Vol. XXX Issue 2
Article 1

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><!-- [et_pb_line_break_holder] --><html xmlns="http://www.w3.org/1999/xhtml"><!-- [et_pb_line_break_holder] --><head><!-- [et_pb_line_break_holder] --><meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1" /><!-- [et_pb_line_break_holder] --><title>Documento sin título</title><!-- [et_pb_line_break_holder] --></head><!-- [et_pb_line_break_holder] --><!-- [et_pb_line_break_holder] --><body><!-- [et_pb_line_break_holder] --><p align="right"><font size="3" face="Arial, Helvetica, sans-serif"><strong>ARTÍCULOS ORIGINALES</strong></font></p><!-- [et_pb_line_break_holder] --><p><font size="4" face="Arial, Helvetica, sans-serif"><strong>Molecular marker analysis of spike fertility index and</strong> <!-- [et_pb_line_break_holder] --> <strong>related traits in a bread wheat recombinant inbred line</strong> <!-- [et_pb_line_break_holder] --> <strong>population</strong></font></p><!-- [et_pb_line_break_holder] --><p><i><b><font size="3" face="Arial, Helvetica, sans-serif">Análisis de marcadores moleculares para el índice de <!-- [et_pb_line_break_holder] --> fertilidad de espiga y caracteres asociados en una población <!-- [et_pb_line_break_holder] --> de líneas endocriadas recombinantes de trigo pan</font></b></i></p><!-- [et_pb_line_break_holder] --><p> </p><!-- [et_pb_line_break_holder] --><p><b><font size="3" face="Arial, Helvetica, sans-serif">Panelo J.S.<sup>1,2</sup>, Alonso M.P.<sup>1,3</sup>, Mirabella N.E.<sup>1</sup>, Pontaroli A.C.<sup>1,3*</sup></font></b></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><font size="2"><sup>1</sup> Unidad Integrada Balcarce <!-- [et_pb_line_break_holder] --> (Estacion Experimental <!-- [et_pb_line_break_holder] --> Agropecuaria Balcarce, Instituto <!-- [et_pb_line_break_holder] --> Nacional de Tecnologia <!-- [et_pb_line_break_holder] --> Agropecuaria – Facultad de <!-- [et_pb_line_break_holder] --> Ciencias Agrarias, Universidad <!-- [et_pb_line_break_holder] --> Nacional de Mar del Plata); CC 276 <!-- [et_pb_line_break_holder] --> (7620) Balcarce, Argentina.<br /><!-- [et_pb_line_break_holder] --> <sup>2</sup> Comision de Investigaciones <!-- [et_pb_line_break_holder] --> Cientificas de la Provincia de <!-- [et_pb_line_break_holder] --> Buenos Aires; CC 276 (7620) <!-- [et_pb_line_break_holder] --> Balcarce, Argentina.<br /><!-- [et_pb_line_break_holder] --> <sup>3</sup> Consejo Nacional de <!-- [et_pb_line_break_holder] --> Investigaciones Cientificas y <!-- [et_pb_line_break_holder] --> Tecnicas; CC 276 (7620) Balcarce, <!-- [et_pb_line_break_holder] --> Argentina.<br /><!-- [et_pb_line_break_holder] --></font><font size="2"> <b>Corresponding author</b>: <!-- [et_pb_line_break_holder] --> Ana Clara Pontaroli <!-- [et_pb_line_break_holder] --> <a href="mailto:pontaroli.ana@inta.gob.ar">pontaroli.ana@inta.gob.ar</a></font></font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif">* This work is part of a thesis by M.P. Alonso in partial fulfillment of the requirements for the Doctor´s <!-- [et_pb_line_break_holder] --> degree (Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Argentina).<br /><!-- [et_pb_line_break_holder] -->The authors contributed equally to this work (<em>ex-aequo</em>).</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif">DOI: 10.35407/bag.2019.xxx.02.01</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><b>Received</b>: 05/28/2019<br /><!-- [et_pb_line_break_holder] --> <b>Revised version received</b>: 07/25/2019<br /><!-- [et_pb_line_break_holder] --> <b>Accepted</b>: 08/13/2019</font></p><!-- [et_pb_line_break_holder] --><hr /><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><strong>ABSTRACT</strong></font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><!-- [et_pb_line_break_holder] --> Spike fertility index (SF) has been well established as an ecophysiological trait related to<!-- [et_pb_line_break_holder] --> grain number per unit area and a promising selection target in wheat breeding programs.<!-- [et_pb_line_break_holder] --> Scarce information on the molecular basis of SF is available thus far. In this study, a<!-- [et_pb_line_break_holder] --> preliminary molecular marker analysis was carried out in a RIL population derived from the<!-- [et_pb_line_break_holder] --> cross between two Argentinean cultivars with contrasting SF to identify candidate genomic<!-- [et_pb_line_break_holder] --> regions associated with SF. Twenty-four microsatellites and two functional markers that had<!-- [et_pb_line_break_holder] --> been found to co-segregate with SF in a bulked-segregant analysis of the F3 generation of<!-- [et_pb_line_break_holder] --> the population were analyzed. Phenotypic data were collected from three field experiments<!-- [et_pb_line_break_holder] --> carried out during 2013, 2014 and 2015 growing seasons at Balcarce, Argentina. Two genomic<!-- [et_pb_line_break_holder] --> regions associated with SF in chromosomes 5BS and 7AS were detected, which merit further<!-- [et_pb_line_break_holder] --> investigation.</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><b>Key words</b>: Selection; Genomic regions; Grain number; Yield; QTL; Spike fertility index; <!-- [et_pb_line_break_holder] --> Fruiting efficiency</font>.</p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><strong>RESUMEN</strong></font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif">El índice de fertilidad de espiga (FE) ha sido propuesto como un carácter ecofisiológico<!-- [et_pb_line_break_holder] --> asociado con el número de granos por unidad de área y como criterio de selección prometedor<!-- [et_pb_line_break_holder] --> para los programas de mejoramiento de trigo. Sin embargo, la información sobre las bases<!-- [et_pb_line_break_holder] --> moleculares de la FE aún es escasa. En este estudio, se realizó un análisis preliminar de<!-- [et_pb_line_break_holder] --> marcadores moleculares en una población RIL derivada del cruce entre dos cultivares<!-- [et_pb_line_break_holder] --> argentinos con FE contrastante con el objetivo de identificar regiones genómicas candidatas<!-- [et_pb_line_break_holder] --> asociadas con el carácter. Se analizaron 24 microsatélites y dos marcadores funcionales que<!-- [et_pb_line_break_holder] --> se había encontrado que se co-segregaban con la FE en un análisis de segregantes en <em>“bulk”</em><!-- [et_pb_line_break_holder] --> en la generación F3 de la población. Se recopilaron datos fenotípicos de tres experimentos<!-- [et_pb_line_break_holder] --> de campo llevados a cabo durante las temporadas de cultivo 2013, 2014 y 2015 en Balcarce,<!-- [et_pb_line_break_holder] --> Argentina. Se detectaron dos regiones genómicas asociadas con la FE en los cromosomas<!-- [et_pb_line_break_holder] --> 5BS y 7AS, que mostraron ser estables a través de los años de evaluación. Este trabajo aporta<!-- [et_pb_line_break_holder] --> información novedosa acerca de las bases moleculares de la FE, las cuales deberán ser<!-- [et_pb_line_break_holder] --> estudiadas con mayor profundidad.</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><b>Palabras clave</b>: Selección; Regiones genómicas; Número de granos; Rendimiento; QTL; Indice <!-- [et_pb_line_break_holder] --> de fertilidad de espiga; Eficiencia de fructificación</font><font size="2">.</font></p><!-- [et_pb_line_break_holder] --><hr /><!-- [et_pb_line_break_holder] --><p> </p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>INTRODUCTION</strong></font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><!-- [et_pb_line_break_holder] --> Bread wheat (<em>Triticum aestivum </em>L.) is one of the most<!-- [et_pb_line_break_holder] --> important field crops in the world. It provides ~20% of<!-- [et_pb_line_break_holder] --> human food calories and protein (FAO 2018). Prospects<!-- [et_pb_line_break_holder] --> indicate a steady growth in the global population, which<!-- [et_pb_line_break_holder] --> will be encompassed by an increase in food demand.<!-- [et_pb_line_break_holder] --> However, this demand will hardly be attained through<!-- [et_pb_line_break_holder] --> the expansion of farming areas (Albajes <em>et al. </em>2013).