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v More	 than	 200	 breeders	 and	 agriculturists	
from	NARS	trained	to	use	modern	breeding	
management	and	decision	tool	(Fig.	1)	
v Establishment	 of	 nodes	 and	 migra@on	 of	
historical	 data	 from	 several	 breeding	
programs	in	Africa	into	the	BMS	tool		
v 15	research	scien@sts	trained	in	Demand-led	
variety	design	approach	
v SSR	 genotyping	 of	 tubers	 (yam,	 cassava),	
cereals	 (rice,	 maize),	 tree	 crops	 (mango,	
eucalyptus),	 vegetable	 (biJer	 leaf),	 forage,	
fish	 and	 livestock	 (caJle,	 goat)	 from	 sub	
Saharan	Africa,	USA	and	Ecuador	(Fig	2)	
v SNP	 genotyping	 of	 3230	 samples	 including	
forage,	maize,	rice,	sorghum,	fish	and	sheep	
from	CG	centers	(70%),	Na@onal	Agricultural	
Research	 System	 (NARS)	 (15%)	 and	 the	
Program	 for	 African	 Seed	 System	 (PASS)	
(15%)	(Fig.	3).		
v Genomic	 assisted	 breeding	 jointly	
implemented		with	partners	in	Tanzania	and	
Eritrea	
Molecular	Breeding	Pla0orm	at	the	BecA-ILRI	Hub:		
Suppor>ng	breeders	in	the	region	
	
*Nasser	Yao,	Mary	Wambugu,	Francesca	Stomeo,	Mark	Wamalwa,	Appolinaire	Djikeng	and	*Josephine	Birungi	
Biosciences	eastern	and	central	Africa-InternaFonal	Livestock	Research	InsFtute,	(BecA-ILRI)	Hub,Nairobi,	Kenya	
About	the	Molecular	Breeding	Pla0orm	
The	Breeding	plaWorm	at	the	BecA-ILRI	Hub	aims	at	enhancing	plant	breeding	and	livestock	research	using	modern	breeding	tools.	The	plaWorm	is	built	
around	three	key	components:	the	Breeding	Management	System	(BMS),	the	Integrated	Genotyping	Service	and	Support	(IGSS)	and	the	Demand-led	
variety	design.	BMS	is	a	suite	of	comprehensive	tools,	embedded	in	a	single	place,	and	commonly	used	by	breeders	for	their	day	to	day	breeding	
ac@vi@es.	IGSS	is	an	innova@ve	genotyping	service	and	support	to	local	plant/livestock	breeding	programs	and	small	and	medium	enterprises	in	sub-
Saharan	Africa	to	accelerate	the	rate	of	gene@c	gain,	development	and	release	of	new	crop	varie@es	and	improved	livestock.	Through	the	demand-led	
variety	design	approach,	the	breeding	plaWorm	aims	at	making	plant	breeding	in	Africa	a	business	model	responsive	to	market	demand.	The	plaWorm	
also	generates	and	manages	genomic	and	marker	data	and	provides	support	to	breeders	and	other	scien@sts	in	integra@ng	DNA	marker	assays	and	
genomic	tools	into	their	breeding	program	and	research	ac@vi@es	for	both	cul@vated	and	orphan	crops	and	livestock	species.	Major	fields	covered	
under	the	plaWorm	include:	1)	training	of	breeders	to	make	efficient	use	of	breeding	decision	support	tools	(BMS	and	IGSS),	2)	provision	of	advice	and	
guidance	in	elabora@ng	an	holis@c	breeding	strategies	(demand-led	variety	design)	using	modern	breeding	tools,	3)	genome	sequencing-based	gene@c	
profiling	 and	 genera@on	 of	 various	 genotyping	 data	 types	 from	 low	 to	 high	 throughput	 facili@es,	 4)	 support	 in	 popula@on	 development	 and	 data	
management	followed	by	required	analyses,	interpreta@on	of	result	and	suppor@ng	scien@sts	to	get	the	suitable	breeding	decision	for	the	next	step,	5)	
genome	wide	associa@on	study	and	6)	genomic	selec@on.		
