf14) in a panel of European cultivars, landraces and H. spontaneum accessions. In the cultivars a reduction of nucleotide and haplotype diversities in CBFs compared with the landraces and the wild ancestor H. spontaneum, was evident. In particular, in cultivars the loss of HvCbf9 genetic variants was higher compared to other sequences. In order to verify if the pattern of CBF genetic variants correlated with the level of FT, an association procedure was adopted. The pairwise analysis of linkage disequilibrium (LD) among the genetic variants in four CBF genes was computed to evaluate the resolution of the association procedure. The pairwise plotting revealed a low level of LD in cultivated varieties, despite the tight physical linkage of CBF genes analysed. A structured association procedure based on a general liner model was implemented, including the variants in CBFs,of Vrn-H1, and of two reference genes not involved in FT (alpha-Amy1 and Gapdh) and considering the phenotypic data for FT. Association analysis recovered two nucleotide variants of HvCbf14 and one nucleotide variant of Vrn-H1 as statistically associated to FT.

A systems genetics approach implicates USF1, FADS3, and other causal candidate genes for familial combined hyperlipidemia

We hypothesized that a common SNP in the 3' untranslated region of the upstream transcription factor 1 (USF1), rs3737787, may affect lipid traits by influencing gene expression levels, and we investigated this possibility utilizing the Mexican population, which has a high predisposition to dyslipidemia. We first associated rs3737787 genotypes in Mexican Familial Combined Hyperlipidemia (FCHL) case/control fat biopsies, with global expression patterns. To identify sets of co-expressed genes co-regulated by similar factors such as transcription factors, genetic variants, or environmental effects, we utilized weighted gene co-expression network analysis (WGCNA). Through WGCNA in the Mexican FCHL fat biopsies we identified two significant Triglyceride (TG)-associated co-expression modules. One of these modules was also associated with FCHL, the other FCHL component traits, and rs3737787 genotypes. This USF1-regulated FCHL-associated (URFA) module was enriched for genes involved in lipid metabolic processes. Using systems genetics procedures we identified 18 causal candidate genes in the URFA module. The FCHL causal candidate gene fatty acid desaturase 3 (FADS3) was associated with TGs in a recent Caucasian genome-wide significant association study and we replicated this association in Mexican FCHL families. Based on a USF1-regulated FCHL-associated co-expression module and SNP rs3737787, we identify a set of causal candidate genes for FCHL-related traits. We then provide evidence from two independent datasets supporting FADS3 as a causal gene for FCHL and elevated TGs in Mexicans.

By integrating a genetic polymorphism with genome-wide gene expression levels, we were able to attribute function to a genetic polymorphism in the USF1 gene. The USF1 gene has previously been associated with a common dyslipidemia, FCHL. FCHL is characterized by elevated levels of total cholesterol, triglycerides, or both. We demonstrate that this genetic polymorphism in USF1 contributes to FCHL disease risk by modulating the expression of a group of genes functionally related to lipid metabolism, and that this modulation is mediated by USF1. One of the genes whose expression is modulated by USF1 is FADS3, which was also implicated in a recent genome-wide association study for lipid traits. We demonstrated that a genetic polymorphism from the FADS3 region, which was associated with triglycerides in a GWAS study of Caucasians, was also associated with triglycerides in Mexican FCHL families. Our analysis provides novel insight into the gene expression profile contributing to FCHL disease risk, and identifies FADS3 as a new gene for FCHL in Mexicans.

On the adjustment for covariates in genetic association analysis: a novel, simple principle to infer direct causal effects

In genetic association studies, different complex phenotypes are often associated with the same marker. Such associations can be indicative of pleiotropy (i.e. common genetic causes), of indirect genetic effects via one of these phenotypes, or can be solely attributable to non-genetic/environmental links between the traits. To identify the phenotypes with the inducing genetic association, statistical methodology is needed that is able to distinguish between the different causes of the genetic associations. Here, we propose a simple, general adjustment principle that can be incorporated into many standard genetic association tests which are then able to infer whether an SNP has a direct biological influence on a given trait other than through the SNP's influence on another correlated phenotype. Using simulation studies, we show that, in the presence of a non-marker related link between phenotypes, standard association tests without the proposed adjustment can be biased. In contrast to that, the proposed methodology remains unbiased. Its achieved power levels are identical to those of standard adjustment methods, making the adjustment principle universally applicable in genetic association studies. The principle is illustrated by an application to three genome-wide association analyses.

Methods for optimizing statistical analyses in pharmacogenomics research

Pharmacogenomics is a rapidly developing sector of human genetics research with arguably the highest potential for immediate benefit. There is a considerable body of evidence demonstrating that variability in drug-treatment response can be explained in part by genetic variation. Subsequently, much research has ensued and is ongoing to identify genetic variants associated with drug-response phenotypes. To reap the full benefits of the data we collect we must give careful consideration to the study population under investigation, the phenotype being examined and the statistical methodology used in data analysis. Here, we discuss principles of study design and optimizing statistical methods for pharmacogenomic studies when the outcome of interest is a continuous measure. We review traditional hypothesis testing procedures, as well as novel approaches that may be capable of accounting for more variance in a quantitative pharmacogenomic trait. We give examples of studies that have employed the analytical methodologies discussed here, as well as resources for acquiring software to run the analyses.

Combining case-control and case-trio data from the same population in genetic association analyses: overview of approaches and illustration with a candidate gene study

In genetic association studies, investigators compare allele or genotype frequencies in unrelated case and control subjects or examine preferential allele transmissions from parents to affected offspring. In many genetic case-control studies, the collection of DNA material extends to relatives such as parents of cases. Thus, case-control and case-parent trio association analyses are possible. Whereas the goal of collecting genetic information from family members in a study initially designed as a case-control study is to enrich the genetic analysis, increase power, or address concern about population structure bias, methods of combining genetic data from unrelated case and control subjects with genetic trio data from the same study population are not well known. A number of hybrid approaches have been developed that utilize such data together. In this paper, the authors describe key features of genetic case-control and case-parent trio studies and review commonly used methods of genetic analysis for case-parent trio designs. In addition, they provide a pragmatic review of statistical methods and available software for existing hybrid approaches that combine various components of case-control and genetic trio data. The application of all methods is illustrated using a candidate gene study of childhood leukemia that included case-control subjects and their parents.

