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BackgroundAsthma is a chronic inflammatory disease with a strong genetic predisposition. A major challenge for candidate gene association studies in asthma is the selection of biologically relevant genes.Methodology/principal findingsUsing epithelial RNA expression arrays, HapMap allele frequency variation, and the literature, we identified six possible candidate susceptibility genes for childhood asthma including ADCY2, DNAH5, KIF3A, PDE4B, PLAU, SPRR2B. To evaluate these genes, we compared the genotypes of 194 predominantly tagging SNPs in 790 asthmatic, allergic and non-allergic children. We found that SNPs in all six genes were nominally associated with asthma (p

Abstract Emerging technologies now make it possible to genotype hundreds of thousands of genetic variations in individuals, across the genome. The study of loci at finer scales will facilitate the understanding of genetic variation at genomic and geographic levels. We examined global and chromosomal variations across HapMap populations using 3.7 million single nucleotide polymorphisms to search for the most stratified genomic regions of human populations and linked these regions to ontological annotation and functional network analysis. To achieve this, we used five complementary statistical and genetic network procedures: principal component (PC), cluster, discriminant, fixation index (FST) and network/pathway analyses. At the global level, the first two PC scores were sufficient to account for major population structure; however, chromosomal level analysis detected subtle forms of population structure within continental populations, and as many as 31 PCs were required to classify individuals into homogeneous groups. Using recommended population ancestry differentiation measures, a total of 126 regions of the genome were catalogued. Gene ontology and networks analyses revealed that these regions included the genes encoding oculocutaneous albinism II (OCA2), hect domain and RLD 2 (HERC2), ectodysplasin A receptor (EDAR) and solute carrier family 45, member 2 (SLC45A2). These genes are associated with melanin production, which is involved in the development of skin and hair colour, skin cancer and eye pigmentation. We also identified the genes encoding interferon-γ (IFNG) and death-associated protein kinase 1 (DAPK1), which are associated with cell death, inflammatory and immunological diseases. An in-depth understanding of these genomic regions may help to explain variations in adaptation to different environments. Our approach offers a comprehensive strategy for analysing chromosome-based population structure and differentiation, and demonstrates the application of complementary statistical and functional network analysis in human genetic variation studies.

Abstract Background Admixture mapping is a powerful gene mapping approach for an admixed population formed from ancestral populations with different allele frequencies. The power of this method relies on the ability of ancestry informative markers (AIMs) to infer ancestry along the chromosomes of admixed individuals. In this study, more than one million SNPs from HapMap databases and simulated data have been interrogated in admixed populations using various measures of ancestry informativeness: Fisher Information Content (FIC), Shannon Information Content (SIC), F statistics (FST), Informativeness for Assignment Measure (In), and the Absolute Allele Frequency Differences (delta, δ). The objectives are to compare these measures of informativeness to select SNP markers for ancestry inference, and to determine the accuracy of AIM panels selected by each measure in estimating the contributions of the ancestors to the admixed population. Results FST and In had the highest Spearman correlation and the best agreement as measured by Kappa statistics based on deciles. Although the different measures of marker informativeness performed comparably well, analyses based on the top 1 to 10% ranked informative markers of simulated data showed that In was better in estimating ancestry for an admixed population. Conclusions Although millions of SNPs have been identified, only a small subset needs to be genotyped in order to accurately predict ancestry with a minimal error rate in a cost-effective manner. In this article, we compared various methods for selecting ancestry informative SNPs using simulations as well as SNP genotype data from samples of admixed populations and showed that the In measure estimates ancestry proportion (in an admixed population) with lower bias and mean square error.

Abstract Background New technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously. A wealth of genomic information in the form of publicly available databases is underutilized as a potential resource for uncovering functionally relevant markers underlying complex human traits. Given the huge amount of SNP data available from the annotation of human genetic variation, data mining is a reasonable approach to investigating the number of SNPs that are informative for ancestry information. Methods The distribution and density of SNPs across the genome of African and European populations were extensively investigated by using the HapMap, Affymetrix, and Illumina SNP databases. We exploited these resources by mining the data available from each of these databases to prioritize potential candidate SNPs useful for admixture mapping in complex human diseases and traits. Over 4 million SNPs were compared between Africans and Europeans on the basis of a pre-specified recommended allele frequency difference (delta) value of ≥ 0.3. Results The method identified 15% of HapMap, 11% of Affymetrix, and 14% of Illumina SNP sets as candidate SNPs, termed ancestry informative markers (AIMs). These AIM panels with assigned rs numbers, allele frequencies in each ethnic group, delta value, and map positions are all posted on our website http://www.ssg.uab.edu/downloads/admixture_mapping/SNPAIMs.txt. All marker information in this data set is freely and publicly available without restriction. Conclusion The selected SNP sets represent valuable resources for admixture mapping studies. The overlap between selected AIMs by this single measure of marker informativeness in the different platforms is discussed.