*For	more	informa@on	contact	Dr	Nasser	Yao/Dr	Josephine	Birungi	
N.Yao@cgiar.org/J.Birungi@cgiar.org	●	hub.africabiosciences.org		
			
Funding:	Bill	and	Melinda	Gates	Founda@on	(BMGF)	
																	Syngenta	Founda@on	for	Sustainable	Agriculture	(SFSA)	
This	document	is	licensed	for	use	under	a	Crea@ve	Commons	AJribu@on	–Non	commercial-Share	Alike	3.0	Unported	License																									February	2016	
Partnerships	
Outputs	
v Modern	breeding	tool	adopted	and	used	
in	several	breeding	programs	in	eastern,	
central	 and	 southern	 Africa	 including	
Ethiopia,	 Uganda,	 Tanzania,	 Sudan	 and	
Kenya	
v Breeders	 and	 agriculturists	 beJer	
integra@ng	 historical	 and	 manual	 data	
directly	into	BMS	to	accelerate	breeding	
efforts	
v Major	 QTL	 for	 Maize	 Lethal	 Necrosis	
(MLN)	 iden@fied	 and	 used	 to	 develop	
maize	MLN	resistant	lines	
v Gene@c	 profiling	 of	 350	 rice	 accessions,	
comprising	 a	 unique	 collec@on	 of	 the	
African	 Oryza	 and	 key	 rice	 germplasms	
used	by	the	rice	community	as	parental	
lines	 or	 elite	 material,	 now	 serving	
several	 breeding	 purposes	 at	 the	 Africa	
Rice	Centre	
v Ins@tu@ons	 (Naliendele	 Agricultural	
Research	Ins@tute	(NARI)	in	Tanzania	and	
the	 Hamalmelo	 College	 of	 Agriculture,	
Eritrea)	 implemen@ng	 genomic-based/
molecular	assisted		breeding	
Outcomes	
•  Gene@c	 profiling	 of	 key	 crop	 and	 livestock	 commodi@es	 from	 breeding	
programs	in	Ethiopia,	Kenya,	Rwanda,	Tanzania	and	Uganda	
•  Design	of	product	profiles	for	major	crops/livestock	
•  Development	of	cul@var	development	pipelines	for	major	crop/livestock		
•  Linkage	 with	 the	 Global	 Open-source	 Breeding	 and	 Informa@cs	 Ini@a@ve	
(GOBII)	and	Diversity	Seek	(DivSeek)	ini@a@ves		
Poten>al	to	scale-up	
0	 10	 20	 30	 40	 50	 60	 70	 80	 90	 100	
Private	Sector	
CG	center	
ABCF	
NARS	
University	
Number	of	trainees	per	ins>tu>on	
Figure	1:	Number	of	trainees	per	country	and	per	ins>tu>on	
Figure	 3:	 Number	 of	 samples	 processed	 per	 commodity	 and	 per	
ins>tu>on	under	IGSS	
0	
50000	
100000	
150000	
200000	
250000	
300000	
2009	 2010	 2011	 2012	 2013	 2014	
Number	of	samples		
Years		
Trends	in	Genotyping	Service	Provision	
Figure	2:	SSR	genotyping	service	provision	and	trend	from	2009	to	2014	
0	 5	 10	 15	 20	 25	 30	 35	 40	
Zambia	
Benin	
Cote	d'Ivoire	
Cameroon	
DRC	
Burundi	
Rwanda	
Eritrea	
Tanzania	
Ethiopia	
Kenya	
Sudan	
Uganda	
Number	of	trainees	per	country

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Molecular Breeding Platform at the BecA-ILRI Hub: Supporting breeders in the region

  • 1. v More than 200 breeders and agriculturists from NARS trained to use modern breeding management and decision tool (Fig. 1) v Establishment of nodes and migra@on of historical data from several breeding programs in Africa into the BMS tool v 15 research scien@sts trained in Demand-led variety design approach v SSR genotyping of tubers (yam, cassava), cereals (rice, maize), tree crops (mango, eucalyptus), vegetable (biJer leaf), forage, fish and livestock (caJle, goat) from sub Saharan Africa, USA and Ecuador (Fig 2) v SNP genotyping of 3230 samples including forage, maize, rice, sorghum, fish and sheep from CG centers (70%), Na@onal Agricultural Research System (NARS) (15%) and the Program for African Seed System (PASS) (15%) (Fig. 3). v Genomic assisted breeding jointly implemented with partners in Tanzania and Eritrea Molecular Breeding Pla0orm at the BecA-ILRI Hub: Suppor>ng breeders in the region *Nasser Yao, Mary Wambugu, Francesca Stomeo, Mark Wamalwa, Appolinaire Djikeng and *Josephine Birungi Biosciences eastern and central Africa-InternaFonal Livestock Research InsFtute, (BecA-ILRI) Hub,Nairobi, Kenya About the Molecular Breeding Pla0orm The Breeding plaWorm at the BecA-ILRI Hub aims at enhancing plant breeding and livestock research using modern breeding