Development of a 63K SNP Array for Cotton and High-Density Mapping of Intraspecific and Interspecific Populations of Gossypium spp.

G3: Genes|Genomes|Genetics Early Online, published on April 22, 2015 as doi:10.1534/g3.115.018416

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Development of a 63K SNP Array for Cotton and High‐density Mapping of Intra‐

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and Inter‐specific Populations of Gossypium spp.

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Amanda M. Hulse‐Kemp1,2, Jana Lemm3, Joerg Plieske3, Hamid Ashrafi4, Ramesh Buyyarapu5,

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David D. Fang6, James Frelichowski7, Marc Giband8,9, Steve Hague1, Lori L. Hinze7, Kelli J.

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Kochan10, Penny K. Riggs10,2, Jodi A. Scheffler11, Joshua A. Udall12, Mauricio Ulloa13, Shirley S.

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Wang7, Qian‐Hao Zhu14, Sumit K. Bag15, Archana Bhardwaj15, John J. Burke13, Robert L.

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Byers12, Michel Claverie8, Michael A. Gore16, David B. Harker12, Md S. Islam6, Johnie N.

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Jenkins17, Don C. Jones18, Jean‐Marc Lacape8, Danny J. Llewellyn14, Richard G. Percy7, Alan E.

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Pepper19,2, Jesse A. Poland20, Krishan Mohan Rai15, Samir V. Sawant15, Sunil Kumar Singh15,

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Andrew Spriggs14, Jen M. Taylor14, Fei Wang1, Scott M. Yourstone12, Xiuting Zheng1, Cindy T.

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Lawley21, Martin W. Ganal3, Allen Van Deynze4, Iain W. Wilson14, David M. Stelly1,2,22

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Department of Soil & Crop Sciences, Texas A&M University, College Station, Texas 77843, USA

Interdisciplinary Degree Program in Genetics, Texas A&M University, College Station, Texas 77843, USA

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TraitGenetics GmbH, 06466 Gatersleben, Germany

Department of Plant Sciences and Seed Biotechnology Center, University of California‐Davis, Davis, California 95616, USA

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Dow AgroSciences, Trait Genetics & Technologies, Indianapolis, Indiana 46268, USA USDA‐ARS‐SRRC, Cotton Fiber Bioscience Research Unit, New Orleans, Louisiana 70124, USA USDA‐ARS‐SPARC, Crop Germplasm Research Unit, College Station, Texas 77845, USA CIRAD, UMR AGAP, Montpellier, F34398, France EMBRAPA, Algodão, Nucleo Cerrado, 75.375‐000 Santo Antônio de Goias, GO, Brazil Department of Animal Science, Texas A&M University, College Station, Texas, 77843, USA 1

© The Author(s) 2013. Published by the Genetics Society of America.

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USDA‐ARS, Jamie Whitten Delta States Research Center, Stoneville, Mississippi 38776, USA Brigham Young University, Plant and Wildlife Science Department, Provo, Utah 84602, USA

USDA‐ARS, PA, Plant Stress and Germplasm Development Research Unit, Lubbock, Texas 79415, USA

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CSIRO Agriculture Flagship, Black Mountain Laboratories, ACT 2601, Australia

CSIR‐National Botanical Research Institute, Plant Molecular Biology Division, Lucknow‐226001, UP, India 16

Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York, 14853, USA 17

USDA‐ARS, Genetics and Precision Agriculture Research, Missississippi State, Mississippi, 39762, USA

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Cotton Incorporated, Agricultural Research, Cary, North Carolina, 27513, USA Department of Biology, Texas A&M University, College Station, Texas, 77843, USA

Wheat Genetics Resource Center, Department of Plant Pathology and Department of Agronomy, Kansas State University, Manhattan, Kansas, 66506, USA

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Cotton sequences utilized for array construction are available at: http://www.cottongen.org

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Illumina Inc., 499 Illinois Street, San Francisco, California, 94158, USA

Corresponding Author, email: [email protected]

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Running title:  The CottonSNP63K Array: Development and High‐Density Genetic Mapping

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Key words:  linkage analysis, recombination, interspecific SNPs, intraspecific SNPs, breeding

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Corresponding author:  David M. Stelly, Texas A&M University, 370 Olsen Blvd., 2474 TAMU,

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College Station, TX 77843‐2474.  979‐845‐2745 (Phone). 979‐845‐0456 (Fax).  [email protected].

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ABSTRACT

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High‐throughput genotyping arrays provide a standardized resource for plant breeding

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communities that are useful for a breadth of applications including high‐density genetic

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mapping, genome‐wide association studies (GWAS), genomic selection (GS), complex trait

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dissection and studying patterns of genomic diversity among cotton cultivars and wild

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accessions. We have developed the CottonSNP63K, an Illumina Infinium array containing assays

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for 45,104 putative intra‐specific single nucleotide polymorphism (SNP) markers for use within

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the cultivated cotton species Gossypium hirsutum L. and 17,954 putative inter‐specific SNP

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markers for use with crosses of other cotton species with G. hirsutum. The SNPs on the array

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are developed from 13 different discovery sets that represent a diverse range of G. hirsutum

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germplasm and five other species: G. barbadense L., G. tomentosum Nuttal ex Seemann, G.