<!-- [et_pb_line_break_holder] --> Breeding efforts should rather concentrate on achieving<!-- [et_pb_line_break_holder] --> higher grain yield-increase rates (Reynolds <em>et al. </em>2012).<!-- [et_pb_line_break_holder] --> Grain yield in wheat is more strongly associated<!-- [et_pb_line_break_holder] --> with grain number per unit area (hereinafter referred<!-- [et_pb_line_break_holder] --> to as GN m<sup>-2</sup>) than it is with grain weight (Sadras 2007;<!-- [et_pb_line_break_holder] --> Fischer 2011). Hence, breeding efforts have focused on<!-- [et_pb_line_break_holder] --> increasing grain yield through increasing GN m<sup>-2</sup> (Slafer<!-- [et_pb_line_break_holder] --> <em>et al. </em>2014, 2015; Lo Valvo <em>et al. </em>2018). However, this is a<!-- [et_pb_line_break_holder] --> difficult trait to select for in early breeding stages. Thus,<!-- [et_pb_line_break_holder] --> the use of GN m<sup>-2</sup>-related traits as selection targets may<!-- [et_pb_line_break_holder] --> be helpful to increase grain yield at the pace it is required<!-- [et_pb_line_break_holder] --> (Slafer 2003; Fischer and Rebetzke 2018). A conceptual<!-- [et_pb_line_break_holder] --> model proposed by Fischer (1984) suggests that, under<!-- [et_pb_line_break_holder] --> non-limiting growing conditions, GN m<sup>-2</sup> in wheat can<!-- [et_pb_line_break_holder] --> be considered as the product of (i) the duration of the<!-- [et_pb_line_break_holder] --> spike growth period, (ii) the crop growth rate during the<!-- [et_pb_line_break_holder] --> spike growth period, (iii) the dry weight partitioning<!-- [et_pb_line_break_holder] --> to spikes during the spike growth period and (iv) the<!-- [et_pb_line_break_holder] --> number of grains per unit of spike chaff dry weight,<!-- [et_pb_line_break_holder] --> i.e. a spike fertility index (SF), also termed “fruiting<!-- [et_pb_line_break_holder] --> efficiency” (Ferrante <em>et al. </em>2012).<!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> Many authors have described SF as an ecophysiological<!-- [et_pb_line_break_holder] --> component which explains a substantial proportion of<!-- [et_pb_line_break_holder] --> the differences in GN m<sup>-2</sup> between cultivars (Acreche<!-- [et_pb_line_break_holder] --> <em>et al</em>. 2008; Gonzalez <em>et al. </em>2014; Aisawi <em>et al. </em>2015;<!-- [et_pb_line_break_holder] --> Gonzalez-Navarro <em>et al. </em>2016), with high stability across<!-- [et_pb_line_break_holder] --> environments (Abbate <em>et al. </em>2013; Elía <em>et al. </em>2016; Guo <em>et</em><!-- [et_pb_line_break_holder] --> <em>al. </em>2016) and moderate to high heritability (Martino <em>et al.</em><!-- [et_pb_line_break_holder] --> 2015; Mirabella <em>et al. </em>2016; Alonso <em>et al. </em>2018b). <br /><!-- [et_pb_line_break_holder] --> In turn,<!-- [et_pb_line_break_holder] --> a fast and high-throughput method was developed for<!-- [et_pb_line_break_holder] --> SF determination at maturity, using as few as 15 spikes<!-- [et_pb_line_break_holder] --> per plot (Abbate <em>et al. </em>2013). Adding up all these features,<!-- [et_pb_line_break_holder] --> SF emerges as a promising trait to select for in the early<!-- [et_pb_line_break_holder] --> generations of breeding programs, in which little seed<!-- [et_pb_line_break_holder] --> is available for GN m<sup>-2</sup> determinations (Fischer and<!-- [et_pb_line_break_holder] --> Rebetzke 2018). Furthermore, a recent study showed<!-- [et_pb_line_break_holder] --> that the use of SF as a selection criterion, either solely or<!-- [et_pb_line_break_holder] --> in combination with selection for high yield, effectively<!-- [et_pb_line_break_holder] --> increased yield, resulting in superior and more stable<!-- [et_pb_line_break_holder] --> grain yields than selecting just for high yield (Alonso <em>et</em><!-- [et_pb_line_break_holder] --> <em>al. </em>2018b).<!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> Molecular markers associated with agronomically<!-- [et_pb_line_break_holder] --> valuable traits have been successfully used to select for<!-- [et_pb_line_break_holder] --> in wheat breeding programs’ early generations (Collard<!-- [et_pb_line_break_holder] --> and Mackill 2008). For example, some authors reported<!-- [et_pb_line_break_holder] --> microsatellites linked to traits as plant height (Wang<!-- [et_pb_line_break_holder] --> <em>et al</em>. 2010; Zhang <em>et al</em>. 2011), grain number per spike<!-- [et_pb_line_break_holder] --> (Quarrie <em>et al</em>. 2005, 2006; Hai <em>et al</em>. 2008) and yield <em>per</em><!-- [et_pb_line_break_holder] --> <em>se </em>(Kobiljski <em>et al. </em>2007). The availability of molecular<!-- [et_pb_line_break_holder] --> markers associated with SF would be very helpful for<!-- [et_pb_line_break_holder] --> increasing the genetic gain per selection cycle. Markerassisted<!-- [et_pb_line_break_holder] --> selection could allow seed or plantlet selection<!-- [et_pb_line_break_holder] --> of transgressive genotypes at early generations of<!-- [et_pb_line_break_holder] --> segregating populations, and molecular characterization<!-- [et_pb_line_break_holder] --> of the crossing block. Despite the prospective relevance<!-- [et_pb_line_break_holder] --> of SF for wheat breeding, about the genetic and molecular<!-- [et_pb_line_break_holder] --> control of this trait little is known, except for a couple<!-- [et_pb_line_break_holder] --> of recent studies which respectively reported a QTL for<!-- [et_pb_line_break_holder] --> SF in chromosome 2AL (candidate gene <em>CO4</em>; Guo <em>et al</em>.<!-- [et_pb_line_break_holder] --> 2017) and a significant effect of photoperiod sensitivity<!-- [et_pb_line_break_holder] --> genes <em>Ppd-B1 </em>and <em>Ppd-D1 </em>on SF (Ramirez <em>et al. </em>2018).<!-- [et_pb_line_break_holder] --> In the present study, a preliminary molecular marker<!-- [et_pb_line_break_holder] --> analysis was carried out in a recombinant inbred line<!-- [et_pb_line_break_holder] --> (RIL) population segregating for SF in order to identify<!-- [et_pb_line_break_holder] --> candidate genomic regions associated with this trait.<!-- [et_pb_line_break_holder] --> These results provide valuable information as a first<!-- [et_pb_line_break_holder] --> step in mapping QTL/genes controlling SF and possibly<!-- [et_pb_line_break_holder] --> other related traits.</font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>MATERIALS AND METHODS</strong></font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>Phenotypic data generation</strong><br /><!-- [et_pb_line_break_holder] --> In the present study, molecular marker analysis of<!-- [et_pb_line_break_holder] --> SF and related traits was carried out with previously<!-- [et_pb_line_break_holder] --> published phenotypic data (Alonso <em>et al. </em>2018a, b). A brief<!-- [et_pb_line_break_holder] --> description of the mapping population, experiments,<!-- [et_pb_line_break_holder] --> environmental conditions, and measurements and<!-- [et_pb_line_break_holder] --> calculations, is included below. For further details see<!-- [et_pb_line_break_holder] --> Alonso <em>et al. </em>(2018a).<br /><!-- [et_pb_line_break_holder] --> <em>Plant material</em>. A mapping population of 146<!-- [et_pb_line_break_holder] --> recombinant inbred lines (RILs) derived from the<!-- [et_pb_line_break_holder] --> cross between the Argentinean spring bread wheat<!-- [et_pb_line_break_holder] --> cultivars ‘Baguette 10’ and ‘Klein Chajá’ was used in<!-- [et_pb_line_break_holder] --> all field experiments. Both parental cultivars were<!-- [et_pb_line_break_holder] --> also included. ‘Baguette 10’ and ‘Klein Chajá’ were<!-- [et_pb_line_break_holder] --> commercially released in 2000 and 2002 respectively<!-- [et_pb_line_break_holder] --> and are contrasting for SF and other yield-related traits<!-- [et_pb_line_break_holder] --> (Martino <em>et al</em>. 2015; Alonso <em>et al</em>. 2018a, b).<br /><!-- [et_pb_line_break_holder] --> <em>Field experiments</em>. During the 2013, 2014 and 2015<!-- [et_pb_line_break_holder] --> crop seasons, field experiments were carried out at<!