Tests of association for quantitative traits in nuclear families using principal components to correct for population stratification

Traditional transmission disequilibrium test (TDT) based methods for genetic association analyses are robust to population stratification at the cost of a substantial loss of power. We here describe a novel method for family-based association studies that corrects for population stratification with the use of an extension of principal component analysis (PCA). Specifically, we adopt PCA on unrelated parents in each family. We then infer principal components for children from those for their parents through a TDT-like strategy. Two test statistics within the variance-components model are proposed for association tests. Simulation results show that the proposed tests have correct type I error rates regardless of population stratification, and have greatly improved power over two popular TDT-based methods: QTDT and FBAT. The application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility of the proposed method.

Univariate/multivariate genome-wide association scans using data from families and unrelated samples

As genome-wide association studies (GWAS) are becoming more popular, two approaches, among others, could be considered in order to improve statistical power for identifying genes contributing subtle to moderate effects to human diseases. The first approach is to increase sample size, which could be achieved by combining both unrelated and familial subjects together. The second approach is to jointly analyze multiple correlated traits. In this study, by extending generalized estimating equations (GEEs), we propose a simple approach for performing univariate or multivariate association tests for the combined data of unrelated subjects and nuclear families. In particular, we correct for population stratification by integrating principal component analysis and transmission disequilibrium test strategies. The proposed method allows for multiple siblings as well as missing parental information. Simulation studies show that the proposed test has improved power compared to two popular methods, EIGENSTRAT and FBAT, by analyzing the combined data, while correcting for population stratification. In addition, joint analysis of bivariate traits has improved power over univariate analysis when pleiotropic effects are present. Application to the Genetic Analysis Workshop 16 (GAW16) data sets attests to the feasibility and applicability of the proposed method.

Genome-wide association studies and the genetic dissection of complex traits

The availability of affordable high throughput technology for parallel genotyping has opened the field of genetics to genome-wide association studies (GWAS), and in the last few years hundreds of articles reporting results of GWAS for a variety of heritable traits have been published. What do these results tell us? Although GWAS have discovered a few hundred reproducible associations, this number is underwhelming in relation to the huge amount of data produced, and challenges the conjecture that common variants may be the genetic causes of common diseases. We argue that the massive amount of genetic data that result from these studies remains largely unexplored and unexploited because of the challenge of mining and modeling enormous data sets, the difficulty of using nontraditional computational techniques and the focus of accepted statistical analyses on controlling the false positive rate rather than limiting the false negative rate. In this article, we will review the common approach to analysis of GWAS data and then discuss options to learn more from these data. We will use examples from our ongoing studies of sickle cell anemia and also GWAS in multigenic traits.

Filaggrin gene defects and risk of developing allergic sensitisation and allergic disorders: systematic review and meta-analysis

OBJECTIVE

To investigate whether filaggrin gene defects, present in up to one in 10 western Europeans and North Americans, increase the risk of developing allergic sensitisation and allergic disorders.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Medline, Embase, ISI Science Citation Index, BIOSIS, ISI Web of Knowledge, UK National Research Register, clinical trials.gov, the Index to Theses and Digital dissertations, and grey literature using OpenSIGLE.

STUDY SELECTION

Genetic epidemiological studies (family, case-control) of the association between filaggrin gene defects and allergic sensitisation or allergic disorders.

DATA EXTRACTION

Atopic eczema or dermatitis, food allergy, asthma, allergic rhinitis, and anaphylaxis, along with relevant immunological variables relating to the risk of allergic sensitisation as assessed by either positive skin prick testing or increased levels of allergen specific IgE.

DATA SYNTHESIS

24 studies were included. The odds of developing allergic sensitisation was 1.91 (95% confidence interval 1.44 to 2.54) in the family studies and 1.57 (1.20 to 2.07) in the case-control studies. The odds of developing atopic eczema was 1.99 (1.72 to 2.31) in the family studies and 4.78 (3.31 to 6.92) in the case-control studies. Three studies investigated the association between filaggrin gene mutations and allergic rhinitis in people without atopic eczema: overall odds ratio 1.78 (1.16 to 2.73). The four studies that investigated the association between filaggrin gene mutations and allergic rhinitis in people with atopic eczema reported a significant association: pooled odds ratio from case-control studies 2.84 (2.08 to 3.88). An overall odds ratio for the association between filaggrin gene mutations and asthma in people with atopic eczema was 2.79 (1.77 to 4.41) in case-control studies and 2.30 (1.66 to 3.18) in family studies. None of the studies that investigated filaggrin gene mutations and asthma in people without atopic eczema reported a significant association; overall odds ratio was 1.30 (0.7 to 2.30) in the case-control studies. The funnel plots suggested that publication bias was unlikely to be an explanation for these findings. No studies investigated the association between filaggrin gene mutations and food allergy or anaphylaxis.

CONCLUSIONS

Filaggrin gene defects increase the risk of developing allergic sensitisation, atopic eczema, and allergic rhinitis. Evidence of the relation between filaggrin gene mutations and atopic eczema was strong, with people manifesting increased severity and persistence of disease. Filaggrin gene mutations also increased the risk of asthma in people with atopic eczema. Restoring skin barrier function in filaggrin deficient people in early life may help prevent the development of sensitisation and halt the development and progression of allergic disease.

A Bayesian approach to efficient differential allocation for resampling-based significance testing

BACKGROUND

Large-scale statistical analyses have become hallmarks of post-genomic era biological research due to advances in high-throughput assays and the integration of large biological databases. One accompanying issue is the simultaneous estimation of p-values for a large number of hypothesis tests. In many applications, a parametric assumption in the null distribution such as normality may be unreasonable, and resampling-based p-values are the preferred procedure for establishing statistical significance. Using resampling-based procedures for multiple testing is computationally intensive and typically requires large numbers of resamples.

RESULTS

We present a new approach to more efficiently assign resamples (such as bootstrap samples or permutations) within a nonparametric multiple testing framework. We formulated a Bayesian-inspired approach to this problem, and devised an algorithm that adapts the assignment of resamples iteratively with negligible space and running time overhead. In two experimental studies, a breast cancer microarray dataset and a genome wide association study dataset for Parkinson's disease, we demonstrated that our differential allocation procedure is substantially more accurate compared to the traditional uniform resample allocation.

CONCLUSION

Our experiments demonstrate that using a more sophisticated allocation strategy can improve our inference for hypothesis testing without a drastic increase in the amount of computation on randomized data. Moreover, we gain more improvement in efficiency when the number of tests is large. R code for our algorithm and the shortcut method are available at http://people.pcbi.upenn.edu/~lswang/pub/bmc2009/.

