תפריט נגישות
ניגודיות עדינהניגודיות גבוהה
מונוכרום
הדגשת קישורים
חסימת אנימציה
פונט קריא
סגור
איפוס הגדרות נגישות
להורדת מודול נגישות חינם תפריט נגישותניגודיות עדינהניגודיות גבוההמונוכרוםהדגשת קישוריםחסימת אנימציהפונט קריאסגוראיפוס הגדרות נגישותלהורדת מודול נגישות חינםNathan Fumia,
Michael Kantar,
Ya-ping Lin,
Roland Schafleitner,
Véronique Lefebvre,
Ilan Paran,
Andreas Börner,
Maria José Diez,
Jaime Prohens,
Arnaud Bovy,
Filiz Boyaci,
Gancho Pasev,
Pasquale Tripodi,
Lorenzo Barchi,
Giovanni Giuliano,
Derek W. Barchenger
Recently, there has been a substantial increase in high-throughput technologies that generate highly complex large datasets for use in the sciences. Plant breeding and genetics have benefited from this data explosion where many public and private institutions now implement genomic and phenomic data to predict performance thus informing germplasm selection. However, the multitude of methodologies and data generates a situation of strategic uncertainty. We set out to compare different methods of genomic and phenomic selection in the Capsicum core collection, developed through the G2P-SOL project, producing a combination of unique and similar selected genotypes for heat tolerance. Combined, the methods tested identified a total of 33 genotypes that show tremendous promise for use as parents in heat tolerance breeding: with 13 of these being present in more than 1 selection method. Combining classical and multispectral phenotyping methods produced better selection results than either method alone. When each method was conducted without being informed by the other, similar results were obtained. Our weighted rank-sum selection index identified 10 entries across environments that show heat tolerance, 8 of which are also selected within heat environments. This suggests that different breeding programs can reach similar results despite having different logistical constraints. Our case study within pepper germplasm using phenomic and genomic data exhibits the potential to compensate for the dearth of germplasm knowledge with high-throughput data as well as the converse, to compensate for logistical or financial constraint to new technologies with breeder knowledge.
Nathan Fumia,
Michael Kantar,
Ya-ping Lin,
Roland Schafleitner,
Véronique Lefebvre,
Ilan Paran,
Andreas Börner,
Maria José Diez,
Jaime Prohens,
Arnaud Bovy,
Filiz Boyaci,
Gancho Pasev,
Pasquale Tripodi,
Lorenzo Barchi,
Giovanni Giuliano,
Derek W. Barchenger
Recently, there has been a substantial increase in high-throughput technologies that generate highly complex large datasets for use in the sciences. Plant breeding and genetics have benefited from this data explosion where many public and private institutions now implement genomic and phenomic data to predict performance thus informing germplasm selection. However, the multitude of methodologies and data generates a situation of strategic uncertainty. We set out to compare different methods of genomic and phenomic selection in the Capsicum core collection, developed through the G2P-SOL project, producing a combination of unique and similar selected genotypes for heat tolerance. Combined, the methods tested identified a total of 33 genotypes that show tremendous promise for use as parents in heat tolerance breeding: with 13 of these being present in more than 1 selection method. Combining classical and multispectral phenotyping methods produced better selection results than either method alone. When each method was conducted without being informed by the other, similar results were obtained. Our weighted rank-sum selection index identified 10 entries across environments that show heat tolerance, 8 of which are also selected within heat environments. This suggests that different breeding programs can reach similar results despite having different logistical constraints. Our case study within pepper germplasm using phenomic and genomic data exhibits the potential to compensate for the dearth of germplasm knowledge with high-throughput data as well as the converse, to compensate for logistical or financial constraint to new technologies with breeder knowledge.