We propose a nonparametric Bayes-based clustering algorithm to detect associations with rare and common single-nucleotide polymorphisms (SNPs) for quantitative traits. Unlike current methods, our approach identifies associations with rare genetic variants at the variant level, not the gene level. In this method, we use a Dirichlet process prior for the distribution of SNP-specific regression coefficients, conduct hierarchical clustering with a distance measure derived from posterior pairwise probabilities of two SNPs having the same regression coefficient, and explore data-driven approaches to select the number of clusters. SNPs falling inside the largest cluster have relatively low or close to zero estimates of regression coefficients and are considered not associated with the trait. SNPs falling outside the largest cluster have relatively high estimates of regression coefficients and are considered potential risk variants. Using the data from the Genetic Analysis Workshop 17, we successfully detected associations with both rare and common SNPs for a quantitative trait. We conclude that our method provides a novel and broadly applicable strategy for obtaining association results with a reasonably low proportion of false discovery and that it can be routinely used in resequencing studies. [ABSTRACT FROM AUTHOR]

Both family- and population-based samples are used to identify genetic variants associated with phenotypes. Each strategy has demonstrated advantages, but their ability to identify rare variants and genes containing rare variants is unclear. To compare these two study designs in the identification of rare causal variants, we applied various methods to the population- and family-based data simulated by the Genetic Analysis Workshop 17 with knowledge of the simulated model. Our results suggest that different variants can be identified by different study designs. Family-based and population-based study designs can be complementary in the identification of rare causal variants and should be considered in future studies. [ABSTRACT FROM AUTHOR]

Principal components analysis (PCA) has been successfully used to correct for population stratification in genomewide association studies of common variants. However, rare variants also have a role in common disease etiology. Whether PCA successfully controls population stratification for rare variants has not been addressed. Thus we evaluate the effect of population stratification analysis on false-positive rates for common and rare variants at the single-nucleotide polymorphism (SNP) and gene level. We use the simulation data from Genetic Analysis Workshop 17 and compare false-positive rates with and without PCA at the SNP and gene level. We found that SNPs' minor allele frequency (MAF) influenced the ability of PCA to effectively control false discovery. Specifically, PCA reduced false-positive rates more effectively in common SNPs (MAF > 0.05) than in rare SNPs (MAF < 0.01). Furthermore, at the gene level, although false-positive rates were reduced, power to detect true associations was also reduced using PCA. Taken together, these results suggest that sequence-level data should be interpreted with caution, because extremely rare SNPs may exhibit sporadic association that is not controlled using PCA. [ABSTRACT FROM AUTHOR]

Vernonia galamensisis a wild plant from the familyAsteraceaewhich is endemic to East Africa and has the potential to become a new oil crop for industrial uses. Its seed oil is rich in vernolic acid, a fatty acid of high interest for oleochemical applications. However, a breeding program forVernonia galamensiscultivars with high seed and oil yields requires knowledge about the genetic variability of traits that influence seed and oil production. This study was undertaken to examine phenotypic and genotypic variability, broad-sense heritability, genetic advance under selection and interrelationships of agronomic and seed quality traits. A total of 122Vernoniaaccessions, 115 collected from different regions of Ethiopia and seven introduced, were grown at two locations in Ethiopia (Alemaya and Babile), in 2001/2002 and were analyzed for 20 traits including phenology, yield, yield components, and seed quality with special emphasis on fatty acid composition. The collections exhibited significant variation for all traits except for days to emergence. Genotypes and locations interacted significantly (P= 0.01) for all traits. Broad-sense heritability estimates ranged from 11% (for days to emergence) up to 79% (for days to maturity). Expected genetic advance was between 1.3% (for days to emergence) and 44.8% (for seed oil yield). Genetic correlation analysis revealed that seed yield per plant is highly and positively correlated with seed weight and head number; highly significant and negative correlations (r= -0.59, -0.82, -0.85, and -0.89) were found between vernolic acid and palmitic, stearic, oleic, and linoleic acid, respectively. Highly significant positive correlations (r= 0.55, 0.44, and 0.36) were observed between vernolic acid and oil content, meal protein content and seed oil yield, respectively. Path-coefficient analysis indicated seed weight and secondary head number to be the most important components of seed yield per plant. Vernolic acid, oleic acid and linoleic acid had positive direct effects and stearic acid had a negative direct effect on oil content. The direct positive effect of oleic acid on oil content was, however, compensated by the negative indirect effects of stearic and vernolic acid resulting in a negative correlation (r= -0.60) between oleic acid and oil content. These observations will support the selection of accessions with high seed and oil yield, high meal protein contents, and high vernolic acid content. [ABSTRACT FROM AUTHOR]

Vernonia galamensis is a potential new industrial oilseed crop from the Asteraceae family. The interest in this species is due to the presence of a high vernolic acid content of its seed oil, which is useful in the oleochemical industry for paints and coatings. The development of a rapid, precise, robust, nondestructive, and economical method to evaluate quality components is of major interest to growers, processors, and breeders. NIR reflectance spectroscopy (NIRS) is routinely used for the prediction of quality traits in many crops. This study was conducted to establish a rapid analytical method for determining the quality of intact seeds of V. galamensis. A total of 114 Vernonia accessions were scanned to determine seed weight, FA composition, oil, and protein contents using NIRS. Conventional chemical analysis for FA composition, total oil, and protein contents were performed by GC, Soxhlet extraction, and the Dumas combustion method, respectively. Calibration equations were developed and tested through cross-validation. The coefficient of determination in cross-validation for FA ranged from 0.47 (linoleic acid) to 0.55 (vernolic acid), and for oil, protein, and seed weight from 0.71 (oil) to 0.86 (seed protein). It was concluded that NIRS calibration equations developed for seed weight and seed quality traits can be satisfactorily used as early screening methods in V. galamensis breeding programs. [ABSTRACT FROM AUTHOR]