Comprehensive linkage and association analyses identify haplotype, near to the TNFSF15 gene, significantly associated with spondyloarthritis

Spondyloarthritis (SpA) is a chronic inflammatory disorder with a strong genetic predisposition dominated by the role of HLA-B27. However, the contribution of other genes to the disease susceptibility has been clearly demonstrated. We previously reported significant evidence of linkage of SpA to chromosome 9q31–34. The current study aimed to characterize this locus, named SPA2. First, we performed a fine linkage mapping of SPA2 (24 cM) with 28 microsatellite markers in 149 multiplex families, which allowed us to reduce the area of investigation to an 18 cM (13 Mb) locus delimited by the markers D9S279 and D9S112. Second, we constructed a linkage disequilibrium (LD) map of this region with 1,536 tag single-nucleotide polymorphisms (SNPs) in 136 families (263 patients). The association was assessed using a transmission disequilibrium test. One tag SNP, rs4979459, yielded a significant P-value (4.9×10⁻⁵). Third, we performed an extension association study with rs4979459 and 30 surrounding SNPs in LD with it, in 287 families (668 patients), and in a sample of 139 cases and 163 controls. Strong association was observed in both familial and case/control datasets for several SNPs. In the replication study, carried with 8 SNPs in an independent sample of 232 cases and 149 controls, one SNP, rs6478105, yielded a nominal P-value<3×10⁻². Pooled case/control study (371 cases and 312 controls) as well as combined analysis of extension and replication data showed very significant association (P<5×10⁻⁴) for 6 of the 8 latter markers (rs7849556, rs10817669, rs10759734, rs6478105, rs10982396, and rs10733612). Finally, haplotype association investigations identified a strongly associated haplotype (P<8.8×10⁻⁵) consisting of these 6 SNPs and located in the direct vicinity of the TNFSF15 gene. In conclusion, we have identified within the SPA2 locus a haplotype strongly associated with predisposition to SpA which is located near to TNFSF15, one of the major candidate genes in this region.

Spondyloarthritis (SpA) is a common variety of articular inflammatory disorder characterized by axial and/or peripheral arthritis, frequently associated with extra-articular manifestations such as psoriasis, uveitis, and inflammatory bowel diseases (ulcerative colitis or Crohn's disease (CD)). SpA is a complex disorder with high heritability. The MHC class I HLA-B27 allele is a very strong risk factor for its development, but other genetic factors located outside the MHC also play a role in disease susceptibility. By a previous whole-genome linkage investigation, we have demonstrated that a region located on the chromosome 9q31–34 was involved in SpA susceptibility. The present study aimed to further characterize this locus. Using a stepwise linkage and association approach, we identified a haplotype spanning 6 single-nucleotide polymorphisms strongly associated with SpA and located in a genomic region paralogous to the MHC, near to the TNFSF15 gene. Interestingly, polymorphisms of this gene have previously been shown to be associated with CD. This original finding offers a new research track for the understanding of SpA pathophysiology, which is still poorly understood, as well as new hope for diagnostic and therapeutic innovation.

Approaches to the identification of susceptibility genes

Although previous studies have revealed a great deal about the genetic basis of susceptibility and resistance to parasite infection, there is now an opportunity to considerably enhance understanding through genome-wide association mapping. The application of association mapping to complex inheritance has recently become achievable given reduced costs, sophisticated genotyping platforms and powerful statistical methods which build upon increased knowledge of the linkage disequilibrium structure of the human genome. Linkage mapping and related approaches remain useful for the localization of the rarer genetic variants and candidate region association studies can be a very cost-effective route to progress. However, genome-wide association offers the greatest promise, despite the challenges posed by phenotype complexity, ensuring genotype coverage/quality and robust statistical analysis. The available approaches for mapping genes underlying susceptibility are reviewed here, emphasizing their relative merits and drawbacks and highlighting specific software tools and resources that enable successful mapping.

Two-stage analysis for gene-environment interaction utilizing both case-only and family-based analysis

The case-only study and family-based study are two popular study designs for detecting gene-environment interactions. It is well known that the case-only analysis is efficient, but its validity relies crucially on the assumption of gene-environment independence in the study population. In contrast, the family-based analysis is robust to the violation of such an assumption, but is less efficient. We propose a two-stage study design for detecting gene-environment interactions, where a case-only study is performed at the first stage, and a case-parent/case-sibling study is performed at the second stage on a random subsample of the first-stage case sample as well as their parents/unaffected siblings. Statistical inference procedures are developed for the proposed two-stage study designs, which not only preserve the robustness property of the family-based analysis, but also utilize information from the case-only analysis to enhance estimation efficiency and testing power. Simulation results reveal both the robustness and efficiency of the proposed strategies.

Analysis of FTO gene variants with measures of obesity and glucose homeostasis in the IRAS Family Study

Multiple studies have identified FTO gene variants associated with measures of adiposity in European-derived populations. The objective of the study was to determine whether FTO variants were associated with adiposity, including visceral and subcutaneous adipose tissue (VAT, SAT), and glucose homeostasis measures in the Insulin Resistance Atherosclerosis Family Study (IRASFS). A total of 27 SNPs in FTO intron 1, including SNPs prominent in the literature (rs9939609, rs8050136, rs1121980, rs17817449, rs1421085, and rs3751812), were genotyped in 1,424 Hispanic Americans and 604 African Americans. Multiple SNPs were associated with BMI and SAT (P values ranging from 0.001 to 0.033), and trending or associated with waist circumference (P values ranging from 0.008 to 0.099) in the Hispanic Americans. No association was observed with VAT, illustrating that FTO variants are associated with overall fat mass instead of specific fat depots. For the glucose homeostasis measures, variants were associated with fasting insulin but, consistent with other studies, after BMI adjustment, no evidence of association remained. The lack of association of FTO SNPs with insulin sensitivity is consistent with the lack of association with VAT, since these traits are strongly correlated. In the African Americans, only rs8050136 and rs9939609 were associated with BMI and WAIST (P values of 0.011 and 0.034), and associated or trending towards association with SAT (P values of 0.038 and 0.058). These results confirm that FTO variants are associated with adiposity measures, predisposing individuals to obesity by increasing overall fat mass in Hispanic Americans and to a lesser degree in African Americans.

Why Do We Test Multiple Traits in Genetic Association Studies?

In studies of complex disorders such as nicotine dependence, it is common that researchers assess multiple variables related to a disorder as well as other disorders that are potentially correlated with the primary disorder of interest. In this work, we refer to those variables and disorders broadly as multiple traits. The multiple traits may or may not have a common causal genetic variant. Intuitively, it may be more powerful to accommodate multiple traits in genetic traits, but the analysis of multiple traits is generally more complicated than the analysis of a single trait. Furthermore, it is not well documented as to how much power we may potentially gain by considering multiple traits. Our aim is to enhance our understanding on this important and practical issue. We considered a variety of correlation structures between traits and the disease locus. To focus on the effect of accommodating multiple traits, we examined genetic models that are relatively simple so that we can pinpoint the factors affecting the power. We conducted simulation studies to explore the performance of testing multiple traits simultaneously and the performance of testing a single trait at a time in family-based association studies. Our simulation results demonstrated that the performance of testing multiple traits simultaneously is better than that of testing each trait individually for almost models considered. We also found that the power of association tests varies among the underlying models. The advantage of conducting a multiple traits test is minimized when some traits are influenced by the gene only through other traits; and it is maximized when there are causal relations between the traits and the gene, and among the traits themselves or when there are extraneous traits.

