Knowledge Center Catalog

Association analysis in structured plant populations, an adaptive mixed LASSO approach (Record no. 8011)

MARC details
000 -LEADER
fixed length control field 02553nam a22003017a 4500
001 - CONTROL NUMBER
control field G94606
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240919020942.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 121211s ||||f| 0 p|p||0|| |
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) CIS-6161
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Dong Wang
Affiliation Plant and Animal Genomes Conference, XVIII; San Diego, CA (USA); 9-13 Jan 2010. Abstracts of oral and poster presentations
245 00 - TITLE STATEMENT
Title Association analysis in structured plant populations, an adaptive mixed LASSO approach
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Date of publication, distribution, etc. 2010
300 ## - PHYSICAL DESCRIPTION
Extent 1 page
520 ## - SUMMARY, ETC.
Summary, etc. Recently, there has been heightened interest in performing association analysis in important crop species. The development of mixed linear models for plant association mapping has significantly advanced the statistical methodology in this field. However, the mixed linear model has been mostly limited to single marker analysis. On the other hand, the lack of knowledge on epistasis and GxE interactions has become one of the major impediments of utilizing genomic information for crop improvement. We report the development of the adaptive mixed LASSO method that can incorporate a large number of predictors while simultaneously accounting for the population structure. LASSO can deal with situations where the number of explanatory variables is much larger than the sample size, which is not feasible for traditional regression methods. By extending adaptive LASSO to include random effects for structured populations, we can readily apply our method to the setting of plant association mapping. Our results show that the adaptive mixed LASSO method is very promising in modeling multiple genetic effects (main QTL effects and epistasis) as well as modeling gene by environment interactions when a large number of markers are available and the population structure cannot be ignored. Since no equivalent method has been proposed in the setting of crop association analysis, it is expected to have a significant impact on the study of complex traits in important crop species. Applications to wheat breeding programs has been planned with the potential of influencing plant breeding practices.
536 ## - FUNDING INFORMATION NOTE
Text of note Genetic Resources Program
546 ## - LANGUAGE NOTE
Language note English
594 ## - STAFFID
StaffID INT2542|CCJL01
595 ## - COLLECTION
Collection CIMMYT Staff Publications Collection
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Baenziger, S.P.,
Relator term coaut.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Dweikat, I.,
Relator term coaut.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Eskridge, K.M.,
Relator term coaut.
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 842
Personal name Jiankang Wang
Miscellaneous information Genetic Resources Program
Field link and sequence number INT2542
Relator term coaut.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Crossa, J.
Miscellaneous information Genetic Resources Program
Field link and sequence number CCJL01
9 (RLIN) 59
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Conference proceedings
Holdings
Date last seen Total Checkouts Full call number Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
07/19/2017   CIS-6161 07/19/2017 Conference proceedings Not Lost Dewey Decimal Classification     CIMMYT Staff Publications Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 07/19/2017

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