Molecular identification of genetically distinct accessions in the USDA chickpea core collection

Pisum Genetics

2007—Volume 39

Brief Communications

Molecular identification of genetically distinct accessions in the USDA chickpea core collection

Varshney, R.K.¹,

Coyne, C.J.²,

Swamy, P.¹ and Hoisingto

¹Intl. Crops Res. Inst. for the Semi-Arid Tropics (ICRISAT), Patancheru, India

²U.S. Dept of Agri.-Agric. Res. Stat. (USDA-ARS)
n, D.¹ Washington State Univ., Pullman, WA, U.S.A.

Summary

Knowledge of the molecular genetic variation of the accessions of core collections will be important for their
efficient use in breeding programs, and for conservation purposes. The present study was undertaken for
genotyping the part of the USDA chickpea core collection (Hannan et al 1994) with 20 microsatellite or simple
sequence repeat (SSR) markers. In addition to understand the molecular diversity in the core collection, the
genetic relationship was studied. A total of 376 accessions from the USDA chickpea core collection were
genotyped. Twenty SSR markers revealed a total of 388 alleles among the 376 accessions. In the USDA core
collection, the shared allele frequency (SAF) varied from 7.5% to 47.5% with an average of 21.6%. In the present
study, the structure of the population was determined by using K=4 based on model-based (Bayesian) clustering
algorithm.

Understanding of the molecular diversity in germplasm collections has several applications and advantages.
These include genebank management issues and association mapping studies. Genebank management uses of
molecular diversity information include maintaining genetic diversity, increasing diversity through knowledge-
based acquisition, reducing redundancy and creating association mapping studies populations. Association
mapping studies have increased the possible ways of utilizing the genetic diversity of the 6,193 accessions in the
USDA chickpea germplasm collection. The initial step for using the chickpea core collection for these studies is
determining the underlying population structure to improve the likelihood of finding true associations between a
gene and trait.

A total of 376 accessions from the USDA chickpea core collection were used and DNA was extracted from
bulks of ten plants per accession. Twenty SSRs were used to genotype the collection (Table 1). Standard SSR
PCR followed by capillary electrophoresis was used to collect the genotypes. A dendogram based on similarities
was generated using NTSYS pc software (Rolf 2000). The genetic diversity structure of the collection was
determined by using the clustering algorithm of STRUCTURE software (Pritchard et al. 2000).

Pisum Genetics

2007—Volume 39

Brief Communications

Table 1. Microsatellites used to genotype the USDA chickpea core collection from Huttel et al 1999 and Winter
et al. 1999.


CaSTMS15	TA113	TA200	TA72
CaSTMS2	TA118	TA206	TR29
CaSTMS21	TA130	TA22	TR31
NCPGR19	TA135	TA64	TR7
NCGRP6	TA14	TA71	TS84

Twenty SSR markers revealed a total of 388 alleles among the 376 accessions. The shared allele frequency
varied from 7.5% to 47.5% with an average of 21.6%. These results suggest a high level of genetic diversity
present in the germplasm investigated. Our preliminary determination of the population structure is K=4

Table 2. The accessions that were the most distant from the rest of the core are listed. All but one accession
are from the USDA Regional Pulse Improvement Program conducted in Iran and India in the 1960's and early
70's.


Accession	RPIP	Origin	Accession	RPIP	Origin
PI 360108	12-069-06278	India	PI 360641	12-071-06533	Iran
PI 426536	K202 (not RPIP)	Pakistan	PI 360680	12-069-06208	India
PI 360662	12-075-00858	Italy	PI 450658	12-069-00434	India
PI 360687	12-139-00922	Spain	PI 450669	12-069-0475	India
PI 450843	12-069-01490	India	PI 450717	12-069-00711	IndIA
PI 451671	12-153-06965	Turkey	PI 451597	12-071-07051	Iran

Table 3. Thirty-seven sets of accessions that were indistinct based on 20 SSRs indicated by same cell location
in this table.


PI 359009 PI 451501		PI 359805 PI 359815	PI 439831 PI 439858	PI 450852 PI 450902	PI 359607 PI 359968	PI 450654 PI 451394

	PI 360660 PI 360664	PI 426536 PI 439834	PI 359245 PI 462168	PI 359186 PI 502991	PI 193487 PI 343016	PI 207470 PI 451085

PI 359481 PI 359913		PI 214311 PI 426571	PI 360133 PI 360328	PI 360342 PI 360574	PI 451032 Annigeri	PI 359260 PI 450772

	PI 359489 PI 509156	PI 360672 PI 426546	PI 368485 PI 451664	PI 360358 PI 450585	PI 360348 PI 450622	PI 426195 PI 503010

PI 360078 PI 451127		PI 215588 PI 360609 PI 360630	PI 219728 PI 359363 PI 359969	PI 359228 PI 359316 PI 450930	PI 359372 PI 359544 PI 451054	PI 273879 PI 359213 PI 450975


	PI 358916 PI 360063 PI 451688	PI 360418 PI 360470 PI 451622	PI 257584 PI 315813 PI 358930 PI 360194	PI 359374 ICCV2 PI 359560 PI 462176	PI 359801 PI 360292 PI 360315 PI 426193	PI 359007 PI 359555 PI 359673 PI 360599

PI 359595, PI 360230, PI 360304, PI 360344, PI 360425, PI 360456

1. Hannan, R.M., Kaiser, W.J. and Muehlbauer, F.J. 1994. In: Agronomy Abstracts. ASA, Madison, WI. p.

217.

2. Huttel, B., Winter, P., Weising, K., Choumane, W., Weigand, F. and Kahl, G. 1999. Genome 42: 210—217.

3. Pritchard, J.K., Stephans, M., Rosenberg, N.A. and Donnelly, P. 2000. Amer. J. Genet. 67: 170-181.

4. Rohlf, F.J. 2000. NTSYSpc: Exeter Software, NY.

5. Winter, P., Pfaff, T., Udupa, S.M., Huttel, B., Sharma, P.C., Sahim, S., Arreguin-Espinoza, R., Weigand, F.,

Muehlbauer, F.J. and Kahl, G. 1999. Mol. Gen. Genet. 262: 90—101.