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mustelinum Miers x Watt, G. armourianum Kearny, and G. longicalyx J.B. Hutchinson & Lee. The

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array was validated with 1,156 samples to generate cluster positions to facilitate automated

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analysis of 38,822 polymorphic markers. Two high‐density genetic maps containing a total of

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22,829 SNPs were generated for two F2 mapping populations, one intra‐specific and one inter‐

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specific, 3,533 SNP markers were co‐occurring in both maps. The produced intra‐specific

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genetic map is the first saturated map that associates into 26 linkage groups corresponding to

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the number of cotton chromosomes for a cross between two G. hirsutum lines. The linkage

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maps were shown to have high levels of collinearity to the JGI G. raimondii Ulbrich reference

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genome sequence. The developed CottonSNP63K array and cluster file along with the marker

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sequences is a valuable new resource for the global cotton research community.

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Introduction

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Cotton (Gossypium spp.) is the world’s most important renewable natural textile fiber crop and

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also a significant source of oilseed. Cotton is grown in over 75 countries with over 118 million

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bales of cotton fiber produced in 2013 (National Cotton Council, www.cotton.org). In the

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United States, the 2013 crop of 12.9 million bales was valued at $5.2 billion and had an

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estimated overall direct economic impact of $27.6 billion (US Department of Agriculture;

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National Agriculture Statistics Service, www.nass.usda.gov). Roughly 75% of cotton fiber is used

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for apparel products, and 18% for home furnishings and 7% for industrial products (National

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Cotton Council, www.cotton.org). In the past, cottonseed was often regarded as waste, but has

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recently become marketable as a protein source for livestock including dairy cattle and poultry,

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as well as for production of cottonseed oil, which is used in the food product industry. While 52

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different Gossypium species have been identified including 7 tetraploids designated AD# and 45

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diploids classified into eight genome designations (A‐G & K) (Wendel et al. 2009, Grover et al.

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2014), contemporary cotton production relies primarily on allotetraploid Gossypium hirsutum L.

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(2n=4x=52), which accounts for over 95% of the world crop. The remaining production relies

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largely on G. barbadense L., another allotetraploid, and to lesser extents on two A‐genome

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diploids (2n=2x=26), G. arboreum L. and G. herbaceum L., primarily in Asia. The Gossypium

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genus contains approximately 51 species, including 6 allotetraploid species and 45 diploid

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species (Wendel et al. 2009, Ulloa et al. 2013, Grover et al. 2014). While diploid species of

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interest such as G. longicalyx, an F‐genome diploid, and G. armourianum, a D‐genome diploid,

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cannot be readily crossed with the primary cultivated species G. hirsutum, allotetraploid species

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such as G. barbadense, G. tomentosum, and G. mustelinum can be readily intercrossed.   5

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Allotetraploid cotton was formed approximately 1‐2 million years ago (mya) (Wendel et al.

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2009) in a polyploidization event between two diploids: one with an A‐like genome and the

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other with a D‐like genome. The ancestral genomes resemble the genomes of extant diploids G.

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arboreum (A2) and G. raimondii (D5). The G. hirsutum genome is designated [AD]1, and has a C‐

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value of approximately 2.4 Gbp; the 26 chromosomes are numbered according to genome

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ancestry, where chromosomes 1‐13 are of A‐genome origin and 14‐26 are of D‐genome origin

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(Brown 1980, Wendel et al. 2009). Because the ancestral genomes of cotton diverged only 5‐10

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mya, and the polyploidization event was 1‐2 mya, initial efforts to develop single nucleotide

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polymorphism (SNP) markers were hindered by the co‐identification of interlocus SNP variants

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between the two subgenomes in the tetraploid (homeo‐SNPs). Recent developments from the

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cotton community including the publication of a high‐quality genome reference sequence for G.

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raimondii (Paterson et al. 2012) and draft sequences for G. arboreum (Li et al. 2014) and G.

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raimondii (Wang et al. 2012) have aided development of large SNP data sets in gene‐based and

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genome‐based identification efforts (Van Deynze et al. 2009, Byers et al. 2012, Lacape et al.

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2012, Rai et al. 2013, Gore et al. 2014, Islam et al. 2014, Zhu et al. 2014, Hulse‐Kemp et al. 2014,

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Hulse‐Kemp et al. 2015). Increasingly larger genetic maps with greater resolution and saturation

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have been generated as numbers of primarily SSR markers increased, culminating in the recent

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publication of a consensus genetic map that comprises more than 8,200 loci based on primarily

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six inter‐specific (G. hirsutum x G. barbadense) populations (Blenda et al. 2012). Few of the

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published genetic maps contain SNP markers, and those which have included SNPs are limited

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to inclusion of a couple hundred SNPs with SSRs (Byers et al. 2012, Yu et al. 2012, Gore et al.

