A new method for estimating the risk ratio in studies using case-parental control design

Benchmarking statistical methods for analyzing parent-child dyads in genetic association studies

Genetic association studies of child health outcomes often employ family-based study designs. One of the most popular family-based designs is the case-parent trio design that considers the smallest possible nuclear family consisting of two parents and their affected child. This trio design is particularly advantageous for studying relatively rare disorders because it is less prone to type 1 error inflation due to population stratification compared to population-based study designs (e.g., case-control studies). However, obtaining genetic data from both parents is difficult, from a practical perspective, and many large studies predominantly measure genetic variants in mother-child dyads. While some statistical methods for analyzing parent-child dyad data (most commonly involving mother-child pairs) exist, it is not clear if they provide the same advantage as trio methods in protecting against population stratification, or if a specific dyad design (e.g., case-mother dyads vs. case-mother/control-mother dyads) is more advantageous. In this article, we review existing statistical methods for analyzing genome-wide marker data on dyads and perform extensive simulation experiments to benchmark their type I errors and statistical power under different scenarios. We extend our evaluation to existing methods for analyzing a combination of case-parent trios and dyads together. We apply these methods on genotyped and imputed data from multiethnic mother-child pairs only, case-parent trios only or combinations of both dyads and trios from the Gene, Environment Association Studies consortium (GENEVA), where each family was ascertained through a child affected by nonsyndromic cleft lip with or without cleft palate. Results from the GENEVA study corroborate the findings from our simulation experiments. Finally, we provide recommendations for using statistical genetic association methods for dyads.

Investigations of single nucleotide polymorphisms in folate pathway genes in Chinese families with neural tube defects

AIMS

We investigated the hypothesis that there are interactions between SNPs in folate metabolism pathway genes and environmental risk factors to the etiology of neural tube defects (NTDs).

METHOD

In 602 Chinese families, 609 aborted fetus tissues or blood samples were collected from NTD individuals, and 1106 parental blood samples were detected as controls. We analyzed 28 SNPs in 12 folate pathway genes. Folate supplementation, gestational diabetes mellitus (GDM) and medicine administration before and during pregnancy were investigated. Case-parental control study and transmission/disequilibrium tests were performed according to environmental cofactor stratification.

RESULTS

Association between 5,10-methylenetetrahydrofolate reductase (MTHFR) 677C>T and NTDs was significant in all stratifications (all P<.05), and synergistic effects of no folate supplementation and GDM were shown on NTD occurrence. 5-Methyltetrahydrofolate-homocysteine methyltransferase (MTHM) 501A>G in case of GDM, and betaine-homocysteine methyltransferase (BHMT) 716G>A in case of no folate supplementation significantly associated with NTDs (both P<.05), whereas the two genotypes alone did not significantly associate with NTDs (both P>.05).

CONCLUSIONS

MTHFR 677C>T genotype, especially in case of no folate supplementation and GDM, promotes NTD occurrence. MTHM 501A>G only in case of GDM, and BHMT 716G>A only in case of no folate supplementation contribute to the etiology of NTDs.

Bootstrap calibration of TRANSMIT for informative missingness of parental genotype data

Informative missingness of parental genotype data occurs when the genotype of a parent influences the probability of the parent's genotype data being observed. Informative missingness can occur in a number of plausible ways and can affect both the validity and power of procedures that assume the data are missing at random (MAR). We propose a bootstrap calibration of MAR procedures to account for informative missingness and apply our methodology to refine the approach implemented in the TRANSMIT program. We illustrate this approach by applying it to data on hypertensive probands and their parents who participated in the Framingham Heart Study.

Relative risk for genetic associations: the case-parent triad as a variant of case-cohort design

The contribution of this paper is to conceptualize the case-parent triad within an epidemiological framework. We propose that the case-parent triad design is a variant of the case-cohort design. The affected offspring of case-parent triads come from a source cohort of all offspring of parents in a population. We first demonstrate that if the source cohort is restricted to offspring of a certain parental mating type then the relative risk in relation to genetic exposure can be estimated simply from the ratio of the number of exposed to the number of unexposed affected offspring. We then extend the logic to studies including offspring of all parental mating types; provided that the allele frequencies and possible parental mating types are specified, a valid relative risk can still be estimated. Compared to prior descriptions of the case-parent triad design, the proposed approach is readily understandable, epidemiologically meaningful and provides a relatively simple perspective for estimating valid measure of effect. Also, by allowing the potential sources of selection bias to be revealed more easily the design is made more accessible both conceptually and practically to epidemiologists.

Family-based association studies

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.

Whole genome association studies for genes affecting alcohol dependence

We applied the transmission/disequilibrium test (TDT) for sibs (S-TDT) and for families with one parent (1-TDT), to the Collaborative Study on the Genetics of Alcoholism data set. The combined test is used to screen the whole genome to locate genes responsible for alcohol dependence. This analysis supports the previous finding that the region close to GABRB1 on chromosome 4 might be associated with alcohol dependence. The regions close to D6S474 and D11S1998 are also of particular interest. We found segregation distortion at the GR1K1 locus. The segregation distortion might be due to the binning method used in genotyping at this locus.

Allowing for missing parents in genetic studies of case-parent triads

In earlier work, my colleagues and I described a log-linear model for genetic data from triads composed of affected probands and their parents. This model allows detection of and discrimination between effects of an inherited haplotype versus effects of the maternal haplotype, which presumably would be mediated by prenatal factors. Like the transmission disequilibrium test (TDT), the likelihood-ratio test (LRT) based on this model is not sensitive to associations that are due to genetic admixture. When used as a method for testing for linkage disequilibrium, the LRT can be regarded as an alternative to the TDT. When one or both parents are missing, the resulting incomplete triad must be discarded to ensure validity of the TDT, thereby sacrificing information. By contrast, when the problem is set in a likelihood framework, the expectation-maximization algorithm allows the incomplete triads to contribute their information to the LRT without invalidation of the analysis. Simulations demonstrate that much of the lost statistical power can be recaptured by means of this missing-data technique. In fact, power is reasonably good even when no triad is complete-for example, when a study is designed to include only mothers of cases. Information from siblings also can be incorporated to further improve the statistical power when genetic data from parents or probands are missing.