Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple, correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in different scales such as dichotomous, ordinal and quantitative. In the absence of covariates, nonparametric association tests have been developed for multiple complex traits to study comorbidity. However, genetic studies generally contain measurements of some covariates that may affect the relationship between the risk factors of major interest (such as genes) and the outcomes. While it is relatively easy to adjust these covariates in a parametric model for quantitative traits, it is challenging for multiple complex traits with possibly different scales. In this article, we propose a nonparametric test for multiple complex traits that can adjust for covariate effects. The test aims to achieve an optimal scheme of adjustment by using a maximum statistic calculated from multiple adjusted test statistics. We derive the asymptotic null distribution of the maximum test statistic, and also propose a resampling approach, both of which can be used to assess the significance of our test. Simulations are conducted to compare the type I error and power of the nonparametric adjusted test to the unadjusted test and other existing adjusted tests. The empirical results suggest that our proposed test increases the power through adjustment for covariates when there exist environmental effects, and is more robust to model misspecifications than some existing parametric adjusted tests. We further demonstrate the advantage of our test by analyzing a data set on genetics of alcoholism.

Identifying the risk factors for mental illnesses is of significant public health importance. Diagnosis, stigma associated with mental illnesses, comorbidity, and complex etiologies, among others, make it very challenging to study mental disorders. Genetic studies of mental illnesses date back at least a century ago, beginning with descriptive studies based on Mendelian laws of inheritance. A variety of study designs including twin studies, family studies, linkage analysis, and more recently, genomewide association studies have been employed to study the genetics of mental illnesses, or complex diseases in general. In this paper, I will present the challenges and methods from a statistical perspective and focus on genetic association studies.

In many genetics studies, especially in the investigation of mental illness and behavioral disorders, it is common for researchers to collect multiple phenotypes to characterize the complex disease of interest. It may be advantageous to analyze those phenotypic measurements simultaneously if they share a similar genetic mechanism. In this study, we present a nonparametric approach to studying multiple traits together rather than examining each trait separately. Through simulation we compared the nominal type I error and power of our proposed test to an existing test, i.e., a generalized family-based association test. The empirical results suggest that our proposed approach is superior to the existing test in the analysis of ordinal traits. The advantage is demonstrated on a data set concerning alcohol dependence. In this application, the use of our methods enhanced the signal of the association test.

Recent advances in genotyping technology make it possible to utilize large-scale association analysis for disease-gene mapping. Powerful and robust family-based association methods are crucial for successful gene mapping. We propose a family-based association method, the generalized disequilibrium test (GDT), in which the genotype differences of all discordant relative pairs are utilized in assessing association within a family. The improvement of the GDT over existing methods is threefold: (1) information beyond first-degree relatives is incorporated efficiently, yielding substantial gains in power in comparison to existing tests; (2) the GDT statistic is implemented via a robust technique that does not rely on large sample theory, resulting in further power gains, especially at high levels of significance; and (3) covariates and weights based on family size are incorporated. Advantages of the GDT over existing methods are demonstrated by extensive computer simulations and by application to recently published large-scale genome-wide linkage data from the Type 1 Diabetes Genetics Consortium (T1DGC). In our simulations, the GDT consistently outperforms other tests for a common disease and frequently outperforms other tests for a rare disease; the power improvement is > 13% in 6 out of 8 extended pedigree scenarios. All of the six strongest associations identified by the GDT have been reported by other studies, whereas only three or four of these associations can be identified by existing methods. For the T1D association at gene UBASH3A, the GDT resulted in a genome-wide significance (p = 4.3 x 10(-6)), much stronger than the published significance (p = 10(-4)).

Meningomyelocele (MM) is a common human birth defect. MM is a disorder of neural development caused by contributions from genes and environmental factors that result in the NTD and lead to a spectrum of physical and neurocognitive phenotypes.

A multidisciplinary approach has been taken to develop a comprehensive understanding of MM through collaborative efforts from investigators specializing in genetics, development, brain imaging, and neurocognitive outcome. Patients have been recruited from five different sites: Houston and the Texas-Mexico border area; Toronto, Canada; Los Angeles, California; and Lexington, Kentucky. Genetic risk factors for MM have been assessed by genotyping and association testing using the transmission disequilibrium test.

A total of 509 affected child/parent trios and 309 affected child/parent duos have been enrolled to date for genetic association studies. Subsets of the patients have also been enrolled for studies assessing development, brain imaging, and neurocognitive outcomes. The study recruited two major ethnic groups, with 45.9% Hispanics of Mexican descent and 36.2% North American Caucasians of European descent. The remaining patients are African-American, South and Central American, Native American, and Asian. Studies of this group of patients have already discovered distinct corpus callosum morphology and neurocognitive deficits that associate with MM. We have identified maternal MTHFR 667T allele as a risk factor for MM. In addition, we also found that several genes for glucose transport and metabolism are potential risk factors for MM.