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2014) to at most the inclusion of a couple of thousand SNPs (Islam et al. 2014, Zhu et al. 2014). 6

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SNPs have been difficult to develop in silico, due to low polymorphism and low divergence

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among polyploid genomes (Van Deynze et al. 2009). The increasing efficiency of next

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generation sequencing and improved in silico methods have allowed SNP development at the

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whole genome level even for the 2.4 Gbp allotetraploid cotton genome (Hulse‐Kemp et al.

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2014).

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In cultivated cotton, as well as in other crop species, there is considerable interest in being able

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to genotype a large number of SNP markers in a high‐throughput manner. With advancing array

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technology, large‐scale genotyping can be performed in a massively parallel fashion to assay

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thousands of loci simultaneously in a short time. Genotype data obtained from the high‐

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throughput assay can then be utilized to evaluate genetic diversity, construct genetic linkage

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maps, dissect the genetic architecture of important traits (Truco et al. 2013) and in many more

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novel applications (Ganal et al. 2012). SNP arrays for many crop species have recently been

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developed and have been utilized to advance breeding and discoveries in those crop systems

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(Ganal et al. 2011, Hamilton et al. 2011, Truco et al. 2013, Chen et al. 2013, Song et al. 2013,

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Tinker et al. 2014, Bianco et al. 2014, Wang et al. 2014, Dalton‐Morgan et al. 2014).

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The objectives of this study were 1) to utilize recently identified SNPs to develop a standardized

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large‐scale SNP genotyping array for cotton, 2) to evaluate the performance and reproducibility

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of the array on a large set of samples, 3) to develop a cluster file which can be used to help

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automate genotyping for allotetraploid cotton, and 4) to produce high‐density linkage maps

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based on two biparental F2 populations in an intra‐specific cross (G. hirsutum x G. hirsutum) and

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an inter‐specific cross (G. hirsutum x G. barbadense). Completion of these objectives are

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important for allowing reliable standardized high‐throughput SNP genotyping on large cotton

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populations for many diverse applications including, high‐density genetic mapping, genome‐

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wide associated studies, genomic selection, complete trait dissection and studying patterns of

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genomic diversity among cotton cultivars and wild accessions.

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MATERIALS AND METHODS

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Plant materials

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Seeds for each line were planted in peat pellets (Jiffy, Canada) at Texas A&M University,

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CIRAD/EMBRAPA, USDA‐ARS‐SPARC, USDA‐ARS‐SRRC, or CSIRO greenhouses. Plants were

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allowed to grow until first true leaves were available. Young true leaves were sampled and

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extracted using the Macherey‐Nagel Plant Nucleo‐spin kit (Pennsylvania) according to the

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manufacturer’s instructions. All DNA samples were quantified using PicoGreen® and then

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diluted to 50ng/µl. The samples included reference lines from the various SNP development

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efforts, duplicated DNA samples, individual plants and plant pools from the same seed source,

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individual plant samples from different seed sources, parent/F1 combinations, segregating

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samples from wild Gossypium species, inbred cultivar lines, wild G. hirsutum lines, and two

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mapping populations, one intra‐specific and one inter‐specific (Table 1). This material

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represented samples from most of the cross‐compatible range of G. hirsutum. Mapping

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samples included 93 F2 lines from a G. hirsutum cv. Phytogen 72 x G. hirsutum cv. Stoneville 474

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population designated as “PS” for intra‐specific mapping and 118 F2 lines from a G. barbadense

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doubled haploid line 3‐79 x G. hirsutum cv. Texas Marker‐1 population designated as “T3” for

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inter‐specific mapping. Samples for the mapping populations were chosen randomly from

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germinated seed within each population.

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Array design

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SNP data sets were obtained for nine intra‐specific (Table 2) and four inter‐specific SNP

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development efforts (Table 3). Classification of SNPs into intra‐specific or inter‐specific content

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on the array was based on where the SNP was identified in our data sets (Tables 2 & 3) The

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datasets obtained included SNPs from: [1] restriction enzyme double‐digest procedure in G.

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hirsutum between a cultivated and wild accession (Byers et al. 2012), [2] long‐read 454

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sequencing of restriction‐based genic enrichment libraries of six G. hirsutum lines (Rai et al.