Impact of population stratification on family-based association tests with longitudinal measurements

Several family-based approaches for testing genetic association with traits obtained from longitudinal or repeated measurement studies have been previously proposed. These approaches utilize the multivariate data more efficiently by using estimated optimal weights to combine univariate tests. We show that these FBAT approaches are still robust against hidden population stratification, but their power can be heavily affected since the estimated weights might provide poor approximation of the true theoretical optimal weights with the presence of population stratification. We introduce a permutation-based approach FBAT-MinP and an equal combination approach FBAT-EW, both of which do not involve the use of estimated weights. Through simulation studies, FBAT-MinP and FBAT-EW are shown to be powerful even in the presence of population stratification, when other approaches may substantially lose their power. An application of these approaches to the Childhood Asthma Management Program (CAMP) study data for testing an association between body mass index and a previously reported candidate SNP is given as an example.

Family-based genome-wide association studies

In the last 2 years, the effort to identify genes affecting common diseases and complex traits has been accelerated through the use of genome-wide association studies (GWAS). The availability of existing large collections of linkage data paved the way for the use of family-based GWAS. Although most published GWAS used population-based designs, family-based designs have played an important role, particularly in replication stages. Family-based designs offer advantages in terms of quality control, the robustness to population stratification and the ability to perform genetic analyses that cannot be achieved using a sample of unrelated individuals, such as testing for the effect of imprinted genes on phenotypes, testing whether a genetic variant is inherited or de novo and combined linkage and association analysis.

Accounting for ancestry: population substructure and genome-wide association studies

Accounting for the genetic substructure of human populations has become a major practical issue for studying complex genetic disorders. Allele frequency differences among ethnic groups and subgroups and admixture between different ethnic groups can result in frequent false-positive results or reduced power in genetic studies. Here, we review the problems and progress in defining population differences and the application of statistical methods to improve association studies. It is now possible to take into account the confounding effects of population stratification using thousands of unselected genome-wide single-nucleotide polymorphisms or, alternatively, selected panels of ancestry informative markers. These methods do not require any demographic information and therefore can be widely applied to genotypes available from multiple sources. We further suggest that it will be important to explore results in homogeneous population subsets as we seek to define the extent to which genomic variation influences complex phenotypes.

Alzheimer's disease genetics current status and future perspectives

Alzheimer's disease (AD) is a genetically complex disease whose pathogenesis is largely influenced by genetic factors. Three decades of intensive research have yielded four established AD genes (APP, PSEN1, PSEN2, APOE), and hundreds of potential susceptibility loci, none of which has been unequivocally shown to modify disease risk using conventional methodologies. The results of genome-wide association studies (GWAS) are now adding to an already vast and complicated body of data. To facilitate the evaluation and interpretation of these findings, we have recently created a database for genetic association studies in AD ("AlzGene"; available at http://www.alzgene.org). In addition to systematically screening and summarizing the scientific literature for eligible studies, AlzGene provides the results of allele-based meta-analyses for all polymorphisms with sufficient genotype data. Currently, these meta-analyses highlight over 20 different potential AD genes, several of which were originally implicated by a GWAS. First follow-up analyses in a large collection of over 1300 AD families reveal that-in addition to APOE-genetic variants in ACE, CHRNB2, GAB2, and TF show the most consistent risk effects across a wide range of independent samples and study designs. The chapter highlights these and other promising findings from the recent AD genetics literature and provides an overview of the powerful new tools aiding researchers today to unravel the genetic underpinnings of this devastating disease.

Role of in silico tools in gene discovery

Common complex diseases remain a major health challenge and involve the interaction of multiple genes and environmental factors. Discovering the relevant genes is difficult although it is known that disease risk can originate from the variation of an individual's genome. Application of in silico tools can significantly improve the detection of genes and variation. Data mining and automated tracking of new knowledge facilitate locus mapping. At the gene search stage, in silico prioritization of candidate genes plays an indispensable role in dealing with linked or associated loci. In silico analysis can also differentiate subtle consequences of coding DNA variants and remains the major tool to predict potential effects of non-coding DNA variants on gene transcription and/or pre-mRNA splicing.

On combining triads and unrelated subjects data in candidate gene studies: an application to data on testicular cancer

Combining data collected from different sources is a cost-effective and time-efficient approach for enhancing the statistical efficiency in estimating weak-to-modest genetic effects or gene-gene or gene-environment interactions. However, combining data across studies becomes complicated when data are collected under different study designs, such as family-based and unrelated individual-based (e.g., population-based case-control design). In this paper, we describe a general method that permits the joint estimation of effects on disease risk of genes, environmental factors, and gene-gene/gene-environment interactions under a hybrid design that includes cases, parents of cases, and unrelated individuals. We provide both asymptotic theory and statistical inference. Extensive simulation experiments demonstrate that the proposed estimation and inferential methods perform well in realistic settings. We illustrate the method by an application to a study of testicular cancer.

Assessing genuine parents-offspring trios for genetic association studies

OBJECTIVES

Family-based association tests such as the transmission disequilibrium test (TDT) are dependent on the successful ascertainment of true nuclear family trios. Relationship misspecification inevitably occurs in a proportion of trios collected for genotyping which undetected can lead to a loss of power and increased Type I error due to biases in over-transmission of common alleles. Here, we introduce a method for evaluating the authenticity of nuclear family trios.

METHODS

Operating in a Bayesian framework, our approach assesses the extent of pedigree inconsistent genotype configurations in the presence of genotyping errors. Unlike other approaches, our method: (i) utilizes information from three individuals collectively (the whole trio) rather than consider two independent pairwise relationships; (ii) down-weighs SNPs with poor performance; (iii) does not require the user to pre-define a rate of genotyping error, which is often unknown to the user and seldom fixed across the different SNPs considered which available methods unrealistically assumed.

RESULTS

Simulation studies and comparisons with a real set of data showed that our approach is more likely to correctly identify the presence of true and misspecified trios compared to available software, accurately infers the extent of relationship misspecification in a trio and accurately estimates the genotyping error rates.

