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Abstract Background Localization of complex traits by genetic linkage analysis may involve exploration of a vast multidimensional parameter space. The posterior probability of linkage (PPL), a class of statistics for complex trait genetic mapping in humans, is designed to model the trait model complexity represented by the multidimensional parameter space in a mathematically rigorous fashion. However, the method requires the evaluation of integrals with no functional form, making it difficult to compute, and thus further test, develop and apply. This paper describes MLIP, a multiprocessor two-point genetic linkage analysis system that supports statistical calculations, such as the PPL, based on the full parameter space implicit in the linkage likelihood. Results The fundamental question we address here is whether the use of additional processors effectively reduces total computation time for a PPL calculation. We use a variety of data – both simulated and real – to explore the question "how close can we get?" to linear speedup. Empirical results of our study show that MLIP does significantly speed up two-point log-likelihood ratio calculations over a grid space of model parameters. Conclusion Observed performance of the program is dependent on characteristics of the data including granularity of the parameter grid space being explored and pedigree size and structure. While work continues to further optimize performance, the current version of the program can already be used to efficiently compute the PPL. Thanks to MLIP, full multidimensional genome scans are now routinely being completed at our centers with runtimes on the order of days, not months or years.

Background: This study examined the extent to which genetic variability modifies Transcutaneous Electrical Nerve Stimulation (TENS) effectiveness in osteoarthritic knee pain.Methods: Seventy-five participants with knee osteoarthritis were randomly assigned to either: (a) High-frequency TENS, (b) Low-frequency TENS or (c) Transient Placebo TENS. Pain measures were collected pre- and post-treatment. Participants were genotyped on genes implicated in central or peripheral pain pathways: NGFB, NTRK1, EDNRA, EDNRB, EDN1, OPRM1, TAC1, TACR1, BDNF, BDKRB1, 5HTT, COMT, ESR2, IL6 and IL1B. Genetic association using linear regression modelling was performed separately for the transient placebo TENS subjects, and within the High-frequency TENS + Low-frequency TENS participants, including TENS level as a covariate.Results: In the placebo group, SNPs rs165599 (COMT) was significantly associated with an increased heat pain threshold (β = -1.87; p = .003) and rs6827096 (EDNRA) with an increased resting pain (β = 2.68; p = .001). Within the treatment groups, TENS effectiveness was reduced by the SNP rs6537485 (EDNRA) minor allele in relationship to mechanical sensation (β = 184.13; p = 5.5E-9). Individuals with the COMT rs4680 minor allele reported lowered pain at rest after TENS (β = -42.30; p = .001), with a higher magnitude of pain reduction (28 unit difference) in the low-frequency TENS group compared to the high-frequency TENS group (β = 28.37; p = .0004).Conclusions: EDNRA and COMT are implicated in osteoarthritic knee pain and provide a basis for tailoring TENS interventions according to individual characteristics.Significance: Findings from this study demonstrate that genetic variation within the COMT and EDNRA genes influences the effectiveness of TENS, a non-pharmacologic pain-reduction intervention, in the context of osteoarthritic knee pain. Evidence such as this may contribute to risk models that provide a clinically useful tool for personalizing TENS interventions according to individual characteristics in order to best control pain and maximize functional status. [ABSTRACT FROM AUTHOR]

Human linkage and association studies suggest a gene(s) for nonsyndromic cleft lip with or without cleft palate (CL/P) on chromosome 4q31–q32 at or near the platelet-derived growth factor-C (PDGF-C) locus. The mouse Pdgfc−/− knockout shows that PDGF-C is essential for palatogenesis. To evaluate the role of PDGF-C in human clefting, we performed sequence analysis and SNP genotyping using 1048 multiplex CL/P families and 1000 case–control samples from multiple geographic origins. No coding region mutations were identified, but a novel −986 C>T SNP (rs28999109) was significantly associated with CL/P (P=0.01) in cases from Chinese families yielding evidence of linkage to 4q31–q32. Significant or near-significant association was also seen for this and several other PDGF-C SNPs in families from the United States, Spain, India, Turkey, China, and Colombia, whereas no association was seen in families from the Philippines, and Guatemala, and case–controls from Brazil. The −986T allele abolished six overlapping potential transcription regulatory motifs. Transfection assays of PDGF-C promoter reporter constructs show that the −986T allele is associated with a significant decrease (up to 80%) of PDGF-C gene promoter activity. This functional polymorphism acting on a susceptible genetic background may represent a component of human CL/P etiology.European Journal of Human Genetics (2009) 17, 774–784; doi:10.1038/ejhg.2008.245; published online 17 December 2008 [ABSTRACT FROM AUTHOR]

Previously we have shown that nonsyndromic cleft lip with or without cleft palate (NSCL/P) is strongly associated with SNPs in IRF6 (interferon regulatory factor 6). Here, we use multispecies sequence comparisons to identify a common SNP (rs642961, G>A) in a newly identified IRF6 enhancer. The A allele is significantly overtransmitted (P = 1 × 10−11) in families with NSCL/P, in particular those with cleft lip but not cleft palate. Further, there is a dosage effect of the A allele, with a relative risk for cleft lip of 1.68 for the AG genotype and 2.40 for the AA genotype. EMSA and ChIP assays demonstrate that the risk allele disrupts the binding site of transcription factor AP-2α and expression analysis in the mouse localizes the enhancer activity to craniofacial and limb structures. Our findings place IRF6 and AP-2α in the same developmental pathway and identify a high-frequency variant in a regulatory element contributing substantially to a common, complex disorder. [ABSTRACT FROM AUTHOR]

The research presented in group 11 of the Genetic Analysis Workshop 15 (GAW15) falls into two major themes: Model selection approaches for gene mapping (both Bayesian and Frequentist); and other Bayesian methods. These methods either allow relaxation of some of the common assumptions, such as mode of inheritance, for studying complicated genetic systems, or allow incorporation of additional information into the model. Over half of the groups applied model selection methods on all three data sets, using models in which genetic markers were used as predictors for linkage, phenotype expression, or transmission to an affected offspring. Most groups employed variations of Stochastic Search Variable Selection as the model selection method of choice. A brief review of this class of methods is given in this summary paper, followed by highlights of other methods and overall summaries of each contribution to the GAW15 presentation group 11. These group contributions exhibit the value of framing genetic problems in terms of model selection, and highlight the impact of variable selection for gene mapping., ((c) 2007 Wiley-Liss, Inc.)