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2013), [3] genotyping‐by‐sequencing (GBS) mapping of a cross between two G. hirsutum lines

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(Gore et al. 2014), [4] transcriptome sequencing of five G. hirsutum lines (Ashrafi et al. 2015),

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[5] GBS mapping of a multi‐parent population (Islam et al. 2014), [6] transcriptome sequencing

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and restriction‐based approach of 18 G. hirsutum lines (Zhu et al. 2014), [7&8] re‐sequencing of

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12 G. hirsutum lines (Hulse‐Kemp et al. 2015 and Ashrafi et al. – Personal Communication), [9]

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unclassified markers mapped to known chromosomes (unpublished, DOW AgroSciences), [10]

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single‐copy sequence derived SNPs between G. hirsutum and G. barbadense (Van Deynze et al.

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2009), [11] differential expression analysis using RNA‐sequencing of G. hirsutum versus G.

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barbadense (Lacape et al. 2012), [12] transcriptome sequencing of G. barbadense, G.

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tomentosum, G. mustelinum, G. armourianum, and G. longicalyx (Hulse‐Kemp et al. 2014), and

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[13] re‐sequencing of G. barbadense (Hulse‐Kemp et al. 2015).

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The Illumina Infinium technology utilizes a bead‐based approach where 50 base pair

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oligonucleotide probes are used to hybridize to SNP‐adjacent sequences of a sample and then a

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single base pair extension is performed to assay the SNP base with fluorescently labeled

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nucleotides. The technology utilizes a two‐fluorophore system, necessitating the use of two

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beadtypes to discriminate only those SNPs that target alleles sharing the same fluorophore

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(transversions) and only one beadtype to capture all other SNP types (transitions). Based on the

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Infinium technology, putative markers were filtered to maximize value from this technology,

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i.e., to select for unique 1‐beadtype assays to populate the array. Putative SNPs were filtered by

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design score >0.8 (Illumina, Inc), identity match >99% within 100bp of flanking sequence, >99%

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identity match for designed probe sequence, with final SNPs retained based upon precedent

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publication order in the hierarchy listed above. Subsequent filtering steps included

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prioritization for 1) one beadtype (Infinium II) assays, 2) validated markers, 3) experimental

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screening success rates, and 4) representation of genic and non‐genic SNPs to optimally cover

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the cotton genome, to obtain a final set of 70,000 putative SNP markers for inclusion on the

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array (Supporting Information Table S1). All genic‐based data sets were filtered during dataset

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development to eliminate SNPs nearby predicted intron boundaries (Hulse‐Kemp et al. 2014,

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Ashrafi et al. 2015, Zhu et al. 2014), so SNPs and flanking sequences could be aligned directly

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with genomic‐based markers to filter putative SNPs as indicated above .

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Due to limited prior validation, 500 markers were randomly selected for inclusion from the UC‐

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Davis/TAMU Genomic – Set 2 and 386 markers were randomly selected from the CSIR‐NBRI

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data set including 252 that were chosen randomly and 134 markers that were selected for

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inclusion based on overlap as identified with BLAST to have 100% identity with markers from 10

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other data sets over the entire length of SNP and flanking sequences. For USDA‐Set 2, markers

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were primarily included for contigs that had low missing data and were detected across

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multiple lines with minor allele frequency greater than 0.1. The markers chosen from the CIRAD

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set primarily represent markers from the most highly differentially expressed transcripts

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identified between G. hirsutum and G. barbadense. From the TAMU/UC‐Davis Inter RNA‐Seq

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data set, as many as possible common SNPs between the five species represented and G.

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hirsutum were selected for inclusion, as well as sets of private SNPs unique to each of the five

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species (Figure 1).

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Genotyping with the array

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Standardized DNA at 50ng/µl for each of the cotton lines described above was processed

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according to Illumina protocols and hybridized to the CottonSNP63K array at Texas A&M

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University or CSIRO. Single‐base extension was performed and the chips were scanned using

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the Illumina iScan. Image files were saved for cluster file analysis. All image files were uploaded

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into a single GenomeStudio project containing 1,156 individual samples. Of the 70,000 SNPs

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targeted for manufacture on the array, 6,942 markers failed to meet standards for bead

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representation and decoding metrics during the array construction process at Illumina and

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were removed from the manifest. Data for the remaining markers were clustered using the

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GenomeStudio Genotyping Module (V 1.9.4, Illumina, Inc.). All markers were viewed and

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manually curated, taking into account the sample type and known segregation ratios, for

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construction of the best cluster file for genotyping tetraploid cotton (available at:

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http://www.cottongen.org/node/add/cotton‐cluster‐file‐request).

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Reproducibility and call rate in a diversity set of cotton samples

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Three technical replicates were processed for an individual DNA sample of G. hirsutum cultivar

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TM‐1, G. barbadense inbred line 3‐79, and for the F1 individual from a G. hirsutum (TM‐1) by G.

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barbadense (3‐79) cross. Different individual plants from the same line (cultivar or accession)

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were analyzed for 11 lines where the individual plants were obtained from the same seed

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source and for 11 lines where the individual plants were obtained from different seed sources.

