FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4295845822> ?p ?o ?g. }

• W4295845822 endingPage "1644" @default.
• W4295845822 startingPage "1644" @default.
• W4295845822 abstract "Heritability enrichment analysis is an important means of exploring the genetic architecture of complex traits in human genetics. Heritability enrichment is typically defined as the proportion of an SNP subset explained heritability, divided by the proportion of SNPs. Heritability enrichment enables better study of underlying complex traits, such as functional variant/gene subsets, biological networks and metabolic pathways detected through integrating explosively increased omics data. This would be beneficial for genomic prediction of disease risk in humans and genetic values estimation of important economical traits in livestock and plant species. However, in livestock, factors affecting the heritability enrichment estimation of complex traits have not been examined. Previous studies on humans reported that the frequencies, effect sizes, and levels of linkage disequilibrium (LD) of underlying causal variants (CVs) would affect the heritability enrichment estimation. Therefore, the distribution of heritability across the genome should be fully considered to obtain the unbiased estimation of heritability enrichment. To explore the performance of different heritability enrichment models in livestock populations, we used the VanRaden, GCTA and α models, assuming different α values, and the LDAK model, considering LD weight. We simulated three types of phenotypes, with CVs from various minor allele frequency (MAF) ranges: genome-wide (0.005 ≤ MAF ≤ 0.5), common (0.05 ≤ MAF ≤ 0.5), and uncommon (0.01 ≤ MAF < 0.05). The performances of the models with two different subsets (one of which contained known CVs and the other consisting of randomly selected markers) were compared to verify the accuracy of heritability enrichment estimation of functional variant sets. Our results showed that models with known CV subsets provided more robust enrichment estimation. Models with different α values tended to provide stable and accurate estimates for common and genome-wide CVs (relative deviation 0.5–2.2%), while tending to underestimate the enrichment of uncommon CVs. As the α value increased, enrichments from 15.73% higher than true value (i.e., 3.00) to 48.93% lower than true value for uncommon CVs were observed. In addition, the long-range LD windows (e.g., 5000 kb) led to large bias of the enrichment estimations for both common and uncommon CVs. Overall, heritability enrichment estimations were sensitive for the α value assumption and LD weight consideration of different models. Accuracy would be greatly improved by using a suitable model. This study would be helpful in understanding the genetic architecture of complex traits and provides a reference for genetic analysis in the livestock population." @default.
• W4295845822 created "2022-09-15" @default.
• W4295845822 creator A5005244980 @default.
• W4295845822 creator A5012252352 @default.
• W4295845822 creator A5036248545 @default.
• W4295845822 creator A5052246367 @default.
• W4295845822 creator A5069435054 @default.
• W4295845822 creator A5084644313 @default.
• W4295845822 date "2022-09-13" @default.
• W4295845822 modified "2023-10-14" @default.
• W4295845822 title "Model Comparison of Heritability Enrichment Analysis in Livestock Population" @default.
• W4295845822 cites W1844405070 @default.
• W4295845822 cites W1980003775 @default.
• W4295845822 cites W1983539381 @default.
• W4295845822 cites W1997751006 @default.
• W4295845822 cites W2067715889 @default.
• W4295845822 cites W2076300862 @default.
• W4295845822 cites W2135370544 @default.
• W4295845822 cites W2153860431 @default.
• W4295845822 cites W2155496693 @default.
• W4295845822 cites W2156886328 @default.
• W4295845822 cites W2157211770 @default.
• W4295845822 cites W2188707104 @default.
• W4295845822 cites W2275208244 @default.
• W4295845822 cites W2493925084 @default.
• W4295845822 cites W2613260256 @default.
• W4295845822 cites W2617742891 @default.
• W4295845822 cites W2740758692 @default.
• W4295845822 cites W2797293309 @default.
• W4295845822 cites W2901957554 @default.
• W4295845822 cites W2950016959 @default.