The enrolled patient population provides a valuable resource for elucidating the disease characteristics and mechanisms for MM development.

The authors test single nucleotide polymorphisms (SNPs) in coding sequences of 12 candidate genes involved in glucose metabolism and obesity for associations with spina bifida. Genotyping was performed on 507 children with spina bifida and their parents plus anonymous control DNAs from Hispanic and Caucasian individuals. The transmission disequilibrium test was performed to test for genetic associations between transmission of alleles and spina bifida in the offspring (P < .05). A statistically significant association between Lys481 of HK1 (G allele), Arg109Lys of LEPR (G allele), and Pro196 of GLUT1 (A allele) was found ( P = .019, .039, and .040, respectively). Three SNPs on 3 genes involved with glucose metabolism and obesity may be associated with increased susceptibility to spina bifida.

We examined the BRCA1 gene in 268 patients, and their parents, with a specific diagnosis of spina bifida meningomyelocele (SBMM). We genotyped two intragenic microsatellite markers (BRCA1 D17S1323, BRCA1 D17S1322) and 2 single nucleotide polymorphisms (A1186G, A4956G) in our patients. Transmission disequilibrium testing (TDT) showed significant association with A4956G, but not with A1186G. Extended TDT demonstrated over-transmission of the 17GT allele in BRCA1 D17S1323 and the 14GTT allele in BRCA1 D17S1322, and under-transmission of the 20GT allele in BRCA1 D17S1323 and the 16GTT allele in BRCA1 D17S1322. Our data included location of the rostral edge of the lesion. Individuals homozygous for the 17GT allele for BRCA1 D17S1323 were more likely to have SB lesions located caudally, while heterozygotes with the 17GT allele for BRCA1 D17S1323 had a more rostral lesion. Individuals heterozygous for the 16GTT allele of BRCA1 D17S1322 were more likely to have rostral lesions. We measured gene expression in CEPH members and demonstrated differential expression levels of BRCA1 associated with these polymorphisms. Integrating our data with HapMap findings showed that the polymorphic markers are associated with distinct haplotypes. We conclude that the BRCA1 gene is associated with SBMM and participates in the phenotypic variability seen in SBMM.

Neural tube defects (NTDs) constitute a major group of congenital malformations with an overall incidence of approximately 1-2 in 1,000 live births in the United States. Hispanic Americans have a 2.5 times higher risk than the Caucasian population. Spina bifida meningomyelocele (SBMM) is a major clinical presentation of NTDs resulting from lack of closure of the spinal cord caudal to the head. In a previous study of spina bifida (SB) patients of European Caucasian descent, it was suggested that specific haplotypes of the platelet-derived growth factor receptor-alpha (PDGFRA) gene P1 promoter strongly affected the rate of NTD genesis. In our study, we evaluated the association of PDGFRA P1 in a group of 407 parent-child triads (167 Caucasian, 240 Hispanics) and 164 unrelated controls (89 Caucasian, 75 Hispanic). To fully evaluate the association of PDGFRA P1, we performed both transmission-disequilibrium test (TDT) and association analyses to test the hypotheses that PDGFRA P1 was (1) transmitted preferentially in SBMM affected children and (2) associated with the condition of SBMM comparing affected children to unaffected controls. We did find that there was a different allelic and genotypic distribution of PDGFRA P1 when comparing Hispanics and Caucasians. However, neither ethnic group showed strong association between SBMM and the PDGFRA P1 region. These findings suggest that PDGFRA P1 does not have a major role in the development of SBMM.

By definition, myeloproliferative disorders (MPDs) are caused by an acquired somatic mutation of a hematopoietic progenitor/stem cell and have sporadic occurrence. However, well-documented families exist with first-degree relatives acquiring one or several MPDs. It is reasonable to assume that the germ-line mutation(s) or genetic background must facilitate or predispose for one or several somatic mutation(s) that lead to the MPD that is indistinguishable from the sporadic form. This is best documented in familial polycythemia vera (PV), which appears to be inherited as an autosomal dominant disorder with incomplete penetrance. However, there are also families wherein members develop any combination of MPDs, including PV, essential thrombocythemia (ET), chronic myelocytic leukemia (CML), and idiopathic myelofibrosis (IMF). A separate group of familial diseases is the familial thrombocythemias, wherein germ-line mutations in the genes for thrombopoietin or its receptor, MPL, cause polyclonal hereditary thrombocythemia, which may be clinically indistinguishable from ET. Patients with the congenital polycythemic condition "primary familial and congenital polycythemia" (PFCP) have characteristically decreased erythropoietin (Epo) levels similar to PV, hypersensitive erythroid progenitors, and low Epo levels; as such, this condition is often confused with PV. Therefore, PFCP will also be discussed here, while other congenital polycythemic states such as the Chuvash polycythemia that have elevated or inappropriately normal Epo levels will be omitted from this review in view of their distinct phenotype and unique laboratory features.