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Genotypes from these lines were compared to determine similarity across technical replicates,

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different seed sources and the same seed source. Multiple individuals (12) were pooled into the

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same DNA sample and then compared to the genotype from a single individual to determine

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variability within a seed source as well as percent residual heterozygosity. Polymorphic SNPs

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were classified based on Illumina GenTrain score (proximity of clusters) and call frequencies

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across samples. Minor allele frequencies of polymorphic markers were determined using only

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inbred line samples.

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Genetic linkage analysis – intra‐specific and inter‐specific

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Genotyping data were transformed into mapping data format (“ABH”) for the 93 intra‐specific

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PS F2 samples and the 118 inter‐specific T3 F2 samples. Only markers that had opposite

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homozygous allele calls between parental samples and behaved co‐dominantly were retained.

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Subsequently the data files were initially mapped with JoinMap 4.0 (Van Ooijen 2006) with

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respect to verification of segregation patterns, the formation of linkage groups and the

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preliminary map position of the markers on the chromosomes using the default grouping

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settings and the maximum likelihood mapping algorithm.

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The map data resulting from the initial mapping analysis were manually curated to identify and

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remove problematic markers that caused elevated numbers of double crossovers and

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expansions in the length of the linkage group. After removing the problematic markers from the

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ABH mapping data file in Microsoft Excel, the revised ABH mapping data file was used to

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construct the final linkage groups in MapManager QTX (Manly, Cudmore, and Meer 2001) with

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the following settings: linkage evaluation F2, search linkage criterion P=0.05, map function

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Kosambi, cross type – line cross. Markers were eliminated from the final mapping if they caused

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unexpected expansion of the linkage group because of too many crossovers since this is likely

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the result of low scoring quality of the individual marker analysis, i.e. markers with low

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GenTrain score and/or call frequency. The final maps were drawn with MapChart version 2.2

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(Voorrips 2002) with one marker per centimorgan (cM) to allow easier visualization, positions

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for all markers are listed in Supporting Information Table S1. Linkage disequilibrium and

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visualization of crossovers for each determined linkage group were plotted using the

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CheckMatrix software (www.atgc.org/XLinkage/) to analyze the mapping population genotypes.

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The correlation between the inter‐specific and intra‐specific map orders was visualized using

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MapChart. Discordant linkage groups in the inter‐specific map identified using CheckMatrix and

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MapChart were remapped in MapManager using a framework map from the corresponding

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intra‐specific linkage group. The number of recombination events per each F2 individual was

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calculated for both the intra‐specific and the reordered inter‐specific linkage maps. Average

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numbers of recombination bins across both linkage maps were also calculated. Recombination

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bins were considered as the interval between one recombination breakpoint and the next

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breakpoint within the mapping populations.   13

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Synteny analyses

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All SNP marker sequences were aligned to the high‐quality JGI G. raimondii (D5) reference

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genome (Paterson et al. 2012) using Burrows Wheeler Alignment (BWA) software in GALAXY

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(Goecks, Nekrutenko, and Taylor 2010) with default parameters. Linkage map positions were

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plotted against D5 alignment position for both the inter‐specific and intra‐specific mapped

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markers. The corresponding allotetraploid chromosomes of the linkage groups were identified

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using mapped markers (Dow AgroSciences ‐ unpublished; Yu et al. 2014; Blenda et al. 2012) and

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D5 alignment information. Linkage groups were oriented using the cotton consensus map which

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incorporated SSR marker chromosome assignment information available (Blenda et al. 2012).

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All markers were also aligned to the BGI G. arboreum (A2) draft sequence (Li et al. 2014) and

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plotted.

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RESULTS

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Genotyping array content

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An Illumina Infinium genotyping array was developed targeting 70,000 putative SNP markers

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(Supporting Information File 1). The 70,000 putative markers represent 50,000 SNPs that have

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been identified for use in intra‐specific crosses between G. hirsutum lines and 20,000 SNPs for

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use in inter‐specific crosses between G. hirsutum and other Gossypium species, such as G.

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barbadense, G. tomentosum, G. mustelinum, G. armourianum, and G. longicalyx. The intra‐

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specific set was developed from an initial set of 1,658,397 putative SNP markers; however, the

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largest data set (UC‐Davis/TAMU Intra Genomic ‐ Set 2) had not been experimentally validated

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previously and another large data set (CSIR‐NBRI) had limited validation, so only small random

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samples of SNPs for these sets were included. Therefore, selection of the intra‐specific markers 14

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for the array was based primarily on 91,953 markers. All markers were submitted through the

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Illumina Design Tool to determine assay design scores for each marker. The data set was

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filtered to retain only Infinium II, or one‐bead type assays that assay one SNP per bead, and

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those with design scores above 0.8. This resulted in 69,306 SNPs or 75.4% retention. Results of

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probe design for each of the data sets are shown in Supporting Information Table S2.