• W4295845822 cites W2951427191 @default.
• W4295845822 cites W2952882474 @default.
• W4295845822 cites W2953357410 @default.
• W4295845822 cites W2955590900 @default.
• W4295845822 cites W2957675127 @default.
• W4295845822 cites W2966861845 @default.
• W4295845822 cites W2973029867 @default.
• W4295845822 cites W2983904653 @default.
• W4295845822 cites W2998614679 @default.
• W4295845822 cites W3006330340 @default.
• W4295845822 cites W3012787339 @default.
• W4295845822 cites W3014090301 @default.
• W4295845822 cites W3046013264 @default.
• W4295845822 cites W3046345618 @default.
• W4295845822 cites W3087553723 @default.
• W4295845822 cites W3087569904 @default.
• W4295845822 cites W3113046911 @default.
• W4295845822 cites W3160007175 @default.
• W4295845822 cites W3176565686 @default.
• W4295845822 cites W4283699638 @default.
• W4295845822 doi "https://doi.org/10.3390/genes13091644" @default.
• W4295845822 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36140810" @default.
• W4295845822 hasPublicationYear "2022" @default.
• W4295845822 type Work @default.
• W4295845822 citedByCount "0" @default.
• W4295845822 crossrefType "journal-article" @default.
• W4295845822 hasAuthorship W4295845822A5005244980 @default.
• W4295845822 hasAuthorship W4295845822A5012252352 @default.
• W4295845822 hasAuthorship W4295845822A5036248545 @default.
• W4295845822 hasAuthorship W4295845822A5052246367 @default.
• W4295845822 hasAuthorship W4295845822A5069435054 @default.
• W4295845822 hasAuthorship W4295845822A5084644313 @default.
• W4295845822 hasBestOaLocation W42958458221 @default.
• W4295845822 hasConcept C104317684 @default.
• W4295845822 hasConcept C105795698 @default.
• W4295845822 hasConcept C106208931 @default.
• W4295845822 hasConcept C135763542 @default.
• W4295845822 hasConcept C144621757 @default.
• W4295845822 hasConcept C153209595 @default.
• W4295845822 hasConcept C161890455 @default.
• W4295845822 hasConcept C167928553 @default.
• W4295845822 hasConcept C186413461 @default.
• W4295845822 hasConcept C33923547 @default.
• W4295845822 hasConcept C35605836 @default.
• W4295845822 hasConcept C54355233 @default.
• W4295845822 hasConcept C61420037 @default.
• W4295845822 hasConcept C68873052 @default.
• W4295845822 hasConcept C70616004 @default.
• W4295845822 hasConcept C81941488 @default.
• W4295845822 hasConcept C86803240 @default.
• W4295845822 hasConcept C9287583 @default.
• W4295845822 hasConceptScore W4295845822C104317684 @default.
• W4295845822 hasConceptScore W4295845822C105795698 @default.
• W4295845822 hasConceptScore W4295845822C106208931 @default.
• W4295845822 hasConceptScore W4295845822C135763542 @default.
• W4295845822 hasConceptScore W4295845822C144621757 @default.
• W4295845822 hasConceptScore W4295845822C153209595 @default.
• W4295845822 hasConceptScore W4295845822C161890455 @default.
• W4295845822 hasConceptScore W4295845822C167928553 @default.
• W4295845822 hasConceptScore W4295845822C186413461 @default.
• W4295845822 hasConceptScore W4295845822C33923547 @default.
• W4295845822 hasConceptScore W4295845822C35605836 @default.
• W4295845822 hasConceptScore W4295845822C54355233 @default.
• W4295845822 hasConceptScore W4295845822C61420037 @default.
• W4295845822 hasConceptScore W4295845822C68873052 @default.
• W4295845822 hasConceptScore W4295845822C70616004 @default.
• W4295845822 hasConceptScore W4295845822C81941488 @default.

• next