Multivariate selection of AFLP markers associated with β-carotene in sweetpotatoes

Abstract

A population that consisted of F 1 half-sibs and their parents was replicated in field plantings to generate roots to identify AFLP molecular markers and compare statistical models for selection of traitlinked markers using b-carotene content as the grouping variable. Genotypes were grouped into high and low b-carotene classes based on hierarchical cluster analysis. Association parameters between the high and low phenotypic classes of b-carotene and molecular marker profiles were made using logistic regression and discriminant analysis. Logistic regression selected 8 markers less that were associated with b-carotene content compared to discriminant analysis. Further analysis showed that logistic regression achieved 100% correct classification with the 9 markers selected compared to 17 markers that were required by discriminant analysis thus making logistic regression more efficient than discriminant analysis. Logistic regression based on Frequentist hypothesis testing differs from Bayesian based discriminant analysis. This difference may account for the percent correct classification observed.

Genetic analysis and association of simple sequence repeat markers with storage root yield, dry matter, starch and β-carotene content in sweetpotato

“…Earlier marker trait associations of dry matter, starch and β-carotene content were done through QTL and discriminant analysis with dominant markers ( Cervantes-Flores et al 2011 , Mcharo and LaBonte 2010 , Zhao et al 2013 ). Cervantes-Flores et al (2011) mapped 12 QTL of storage root starch content, 13 QTL of dry matter content and 8 QTL of β-carotene content using AFLP markers.…”

“…In the absence of high-density genetic linkage maps for sweetpotato, discriminant analysis and logistic regression have been employed to identify molecular markers linked to key traits. AFLP markers linked to root-knot nematode resistance ( Mcharo et al 2005b ), sweetpotato virus disease (SPVD) resistance ( Mcharo et al 2005a , Miano et al 2008 ) and storage root β-carotene content ( Mcharo and LaBonte 2010 ) were identified by breeders. However, to the best of our knowledge, none of these markers are being used in a MAS breeding program.…”

Exploring and exploiting genetics and genomics for sweetpotato improvement: Status and perspectives

“…(2017) Dry-matter and starch content (DMSC), BC, starch composition (SCP) 239 genotypes have various morphological types and geographical origins 32 QTLs for DMSC; 16 QTLs for BC; 17 QTLs for SC 887 SSRs GWAS TASSEL 5.0 Zhang et al., (2016) starch content 202 F 1 progenies derived from Xushu 18 × Xu 781 2 QTLs in Xushu 18 and 6 QTLs in Xu 781 Xushu 18: 1936 AFLPs and 141 SSRs; Xu 781: 1824 AFLPs and 130 SSRs QTL MapQTL 4.0 Yu et al., (2014) DMC 202 F 1 progenies derived from Xushu 18 × Xu 781 5 QTLs in Xushu 18 and 22 QTLs in Xu 781 Xushu 18: 1936 AFLPs and 141 SSRs; Xu 781: 1824 AFLPs and 130 SSRs QTL MapQTL 4.0 Zhao et al., (2013a) BC, DMC, starch content (SC) 240 F 1 progenies derived from Tanzania × Beauregard 3 positive QTLs and 1 negative QTL in Beauregard and 3 positive QTLs and 1 negative QTL in Tanzania for BC; 4 positive QTLs and 4 negative QTLs in Beauregard and 4 positive QTLs and 1 negative QTL in Tanzania for DM; 3 positive QTLs and 4 negative QTLs in Beauregard and 3 positive QTLs and 2 negative QTLs in Tanzania for SC Beauregard: 726 AFLPs; Tanzania: 927 AFLPs QTL WinQTL Cartographer Cervantes-Flores et al. (2011) BC 55 genotypes were selected from 73 F 1 genotypes developed from the maternal clones Beauregard, Excel, L94-96, L89-110, L86-33, and L96-117 17 markers were selected by discriminant analysis, and 9 markers were selected by logistic regression 259 AFLPs DA/LRG SAS Mwamburi Mcharo and LaBonte (2010) RSC, FC, root shape (RS) NT: 119 F 1 progenies derived from Nancy Hall (♀) × Tainung 27 (♂) TN: 112 F 1 progenies derived from Tainung 27 (♀) × Nancy Hall (♂) 3 QTLs in population NT and 3 QTLs in population TN for RSC; 4 QTLs in population NT and 2 QTLs in population TN for FC; 3 QTLs in population NT for RS 100 ISSRs QTL QTL Cartographer Chang et al. (2009) basal branching number (FZS), diameter of the stem between the fifth and sixth expanded leaves (JC), length of the stem between the fifth and sixth expanded leaves (JJC), color of the stem between the fifth and sixth expanded leaves (JS), color of the leaf-petiol...…”

Construction of a Genetic Linkage Map based on SSR Markers and Identification of QTLs Related to β-carotene, Dry Matter and Starch Content in Sweet Potato(Ipomoea batatas L. Lam)

“…Fifty seven sweet potato genotypes with high dry matter content and resistant to sweet potato virus disease (SPVD) were characterized using four SSR markers. Mcharo and Labonte (2010) developed the molecular markers associated with β-carotene of sweet potato using multivariate selection of AFLP markers in F 1 half-sib population and their parents. The genetic linkage maps and phenotypic data were imported into QTL Cartographer to identify significant QTL for the traits analyzed (Wang et al, 2005).…”