-- [et_pb_line_break_holder] --> the experimental station of the Instituto Nacional de<!-- [et_pb_line_break_holder] --> Tecnología Agropecuaria (INTA) Balcarce (37º 45’ S; 55º<!-- [et_pb_line_break_holder] --> 18’ W; 130 m a.s.l.), Balcarce, Buenos Aires, Argentina.<!-- [et_pb_line_break_holder] --> Experiments are fully described in Alonso <em>et al</em>. (2018a, b).<!-- [et_pb_line_break_holder] --> <em>Measurements and calculations</em>. Plant height was<!-- [et_pb_line_break_holder] --> measured from the ground to the ear tip at maturity; the<!-- [et_pb_line_break_holder] --> average of two measurements per plot was registered.<!-- [et_pb_line_break_holder] --> At the same time, a sample of 20 spikes was drawn at<!-- [et_pb_line_break_holder] --> random from the three or five central rows of each plot<!-- [et_pb_line_break_holder] --> and air-dried for further SF determination according<!-- [et_pb_line_break_holder] --> to Abbate <em>et al. </em>(2013). Briefly, the sample was weighed<!-- [et_pb_line_break_holder] --> (total weight) and threshed, and grains were weighed<!-- [et_pb_line_break_holder] --> (grain weight) and counted (grain number). Spike<!-- [et_pb_line_break_holder] --> fertility index was calculated as the quotient between<!-- [et_pb_line_break_holder] --> grain number and chaff weight (<em>i.e</em>., the difference<!-- [et_pb_line_break_holder] --> between total weight and grain weight).<!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> Grain yield was determined by mechanical harvest. For<!-- [et_pb_line_break_holder] --> grain weight determination, a clean and dry subsample<!-- [et_pb_line_break_holder] --> of <em>~</em>30 g was taken from the yield sample, weighted and<!-- [et_pb_line_break_holder] --> counted in an automatic counter. Grain number m<sup>-2</sup> was calculated as the quotient between grain yield and<!-- [et_pb_line_break_holder] --> grain weight. Grain test weight was measured using a<!-- [et_pb_line_break_holder] --> Schopper cylinder.</font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>Molecular marker analysis</strong><!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> Genomic DNA extraction from fresh tissue of ten-daysold<!-- [et_pb_line_break_holder] -->seedling leaves was carried out according to Haymes<!-- [et_pb_line_break_holder] --><em>et al. </em>(1996). Approximately 200 molecular markers were<!-- [et_pb_line_break_holder] --> analyzed for polymorphism between the parents of the<!-- [et_pb_line_break_holder] --> RIL population. Bulked segregant analysis (Michelmore<!-- [et_pb_line_break_holder] --> <em>et al</em>. 1991) was carried out in the F3 generation of the<!-- [et_pb_line_break_holder] --> population (Deperi, 2012). Those markers which showed<!-- [et_pb_line_break_holder] --> co-segregation with SF were used in the present study<!-- [et_pb_line_break_holder] --> (24 microsatellites and two functional markers, <a href="#tab1">Table 1</a>,<!-- [et_pb_line_break_holder] --> <a href="#fig1">Fig. S1</a>). In all cases, PCR reactions were performed using<!-- [et_pb_line_break_holder] --> a final volume of 15 μl in a Veriti™ (Applied Biosystems)<!-- [et_pb_line_break_holder] --> thermal cycler. The reaction buffer contained 1X <em>Taq</em><!-- [et_pb_line_break_holder] --> DNA Polymerase buffer (Promega), 0.8 U <em>Taq </em>DNA<!-- [et_pb_line_break_holder] --> Polymerase (Promega), 0.2 mM of each dNTP, 0.2 μM of<!-- [et_pb_line_break_holder] --> each primer, 1.5 mM of MgCl2 and 100 ng of genomic DNA<!-- [et_pb_line_break_holder] --> (template). Cycling conditions were as follows: 3’ initial<!-- [et_pb_line_break_holder] --> denaturation at 95°C, 18 cycles of 30” denaturation at<!-- [et_pb_line_break_holder] --> 95°C, 30” annealing at 65°C to 56°C (“touchdown”)<!-- [et_pb_line_break_holder] --> and 30” extension at 72°C, followed by 22 additional,<!-- [et_pb_line_break_holder] --> similar cycles but with annealing at 56°C, and 5’ final<!-- [et_pb_line_break_holder] --> extension at 72°C. Primer names and sequences, linkage<!-- [et_pb_line_break_holder] --> group, allele sizes and cycling conditions for each<!-- [et_pb_line_break_holder] --> molecular marker are detailed in <a href="#tab1">Table 1</a>. Amplified<!-- [et_pb_line_break_holder] --> fragments were separated and analyzed through<!-- [et_pb_line_break_holder] --> horizontal electrophoresis in 2% agarose gels in 1X TBE<!-- [et_pb_line_break_holder] --> buffer, stained with GelRed® (Biotium) during 15 min<!-- [et_pb_line_break_holder] --> at 100V and exposed to UV light. Also, fragments were<!-- [et_pb_line_break_holder] --> analyzed through electrophoresis in denaturing 6%<!-- [et_pb_line_break_holder] --> polyacrylamide-urea gels (Sambrook <em>et al. </em>2001) stained<!-- [et_pb_line_break_holder] --> with silver nitrate following the protocol described<!-- [et_pb_line_break_holder] --> by Benbouza <em>et al. </em>(2006). In this case, fragment<!-- [et_pb_line_break_holder] --> visualization was performed by exposing gels to white<!-- [et_pb_line_break_holder] --> light. Allelic variants were assigned to each RIL according<!-- [et_pb_line_break_holder] --> to their parent of origin, as ‘B’ for ‘Baguette 10’ and ‘K’<!-- [et_pb_line_break_holder] --> for ‘Klein Chajá’. A few heterozygous individuals were<!-- [et_pb_line_break_holder] --> detected and discarded from further analyses.</font></p><!-- [et_pb_line_break_holder] --><p><a name="tab1" id="tab1"></a></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="3" face="Arial, Helvetica, sans-serif"><strong><font size="2">Table 1. </font></strong><font size="2">Molecular markers used in this study.</font></font><br /><!-- [et_pb_line_break_holder] --> <img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01tab1.jpg" width="562" height="797" /></p><!-- [et_pb_line_break_holder] --><p><a name="fig1" id="fig1"></a></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="2" face="Arial, Helvetica, sans-serif"><strong><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01fig1.jpg" width="497" height="531" /><br /><!-- [et_pb_line_break_holder] --> Figure S1. </strong>Approximate chromosome location of polymorphic markers used in this study and traits with which markers were<!-- [et_pb_line_break_holder] -->associated.</font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>Statistical analysis<br /><!-- [et_pb_line_break_holder] --></strong><!-- [et_pb_line_break_holder] --> Statistical analysis was performed using the package<!-- [et_pb_line_break_holder] --> <em>nlme </em>(Pinheiro <em>et al. </em>2017) of the Rsoftware (R-Core<!-- [et_pb_line_break_holder] --> Team 2017). A linear fixed effects model including<!-- [et_pb_line_break_holder] --> year, genotype, block nested in year, and genotype-byyear<!-- [et_pb_line_break_holder] --> interaction effects on phenotypic variables, was<!-- [et_pb_line_break_holder] --> used. Variances from the model were used to calculate<!-- [et_pb_line_break_holder] --> broad-sense heritability (<em>H2</em>) for each trait according to<!-- [et_pb_line_break_holder] --> Hallauer <em>et al</em>. (2010) as:</font></p><!-- [et_pb_line_break_holder] --><p align="center"><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01for1.jpg" width="184" height="68" /></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"> with <img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01for2.jpg" width="25" height="25" />as genotypic variance, <img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01for3.jpg" width="24" height="32" />as environmental<!-- [et_pb_line_break_holder] --> variance, <img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01for4.jpg" width="33" height="23" />as the genotype-by-environment (year)<!-- [et_pb_line_break_holder] --> variance, <strong><em>r </em></strong>as the number of replications or blocks nested<!-- [et_pb_line_break_holder] --> in environment and <strong><em>e </em></strong>as the number of environments<!-- [et_pb_line_break_holder] --> (years).<br /><!-- [et_pb_line_break_holder] --> In order to detect genomic regions associated with<!-- [et_pb_line_break_holder] --> the evaluated traits, a linear fixed effects model was<!