FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2746592810> ?p ?o ?g. }

• W2746592810 abstract "A quantitative trait is controlled both by major variants with large genetic effects and by minor variants with small effects. Genome-wide association studies (GWAS) are an efficient approach to identify quantitative trait loci (QTL), and genomic selection (GS) with high-density single nucleotide polymorphisms (SNPs) can achieve higher accuracy of estimated breeding values than conventional best linear unbiased prediction (BLUP). GWAS and GS address different aspects of quantitative traits, but, as statistical models, they are quite similar in their description of the genetic mechanisms that underlie quantitative traits.Here, we propose a stepwise linear regression mixed model (StepLMM) to unify GWAS and GS in a single statistical model. First, the variance components of the genomic-BLUP (GBLUP) model are estimated. Then, in the SNP selection step, the linear mixed model (LMM) for GWAS is equivalently transformed into a simple linear regression to improve computation speed, and the most significant SNP is selected and included into the evaluation model. In the SNP dropping step, the SNPs in the evaluation model are tested according to the standard errors of their estimated effects. If non-significant SNPs are present, the least significant one is dropped from the model and variance components are re-estimated. We used extended Bayesian information criteria (eBIC) to evaluate the model optimization, i.e. the model with the smallest eBIC is the final one and includes only significant SNPs.We simulated scenarios with different heritabilities with 100 QTL. StepLMM estimated heritability accurately and mapped QTL precisely. Genomic prediction accuracy was much higher with StepLMM than with GBLUP. The comparison of StepLMM with other GWAS and GS methods based on a dataset from the 16th QTLMAS Workshop showed that StepLMM had medium mapping power, the lowest rate of false positives for QTL mapping, and the highest accuracy for genomic prediction.StepLMM is a combination of GWAS and GBLUP. GWAS and GBLUP are beneficial to each other in a single statistical model, GWAS improves genomic prediction accuracy, while GBLUP increases mapping precision and decreases the rate of false positives of GWAS. StepLMM has a high performance in both GWAS and GS and is feasible for agricultural breeding programs and human genetic studies." @default.
• W2746592810 created "2017-08-31" @default.
• W2746592810 creator A5034681325 @default.
• W2746592810 creator A5040584913 @default.
• W2746592810 creator A5046798136 @default.
• W2746592810 creator A5049446746 @default.
• W2746592810 date "2017-08-24" @default.
• W2746592810 modified "2023-10-06" @default.
• W2746592810 title "An efficient unified model for genome-wide association studies and genomic selection" @default.
• W2746592810 cites W1531814879 @default.
• W2746592810 cites W1928998639 @default.
• W2746592810 cites W1980168725 @default.
• W2746592810 cites W1991415175 @default.
• W2746592810 cites W1998447523 @default.
• W2746592810 cites W2019961888 @default.
• W2746592810 cites W2023673366 @default.
• W2746592810 cites W2029798558 @default.
• W2746592810 cites W2034846276 @default.
• W2746592810 cites W2047875731 @default.
• W2746592810 cites W2053061982 @default.
• W2746592810 cites W2053848388 @default.
• W2746592810 cites W2055372646 @default.
• W2746592810 cites W2067715889 @default.
• W2746592810 cites W2079927753 @default.
• W2746592810 cites W2088486634 @default.
• W2746592810 cites W2106418989 @default.
• W2746592810 cites W2108410869 @default.
• W2746592810 cites W2109085886 @default.
• W2746592810 cites W2110787179 @default.
• W2746592810 cites W2127684760 @default.
• W2746592810 cites W2129813466 @default.
• W2746592810 cites W2134036574 @default.
• W2746592810 cites W2136889170 @default.
• W2746592810 cites W2138566895 @default.
• W2746592810 cites W2141916112 @default.
• W2746592810 cites W2146374090 @default.
• W2746592810 cites W2147272585 @default.
• W2746592810 cites W2148082373 @default.
• W2746592810 cites W2148870018 @default.
• W2746592810 cites W2154670965 @default.
• W2746592810 cites W2155496693 @default.
• W2746592810 cites W2303043072 @default.
• W2746592810 cites W2587687055 @default.
• W2746592810 cites W2962931338 @default.
• W2746592810 cites W4293258550 @default.
• W2746592810 doi "https://doi.org/10.1186/s12711-017-0338-x" @default.
• W2746592810 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5569572" @default.
• W2746592810 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28836943" @default.
• W2746592810 hasPublicationYear "2017" @default.
• W2746592810 type Work @default.
• W2746592810 sameAs 2746592810 @default.
• W2746592810 citedByCount "23" @default.
• W2746592810 countsByYear W27465928102018 @default.
• W2746592810 countsByYear W27465928102019 @default.
• W2746592810 countsByYear W27465928102020 @default.
• W2746592810 countsByYear W27465928102021 @default.
• W2746592810 countsByYear W27465928102022 @default.
• W2746592810 crossrefType "journal-article" @default.
• W2746592810 hasAuthorship W2746592810A5034681325 @default.
• W2746592810 hasAuthorship W2746592810A5040584913 @default.
• W2746592810 hasAuthorship W2746592810A5046798136 @default.
• W2746592810 hasAuthorship W2746592810A5049446746 @default.
• W2746592810 hasBestOaLocation W27465928101 @default.
• W2746592810 hasConcept C103545067 @default.
• W2746592810 hasConcept C104317684 @default.
• W2746592810 hasConcept C105795698 @default.
• W2746592810 hasConcept C106208931 @default.
• W2746592810 hasConcept C119857082 @default.
• W2746592810 hasConcept C135763542 @default.
• W2746592810 hasConcept C153209595 @default.
• W2746592810 hasConcept C153720581 @default.
• W2746592810 hasConcept C16012445 @default.
• W2746592810 hasConcept C161890455 @default.
• W2746592810 hasConcept C163175372 @default.
• W2746592810 hasConcept C186413461 @default.
• W2746592810 hasConcept C33923547 @default.
• W2746592810 hasConcept C41008148 @default.
• W2746592810 hasConcept C54355233 @default.
• W2746592810 hasConcept C81917197 @default.
• W2746592810 hasConcept C81941488 @default.
• W2746592810 hasConcept C86803240 @default.
• W2746592810 hasConceptScore W2746592810C103545067 @default.
• W2746592810 hasConceptScore W2746592810C104317684 @default.
• W2746592810 hasConceptScore W2746592810C105795698 @default.
• W2746592810 hasConceptScore W2746592810C106208931 @default.
• W2746592810 hasConceptScore W2746592810C119857082 @default.
• W2746592810 hasConceptScore W2746592810C135763542 @default.
• W2746592810 hasConceptScore W2746592810C153209595 @default.
• W2746592810 hasConceptScore W2746592810C153720581 @default.
• W2746592810 hasConceptScore W2746592810C16012445 @default.
• W2746592810 hasConceptScore W2746592810C161890455 @default.
• W2746592810 hasConceptScore W2746592810C163175372 @default.
• W2746592810 hasConceptScore W2746592810C186413461 @default.
• W2746592810 hasConceptScore W2746592810C33923547 @default.
• W2746592810 hasConceptScore W2746592810C41008148 @default.
• W2746592810 hasConceptScore W2746592810C54355233 @default.
• W2746592810 hasConceptScore W2746592810C81917197 @default.
• W2746592810 hasConceptScore W2746592810C81941488 @default.
• W2746592810 hasConceptScore W2746592810C86803240 @default.
• W2746592810 hasFunder F4320321001 @default.

• next