Interferon regulatory factor 6 (IRF6) gene variants and the risk of isolated cleft lip or palate.

BACKGROUND: Cleft lip or palate (or the two in combination) is a common birth defect that results from a mixture of genetic and environmental factors. We searched for a specific genetic factor contributing to this complex trait by examining large numbers of affected patients and families and evaluating a specific candidate gene. METHODS: We identified the gene that encodes interferon regulatory factor 6 (IRF6) as a candidate gene on the basis of its involvement in an autosomal dominant form of cleft lip and palate, Van der Woude's syndrome. A single-nucleotide polymorphism in this gene results in either a valine or an isoleucine at amino acid position 274 (V274I). We carried out transmission-disequilibrium testing for V274I in 8003 individual subjects in 1968 families derived from 10 populations with ancestry in Asia, Europe, and South America, haplotype and linkage analyses, and case-control analyses, and determined the risk of cleft lip or palate that is associated with genetic variation in IRF6. RESULTS: Strong evidence of overtransmission of the valine (V) allele was found in the entire population data set (P<10(-9)); moreover, the results for some individual populations from South America and Asia were highly significant. Variation at IRF6 was responsible for 12 percent of the genetic contribution to cleft lip or palate and tripled the risk of recurrence in families that had already had one affected child. CONCLUSIONS: DNA-sequence variants associated with IRF6 are major contributors to cleft lip, with or without cleft palate. The contribution of variants in single genes to cleft lip or palate is an important consideration in genetic counseling.

Methods for detecting gene x gene interaction in multiplex extended pedigrees.

Complex diseases are multifactorial in nature and can involve multiple loci with gene x gene and gene x environment interactions. Research on methods to uncover the interactions between those genes that confer susceptibility to disease has been extensive, but many of these methods have only been developed for sibling pairs or sibships. In this report, we assess the performance of two methods for finding gene x gene interactions that are applicable to arbitrarily sized pedigrees, one based on correlation in per-family nonparametric linkage scores and another that incorporates candidate loci genotypes as covariates into an affected relative pair linkage analysis. The power and type I error rate of both of these methods was addressed using the simulated Genetic Analysis Workshop 14 data. In general, we found detection of the interacting loci to be a difficult problem, and though we experienced some modest success there is a clear need to continue developing new methods and approaches to the problem.

Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations.

In order to detect linkage of the simulated complex disease Kofendrerd Personality Disorder across studies from multiple populations, we performed a genome scan meta-analysis (GSMA). Using the 7-cM microsatellite map, nonparametric multipoint linkage analyses were performed separately on each of the four simulated populations independently to determine p-values. The genome of each population was divided into 20-cM bin regions, and each bin was rank-ordered based on the most significant linkage p-value for that population in that region. The bin ranks were then averaged across all four studies to determine the most significant 20-cM regions over all studies. Statistical significance of the averaged bin ranks was determined from a normal distribution of randomly assigned rank averages. To narrow the region of interest for fine-mapping, the meta-analysis was repeated two additional times, with each of the 20-cM bins offset by 7 cM and 13 cM, respectively, creating regions of overlap with the original method. The 6-7 cM shared regions, where the highest averaged 20-cM bins from each of the three offsets overlap, designated the minimum region of maximum significance (MRMS). Application of the GSMA-MRMS method revealed genome wide significance (p-values refer to the average rank assigned to the bin) at regions including or adjacent to all of the simulated disease loci: chromosome 1 (p < 0.0001 for 160-167 cM, including D1), chromosome 3 (p-value < 0.0000001 for 287-294 cM, including D2), chromosome 5 (p-value < 0.001 for 0-7 cM, including D3), and chromosome 9 (p-value < 0.05 for 7-14 cM, the region adjacent to D4). This GSMA analysis approach demonstrates the power of linkage meta-analysis to detect multiple genes simultaneously for a complex disorder. The MRMS method enhances this powerful tool to focus on more localized regions of linkage.