Power analysis of artificial selection experiments using efficient whole genome simulation of quantitative traits.
about
Combining experimental evolution with next-generation sequencing: a powerful tool to study adaptation from standing genetic variationExperimental Evolution as an Underutilized Tool for Studying Beneficial Animal–Microbe InteractionsLarge-scale assessment of olfactory preferences and learning in Drosophila melanogaster: behavioral and genetic componentsUncovering the genetic signature of quantitative trait evolution with replicated time series data.Genomic Trajectories to Desiccation Resistance: Convergence and Divergence Among Replicate Selected Drosophila Lines.Patterns of linkage disequilibrium and long range hitchhiking in evolving experimental Drosophila melanogaster populationsGenome-wide study of an elite rice pedigree reveals a complex history of genetic architecture for breeding improvement.Pleiotropy, constraint, and modularity in the evolution of life histories: insights from genomic analyses.Reconstruction of Haplotype-Blocks Selected during Experimental Evolution.Genomic Response to Selection for Predatory Behavior in a Mammalian Model of Adaptive Radiation.Drosophila simulans: A Species with Improved Resolution in Evolve and Resequence Studies.Evolution Is an Experiment: Assessing Parallelism in Crop Domestication and Experimental Evolution: (Nei Lecture, SMBE 2014, Puerto Rico).Identifying consistent allele frequency differences in studies of stratified populations.Genome-Wide Analysis of Starvation-Selected Drosophila melanogaster-A Genetic Model of Obesity.Quantifying Selection with Pool-Seq Time Series Data.MimicrEE2: Genome-wide forward simulations of Evolve and Resequencing studies
P2860
Q22122306-F98EEC0B-4245-49C2-BBB2-B9C71B89350BQ27468725-1A1E9EE4-D947-4121-8CC3-ADB4CCEA7796Q28607893-05FF7000-5891-4898-8111-F6D7D0A47E6FQ31142539-5ACEED6A-E6F4-415E-8AA0-6CF69810EADBQ31150861-16CE7D76-8786-48C8-AC88-23D20657FABAQ34981819-D3878D33-A6D1-4CDB-8A55-68EB268A26BFQ37736615-50E85FD5-9970-4832-AA88-8C88ECA7BF8FQ39034291-B13DF0D3-86F4-4C62-BCE2-5FE164A84572Q39323527-CD11C82F-A12C-40A5-9F5E-957243011A1CQ39613139-D775696D-DD6B-4C47-8920-139DFA42BEC7Q40953983-2A0D07EC-F930-4408-BD5B-B476C9C9D3EFQ46723730-FE4EC2B9-6677-443B-B2DA-C8F22BC583F9Q47115590-3E998FF9-96F8-4106-9BE8-EBFFC22D6A4BQ47567388-638B86F2-D49A-4BB1-8721-574DD9594042Q47898799-81495E11-1752-4EF2-933B-CEC30B2564B7Q58773310-F47826BD-03C5-4F22-860D-B74D02E6D75B
P2860
Power analysis of artificial selection experiments using efficient whole genome simulation of quantitative traits.
description
2015 nî lūn-bûn
@nan
2015 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
Power analysis of artificial s ...... lation of quantitative traits.
@ast
Power analysis of artificial s ...... lation of quantitative traits.
@en
type
label
Power analysis of artificial s ...... lation of quantitative traits.
@ast
Power analysis of artificial s ...... lation of quantitative traits.
@en
prefLabel
Power analysis of artificial s ...... lation of quantitative traits.
@ast
Power analysis of artificial s ...... lation of quantitative traits.
@en
P2860
P1433
P1476
Power analysis of artificial s ...... lation of quantitative traits.
@en
P2093
Darren Kessner
John Novembre
P2860
P304
P356
10.1534/GENETICS.115.175075
P407
P577
2015-02-10T00:00:00Z