Since the discovery of Viliuisk encephalomyelitis (VE) in 1887, scientists have tried to understand the natural history and aetiology of this endemic neurological disorder among the native Sakha population of Central Siberia. Familial aggregation and segregation analysis suggested a genetic influence on VE incidence. However, recent studies have implicated an unknown virus, possibly from the alpha herpesvirus family, as a possible cause for this disease. As VE is a neurological disease characterized by the inflammatory reactions systematically observed in the spinocerebellar fluid and in the brain tissue of deceased patients, we examined 17 single nucleotide polymorphisms (SNPs) across seven inflammation-related candidate gene regions, including chemokine receptors type 2 and 5 (CCR2/CCR5), interferon-gamma (IFN-gamma), interleukin-4 (IL-4), IL-6, IL-10, stromal cell-derived factor (SDF) and chemokine regulated upon activation, normal T-cell expressed and presumably secreted (RANTES). Our main objective was to analyse the degree of genetic association between VE and candidate genes that have been previously implicated in other inflammatory diseases. Samples were collected from 83 affected families comprising 88 verified VE cases, 156 family members, and an additional 69 unrelated, unaffected inhabitants of the same geographical area. This collection included substantially all of the cases that are currently on the VE Registry. The experimental design included both case-control and transmission/disequilibrium test (TDT)-based familial association analyses. None of 17 SNPs analysed was significantly associated with VE occurrence. Exclusion of these eight genes based on the lack of association has important implications for identifying the disease agent, as well as prescribing therapy and understanding Viliuisk encephalomyelitis.

Many diseases or traits exhibit a varying age at onset. Recent data examples of prostate cancer and childhood diabetes show that compared to simply treating the disease outcome as affected vs. unaffected, incorporation of age-at-onset information into the transmission/disequilibrium type of test (TDT) does not appear to change the results much. In this paper, we evaluate the power of TDT as a function of age at onset, and show that age-at-onset information is most useful when the disease is common, or the relative risk associated with the high-risk genotype varies with age. Moreover, an extremely old unaffected subject can contribute substantially to the power of the TDT, sometimes as much as old-onset subjects. We propose a modified test statistic for testing no association between the marker at the candidate locus and age at onset. The simulation study was conducted to evaluate the finite sample properties of proposed and the TDT test statistics under various sampling schemes for trios of parents and offspring, as well as for sibling clusters where unaffected siblings were used as controls.

Using large-sample theory, we present a unified approach to power calculations for family-based association tests. Currently available methods for power calculations are restricted to special designs or require approximations or simulations. Our analytical approach to power calculations is broadly applicable in many settings. We discuss power calculations for two scenarios that have high practical relevance and in which power previously could only be assessed by simulation studies or by approximations: (1) studies using both affected and unaffected offspring and (2) studies with missing parental information. When the population prevalence is high, it can be worthwhile to genotype unaffected offspring. For many scenarios, high power can be achieved with reasonable sample sizes, even when no parental information is available.

Population-based case-control studies have found relationships between risk of prostate cancer and genetic polymorphisms in the CAG repeat and GGC repeat of the X-linked androgen receptor gene (AR) as well as the autosomal gene coding for glutathione S-transferase pi (GSTP1). This family-based study utilized the transmission disequilibrium test to examine whether there was evidence that these polymorphisms could account for familial aggregation of prostate cancer. Seventy-nine North American pedigrees were studied. Most of these families had 3 or more affected first-degree relatives. Genotype information was obtained on 578 individuals. The reconstruction combined transmission disequilibrium test (RC-TDT) was used to test for linkage. There was no evidence of linkage to the CAG and GGC repeat sequences in the AR gene or the pentanucleotide (ATAAA) repeat in the GSTP1 gene when each allele was analyzed separately or when alleles were grouped by repeat length. Our findings do not support the hypothesis that familial clustering of prostate cancer in high-risk families is attributable to these genetic variants.