CONCLUSIONS

Assessing relationship misspecification depends on the fidelity of the genotype data used. Available algorithms are not optimised for genotyping technology with varying rates of errors across the markers. Through our comparison studies, our approach is shown to outperform available methods for assessing relationship misspecifications.

Assessment of Alzheimer's disease case-control associations using family-based methods

The genetics of Alzheimer's disease (AD) is heterogeneous and remains only ill-defined. We have recently created a freely available and continuously updated online database (AlzGene; http://www.alzgene.org ) for which we collect all published genetic association studies in AD and perform systematic meta-analyses on all polymorphisms with sufficient genotype data. In this study, we tested 27 genes (ACE, BDNF, CH25H, CHRNB2, CST3, CTSD, DAPK1, GALP, hCG2039140, IL1B, LMNA, LOC439999, LOC651924, MAPT, MTHFR, MYH13, PCK1, PGBD1, PRNP, PSEN1, SORCS1, SORL1, TF, TFAM, TNK1, GWA_14q32.13, and GWA_7p15.2), all showing significant association with AD risk in the AlzGene meta-analyses, in a large collection of family-based samples comprised of 4,180 subjects from over 1,300 pedigrees. Overall, we observe significant association with risk for AD and polymorphisms in ACE, CHRNB2, TF, and an as yet uncharacterized locus on chromosome 7p15.2 [rs1859849]. For all four loci, the association was observed with the same alleles as in the AlzGene meta-analyses. The convergence of case-control and family-based findings suggests that these loci currently represent the most promising AD gene candidates. Further fine-mapping and functional analyses are warranted to elucidate the potential biochemical mechanisms and epidemiological relevance of these genes.

Screening and replication using the same data set: testing strategies for family-based studies in which all probands are affected

For genome-wide association studies in family-based designs, we propose a powerful two-stage testing strategy that can be applied in situations in which parent-offspring trio data are available and all offspring are affected with the trait or disease under study. In the first step of the testing strategy, we construct estimators of genetic effect size in the completely ascertained sample of affected offspring and their parents that are statistically independent of the family-based association/transmission disequilibrium tests (FBATs/TDTs) that are calculated in the second step of the testing strategy. For each marker, the genetic effect is estimated (without requiring an estimate of the SNP allele frequency) and the conditional power of the corresponding FBAT/TDT is computed. Based on the power estimates, a weighted Bonferroni procedure assigns an individually adjusted significance level to each SNP. In the second stage, the SNPs are tested with the FBAT/TDT statistic at the individually adjusted significance levels. Using simulation studies for scenarios with up to 1,000,000 SNPs, varying allele frequencies and genetic effect sizes, the power of the strategy is compared with standard methodology (e.g., FBATs/TDTs with Bonferroni correction). In all considered situations, the proposed testing strategy demonstrates substantial power increases over the standard approach, even when the true genetic model is unknown and must be selected based on the conditional power estimates. The practical relevance of our methodology is illustrated by an application to a genome-wide association study for childhood asthma, in which we detect two markers meeting genome-wide significance that would not have been detected using standard methodology.

The current state of genotyping technology has enabled researchers to conduct genome-wide association studies of up to 1,000,000 SNPs, allowing for systematic scanning of the genome for variants that might influence the development and progression of complex diseases. One of the largest obstacles to the successful detection of such variants is the multiple comparisons/testing problem in the genetic association analysis. For family-based designs in which all offspring are affected with the disease/trait under study, we developed a methodology that addresses this problem by partitioning the family-based data into two statistically independent components. The first component is used to screen the data and determine the most promising SNPs. The second component is used to test the SNPs for association, where information from the screening is used to weight the SNPs during testing. This methodology is more powerful than standard procedures for multiple comparisons adjustment (i.e., Bonferroni correction). Additionally, as only one data set is required for screening and testing, our testing strategy is less susceptible to study heterogeneity. Finally, as many family-based studies collect data only from affected offspring, this method addresses a major limitation of previous methodologies for multiple comparisons in family-based designs, which require variation in the disease/trait among offspring.

New powerful approaches for family-based association tests with longitudinal measurements

We discuss several new powerful family-based approaches for testing genetic association when the traits are obtained from longitudinal or repeated measurement studies. The popular approach FBAT-PC is based on a linear combination of the individual traits. We propose a one-sided modification, FBAT-PCM, which has a closed-form expression and is always more powerful. We also present two approaches FBAT-LC and FBAT-LCC based on linear combination of the univariate test statistics. Furthermore, all three approaches are shown to be unified to a general form. Through simulation studies, we compare the power of these tests under different models of genetic effect sizes. Compared to original FBAT-PC, our modification achieves a power gain of up to 50%. In addition, all three new approaches gain substantial power compared to the ordinary approach of Bonferroni correction, with the relative performance depending upon the underlying model. Application of these approaches for testing an association between Body Mass Index and a previously reported candidate SNP confirms our results.

Association study of Wnt signaling pathway genes in bipolar disorder

CONTEXT

The Wnt signaling pathways promote cell growth and are best known for their role in embryogenesis and cancer. Several lines of evidence suggest that these pathways might also be involved in bipolar disorder.

OBJECTIVE

To test for an association between candidate genes in the Wnt signaling pathways and disease susceptibility in a family-based bipolar disorder study.

DESIGN

Two hundred twenty-seven tagging single- nucleotide polymorphisms (SNPs) from 34 genes were successfully genotyped. Initial results led us to focus on the gene PPARD, in which we genotyped an additional 13 SNPs for follow-up.

SETTING

Nine academic medical centers in the United States.

PARTICIPANTS

Five hundred fifty-four offspring with bipolar disorder and their parents from 317 families.

MAIN OUTCOME MEASURES

Family-based association using FBAT and HBAT (http://www.biostat.harvard.edu/~fbat/default.html; Harvard School of Public Health, Boston, Massachusetts). Exploratory analyses testing for interactions of PPARD SNPs with clinical covariates and with other Wnt genes were conducted with GENASSOC (Stata Corp, College Station, Texas).

RESULTS

In the initial analysis, the most significantly associated SNP was rs2267665 in PPARD (nominal P < .001). This remained significant at P = .05 by permutation after accounting for all SNPs tested. Additional genotyping in PPARD yielded 4 SNPs in 1 haplotype block that were significantly associated with bipolar disorder (P < .01), the most significant being rs9462082 (P < .001). Exploratory analyses revealed significant evidence (P < .01) for interactions of rs9462082 with poor functioning on the Global Assessment Scale (odds ratio [OR], 3.36; 95% confidence interval [CI], 1.85-6.08) and with SNPs in WNT2B (rs3790606: OR, 2.56; 95% CI, 1.67-4.00) and WNT7A (rs4685048: OR, 1.79; 95% CI, 1.23-2.63).

