ABSTRACT
Table 1 Some successful examples of marker-assisted selection and gene pyramiding in wheat
No. Target trait(s) Target loci Marker type
Effect of selection Reference
1 Powdery mildew resistance
3 genes combinations (Pm1+Pm3a, Pm1+Pm10, Pm3a+Pm10)
RFLP Powdery mildew resistance in introgression lines
Liu et al. (2000)
2 HMW-glutenins Major genes (Glu-A1 and Glu-D1)
AS-PCR Improvement in glutenin quality
de Bustos et al. (2001)
3 Scab resistance One QTL SSR Scab resistance in F2:3
Zhou et al. (2003)
4 Leaf rust resistance 2 genes (Lr19, Lr24)
STS Successful pyramiding in F3 lines
Singh et al. (2004)
5 FHB resistance, orange blossom wheat midge resistance and leaf rust resistance
6 QTL for FHB, Sm1 and Lr21
SSR Successful introduction of FHB, Sm1 and Lr21 resistance genes
Somers et al. (2005)
6 Increased GPC Gpc-B1 gene SSR Improved GPC in BC2F4 plants
Davies et al. (2006)
7 Pre-harvest sprouting (PHS)
2 QTL SSR Increased grain dormancy in white-grained wheat
Kottearachchi et al. (2006)
No. Target trait(s) Target loci Marker type
Effect of selection Reference
8 Fusarium head blight
3 QTL SSR Maximum grain fill phenotypic selection following the marker-based selection
Miedaner et al. (2006)
9 Powdery mildew resistance
3 QTL SSR Effective selection for powdery mildew resistance in both greenhouse and field experiments
Tucker et al. (2006)
10 Cereal cyst nematode resistance
2 genes (CreX, CreY)
SCAR Higher resistance in pyramided line
Barloy et al. (2007)
11 Dough properties, durable rust resistance and height
Rht-B1, Rht-D1, Rht8, Lr24/Sr24, Lr34/Yr18, GluA3, Glu-B3
SSR Increased genetic improvement for specific target genes, particularly at the early stage of a breeding programme
Kuchel et al. (2007)
12 Leaf rust resistance 4 genes (Lr1, Lr9, Lr24, Lr47)
STS, SCAR, CAPS
Effective selection for resistance gene
Nocente et al. (2007)
13 FHB resistance and DON content
3 QTL SSR Increased gain for major QTL only
Wilde et al. (2007)
14 Spot blotch resistance
4QTLs SSR Enhanced spot blotch resistance
Kumar et al. (2009)
15 Spot blotch resistance
4QTLs SSR Enhanced spot blotch resistance
Kumar et al. (2010)
16 Leaf rust resistance Lr24 + Lr28 SCAR Better yield potential than recipient parent
Chhuneja et al. (2011)
17 Adult plant powdery mildew resistance
QPm.caas-1A + QPm.caas-4DL + QPm.caas-2BS + QPm.caas-2BL + QPm.caas-2DL
– Expressed better APR to powdery mildew than the more resistant parent
Bai et al. (2012)
Table 1 (Continued)
(Continued)
No. Target trait(s) Target loci Marker type
Effect of selection Reference
18 Glutenin Glu-A1x, GluB1x and Glu-D1x
SSR Improved baking quality
Izadi-Darbandi and Yazdi-Samadi (2012)
19 Terminal heat tolerance
2QTLs SSR Enhanced heat tolerance
Paliwal et al. (2012)
20 Heat tolerance 14 QTL SSRs Validation of QTL
Sadat et al. (2013)
21 Spot blotch resistance
Co-location with leaf rust resistance gene
SSR Spot blotch resistant genes was co-located with Lr34 and Lr46
Lillemo et al. (2013)
22 Terminal heat tolerance
7 QTLs SSR Enhanced heat tolerance
Tiwari et al. (2013)
23 Zn and Fe content in grain
10 QTLs SNP and DArT
Increased Zn and Fe content in grain
Srinivasa et al. (2014)
24 Grain protein content
Gpc-B1 gene SSR Enhanced grain protein content in a welladopted variety HUW234
Vishwakarama et al. (2014)
25 Grain protein content
Gpc-B1gene SSR Enhanced >3% grain protein content as compared to recipient variety
Mishra et al. (2015)
26 Stem rust resistant genes
Sr25, SrWeb, Sr50
SSR All these genes pyramided in HUW234, a mega variety of NEPZ, India
Yadav et al. (2015)
27 Spot blotch resistance
2 QTLs SSR Pyramided 2 QTLs for spot blotch resistance in to welladopted variety
Vasistha et al. (2015)
Table 1 (Continued)
3.2 Genomic selection Genomic selection is an emerging technique of selection (Meuwissen et al., 2001), in which joint merit of a large number of mapped markers across the genome is used (de Koning and McIntyre, 2012). The major difference between conventional breeding and genomic selection approaches is that in the former, selection is based on the phenotypic performance, whereas in the latter, genetic makeup of plants/lines is used as the deciding
factor. The successful utilization of this approach became possible due to major advances in the development of statistical tools such as BayesB and the GBLUP (Gianola et al., 2009; Gianola, 2013; de los Campos et al., 2013; Daetwyler et al., 2013). Burgueño et al. (2012) first used GBLUP for assessing G × E under genomic selection, while Heslot et al. (2014) used crop-modelling data for same assessment. Jarquin et al. (2014) proposed a random effect GBLUP model where random variance-covariance structures of markers and environmental covariables were used to determine the effects of markers and environmental covariates.
