Functional analysis of the genetic variability in the F7 gene promoter

Sabater‐Lleal, Maria; Chillón, Miguel; Howard, Tom E.; Gil-Guiñón, Estel; Almasy, Laura; Blangero, John; Fontcuberta, Jordi; Soria, José Manuel Nieto

doi:10.1016/j.atherosclerosis.2006.12.031

Cited by 25 publications

(20 citation statements)

References 24 publications

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“…Both software packages provide consistent power and Type 1 errors when testing for association with and without conditioning in the simulated data. The inference of complexity and multiple independent cis- regulatory elements is supported by a functional analysis of the F7 gene in an independent study [7]. …”

Section: Discussionmentioning

confidence: 99%

Genetic complexity at expression quantitative trait loci

2016

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BackgroundIdentifying variants that regulate gene expression and delineating their genetic architecture is a critical next step in our endeavors to better understand the genetic etiology of complex diseases. The appropriate genomic tools are in place, and preliminary analytic strategies have been developed.MethodsHere we used Genetic Analysis Workshop (GAW) 19 data to investigate the genetic complexity of expression quantitative trait loci (eQTL), chromosomal regions likely to harbor regulatory elements responsible for gene expression. For this investigation, we analyzed the lymphocyte expression profiles of 653 individuals in 20 pedigrees who were also genotyped by single nucleotide polymorphism (SNP) arrays, followed by sequencing and imputation. We used these data to examine the degree of allelic heterogeneity, a contributor to genetic complexity at eQTL, by sequentially conditioning on the most significantly associated SNPs.ResultSOLAR (Sequential Oligogenic Linkage Analysis Routines)-MGA (measured genotype approach) and FaST-LMM (Factored Spectrally Transformed Linear Mixed Model) software allowed us to analyze pedigree data. The power and Type 1 error rates for single SNP association testing and multiple SNP sequential association testing were consistent for these programs. Sequential conditioning of the real expression data revealed substantial levels of allelic heterogeneity at the 2 eQTL examined, illustrating this feature of genetic complexity.ConclusionseQTL exhibit substantial genetic complexity among and within pedigrees.

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Section: Discussionmentioning

confidence: 99%

Genetic complexity at expression quantitative trait loci

2016

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“…Association analyses of the 48 polymorphisms identified in the F7 gene by sequencing in individuals from the linkage sample identified four variants with a very high posterior probability of effect and three additional variants with high support for functionality. Several of these variants were in putative promoter regions and their functionality has now been confirmed in in vitro expression assays(29). …”

Section: Human Qtl Linkage Mapping Work – Success Storiesmentioning

confidence: 90%

Human QTL linkage mapping

Almasy

Blangero

2008

Genetica

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Human quantitative trait locus (QTL) linkage mapping, although based on classical statistical genetic methods that have been around for many years, has been employed for genome-wide screening for only the last 10-15 years. In this time, there have been many success stories, ranging from QTLs that have been replicated in independent studies to those for which one or more genes underlying the linkage peak have been identified to a few with specific functional variants that have been confirmed in in vitro laboratory assays. Despite these successes, there is a general perception that linkage approaches do not work for complex traits, possibly because many human QTL linkage studies have been limited in sample size and have not employed the family configurations that maximize the power to detect linkage. We predict that human QTL linkage studies will continue to be productive for the next several years, particularly in combination with RNA expression level traits that are showing evidence of regulatory QTLs of large effect sizes and in combination with high-density genome-wide SNP panels. These SNP panels are being used to identify QTLs previously localized by linkage and linkage results are being used to place informative priors on genome-wide association studies. The methods are old the results are newThe basic statistical genetic methods that are used today for human quantitative trait locus (QTL) linkage mapping are extensions of classic models that were available twenty years ago at the second International Conference on Quantitative Genetics (ICQG2). The methods currently used for human QTL linkage mapping derive from classical variance component methods(1) and from the sibling pair regression methods proposed by Haseman and Elston (2). These methods are based on the simple intuition that relatives who are more alike phenotypically should be more likely to share alleles at the QTL (and at nearby linked markers) than relatives who are phenotypically discordant. Identity by descent (IBD) allele sharing is used to quantify the proportion of alleles a relative pair shares at a particular location in the genome that are copies of the same ancestral chromosome. In the simplest case of a sample of independent sibling pairs, the original Haseman-Elston linkage method proposed that the squared difference in the siblings' phenotypes can be regressed on IBD with evidence of linkage coming from a negative regression slope indicating that siblings who are relatively more discordant share fewer alleles at a marker than siblings who are relatively more phenotypically concordant. The variance component (1,3) and the newer revised Haseman-Elston methods (4,5) are somewhat more complex, allowing for the non-independence of multiple relative pairs from the same pedigree and incorporating polygenic background effects, effects of measured

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“…Fluorescent quantification was measured in a Fluorescence Activated Cell Sorter (Becton Dickinson FACS Analyzer, SN AL01001) as described previously [19–21]. Fluorescence was analyzed with the MXP software v.2.1.…”

Section: Design and Methodsmentioning

confidence: 99%