tools. The plaWorm is built around three key components: the Breeding Management System (BMS), the Integrated Genotyping Service and Support (IGSS) and the Demand-led variety design. BMS is a suite of comprehensive tools, embedded in a single place, and commonly used by breeders for their day to day breeding ac@vi@es. IGSS is an innova@ve genotyping service and support to local plant/livestock breeding programs and small and medium enterprises in sub- Saharan Africa to accelerate the rate of gene@c gain, development and release of new crop varie@es and improved livestock. Through the demand-led variety design approach, the breeding plaWorm aims at making plant breeding in Africa a business model responsive to market demand. The plaWorm also generates and manages genomic and marker data and provides support to breeders and other scien@sts in integra@ng DNA marker assays and genomic tools into their breeding program and research ac@vi@es for both cul@vated and orphan crops and livestock species. Major fields covered under the plaWorm include: 1) training of breeders to make efficient use of breeding decision support tools (BMS and IGSS), 2) provision of advice and guidance in elabora@ng an holis@c breeding strategies (demand-led variety design) using modern breeding tools, 3) genome sequencing-based gene@c profiling and genera@on of various genotyping data types from low to high throughput facili@es, 4) support in popula@on development and data management followed by required analyses, interpreta@on of result and suppor@ng scien@sts to get the suitable breeding decision for the next step, 5) genome wide associa@on study and 6) genomic selec@on. *For more informa@on contact Dr Nasser Yao/Dr Josephine Birungi N.Yao@cgiar.org/J.Birungi@cgiar.org ● hub.africabiosciences.org Funding: Bill and Melinda Gates Founda@on (BMGF) Syngenta Founda@on for Sustainable Agriculture (SFSA) This document is licensed for use under a Crea@ve Commons AJribu@on –Non commercial-Share Alike 3.0 Unported License February 2016 Partnerships Outputs v Modern breeding tool adopted and used in several breeding programs in eastern, central and southern Africa including Ethiopia, Uganda, Tanzania, Sudan and Kenya v Breeders and agriculturists beJer integra@ng historical and manual data directly into BMS to accelerate breeding efforts v Major QTL for Maize Lethal Necrosis (MLN) iden@fied and used to develop maize MLN resistant lines v Gene@c profiling of 350 rice accessions, comprising a unique collec@on of the African Oryza and key rice germplasms used by the rice community as parental lines or elite material, now serving several breeding purposes at the Africa Rice Centre v Ins@tu@ons (Naliendele Agricultural Research Ins@tute (NARI) in Tanzania and the Hamalmelo College of Agriculture, Eritrea) implemen@ng genomic-based/ molecular assisted breeding Outcomes •  Gene@c profiling of key crop and livestock commodi@es from breeding programs in Ethiopia, Kenya, Rwanda, Tanzania and Uganda •  Design of product profiles for major crops/livestock •  Development of cul@var development pipelines for major crop/livestock •  Linkage with the Global Open-source Breeding and Informa@cs Ini@a@ve (GOBII) and Diversity Seek (DivSeek) ini@a@ves Poten>al to scale-up 0 10 20 30 40 50 60 70 80 90 100 Private Sector CG center ABCF NARS University Number of trainees per ins>tu>on Figure 1: Number of trainees per country and per ins>tu>on Figure 3: Number of samples processed per commodity and per ins>tu>on under IGSS 0 50000 100000 150000 200000 250000 300000 2009 2010 2011 2012 2013 2014 Number of samples Years Trends in Genotyping Service Provision Figure 2: SSR genotyping service provision and trend from 2009 to 2014 0 5 10 15 20 25 30 35 40 Zambia Benin Cote d'Ivoire Cameroon DRC Burundi Rwanda Eritrea Tanzania Ethiopia Kenya Sudan Uganda Number of trainees per country