CONCLUSIONS

We found evidence for association of bipolar disorder with PPARD, a gene in the Wnt signaling pathway. The consistency of this result with one from the Wellcome Trust Case-Control Consortium encourages further study. If the finding can be confirmed in additional samples, it may illuminate a new avenue for understanding the pathogenesis of severe bipolar disorder and developing more effective treatments.

Genetic risk factors for placental abruption: a HuGE review and meta-analysis

BACKGROUND

Although the precise pathophysiology that leads to placental abruption is unknown, there is evidence supporting a genetic etiology.

METHODS

We searched PubMed and systematically reviewed all case-control studies that investigated the association between genetic variants and placental abruption. Pooled genetic risks were estimated using fixed and random effects odds ratios.

RESULTS

Twenty-two articles, examining a total of 14 gene polymorphisms were identified. Seven polymorphisms (F5 Arg506Gln, F5 Met385Thr, F2 G20210A, MTHFR A1298C, MTHFD1 Arg653Gln, NOS3 Glu298Asp, AGT Met235Thr) show significant association in individual studies. Six of the 7 (all except F5Met385Thr) were studied more than once and we therefore included them in our meta-analyses. A positive association under the dominant model was found for the F5 Arg506Gln and F2 G20210A polymorphisms. The random-effects odds ratio for the F5 Arg506Gln polymorphism was 3.4 (95% confidence interval = 1.4-8.3) and the fixed-effects odds ratio for the F2 G20210A polymorphism was 6.7 (3.2-13).

CONCLUSION

Considering the multifactorial etiology of abruption and the relatively small numbers of studies and participants, this review provides only the first clues of possible genetic causes. Larger case-control studies that include gene-gene and gene-environment interactions may help to elucidate the genetics of placental abruption further.

Family-based SNP association study on 8q24 in bipolar disorder

Previous linkage studies have identified chromosome 8q24 as a promising positional candidate region to search for bipolar disorder (BP) susceptibility genes. We, therefore, sought to identify BP susceptibility genes on chromosome 8q24 using a family-based association study of a dense panel of SNPs selected to tag the known common variation across the region of interest. A total of 1,458 SNPs across 16 Mb of 8q24 were examined in 3,512 subjects, 1,954 of whom were affected with BP, from 737 multiplex families. Single-locus tests were carried out with FBAT and Geno-PDT, and multi-locus test were carried out with HBAT and multi-locus Geno-PDT. None of the SNPs were associated with BP in the single-locus tests at a level that exceeded our threshold for study-wide significance (P < 3.00 x 10(-5)). However, there was consistent evidence at our threshold for the suggestive level (P < 7.00 x 10(-4)) from both the single locus and multi-locus tests of associations with SNPs in the genes ADCY8, ST3GAL1, and NSE2. Multi-locus analyses suggested joint effects between ADCY8 and ST3GAL1 (P = 3.00 x 10(-4)), with at least one copy of the "high risk" allele required at both genes for association with BP, consistent with a jointly dominant-dominant model of action. These findings with ADCY8 and ST3GAL1 warrant further investigation in order to confirm the observed associations and their functional significance for BP susceptibility.

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

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

Density of common complex ocular traits in the aging eye: analysis of secondary traits in genome-wide association studies

Genetic association studies are identifying genetic risks for common complex ocular traits such as age-related macular degeneration (AMD). The subjects used for discovery of these loci have been largely from clinic-based, case-control studies. Typically, only the primary phenotype (e.g., AMD) being studied is systematically documented and other complex traits (e.g., affecting the eye) are largely ignored. The purpose of this study was to characterize these other or secondary complex ocular traits present in the cases and controls of clinic-based studies being used for genetic study of AMD. The records of 100 consecutive new patients (of any diagnosis) age 60 or older for which all traits affecting the eye had been recorded systematically were reviewed. The average patient had 3.5 distinct diagnoses. A subset of 10 complex traits was selected for further study because they were common and could be reliably diagnosed. The density of these 10 complex ocular traits increased by 0.017 log-traits/year (P = 0.03), ranging from a predicted 2.74 at age 60 to 4.45 at age 90. Trait-trait association was observed only between AMD and primary vitreomacular traction (P = 0.0009). Only 1% of subjects age 60 or older had no common complex traits affecting the eye. Extrapolations suggested that a study of 2000 similar subjects would have sufficient power to detect genetic association with an odds ratio of 2.0 or less for 4 of these 10 traits. In conclusion, the high prevalence of complex traits affecting the aging eye and the inherent biases in referral patterns leads to the potential for confounding by undocumented secondary traits within case-control studies. In addition to the primary trait, other common ocular phenotypes should be systematically documented in genetic association studies so that adjustments for potential trait-trait associations and other bias can be made and genetic risk variants identified in secondary analyses.

On the analysis of copy-number variations in genome-wide association studies: a translation of the family-based association test

Though there is an increasing support for an important contribution of copy number variation (CNV) to the genetic architecture of complex disease, few methods have been developed for the analysis of such variation in the context of genetic association studies. In this paper, we propose a generalization of family-based association tests (FBATs) to allow for the analysis of CNVs at a genome-wide level. We translate the popular FBAT approach so that, instead of genotypes, raw intensity values that reflect copy number are used directly in the test statistic, thereby bypassing the need for a CNV genotyping algorithm. Moreover, both inherited and de novo CNVs can be analyzed without any prior knowledge about the type of CNV, making it easily applicable to large-scale association studies. All robustness properties of the genotype FBAT approach are maintained and all previously developed FBAT extensions, including FBATs for time-to-onset, multivariate FBATs, and FBAT-testing strategies, can be directly transferred to the analysis of CNVs. Using simulation studies, we evaluate the power and the robustness of the new approach. Furthermore, for those CNVs that can be genotyped, we compare FBATs based on genotype calls with FBATs that are directly based on the intensity data. An application to one of the first CNV genome-wide-association studies of asthma identifies a very plausible candidate gene. A software implementation of the approach is freely available at http://www.hsph.harvard.edu/research/iuliana-ionita/software. The approach has also been completely integrated in the PBAT software package.

