scholarly journals Tagging SNP-set selection with maximum information based on linkage disequilibrium structure in genome-wide association studies

2017 ◽  
Vol 33 (14) ◽  
pp. 2078-2081 ◽  
Author(s):  
Shudong Wang ◽  
Sicheng He ◽  
Fayou Yuan ◽  
Xinjie Zhu
Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2006
Author(s):  
David P. Horvath ◽  
Michael Stamm ◽  
Zahirul I. Talukder ◽  
Jason Fiedler ◽  
Aidan P. Horvath ◽  
...  

A diverse population (429 member) of canola (Brassica napus L.) consisting primarily of winter biotypes was assembled and used in genome-wide association studies. Genotype by sequencing analysis of the population identified and mapped 290,972 high-quality markers ranging from 18.5 to 82.4% missing markers per line and an average of 36.8%. After interpolation, 251,575 high-quality markers remained. After filtering for markers with low minor allele counts (count > 5), we were left with 190,375 markers. The average distance between these markers is 4463 bases with a median of 69 and a range from 1 to 281,248 bases. The heterozygosity among the imputed population ranges from 0.9 to 11.0% with an average of 5.4%. The filtered and imputed dataset was used to determine population structure and kinship, which indicated that the population had minimal structure with the best K value of 2–3. These results also indicated that the majority of the population has substantial sequence from a single population with sub-clusters of, and admixtures with, a very small number of other populations. Analysis of chromosomal linkage disequilibrium decay ranged from ~7 Kb for chromosome A01 to ~68 Kb for chromosome C01. Local linkage decay rates determined for all 500 kb windows with a 10kb sliding step indicated a wide range of linkage disequilibrium decay rates, indicating numerous crossover hotspots within this population, and provide a resource for determining the likely limits of linkage disequilibrium from any given marker in which to identify candidate genes. This population and the resources provided here should serve as helpful tools for investigating genetics in winter canola.


2016 ◽  
Author(s):  
Piotr Szulc ◽  
Malgorzata Bogdan ◽  
Florian Frommlet ◽  
Hua Tang

AbstractIn Genome-Wide Association Studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand Admixture Mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry).Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here we extend this approach for population-based GWAS in the direction of multi marker models. A modified version of the Bayesian Information Criterion is developed for building a multi-locus model, which accounts for the differential correlation structure due to linkage disequilibrium and admixture linkage disequilibrium. Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis and modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single-marker analysis combining genotypic and ancestry information. Depending on the signal strength our procedure automatically chooses whether genotypic or locus-specific ancestry markers are added to the model. This results in a good compromise between the power to detect causal mutations and the precision of their localization. The proposed method has been implemented in R and is available at http://www.math.uni.wroc.pl/~mbogdan/admixtures/.


2018 ◽  
Author(s):  
Loic Yengo ◽  
Jian Yang ◽  
Peter M. Visscher

Linkage disequilibrium (LD) score regression is an increasingly popular method used to quantify the level of confounding in genome-wide association studies (GWAS) or to estimate heritability and genetic correlation between traits. When applied to a pair of GWAS, the LD score regression (LDSC) methodology produces a statistic, referred to as the bivariate LDSC intercept, which deviation from 0 is classically interpreted as an indication of sample overlap between the two GWAS. Here we propose an extension of the theory underlying the bivariate LDSC methodology, which accounts for population stratification within and between GWAS. Our extended theory predicts an inflation of the bivariate LDSC intercept when sample sizes and heritability are large, even in the absence of sample overlap. We illustrate our theoretical results with simulations based on actual SNP genotypes and we propose a re-interpretation of previously published results in the light of our extended theory.


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