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Duplicated SNPs were eliminated, as noted in the Methods. All available SNP markers (18,348)

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within genes (genic) and a randomly selected set of genomic sequence‐derived markers

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(31,396) were included, for a total of 50,000 intra‐specific markers. The resulting intra‐specific

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set comprises 36.7% genic SNPs, 62.8% non‐genic SNPs, and 0.5% unclassified SNPs.

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The same selection and filtering method was used for the inter‐specific data set. The SNP

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selection process started with 314,894 potential markers. After the initial step of filtering based

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on assay design score and one‐bead type markers, there were 234,370 markers. As many

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previously mapped markers as possible were included. When feasible, a single marker was

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selected per reference sequence or gene sequence. Additional markers were randomly selected

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from the filtered data set to fill out the final set of 20,000 inter‐specific assays. A total of 14,689

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markers in genes were included and 5,311 genomic derived markers were included. The inter‐

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specific final set comprises 73.5% genic SNPs and 26.5% non‐genic markers. Within the inter‐

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specific set, the TAMU/UC‐Davis Inter RNA‐seq markers were selected to contain the maximum

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number of markers with polymorphism across multiple species (relative to G. hirsutum) as

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possible and approximately 2,000 markers unique to each of the wild species. This allowed for a

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maximum number of markers that can be used for introgression breeding efforts, while

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occupying as few beads as possible for cost purposes (Figure 1). Approximately 18% of the 15

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inter‐specific set is composed of markers chosen to support work for multiple species

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germplasm introgression into G. hirsutum.

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Together, the selected intra‐specific and inter‐specific sets resulted in 70,000 SNP markers

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submitted to manufacturing for inclusion on the array. All these SNPs have been deposited in

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the CottonGen database (Yu et al. 2014 www.cottongen.org). Based on the characteristics of

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the utilized Illumina array format, a total of 90,000 beads can be assayed per sample; therefore

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it is possible to add up to 20,000 assays, which can be included as private add‐on content to

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allow for adaptability of the array.

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Automated genotype calling through cluster definition

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From the 70,000 markers sent into production, 63,058 markers passed Illumina manufacturing

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and quality control, and were analyzed on 1,156 samples for amenability to automated

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genotyping. Detailed analyses of the diversity and population structure represented by the

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germplasm used in this project are underway and will be reported separately. The proportion of

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samples amenable to genotyping, or "call frequency", across all markers on the chip could be

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grouped into four distinct types (Figure 2a‐d) based on all 1,156 samples that were included for

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cluster file development, the distribution of markers within the four types were examined

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(Figure 2e). Importantly the polymorphic markers have an average call frequency of 0.99. The

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first type of classification represents failed markers that did not amplify in the majority of

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samples (Figure 2a). The second type consists of markers that had many samples with uncalled

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genotypes and therefore had a call frequency between 0.50‐0.99 (Figure 2b), which may be

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caused by the presence of an additional null allele. Type 3 includes markers in which only a few

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samples remain uncalled (Figure 2c). The last type is those markers in which all samples are

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called and are highly reproducible (Figure 2d). Call frequency distribution of all synthesized SNP

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markers on the chip are shown in Figure 2e.

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Successful markers primarily produced six distinct clustering patterns (Figure 3a‐f). In the first

5

pattern type essentially all samples fall in a single cluster. This type represents probe sequences

6

that detect a monomorphic locus or loci (Figure 3a). The second pattern type represents

7

markers which are detecting two monomorphic loci which are each homozygous for a different

8

allele (Figure 3b). Markers in this category appear to be heterozygous in all lines and

9

correspond to intergenomic SNPs or “homeo‐SNPs”, which are differences between the two

10

subgenomes of cotton and are often identified as false positives in SNP discovery. Polymorphic

11

markers are attributed to the remaining four patterns; they can be classified according to their

12

GenTrain score. Pattern type 3 are markers which showed three clearly definable clusters and

13

behaved in a traditional co‐dominant, diploid‐like marker with three possible genotypes (AA,

14

AB, BB) and homozygous genotype clusters located near 0 and 1 (Figure 3c). These types of

15

markers did not require any significant manual adjustment of marker positions. Similarly, the

16

fourth pattern type (Figure 3d) showed three clearly identifiable clusters, but the clusters were

17

shifted towards one side of the plot with one homozygous cluster at 0.5 on the X‐axis. Pattern 4

18

markers represent markers that detect two loci, most likely from the homeologous

19

chromosomes: one polymorphic and one monomorphic resulting in three possible genotypes

20

(AAAA, AAAB, AABB). Pattern 5 markers represent a pattern in which the three clusters are

21

quite close together and likely represent three or more loci with one polymorphic locus and

22

multiple monomorphic loci in the background (Figure 3e). Lastly pattern 6 markers represent 17

1

markers which show extremely close clusters due to assaying a large number of loci (Figure 3f).