Until about a decade ago, genetic association testing essentially meant case control association analysis using genetic markers. Concerns about population stratification propelled family-based tests of association into widespread use and challenged the classic case control design. The literature now contains a vast collection of different family-based methods, most of which are based on the transmission/disequilibrium test (TDT). Some methods extend the original TDT to accommodate multiallelic markers, variable pedigree constellations, multiple loci, etc. Other methods go beyond the original design of the TDT to detect genetic association via haplotype sharing. Most recently, we have witnessed a revival of case control methods that control for population stratification. The purpose of this review is to help orient readers to the rapidly developing methods of association testing and enhance their understanding of the basic principles of these approaches. We present an overview of the development of genetic association tests, with practical guidelines on which test might be the most suitable for a given study.

Various family-based association methods have recently been proposed that allow testing for linkage in the presence of linkage disequilibrium between a marker and a disease even if there is only incomplete parental-genotype information. For some families, it may be possible to reconstruct missing parental genotypes from the genotypes of their offspring. Treating such a reconstructed family as if parental genotypes have been typed, however, can introduce bias. The reconstruction-combined transmission/disequilibrium test (RC-TDT) and its X-chromosomal counterpart, XRC-TDT, employ parental-genotype reconstruction and correct for the biases involved in this reconstruction without relying on population marker allele frequencies. For the two tests, exact P values can be obtained by numerically calculating the convolution of the null distributions corresponding to the families in the sample.

Several genome-wide screens for asthma and related phenotypes have been published to date but data on fine-mapping are scarce. For higher resolution we performed a fine-mapping study with 2 cM average spacing in often discussed asthma candidate regions (2p, 5q, 6p, 7p, 9q, 11p, and 12q) to narrow down the regions of interest. All participants of a Caucasian family study (97 families with at least two affected sib pairs) were genotyped for 49 supplementary polymorphic dinucleotide markers. Our results indicate increased evidence for linkage on chromosome 6p, 9q, and 12q. These candidate regions were further analyzed with SNP polymorphisms in the endothelin 1 (EDN1), lymphotoxin alpha (LTA), and neuronal nitric oxide synthase (NOS1) genes. In addition, IL4 -590C>T and IL10 -592C>A, localized on chromosomes 5q and 1q, respectively, have been analyzed for SNP association. Of the six SNPs tested, four revealed weak association with the examined phenotypes. These are the IL10 -592C>A SNP in the interleukin 10 gene (p=0.036 for eosinophil cell counts), the 4124T>C SNP in EDN1 (p=0.044 for asthma), the 3391C>T SNP in NOS1 with eosinophil cell counts (p=0.0086), and the 5266C>T polymorphism, also in the NOS1 gene, for high IgE levels (p=0.022). In summary, fine mapping data enable us to confine asthma candidate regions, while variants of EDN1 and NOS1, or nearby genes, may play an important role in this context.

Over the past decade, attention has turned from positional cloning of Mendelian disease genes to the dissection of complex diseases. Both theoretical and empirical studies have shown that traditional linkage studies may be inferior in power compared to studies that directly utilize allele status. Case-control association studies, as an alternative, are subject to bias due to population stratification. As a compromise between linkage studies and case-control studies, family-based association designs have received great attention recently due to their potentially higher power to identify complex disease genes and their robustness in the presence of population substructure. In this review, we first describe the basic family-based association design involving one affected offspring with its two parents, all genotyped for a biallelic genetic marker. Extensions of the original transmission disequilibrium tests to multiallelic markers, families with multiple siblings, families with incomplete parental genotypes, and general pedigree structures are discussed. Further developments of statistical methods to study quantitative traits, to analyse genes on the X chromosome, to incorporate multiple tightly linked markers, to identify imprinting genes, and to detect gene-environment interactions are also reviewed. Finally, we discuss the implications of the completion of the Human Genome Project and the identification of hundreds of thousands of genetic polymorphisms on employing family-based association designs to search for complex disease genes.

Family-based association methods have recently been introduced that allow testing for linkage in the presence of linkage disequilibrium between a marker and a disease even if there is only incomplete parental-marker information. No such tests are currently available for X-linked markers. This report fills this methodological gap by presenting the X-linked sibling transmission/disequilibrium test (XS-TDT) and the X-linked reconstruction-combination transmission/disequilibrium test (XRC-TDT). As do their autosomal counterparts (S-TDT and RC-TDT), these tests make no assumption about the mode of inheritance of the disease and the ascertainment of the sample. They protect against spurious association due to population stratification. The two tests were compared by simulations, which show that (1) the X-linked RC-TDT is, in general, considerably more powerful than the X-linked S-TDT and (2) the lack of parental-genotype information can be offset by the typing of a sufficient number of sibling controls. A freely available SAS implementation of these tests allows the calculation of exact P values.