-- [et_pb_line_break_holder] --> run for each marker, including marker, year, block<!-- [et_pb_line_break_holder] --> nested in year, and the marker-by-year interaction<!-- [et_pb_line_break_holder] --> effects. Bonferroni correction was applied in multiple<!-- [et_pb_line_break_holder] --> comparisons using a family-wise error rate of 0.05.<!-- [et_pb_line_break_holder] --> When a significant marker-by-year interaction effect<!-- [et_pb_line_break_holder] --> was detected, the marker effect was analyzed for each<!-- [et_pb_line_break_holder] --> year individually. When a significant marker-trait<!-- [et_pb_line_break_holder] --> association was detected, the percentage of phenotypic<!-- [et_pb_line_break_holder] --> variation explained by the marker was calculated as the<!-- [et_pb_line_break_holder] --> quotient between the sum of squares of the marker and<!-- [et_pb_line_break_holder] --> the total sum of squares x 100. The marker effect was<!-- [et_pb_line_break_holder] --> calculated as the difference between the mean in the<!-- [et_pb_line_break_holder] --> group ‘B’ and the mean in the group ‘K’.<!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> Haplotypes were constructed with one marker per<!-- [et_pb_line_break_holder] --> region associated with SF. In chromosome 7AS, the<!-- [et_pb_line_break_holder] --> chosen marker was the one with the lowest p-value.<!-- [et_pb_line_break_holder] --> Differences between these groups were tested with the<!-- [et_pb_line_break_holder] --> Tukey test (α=0.05).</font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>RESULTS AND DISCUSSION</strong></font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>Environmental conditions</strong><br /><!-- [et_pb_line_break_holder] --> The environmental conditions under which the<!-- [et_pb_line_break_holder] --> experiments were performed are fully described<!-- [et_pb_line_break_holder] --> in Alonso <em>et al. </em>(2018a). Conspicuous inter-annual<!-- [et_pb_line_break_holder] --> environmental variation was observed, even though all<!-- [et_pb_line_break_holder] --> experiments were carried out with no water or nutrient<!-- [et_pb_line_break_holder] --> limitations and with chemical control of pests and<!-- [et_pb_line_break_holder] --> fungal diseases.</font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>Phenotypic variation of RIL population</strong><!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> Phenotypic data description is fully detailed in Alonso<!-- [et_pb_line_break_holder] --><em>et al. </em>(2018a, b). Evaluated traits showed a bell-shaped<!-- [et_pb_line_break_holder] --> and symmetrical distribution across all years (<a href="#fig2">Fig. S2</a>).<!-- [et_pb_line_break_holder] --> Mean standard deviation and coefficient of variation for<!-- [et_pb_line_break_holder] --> the analyzed traits in the RIL population are presented in<!-- [et_pb_line_break_holder] --> <a href="#tab2">Table 2</a>. ‘Baguette 10’ had higher values of SF, grain yield<!-- [et_pb_line_break_holder] --> and GN m-2 than those of ‘Klein Chajá’. Significant effects<!-- [et_pb_line_break_holder] --> of genotype, year and genotype-by-year interaction were<!-- [et_pb_line_break_holder] --> detected for all the traits in the RIL population (<a href="#tab3">Table<!-- [et_pb_line_break_holder] --> 3</a>). However, the genetic variance was always greater<!-- [et_pb_line_break_holder] --> than genotype-by-year interaction variance. All traits<!-- [et_pb_line_break_holder] --> showed moderate to high broad-sense heritability (<a href="#tab3">Table<!-- [et_pb_line_break_holder] --> 3</a>), which is essential for meaningful QTL detection.<!-- [et_pb_line_break_holder] --> Although field evaluations comprising a larger number of<!-- [et_pb_line_break_holder] --> environments are needed, these results suggest stability<!-- [et_pb_line_break_holder] --> in SF, in line with previous findings (Abbate <em>et al. </em>2013;<!-- [et_pb_line_break_holder] --> Elía <em>et al. </em>2016; Gonzalez-Navarro <em>et al. </em>2016; Mirabella<!-- [et_pb_line_break_holder] --> <em>et al. </em>2016). This further supports the possibility of using<!-- [et_pb_line_break_holder] --> molecular markers linked to the trait.</font></p><!-- [et_pb_line_break_holder] --><p><a name="fig2" id="fig2"></a></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="2" face="Arial, Helvetica, sans-serif"><strong><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01fig2.jpg" width="509" height="720" /><br /><!-- [et_pb_line_break_holder] --> Figure S2. </strong>Histograms of (A) spike fertility index, (B) spike chaff dry weight, (C) grain number per spike, (D) grain number<!-- [et_pb_line_break_holder] --> per m<sup>2</sup>, (E) grain yield, (F) grain weight, (G) test weight and (H) plant height, in a RIL population (‘Baguette 10’ x ‘Klein Chaja’)<!-- [et_pb_line_break_holder] --> evaluated in 2013 (white), 2014 (grey) and 2015 (black) at Balcarce, Argentina. Values for the parents are indicated with stars<!-- [et_pb_line_break_holder] -->(‘Baguette 10’) and triangles (‘Klein Chaja’).</font></p><!-- [et_pb_line_break_holder] --><p><a name="tab2" id="tab2"></a></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="3" face="Arial, Helvetica, sans-serif"><strong><font size="2">Table 2. </font></strong><font size="2">Mean, standard deviation and coefficient of variation of spike fertility index, spike chaff dry weight, grain number per spike, grain number per<!-- [et_pb_line_break_holder] --> m</font><font size="2" face="Arial, Helvetica, sans-serif"><sup>2</sup></font><font size="2">, grain yield, grain weight, test weight and plant height of a RIL population (‘Baguette 10’ x ‘Klein Chaja’) evaluated in 2013, 2014 and 2015 at Balcarce,<!-- [et_pb_line_break_holder] -->Argentina. Partially published data (Alonso et al. 2018a,b).</font></font><br /><!-- [et_pb_line_break_holder] --><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01tab2.jpg" width="567" height="202" /></p><!-- [et_pb_line_break_holder] --><p><a name="tab3" id="tab3"></a></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="2" face="Arial, Helvetica, sans-serif"><strong>Table 3. </strong>Analysis of variance of spike fertility index, spike chaff dry weight, grain number per spike, grain number per m<sup>2</sup>, grain yield, grain weight, test<!-- [et_pb_line_break_holder] --> weight and plant height of a RIL population (‘Baguette 10’ x ‘Klein Chaja’) evaluated in 2013, 2014 and 2015 at Balcarce, Argentina. Broad-sense heritability<!-- [et_pb_line_break_holder] -->(H2) values. Partially published data (Alonso et al. 2018a,b).</font><br /><!-- [et_pb_line_break_holder] --><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01tab3.jpg" width="531" height="743" /></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>Molecular marker analysis</strong><!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> A total of 24 out of 200 SSR markers, plus two functional<!-- [et_pb_line_break_holder] -->markers, were analyzed in the RIL population for<!-- [et_pb_line_break_holder] -->their polymorphism between the parents and cosegregation<!-- [et_pb_line_break_holder] -->with SF in the F3<!-- [et_pb_line_break_holder] -->generation. Even though<!-- [et_pb_line_break_holder] -->a very low number of polymorphic markers was found,<!-- [et_pb_line_break_holder] -->55 significant single marker-trait associations were<!-- [et_pb_line_break_holder] -->detected (<a href="#tab4">Tables 4</a>, <a href="#tab6">6</a>; <a href="#fig1">Fig. S1</a>). A significant year effect<!-- [et_pb_line_break_holder] -->was found in all cases, whereas significant markerby-<!-- [et_pb_line_break_holder] -->year interaction effects (p<0.05) were detected in<!-- [et_pb_line_break_holder] --> only eleven out of 55 cases (<a href="#tab4">Tables 4</a>, <a href="#tab6">6</a>). No crossover<!-- [et_pb_line_break_holder] --> interactions were found. Genome coverage reached<!-- [et_pb_line_break_holder] --> by these polymorphic markers was low and markers<!-- [et_pb_line_break_holder] --> were not evenly distributed. Also, many linkage groups<!