2

Marker patterns 4 and 5 typically required manual adjustment of locus positions, while pattern

3

6 markers were frequently set as failed.

4

After evaluating and manually curating all markers, a total of 55,201 of the markers produced

5

successful assays. The successful assays were further characterized into 38,822 polymorphic

6

markers, 10,314 monomorphic markers and 6,065 intergenomic markers. Thus the overall

7

success rate of the chip is represented by a minimum of 38,822 polymorphic markers (in the

8

samples assayed) out of the 63,058 markers that were synthesized on the chip (61.6%) that will

9

be useful for genotyping cotton samples. Analysis of minor allele frequency (MAF) across

10

polymorphic markers using only inbred samples showed that 66.8% of the polymorphic markers

11

have a MAF above 0.05, 55.8% above 0.10, and 40.0% above 0.2. The average MAF among

12

polymorphic markers on the CottonSNP63K array was found to be 0.17. Distribution of minor

13

allele frequencies of polymorphic markers in inbred samples is shown in Figure 4.

14

Table 4 shows the distribution of the final markers and their success rates across the designed

15

data sets. While most data sets had a success rate of 50% or greater, some had a much lower

16

success rate. Discovery sets that did not have previous validation or had limited validation with

17

PCR‐based assays frequently resulted in lower success rates, e.g. CSIR‐NBRI and USDA‐Set 1. In

18

the CSIR‐NBRI data set low success is likely due to homeo‐SNPs as it showed the highest

19

percentage of markers classified as (33.82%). The other data set that had a low success rate of

20

8.65% was the USDA‐Set 1 containing markers previously mapped by GBS (Gore et al. 2014).

21

While the CSIR‐NBRI data set showed elevated levels of intergenomic markers, the USDA‐Set 1

22

was primarily found to have monomorphic markers (77.88%). As these markers had been 18

1

previously mapped and their map positions correlate with alignment positions on JGI D5

2

genome (Gore et al. 2014), these are likely true markers that are of very low MAF or specific to

3

the individual bi‐parental population used in their identification (Gore et al. 2012). While lines

4

for the parents of the population were included in the study, this population has been shown to

5

contain non‐parental derived alleles (Gore et al. 2012). Thus without lines that represent the

6

non‐parental derived alleles, polymorphism would not be able to be identified and this would

7

lead to the monomorphic classification of these markers.

8

Reproducibility and call rate in different cotton samples

9

Replicates of samples were analyzed in order to determine reproducibility across genotyping

10

runs as well as reproducibility across seed sources. To do this, four different types of replicates

11

were used: 1) technical replicates running the same DNA on different genotyping runs, 2)

12

individual plants from the same seed source, 3) individual plants from different seed sources,

13

and 4) pooled DNA from multiple plants from the same seed source. The three technical

14

replicates (three samples from each G. hirsutum line TM‐1, G. barbadense line 3‐79 and their

15

F1) showed negligible inconsistencies between runs with average similarity of 99.93% ± 0.0007.

16

Therefore allele calls are highly reproducible across runs. Individual plants from the same seed

17

source showed a range of similarity from 100% for Coker 315 provided by CSIRO to 73.39% for

18

Lu Mien 14 samples provided by CSIRO. A larger inconsistency was seen with duplicated

19

samples from different seed sources. The amount of inconsistency varied considerably across

20

different lines, from 66.61% for VIR‐6615/MCU‐5 samples provided by USDA‐ARS (College

21

Station) and CSIRO to 99.36% for Stoneville 474 samples provided by USDA‐ARS (New Orleans)

22

and CIRAD. When DNA of a pool of individual plants developed from the same seed source was 19

1

analyzed, the percentage of similarity varied between lines from 79.48% for LuMien 14 to

2

99.84% for Sicot 189. Rates determined for individual lines and replications of different types

3

are shown in Table 5.

4

Genetic map construction

5

An intra‐specific map was generated from 93 F2 samples from a cross between lines Phytogen

6

72 and Stoneville 474. A total of 7,171 SNP markers were mapped in 26 linkage groups

7

representing 3,499 cM (Figure 5). These represent 6,938 markers from the G. hirsutum set and

8

235 markers from the other species sets. An average of 254 markers per linkage group were

9

mapped on A‐subgenome chromosomes and 298 markers per linkage group of the D‐

10

subgenome. These correspond to an overall average of 55 bins with 4.98 markers/bin per

11

chromosome, 51 bins with 4.59 markers/bin for A‐subgenome chromosomes and 60 bins with

12

5.40 markers/bin for D‐subgenome chromosomes.