The -1021 C/T DBH polymorphism is associated with neuropsychological performance among children and adolescents with ADHD

Catecholaminergic imbalance has increasingly been implicated in the pathophysiology of attention-deficit/hyperactivity disorder (ADHD). The enzyme dopamine-beta-hydroxylase (D beta H)-critical to catecholaminergic regulation-is under strong genetic control, with the -1021 C/T polymorphism accounting for up to 50% of the enzymatic activity. This work aimed to investigate association between this functional polymorphism and the performance of children and adolescents with ADHD in neuropsychological measures of executive function (EF). Sixty-four drug-naive patients with ADHD undertook a Continuous Performance Test and the Wisconsin Card Sorting Test. By means of a factorial analysis, a composite measure of EF was extracted. Performance according to genotypic group was analyzed, including age as a confounder. In addition, a family-based association test was conducted as a confirmatory analysis. Principal components analysis of neuropsychological measures loaded two factors that explained 83.8% of total variance. Cognitive performance, as measured by the composite score, showed significant difference between genotypic groups after adjustment for age (P = 0.002). The CC homozygosity was associated with a diminished global EF performance, a result that was corroborated by the intra-familial analysis. The present study demonstrated an association between the neuropsychological performance of children with ADHD and a functional polymorphism in the promoter region of the DBH gene. The refinement of the ADHD phenotype by means of composite measures of EF can contribute to uncover the molecular underpinnings of ADHD.

Family-based methods for linkage and association analysis

Traditional epidemiological study concepts such as case-control or cohort designs can be used in the design of genetic association studies, giving them a prominent role in genetic association analysis. A different class of designs based on related individuals, typically families, uses the concept of Mendelian transmission to achieve design-independent randomization, which permits the testing of linkage and association. Family-based designs require specialized analytic methods but they have distinct advantages: They are robust to confounding and variance inflation, which can arise in standard designs in the presence of population substructure; they test for both linkage and association; and they offer a natural solution to the multiple comparison problem. This chapter focuses on family-based designs. We describe some basic study designs as well as general approaches to analysis for qualitative, quantitative, and complex traits. Finally, we review available software.

Kernel-based association test

Association mapping (i.e., linkage disequilibrium mapping) is a powerful tool for positional cloning of disease genes. We propose a kernel-based association test (KBAT), which is a composite function of "P-values of single-locus association tests" and "kernel weights related to intermarker distances and/or linkage disequilibria." The KBAT is a general form of some current test statistics. This method can be applied to the study of candidate genes and can scan each chromosome using a moving average procedure. We evaluated the performance of the KBAT through simulation studies that considered evolutionary parameters, disease models, sample sizes, kernel functions, test statistics, window attributes, empirical P-value estimations, and genetic/physical maps. The results showed that the KBAT had a well-controlled false positive rate and high power compared to existing methods. In addition, the KBAT was also applied to analyze a genomewide data set from the Collaborative Study on the Genetics of Alcoholism. Important genes associated with alcoholism dependence were identified. In summary, the merits of the KBAT are multifold: the KBAT is robust against the inclusion of nuisance markers, is invariant to the map scale, and accommodates different types of genomic data, study designs, and study purposes. The proposed methods are packaged in the user-friendly software, KBAT, available at http://www.stat.sinica.edu.tw/hsinchou/genetics/association/KBAT.htm.

The CHRNA5/A3/B4 gene cluster variability as an important determinant of early alcohol and tobacco initiation in young adults

BACKGROUND

One potential site of convergence of the nicotine and alcohol actions is the family of the neuronal nicotinic acetylcholine receptors. Our study examines the genetic association between variations in the genomic region containing the CHRNA5, A3, and B4 gene cluster (A5A3B4) and several phenotypes of alcohol and tobacco use in an ethnically diverse young adult sample. Significant results were then replicated in a separate adult population-representative sample.

METHODS

In a selected sample, nine single nucleotide polymorphisms (SNPs) were tested for association with various nicotine and alcohol phenotypes, including age of initiation and measures of frequency, quantity, and subjective responses to the substances. Analysis was conducted with the statistical genetics program WHAP in the full sample (1075 subjects) including ethnicities as covariates and within each ethnic group sub-sample. Replication of the significant results in a separate population-based sample was carried out with the PBAT statistical genetics program.

RESULTS

Two linked SNPs (rs8023462 and rs1948) located in a conserved region of the A5A3B4 gene cluster significantly predicted early age of initiation for tobacco with a hazard ratio (HR) of 1.35 (95% confidence interval [CI]1.08-1.70) for the CC genotype of rs8023462 and a HR of 1.29 (95% CI 1.01-1.63) for the TT genotype of rs1948 [corrected]. These findings were then replicated in a separate population-representative sample, showing rs1948 and rs8023462 to be associated with age of initiation for both tobacco and alcohol use (p < .01 and p < .001).

CONCLUSIONS

Variations in A5A3B4 genes might influence behaviors that promote early age of experimentation with drugs.

Application of the Linux cluster for exhaustive window haplotype analysis using the FBAT and Unphased programs

Background

Genetic association studies have been used to map disease-causing genes. A newly introduced statistical method, called exhaustive haplotype association study, analyzes genetic information consisting of different numbers and combinations of DNA sequence variations along a chromosome. Such studies involve a large number of statistical calculations and subsequently high computing power. It is possible to develop parallel algorithms and codes to perform the calculations on a high performance computing (HPC) system. However, most existing commonly-used statistic packages for genetic studies are non-parallel versions. Alternatively, one may use the cutting-edge technology of grid computing and its packages to conduct non-parallel genetic statistical packages on a centralized HPC system or distributed computing systems. In this paper, we report the utilization of a queuing scheduler built on the Grid Engine and run on a Rocks Linux cluster for our genetic statistical studies.

Results

Analysis of both consecutive and combinational window haplotypes was conducted by the FBAT (Laird et al., 2000) and Unphased (Dudbridge, 2003) programs. The dataset consisted of 26 loci from 277 extended families (1484 persons). Using the Rocks Linux cluster with 22 compute-nodes, FBAT jobs performed about 14.4–15.9 times faster, while Unphased jobs performed 1.1–18.6 times faster compared to the accumulated computation duration.

Conclusion

Execution of exhaustive haplotype analysis using non-parallel software packages on a Linux-based system is an effective and efficient approach in terms of cost and performance.

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, discusses selection of genetic markers for use in studies, introduces basic methods of analysis of case-control data, and discusses measures of association and impact. Controlling for confounding (including population stratification), consideration of multiple loci, and haplotype analysis are briefly discussed. Readers are referred to basic texts on epidemiology for more details on general conduct of case-control studies.