-- [et_pb_line_break_holder] --> were not covered. This may lead to biased results, with<!-- [et_pb_line_break_holder] --> phenotypic variation only being explained by covered<!-- [et_pb_line_break_holder] --> regions. Nevertheless, significant regions explaining an<!-- [et_pb_line_break_holder] --> interesting amount of variation (R2=0.6 to 24%; <a href="#tab4">Tables<!-- [et_pb_line_break_holder] --> 4</a>, <a href="#tab6">6</a> and <a href="#tab7">7</a>) were detected for the reported traits.</font></p><!-- [et_pb_line_break_holder] --><p><a name="tab4" id="tab4"></a></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="2" face="Arial, Helvetica, sans-serif"><strong>Table 4. </strong>Molecular markers associated with spike fertility index (grains /g chaff) in a RIL population (‘Baguette 10’ x ‘Klein Chaja’)<!-- [et_pb_line_break_holder] -->evaluated in 2013, 2014 and 2015 at Balcarce, Argentina.</font><br /><!-- [et_pb_line_break_holder] --><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01tab4.jpg" width="527" height="200" /></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="2" face="Arial, Helvetica, sans-serif"><strong>Table 5. </strong>Spike fertility index (grains /g chaff) of haplotypes at the two markers most significantly associated with the trait in a RIL<!-- [et_pb_line_break_holder] --> population (‘Baguette 10’ x ‘Klein Chaja’) evaluated in 2013, 2014 and 2015 at Balcarce, Argentina. The‘B’ and ‘K’ denote lines with the ‘B’ <!-- [et_pb_line_break_holder] -->and the ‘K’ allele, respectively. Same letters within a column indicate non-significant differences (p>0.05).</font><br /><!-- [et_pb_line_break_holder] --><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01tab5.jpg" width="528" height="172" /></p><!-- [et_pb_line_break_holder] --><p><a name="tab6" id="tab6"></a></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="2" face="Arial, Helvetica, sans-serif"><strong>Table 6. </strong>Molecular markers associated with spike chaff dry weight, grain number per spike, grain number per m<sup>2</sup>, grain yield, grain weight, test<!-- [et_pb_line_break_holder] --> weight and plant height of a RIL population (‘Baguette 10’ x ‘Klein Chaja’) evaluated in 2013, 2014 and 2015 at Balcarce, Argentina.<!-- [et_pb_line_break_holder] -->a Average trait difference between lines with the ‘B’ vs. the ‘K’ allele. * significant GxE interaction.</font><br /><!-- [et_pb_line_break_holder] --><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01tab6.jpg" width="560" height="801" /></p><!-- [et_pb_line_break_holder] --><p align="left"><a name="tab7" id="tab7"></a></p><!-- [et_pb_line_break_holder] --><p align="center"><font size="2" face="Arial, Helvetica, sans-serif"><strong>Table 7. </strong>Molecular markers which showed significant GxE interaction, associated with spike chaff dry weight, grain number per spike, grain number per<!-- [et_pb_line_break_holder] -->m<sup>2</sup>, grain yield and plant height of a RIL population (‘Baguette 10’ x ‘Klein Chaja’) evaluated in 2013, 2014 and 2015 at Balcarce, Argentina.</font><br /><!-- [et_pb_line_break_holder] --><img src="https://sag.org.ar/jbag/wp-content/uploads/2020/02/a01tab7.jpg" width="578" height="689" /></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>Markers associated with spike fertility index</strong><!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> Five markers were associated with SF (Table 4), one on<!-- [et_pb_line_break_holder] -->chromosome 5BS (<em>Xgwm540</em>) and four on chromosome<!-- [et_pb_line_break_holder] --> 7AS. The highest proportion of explained variance for<!-- [et_pb_line_break_holder] --> a marker in SF was that of <em>Xwmc790 </em>(R2=3.7%) with<!-- [et_pb_line_break_holder] --> a positive effect of allele ‘B’. On chromosome 5BS,<!-- [et_pb_line_break_holder] --> genotypes carrying the ‘K’ allele in marker <em>Xgwm540</em><!-- [et_pb_line_break_holder] --> showed the highest SF values (<a href="#tab4">Table 4</a>). Presence of high<!-- [et_pb_line_break_holder] --> SF alleles in the low SF parent is expected, as transgressive<!-- [et_pb_line_break_holder] --> segregation was observed in this population (<a href="#fig2">Fig. S2</a>)<!-- [et_pb_line_break_holder] --> (Martino <em>et al. </em>2015; Alonso <em>et al. </em>2018b). Such alleles<!-- [et_pb_line_break_holder] --> are interesting because further variability for the trait<!-- [et_pb_line_break_holder] --> can be exploited even in “bad” genotypes, for stacking<!-- [et_pb_line_break_holder] --> favorable minor alleles. Thus, the selection of extreme<!-- [et_pb_line_break_holder] --> superior phenotypes could further increase SF and, in<!-- [et_pb_line_break_holder] --> extension, raise grain yield (Slafer <em>et al</em>. 2015; Fischer<!-- [et_pb_line_break_holder] --> and Rebetzke 2018).<br /><!-- [et_pb_line_break_holder] --></font><font size="3" face="Arial, Helvetica, sans-serif">Haplotypes constructed with these two markers<!-- [et_pb_line_break_holder] -->yielded four genotypic groups. In all cases (Table 5),<!-- [et_pb_line_break_holder] -->haplotype <em>Xwmc790-B/ Xgwm540-K </em>showed the highest<!-- [et_pb_line_break_holder] -->SF, whereas the haplotype <em>Xwmc790-K/Xgwm540-B</em> showed the lowest SF (p<0.05). Haplotypes with the<!-- [et_pb_line_break_holder] -->remaining allele combinations had intermediate SF<!-- [et_pb_line_break_holder] -->values. Also, haplotype ranking was the same across<!-- [et_pb_line_break_holder] -->years. On average, SF difference between extreme<!-- [et_pb_line_break_holder] -->haplotypes was ~7%; according to results reported by<!-- [et_pb_line_break_holder] -->Alonso <em>et al</em>. (2018b), this could represent a difference<!-- [et_pb_line_break_holder] -->in GN m-2 potentially associated with a significant grain<!-- [et_pb_line_break_holder] -->yield increase.</font></p><!-- [et_pb_line_break_holder] --><p><font size="3" face="Arial, Helvetica, sans-serif"><strong>Markers associated with other traits</strong><!-- [et_pb_line_break_holder] --> <em><br /><!-- [et_pb_line_break_holder] --> Spike chaff dry weight. </em>Eight marker-trait associations<!-- [et_pb_line_break_holder] --> were detected in four chromosomic regions, with effects<!-- [et_pb_line_break_holder] --> ranging between 3.6 and 4.4% (<a href="#tab6">Table 6</a>). Two of these<!-- [et_pb_line_break_holder] --> markers, on chromosome 7AS, co-localized with SF<!-- [et_pb_line_break_holder] --> as well. All markers on 7AS showed a positive effect of<!-- [et_pb_line_break_holder] --> allele ‘B’, except for <em>Xpsp3050</em>. When analyzed by year,<!-- [et_pb_line_break_holder] --> a significant negative effect of <em>Vrn-A1-B </em>on 5AL was<!-- [et_pb_line_break_holder] --> observed at all three years, but the magnitude of such<!-- [et_pb_line_break_holder] --> effect varied across years (<a href="#tab7">Table 7</a>).<!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> <em>Grain number per spike. </em>Ten marker associations with<!-- [et_pb_line_break_holder] --> GN/spike were detected in four chromosomic regions<!-- [et_pb_line_break_holder] --> (<a href="#tab6">Table 6</a>). These results are partly coincident with the<!-- [et_pb_line_break_holder] --> ones reported by Quarrie <em>et al. </em>(2005, 2006) and Hai <em>et</em><!-- [et_pb_line_break_holder] --> <em>al. </em>(2008). These authors detected QTL associated with<!-- [et_pb_line_break_holder] --> grain number per spike in chromosome 7AS of a doubled<!-- [et_pb_line_break_holder] --> haploid population. Marker effect ranged between 3.4<!-- [et_pb_line_break_holder] --> and 7.6% (<a href="#tab6">Table 6</a>). <em>RhtD1 </em>had a significant association<!-- [et_pb_line_break_holder] --> with GN/spike at all three years, with a negative effect of<!-- [et_pb_line_break_holder] --> allele ’B‘. However, the marker effect in 2014 was almost<!-- [et_pb_line_break_holder] --> three times greater than that of 2013 and 2015 (<a href="#tab7">Table 7</a>).<!-- [et_pb_line_break_holder] --> Marker <em>Xgwm335 </em>(5BL) showed significant marker by<!-- [et_pb_line_break_holder] --> year interaction due to its association with GN/spike<!