Inheritance of time of flowering in upland cotton under natural conditions

Abstract

Time to flowering is an essential component of the adaptation and productivity of cotton (Gossipium hirsutum) in various agro-ecological zones. This article presents a study of the genetic control of this trait in two crosses obtained from different early-maturity parental lines. In each cross, multiple generations including P 1 , F 1 , P 2 , B 1 , B 2 and F 2 were evaluated under two natural field conditions in 2004 and 2005. The data on time to flowering in the F 2 populations had a continuous distribution but deviated from normality. A joint segregation analysis (JSA) revealed that time of flowering in upland cotton was controlled by a mixture of an additive major gene and additive-dominant polygenes. The first-and second-order genetic parameters were all calculated based on the mixture of major gene and polygene inheritance models using JSA. These results suggested that there was considerable genetic diversity and complexity in days to anthesis in upland cotton. This variation can be used to formulate the most efficient breeding strategy and to design cotton for a particular environment.

Genetic analysis and associated SRAP markers for flowering traits of chrysanthemum (Chrysanthemum morifolium)

“…In chrysanthemum, flower diameter, central flower diameter, and ray and tubular floret number are all predominantly controlled by additive genes (Ghimiray et al 2005;Pal and George 2002;Sirohi and Behera 2000;Zhang et al 2008Zhang et al , 2010a, but little is known of the inheritance of flowering traits. Although flowering time depends on planting date, photoperiod, temperature and other climatic factors (Chardon et al 2004), genes also play an important part (Anbessa et al 2006;Cai et al 2008;Godoy and Palomo 1999;Hao et al 2008a). The outcome of the present genetic analysis was that both flowering traits were compatible with the action of two pairs of major genes displaying additivity-dominance-epistasis.…”

“…Flowering is photoperiod, temperature and vernalization dependent, so its genetic control is expected to be complex. Flowering time is a quantitative trait in kiwifruit (Zhu et al 2002), snapdragon Stimart 2003, 2005), Vernonia galamensis (Baye and Becker 2005) and cotton (Anbessa et al 2006;Hao et al 2008a). Little is known regarding the genetic control of flowering in chrysanthemum, which has hindered the improvement of this trait to some extent.…”

“…The demand for chrysanthemum production, encompassing cut-flower, garden, potted plants and ground-cover types, is increasing worldwide. The flowering traits of a variety determine its adaptation and productivity in a given agroecological zone (Hao et al 2008a). In China, most cultivars flower in autumn, with only a few flowering in early summer.…”

Candidate Gene Identification of Flowering Time Genes in Cotton

“…In the case of the most important of the cultivated cotton species, G. barbadense and G. hirsutum , this shift happened independently in the two domesticates. While the genes targeted by human selection in this parallel domestication are unknown, previous genetic analyses and quantitative trait loci (QTL) mapping studies suggest that the transition to day‐neutral flowering involved multiple genes (Guo et al, 2008, 2009; Kohel and Richmond, 1962; Lewis and Richmond, 1960), some of which may have had a large effect on photoperiod sensitivity (Guo et al, 2008, 2009; Hao et al, 2008; Kohel and Richmond, 1962; Lewis and Richmond, 1960; Zhu and Kuraparthy, 2014), and the identities of which may differ between the two domesticates (Guo et al, 2008; Lewis and Richmond, 1960).…”

QTL Analysis of Head Splitting Resistance in Cabbage (Brassica oleracea L. var. capitata) Using SSR and InDel Makers Based on Whole-Genome Re-Sequencing

“…To determine an appropriate genetic model for the HSR trait, the cabbage DH population dataset was analyzed by major gene/polygene mixed inheritance analysis [ 38 – 39 ]. Joint segregation analysis was performed following Hao et al (2008) and Zhang et al (2010) under the basic assumptions described by those authors [ 10 , 14 ]. To select the genetic model best explaining the quantitative trait variation, 38 genetic models of 7 different types were considered ( S2 Table ).…”