Association genetics in Pinus taeda L. II. Carbon isotope discrimination

Dissection of complex traits that influence fitness is not only a central topic in evolutionary research but can also assist breeding practices for economically important plant species, such as loblolly pine (Pinus taeda L). In this study, 46 single nucleotide polymorphisms (SNPs) from 41 disease and abiotic stress-inducible genes were tested for their genetic association with carbon isotope discrimination (CID), a time-integrated trait measure of stomatal conductance. A family-based approach to detect genotype/phenotype genetic association was developed for the first time in plants by applying the quantitative transmission disequilibrium test on an association population of 961 clones from 61 families (adopted from previous breeding programs) evaluated for phenotypic expression of CID at two sites. Two particularly promising candidates for their genetic effects on CID are: dhn-1, involved in stabilization of cell structures, and lp5-like, a glycine rich protein putatively related to cell wall reinforcement proteins, both of which were shown in previous studies to be water-deficit inducible. Moreover, association in lp5-like involves a nonsynonymous mutation in linkage disequilibrium with two other nonsynonymous polymorphisms that could, by acting together, enhance overall phenotypic effects. This study highlights the complexity of dissecting CID traits and provides insights for designing second-generation association studies based on candidate gene approaches in forest trees.

Genome-wide association studies for complex traits: consensus, uncertainty and challenges

The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.

Catechol O-methyl transferase and dopamine D2 receptor gene polymorphisms: evidence of positive heterosis and gene-gene interaction on working memory functioning

The COMT Val(108/158)Met polymorphism has been extensively studied in relation to individual differences in working memory (WM) performance. The present study tested the association of the COMT Val(108/158)Met polymorphism with WM performance in two independent family-based Dutch samples: 371 children (mean age 12.4 years) and 391 adults (mean age 36.2 years). A significant association was found between the COMT polymorphism and WM scores in the combined adult and young cohorts. The association reflected positive heterosis such that the Met/Met and Val/Val homozygotes did not perform as well as the Met/Val heterozygotes on the WM tasks. A secondary analysis was conducted in which a DRD2-tagging SNP (rs2075654) was tested for an interactive effect with the COMT polymorphism on WM performance. A significant interactive effect of the DRD2 and COMT genes was found such that heterosis was present only in the DRD2 genotype that has been linked to lower receptor density. Our results support previous findings that WM performance needs an optimal level of dopamine signaling within the PFC. This optimum level depends on enzymatic activity controlling dopamine level as well as dopamine receptor sensitivity, both of which may differ as a function of age and genotype. We conclude that the effects of a single polymorphism in a dopaminergic gene on a well-defined cognitive trait may easily remain hidden if the interaction with age and other genes in the pathway are not taken into account.

A family-based likelihood ratio test for general pedigree structures that allows for genotyping error and missing data

The purpose of this work is the development of a family-based association test that allows for random genotyping errors and missing data and makes use of information on affected and unaffected pedigree members. We derive the conditional likelihood functions of the general nuclear family for the following scenarios: complete parental genotype data and no genotyping errors; only one genotyped parent and no genotyping errors; no parental genotype data and no genotyping errors; and no parental genotype data with genotyping errors. We find maximum likelihood estimates of the marker locus parameters, including the penetrances and population genotype frequencies under the null hypothesis that all penetrance values are equal and under the alternative hypothesis. We then compute the likelihood ratio test. We perform simulations to assess the adequacy of the central chi-square distribution approximation when the null hypothesis is true. We also perform simulations to compare the power of the TDT and this likelihood-based method. Finally, we apply our method to 23 SNPs genotyped in nuclear families from a recently published study of idiopathic scoliosis (IS). Our simulations suggest that this likelihood ratio test statistic follows a central chi-square distribution with 1 degree of freedom under the null hypothesis, even in the presence of missing data and genotyping errors. The power comparison shows that this likelihood ratio test is more powerful than the original TDT for the simulations considered. For the IS data, the marker rs7843033 shows the most significant evidence for our method (p = 0.0003), which is consistent with a previous report, which found rs7843033 to be the 2nd most significant TDTae p value among a set of 23 SNPs.

Likelihood-based association analysis for nuclear families and unrelated subjects with missing genotype data

Missing data occur in genetic association studies for several reasons including missing family members and uncertain haplotype phase. Maximum likelihood is a commonly used approach to accommodate missing data, but it can be difficult to apply to family-based association studies, because of possible loss of robustness to confounding by population stratification. Here a novel likelihood for nuclear families is proposed, in which distinct sets of association parameters are used to model the parental genotypes and the offspring genotypes. This approach is robust to population structure when the data are complete, and has only minor loss of robustness when there are missing data. It also allows a novel conditioning step that gives valid analysis for multiple offspring in the presence of linkage. Unrelated subjects are included by regarding them as the children of two missing parents. Simulations and theory indicate similar operating characteristics to TRANSMIT, but with no bias with missing data in the presence of linkage. In comparison with FBAT and PCPH, the proposed model is slightly less robust to population structure but has greater power to detect strong effects. In comparison to APL and MITDT, the model is more robust to stratification and can accommodate sibships of any size. The methods are implemented for binary and continuous traits in software, UNPHASED, available from the author.

Pesticide exposure and risk of Parkinson's disease: a family-based case-control study

BACKGROUND

Pesticides and correlated lifestyle factors (e.g., exposure to well-water and farming) are repeatedly reported risk factors for Parkinson's disease (PD), but few family-based studies have examined these relationships.

METHODS

Using 319 cases and 296 relative and other controls, associations of direct pesticide application, well-water consumption, and farming residences/occupations with PD were examined using generalized estimating equations while controlling for age-at-examination, sex, cigarette smoking, and caffeine consumption.

RESULTS

Overall, individuals with PD were significantly more likely to report direct pesticide application than their unaffected relatives (odds ratio = 1.61; 95% confidence interval, 1.13-2.29). Frequency, duration, and cumulative exposure were also significantly associated with PD in a dose-response pattern (p </= 0.013). Associations of direct pesticide application did not vary by sex but were modified by family history of PD, as significant associations were restricted to individuals with no family history. When classifying pesticides by functional type, both insecticides and herbicides were found to significantly increase risk of PD. Two specific insecticide classes, organochlorines and organophosphorus compounds, were significantly associated with PD. Consuming well-water and living/working on a farm were not associated with PD.

CONCLUSION

These data corroborate positive associations of broadly defined pesticide exposure with PD in families, particularly for sporadic PD. These data also implicate a few specific classes of pesticides in PD and thus emphasize the need to consider a more narrow definition of pesticides in future studies.

General aspects of the genetics of SLE

The application of genetic techniques to the study of systemic lupus erythematosus (SLE) has identified candidate genes with diverse immunological function. There is a growing understanding that susceptibility to SLE is due to a complex interaction of multiple genes and environmental factors, and that many of these may be shared with other autoimmune diseases. In this first of a series of review articles we outline our current understanding of SLE genetics, in particular summarising the results of recent association studies.