-- [et_pb_line_break_holder] --> only in 2014 and 2015, with a similar magnitude (<a href="#tab7">Table<!-- [et_pb_line_break_holder] --> 7</a>). No additional association was detected for <em>Xgwm540</em>,<!-- [et_pb_line_break_holder] --> even though it had been described as a yield-related<!-- [et_pb_line_break_holder] --> marker in a set of Serbian cultivars (Kobiljski <em>et al. </em>2007).<br /><!-- [et_pb_line_break_holder] --></font><font size="3" face="Arial, Helvetica, sans-serif"><em>Grain number per square meter. </em>Seven markers on four<!-- [et_pb_line_break_holder] --> regions showed a significant effect on GN m<sup>-2</sup> (<a href="#tab6">Table 6</a>),<!-- [et_pb_line_break_holder] --> notably those on chromosome 7AS which in turn were<!-- [et_pb_line_break_holder] --> associated with SF. This is expected, given that these two<!-- [et_pb_line_break_holder] --> traits are positively correlated (Acreche <em>et al. </em>2008, Terrile<!-- [et_pb_line_break_holder] --> <em>et al. </em>2017; Lo Valvo <em>et al. </em>2018; Alonso <em>et al</em>. 2018b). Three<!-- [et_pb_line_break_holder] --> markers had a significant marker-by-year interaction<!-- [et_pb_line_break_holder] --> effect. The <em>Rht-D1 </em>gene (4DS) showed the strongest<!-- [et_pb_line_break_holder] --> effect. In 2013 and 2014 the variation explained by the<!-- [et_pb_line_break_holder] --> marker was notably higher than in 2015 (<a href="#tab7">Table 7</a>).<!-- [et_pb_line_break_holder] --> <em><br /><!-- [et_pb_line_break_holder] --> Grain yield. </em>Grain yield was only associated with allelic<!-- [et_pb_line_break_holder] --> variation at <em>Rht-D1 </em>(4DS). A significant marker-by-year<!-- [et_pb_line_break_holder] --> interaction was detected, similar to the one observed for GN<!-- [et_pb_line_break_holder] --> m-2. Similarly, grain yield variation due to this gene was far<!-- [et_pb_line_break_holder] --> greater in 2013 and 2014 than it was in 2015 (<a href="#tab7">Table 7</a>).<!-- [et_pb_line_break_holder] --> <em><br /><!-- [et_pb_line_break_holder] --> Grain weight. </em>Eight marker-trait associations were<!-- [et_pb_line_break_holder] --> detected for grain weight on five regions, with no markerby-<!-- [et_pb_line_break_holder] --> year interaction. Markers with effect on SF on 7AS<!-- [et_pb_line_break_holder] --> also showed effect for this trait, with the opposite effect.<!-- [et_pb_line_break_holder] --> However, this is unsurprising, as a negative genetic<!-- [et_pb_line_break_holder] --> correlation has been reported between SF and grain<!-- [et_pb_line_break_holder] --> weight (Ferrante <em>et al. </em>2012, 2015; Gonzalez-Navarro<!-- [et_pb_line_break_holder] --> <em>et al. </em>2016; Terrile <em>et al. </em>2017; Alonso <em>et al. </em>2018b). The<!-- [et_pb_line_break_holder] --> variability explained by each marker ranged between<!-- [et_pb_line_break_holder] --> 2.6 and 5.2% (<a href="#tab6">Table 6</a>). In this population, Alonso <em>et</em><!-- [et_pb_line_break_holder] --> <em>al. </em>(2018a, b) reported a negative correlation of SF with<!-- [et_pb_line_break_holder] --> grain weight, which can lead to a tradeoff between SF<!-- [et_pb_line_break_holder] --> and grain weight (Ferrante <em>et al. </em>2015; Slafer <em>et al. </em>2015;<!-- [et_pb_line_break_holder] --> Gonzalez-Navarro <em>et al. </em>2016; Terrile <em>et al. </em>2017), but<!-- [et_pb_line_break_holder] --> also to unbalances in the sink/source ratio (Alonso <em>et al.</em><!-- [et_pb_line_break_holder] --> 2018a). Besides, markers associated with grain weight<!-- [et_pb_line_break_holder] --> were detected in several genomic regions, not linked to<!-- [et_pb_line_break_holder] --> those associated with SF (<a href="#tab6">Table 6</a>).<!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> <em>Test weight. </em>Three independent markers were associated<!-- [et_pb_line_break_holder] --> with test weight, without marker-by-year interaction<!-- [et_pb_line_break_holder] --> effect. The highest association was found with <em>Rht-D1</em><!-- [et_pb_line_break_holder] --> (4DS), which explained ~3% of the total variation (<a href="#tab6">Table 6</a>).<!-- [et_pb_line_break_holder] --> <em><br /><!-- [et_pb_line_break_holder] --> Plant height</em>. Thirteen markers showed a significant<!-- [et_pb_line_break_holder] --> effect on plant height; eleven of them, located on seven<!-- [et_pb_line_break_holder] --> regions, showed no marker-by-year interaction. The<!-- [et_pb_line_break_holder] --> remaining two markers, on chromosomes 4DS and<!-- [et_pb_line_break_holder] --> 5AL, did show such interaction (<a href="#tab6">Table 6</a>). Marker effect<!-- [et_pb_line_break_holder] --> ranged mainly between 3.2 and 6.7%, except for <em>Rht-D1</em>.<!-- [et_pb_line_break_holder] --> It showed a significant marker–by-year interaction<!-- [et_pb_line_break_holder] --> effect. Variation at this gene was associated with plant<!-- [et_pb_line_break_holder] --> height at all three years with a positive effect of allele<!-- [et_pb_line_break_holder] -->‘B’, but it explained a different portion of total variation<!-- [et_pb_line_break_holder] --> depending on the year (~18-24%; <a href="#tab7">Table 7</a>). Marker <em>Xgwm293 </em>on chromosome 5AL showed a similar pattern,<!-- [et_pb_line_break_holder] --> respectively, explaining 3.8 and 5.4% of plant height<!-- [et_pb_line_break_holder] --> variation in 2014 and 2015, with a negative effect of<!-- [et_pb_line_break_holder] --> allele ’B’. Regarding chromosome 5BL, marker <em>Xgwm213</em><!-- [et_pb_line_break_holder] --> was reported as associated with this trait by Wang <em>et al.</em><!-- [et_pb_line_break_holder] --> (2010) in a RIL population, and by Zhang <em>et al. </em>(2011) in<!-- [et_pb_line_break_holder] --> a doubled haploid population. Although not associated<!-- [et_pb_line_break_holder] --> with SF, these results give support for the marker<!-- [et_pb_line_break_holder] --> analysis approach used in the present study.<!-- [et_pb_line_break_holder] --> <br /><!-- [et_pb_line_break_holder] --> In this study, a low genome coverage was reached, partly<!-- [et_pb_line_break_holder] --> due to the lack of polymorphic microsatellites between<!-- [et_pb_line_break_holder] --> the parents of the RIL population. This is also reflected<!-- [et_pb_line_break_holder] --> in the phenotypic variability that was not explained by<!-- [et_pb_line_break_holder] --> the available genotypic information. However, a few<!-- [et_pb_line_break_holder] --> genomic regions associated with SF and related traits<!-- [et_pb_line_break_holder] --> were detected, which were stable trough different years<!-- [et_pb_line_break_holder] --> with different environmental conditions. Using the two<!-- [et_pb_line_break_holder] --> markers that were most associated with SF in this study<!-- [et_pb_line_break_holder] --> (located in chromosomes 5BS and 7AS, respectively), it was<!-- [et_pb_line_break_holder] --> possible to classify lines into high, intermediate and low<!-- [et_pb_line_break_holder] --> SF groups. Further studies with higher genome coverage<!-- [et_pb_line_break_holder] --> and additional phenotypic evaluations are needed to<!-- [et_pb_line_break_holder] --> validate the present results and to delimit genomic regions<!-- [et_pb_line_break_holder] --> containing genes that control SF.</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><strong>ACKNOWLEDGEMENTS</strong></font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><!-- [et_pb_line_break_holder] --> We would like to thank members of the<!-- [et_pb_line_break_holder] --> Grupo Trigo (Unidad Integrada EEA Balcarce<!-- [et_pb_line_break_holder] --> INTA – FCA, UNMdP) for their help with<!-- [et_pb_line_break_holder] --> the experiments and technical assistance.<!-- [et_pb_line_break_holder] --> Scholarships granted to M.P. Alonso by the<!-- [et_pb_line_break_holder] --> CONICET, to N.E. Mirabella by the INTA and to<!-- [et_pb_line_break_holder] --> J.S. Panelo by the CIC, and partial funding by<!-- [et_pb_line_break_holder] --> INTA (PNBIO 1131042), are acknowledged.</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"><strong>REFERENCES</strong></font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif">1. Abbate PE, Pontaroli AC, Lázaro L and Gutheim<!-- [et_pb_line_break_holder] --> F (2013) A method of screening for spike<!-- [et_pb_line_break_holder] --> fertility in wheat. The Journal of Agricultural<!-- [et_pb_line_break_holder] --> Science 151(3): 322-330. doi:10.1017/<!-- [et_pb_line_break_holder] --> S0021859612000068</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 2. Acreche MM, Briceño-Félix G, Sánchez JAM<!-- [et_pb_line_break_holder] --> and Slafer GA (2008) Physiological bases<!-- [et_pb_line_break_holder] --> of genetic gains in Mediterranean bread<!-- [et_pb_line_break_holder] --> wheat yield in Spain. European Journal of<!-- [et_pb_line_break_holder] --> Agronomy 28(3): 162-170. doi:10.1016/j.eja.2007.07.001</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 3. Aisawi KAB, Reynolds MP, Singh RP and<!-- [et_pb_line_break_holder] --> Foulkes MJ (2015) The physiological basis<!-- [et_pb_line_break_holder] --> of the genetic progress in yield potential of<!-- [et_pb_line_break_holder] --> CIMMYT spring wheat cultivars from 1966<!-- [et_pb_line_break_holder] --> to 2009. Crop Science 55(4): 1749-1764.<!-- [et_pb_line_break_holder] --> doi:10.2135/cropsci2014.09.0601</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 4. Albajes R, Cantero-Martínez C, Capell T,<!-- [et_pb_line_break_holder] --> Christou P, Farre A, Galceran J, López-<!-- [et_pb_line_break_holder] --> Gatius F, Marin S, Martín-Belloso O, Motilva<!-- [et_pb_line_break_holder] --> M, Nogareda C, Peman J, Puy J, Recasens J,<!-- [et_pb_line_break_holder] --> Romagosa I, Romero M, Sanchis V, Savin<!-- [et_pb_line_break_holder] --> R, Slafer GA, Soliva-Fortuny R, Viñas I,<!-- [et_pb_line_break_holder] --> and Voltas J (2013) Building bridges: an<!-- [et_pb_line_break_holder] --> integrated strategy for sustainable food<!-- [et_pb_line_break_holder] --> production throughout the value chain.<!-- [et_pb_line_break_holder] --> Molecular Breeding 32(4): 743-770.<!-- [et_pb_line_break_holder] --> doi:10.1007/s11032-013-9915-z</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 5. Alonso MP, Abbate PE, Mirabella NE, Merlos<!-- [et_pb_line_break_holder] --> FA, Panelo JS and Pontaroli AC (2018)<!-- [et_pb_line_break_holder] --> a Analysis of sink/source relations in<!-- [et_pb_line_break_holder] --> bread wheat recombinant inbred lines and<!-- [et_pb_line_break_holder] --> commercial cultivars under a high yield<!-- [et_pb_line_break_holder] --> potential environment. European Journal<!-- [et_pb_line_break_holder] --> of Agronomy 93: 82-87. doi:10.1016/j.eja.2017.11.007</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 6. Alonso MP, Mirabella NE, Panelo JS, Cendoya<!-- [et_pb_line_break_holder] --> MG and Pontaroli AC (2018)b Selection for<!-- [et_pb_line_break_holder] --> high spike fertility index increases genetic<!-- [et_pb_line_break_holder] --> progress in grain yield and stability in bread<!-- [et_pb_line_break_holder] --> wheat. Euphytica 214(7): 112. doi:10.1007/s10681-018-2193-4</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 7. Benbouza H, Jacquemin JM, Baudoin JP and<!-- [et_pb_line_break_holder] --> Mergeai G (2006) Optimization of a reliable,<!-- [et_pb_line_break_holder] --> fast, cheap and sensitive silver staining method<!-- [et_pb_line_break_holder] --> to detect SSR markers in polyacrylamide<!-- [et_pb_line_break_holder] --> gels. Biotechnologie, agronomie, société et<!-- [et_pb_line_break_holder] --> environnement 10(2): 77-81.</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 8. Bryan GJ, Collins AJ, Stephenson P, Orry A,<!-- [et_pb_line_break_holder] --> Smith JB and Gale MD (1997) Isolation<!-- [et_pb_line_break_holder] --> and characterisation of microsatellites<!-- [et_pb_line_break_holder] --> from hexaploid bread wheat. Theoretical<!-- [et_pb_line_break_holder] --> and Applied Genetics 94(5): 557-563.<!-- [et_pb_line_break_holder] --> doi:10.1007/s001220050451</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 9. Collard BC and Mackill DJ (2008) Markerassisted<!-- [et_pb_line_break_holder] --> selection: an approach for<!-- [et_pb_line_break_holder] --> precision plant breeding in the twentyfirst<!-- [et_pb_line_break_holder] --> century. Philosophical Transactions of<!-- [et_pb_line_break_holder] --> the Royal Society of London B: Biological<!-- [et_pb_line_break_holder] --> Sciences 363(1491): 557-572. doi:10.1098/<!-- [et_pb_line_break_holder] --> rstb.2007.2170</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 10. Deperi SI (2012) Detección de marcadores<!-- [et_pb_line_break_holder] --> moleculares asociados a la fertilidad de la<!-- [et_pb_line_break_holder] --> espiga de trigo. Trabajo de Graduación. Ing.<!-- [et_pb_line_break_holder] --> Agr. Universidad Nacional de Mar del Plata.<!-- [et_pb_line_break_holder] --> Facultad de Ciencias Agrarias, Balcarce. 27 p.<!-- [et_pb_line_break_holder] --> dat.num.il.</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 11. Elía M, Savin R and Slafer GA (2016) Fruiting<!-- [et_pb_line_break_holder] --> efficiency in wheat: physiological aspects<!-- [et_pb_line_break_holder] --> and genetic variation among modern<!-- [et_pb_line_break_holder] --> cultivars. Field Crops Research 191: 83-90.<!-- [et_pb_line_break_holder] --> doi:10.1016/j.fcr.2016.02.019</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 12. Ellis M, Spielmeyer W, Gale K, Rebetzke G and<!-- [et_pb_line_break_holder] --> Richards R (2002) “Perfect” markers for<!-- [et_pb_line_break_holder] --> the <em>Rht-B1b </em>and <em>Rht-D1b </em>dwarfing genes<!-- [et_pb_line_break_holder] --> in wheat. Theoretical and Applied Genetics<!-- [et_pb_line_break_holder] --> 105(6-7): 1038-1042. doi:10.1007/s00122-<!-- [et_pb_line_break_holder] --> 002-1048-4</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 13. FAO (2018, December 10) FAOSTAT database.<!-- [et_pb_line_break_holder] --> Agricultural crops: wheat area harvested/<!-- [et_pb_line_break_holder] --> yield. <a href="http://faostat.fao.org/" target="_blank">http://faostat.fao.org/</a></font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 14. Ferrante A, Savin R and Slafer GA (2012)<!-- [et_pb_line_break_holder] --> Differences in yield physiology between<!-- [et_pb_line_break_holder] --> modern, well adapted durum wheat cultivars<!-- [et_pb_line_break_holder] --> grown under contrasting conditions. Field<!-- [et_pb_line_break_holder] --> Crops Research 136: 52-64. doi:10.1016/j.fcr.2012.07.015</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 15. Ferrante A, Savin R and Slafer GA (2015)<!-- [et_pb_line_break_holder] --> Relationship between fruiting efficiency<!-- [et_pb_line_break_holder] --> and grain weight in durum wheat. Field<!-- [et_pb_line_break_holder] --> Crops Research 177: 109-116. doi: 10.1016/j.fcr.2015.03.009</font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif">16. Fischer RA (1984) Wheat. In: Smith, WH, Banta, SJ (eds.) Potential productivity of field crops under different environments; International Rice Research Institute. Los Baños, Philippines, p 129-154. </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 17. Fischer RA (2011) Wheat physiology: a review of recent developments. Crop and Pasture Science 62(2): 95-114. doi:10.1071/CP10344 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 18. Fischer RA and Rebetzke GJ (2018) Indirect selection for potential yield in earlygeneration, spaced plantings of wheat and other small-grain cereals: a review. Crop and Pasture Science 69(5): 439-459. doi:10.1071/CP17409 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 19. González FG, Aldabe ML, Terrile II and Rondanini DP (2014) Grain weight response to different post flowering source: sink ratios in modern high-yielding Argentinean Wheats differing in spike fruiting efficiency. Crop Science 54(1): 297-309. doi:10.2135/cropsci2013.03.0157 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 20. Gonzalez-Navarro OE, Griffiths S, Molero G, Reynolds MP and Slafer GA (2016) Variation in developmental patterns among elite wheat lines and relationships with yield, yield components and spike fertility. Field Crops Research 196: 294-304. doi:10.1016/j. fcr.2016.07.019 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 21. Guo Z, Slafer GA and Schnurbusch T (2016) Genotypic variation in spike fertility traits and ovary size as determinants of floret and grain survival rate in wheat. Journal of experimental botany 67(14): 4221-4230. doi:10.1093/jxb/erw200 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 22. Guo Z, Chen D, Alqudah AM, Röder MS, Ganal MW, and Schnurbusch, T (2017). Genome wide association analyses of 54 traits identified multiple loci for the determination of floret fertility in wheat. New Phytologist 214(1), 257-270. doi: 10.1111/nph.14342 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 23. Hai L, Guo H, Wagner C, Xiao S and Friedt W (2008) Genomic regions for yield and yield parameters in Chinese winter wheat (<em>Triticum aestivum </em>L.) genotypes tested under varying environments correspond to QTL in widely different wheat materials. Plant science 175(3): 226-232. doi:10.1016/j.plantsci.2008.03.006 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 24. Hallauer AR, Carena MJ and Miranda Filho JB (2010) Quantitative genetics in maize breeding. Springer, New York, 664p. </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 25. Haymes KM (1996) Mini-prep method suitable for a plant breeding program. Plant Molecular Biology Reporter 14(3): 280-284. doi:10.1007/BF02671664 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 26. Kobiljski B, Denčić S, Hristov N, Mladenov N, Quarrie S, Stephenson P and Kirby J (2007) Potential uses of microsatellites in markerassisted selection for improved grain yield in wheat. In Wheat Production in Stressed Environments. Springer, Dordrecht, p. 729- 736. doi:10.1007/1-4020-5497-1_89 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 27. Lo Valvo PJ, Miralles D J and Serrago RA (2018) Genetic progress in Argentine bread wheat varieties released between 1918 and 2011: Changes in physiological and numerical yield components. Field Crops Research 221: 314-321. doi:10.1016/j.fcr.2017.08.014 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 28. Martino DL, Abbate PE, Cendoya MG, Gutheim F, Mirabella NE and Pontaroli AC (2015) Wheat spike fertility: inheritance and relationship with spike yield components in early generations. Plant Breeding 134(3): 264-270. doi:10.1111/pbr.12262 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 29. Michelmore RW, Paran I and Kesseli RV (1991) Identification of markers linked to disease resistance genes by bulk segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proceedings of the National Academy of Sciences USA 88: 9828-9832. </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 30. Mirabella NE, Abbate PE, Ramirez IA and Pontaroli AC (2016) Genetic variation for wheat spike fertility in cultivars and early breeding materials. The Journal of Agricultural Science 154(1): 13-22. doi:10.1017/S0021859614001245 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 31. Pinheiro J, Bates D, Debroy S, Sarkar D and R Core Team (2017) Nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-128. 2016. R software. </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 32. Quarrie SA, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusić D, Waterman E, Weyen J, Schondelmaier J, Habash DZ, Farmer P, Saker, L, Clarkson DT, Abugalieva A, Yessimbekova M, Turuspekov Y, Abugalieva, R, Tuberosa R, Sanguineti MC, Hollington PA, Aragués R, Royo A and Dodig D (2005) A high-density genetic map of hexaploid wheat (<em>Triticum aestivum </em>L.) From the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments. Theoretical and Applied Genetics 110(5): 865-880. doi:10.1007/s00122-004-1902-7 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 33. Quarrie SA, Pekic Quarrie S, Radosevic R, Rancic D, Kaminska A, Barnes JD, Leverington M, Ceolini C and Dodig D (2006) Dissecting a wheat QTL for yield present in a range of environments: from the QTL to candidate genes. Journal of Experimental Botany 57(11): 2627-2637. doi10.1093/jxb/erl026 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 34. Ramirez IA, Abbate PE, Redi IW and Pontaroli AC (2018) Effects of photoperiod sensitivity genes <em>Ppd-B1 </em>and <em>Ppd-D1 </em>on spike fertility and related traits in bread wheat. Plant Breeding 137(3): 320-325. doi:10.1111/pbr.12585 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 35. R-Core Team (2017) R: A language and environment for statistical computing. </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 36. Reynolds M, Foulkes J, Furbank R, Griffiths S, King J, Murchie E, Parry M and Slafer GA (2012) Achieving yield gains in wheat. Plant, Cell and Environment 35(10): 1799-1823. doi:10.1111/j.1365-3040.2012.02588.x </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 37. Röder MS, Korzun V, Wendehake K, Plaschke J, Tixier MH, Leroy P and Ganal MW (1998) A microsatellite map of wheat. Genetics 149(4): 2007-2023. </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 38. Sadras VO (2007) Evolutionary aspects of the trade-off between seed size and number in crops. Field Crops Research 100(2-3): 125- 138. doi: 10.1016/j.fcr.2006.07.004 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 39. Sambrook J, Maccallum P and Russell D (2001) Molecular Cloning: A Laboratory Manual (2nd ed.). Cold spring harbor laboratory press. 910 p. </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 40. SCABUSA (2018, December 10). US Wheat and Barley Scab Initiative. <a href="https://scabusa.org/" target="_blank">https://scabusa.org/</a> </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 41. Slafer GA (2003) Genetic basis of yield as viewed from a crop physiologist’s perspective. Annals of Applied Biology 142(2): 117-128. doi:10.1111/j.1744-7348.2003.tb00237.x </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 42. Slafer GA, Savin R and Sadras VO (2014) Coarse and fine regulation of wheat yield components in response to genotype and environment. Field Crops Research 157: 71- 83. doi:10.1016/j.fcr.2013.12.004 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 43. Slafer GA, Elia M, Savin R, García GA, Terrile II, Ferrante A, Miralles DJ and Gonzalez FG (2015) Fruiting efficiency: an alternative trait to further rise wheat yield. Food and Energy Security 4(2): 92-109. doi: 10.1002/fes3.59 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 44. Somers DJ, Isaac P and Edwards K (2004) A high-density microsatellite consensus map for bread wheat (Triticum aestivum L.). Theoretical and Applied Genetics 109(6): 1105-1114. doi:10.1007/s00122-004-1740-7 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 45. Terrile II, Miralles DJ and González FG (2017) Fruiting efficiency in wheat (<em>Triticum</em> <em>aestivum </em>L): Trait response to different growing conditions and its relation to spike dry weight at anthesis and grain weight at harvest. Field Crops Research 201: 86-96. doi: 10.1016/j.fcr.2016.09.026 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 46. Wang Z, Wu X, Ren Q, Chang X, Li R and Jing R (2010) QTL mapping for developmental behavior of plant height in wheat (<em>Triticum</em> <em>aestivum </em>L.). Euphytica 174(3): 447-458. doi:10.1007/s10681-010-0166-3 </font></p><!-- [et_pb_line_break_holder] --><p><font size="2" face="Arial, Helvetica, sans-serif"> 47. Xue S, Zhang Z, Lin F, Kong Z, Cao Y, Li C, Yi H, Mei M, Zhu H, Wu J, Xu H, Zhao D, Tian D, Zhang C and Ma Z (2008) A highdensity intervarietal map of the wheat genome enriched with markers derived from expressed sequence tags. Theoretical and Applied Genetics 117(2): 181-189. doi:10.1007/s00122-008-0764-9 </font></p><!-- [et_pb_line_break_holder] --><font size="2" face="Arial, Helvetica, sans-serif">48. Zhang J, Hao C, Ren Q, Chang X, Liu G and Jing R (2011) Association mapping of dynamic developmental plant height in common wheat. Planta 234(5): 891-902.doi:10.1007/s00425-011-143</font><!-- [et_pb_line_break_holder] --></body><!-- [et_pb_line_break_holder] --></html>
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