13

An initial inter‐specific map was generated from 118 F2 samples from a single inter‐specific

14

cross between G. barbadense line 3‐79 and G. hirsutum standard line TM‐1. A total of 19,198

15

markers mapped into 26 linkage groups initially representing 4,439.6 cM. Markers were largely

16

distributed across the genome with a few moderately sized gaps. Correlation analysis between

17

the initial inter‐specific map and the intra‐specific map showed that the moderately sized gaps

18

were associated with inverted marker orders compared to the intra‐specific map in nine linkage

19

groups corresponding to chromosomes (Chr06, Chr13, Chr15, Chr18, Chr20, Chr21, Chr22,

20

Chr23, and Chr26). Linkage disequilibrium and recombination plots of these linkage groups

21

showed incorrect ordering near the gaps (See Chr06 example in Figure 6). The most likely

22

correct orientation is found in the intra‐specific linkage groups, where the gaps are smaller. For 20

1

example in Figure 6, it is likely that the inter‐specific map is inverted due to low linkage across

2

the gap while the intra‐specific map does not have a corresponding gap and is likely the correct

3

orientation. To correct this issue, the intra‐specific map was used as a framework map to

4

reorder the problematic inter‐specific linkage groups.

5

The final map includes the reordered linkage groups and contains 19,191 markers mapped to

6

26 linkage groups that collectively encompass 3,955.1 cM (Figure 7). This map represents a

7

reduction in size of 484.5 cM (11%) from the initial inter‐specific map. The mapped SNPs

8

represent 11,452 markers from the G. hirsutum set and 7,746 markers from the other species

9

data sets (6,785 ‐ G. barbadense, 614 ‐ G. tomentosum, 280 ‐ G. mustelinum, 39 ‐ G.

10

armourianum, and 28 ‐ G. longicalyx). The map contained an average of 162 recombination bins

11

with 4.55 markers/bin per chromosome across the linkage groups, with an average of 166 bins

12

with 4.52 markers per bin and 159 bins with 4.60 markers per bin in the A‐ and D‐subgenome

13

chromosomes, respectively. The average overall number of markers per linkage group was 738,

14

with an average of 718 per A‐subgenome chromosomes and 759 per D‐subgenome

15

chromosomes.

16

In the intra‐specific and inter‐specific maps, the observed map lengths (cM) for the A‐

17

subgenome chromosomes (137.5cM, 152.8cM) were on average very similar to but slightly

18

larger than for the D‐subgenome chromosomes (131.7 cM, 143.7 cM), respectively. For cotton

19

chromosomes, accurate estimates and comparisons of genetic size with physical distance, e.g.,

20

cM/Mbp, are not yet available.  Accurate cytological estimates for individual allotetraploid

21

chromosome sizes are not available. While a BAC‐by‐BAC reference sequence for the D5 is

22

available, highly repetitive regions are under‐represented, including subtelomeric and large 21

1

peri‐centromeric regions. So, a D5‐based estimate would be limited in scope and would

2

underestimate physical size. However, the genomes of the closest related extant diploids, G.

3

arboreum and G. raimondii, have been estimated to be 1600 and 800 Mb, respectively; sizes of

4

those genomes and chromosomes correspond well cytologically with absolute and relative sizes

5

of chromosomes in the G. hirsutum subgenomes. Using the G. arboreum and G. raimondii

6

estimates as a guide for A‐subgenome and D‐subgenome sizes, one centimorgan in the intra‐

7

specific map represents an average of 895 Kb in A‐subgenome chromosomes and 467 Kb in D‐

8

subgenome chromosomes. In the inter‐specific map, a centimorgan represents an average

9

physical distance of 805 Kb in A‐subgenome chromosomes and 428 Kb in D‐subgenome

10

chromosomes. While sizes and physical distances in A‐ and D‐subgenome chromosomes differ

11

about 2‐fold, the rate of crossing over does not, reflecting that most crossovers occur in non‐

12

heterochromatic regions or gene‐space.

13

Segregation distortion has been previous reported in cotton populations and not unexpectedly

14

a number of markers in both the intra‐specific and inter‐specific maps showed significant

15

distortion from expected F2 segregation ratios. In the intra‐specific map, 612 markers of the

16

7,171 (8.5%) mapped markers showed significant segregation distortion (P0.6, D.) Marker with GenTrain score 0.30-0.59 on half the plot representing two genomes, one monomorphic and one polymorphic locus, E.) Marker with GenTrain score 0.21-0.29 representing multiple monomorphic loci and one polymorphic locus, F.) Marker with GenTrain score less than 0.20 representing many monomorphic loci and one polymorphic locus, G.) Distribution of cluster types in polymorphic markers based on GenTrain score. Figure 4 - Distribution of minor allele frequencies of all polymorphic SNPs on the CottonSNP63K array. Minor allele frequencies were determined using only inbred line samples, mapping samples and other non-inbred line samples used for cluser file development were excluded from this analysis. Figure 5 – Intra-specific linkage map of 26 allotetraploid cotton chromosomes. Map determined using 93 F2 individuals from Phytogen 72 by Stoneville 474 parents. Only one marker is listed on the right per Kosambi centiMorgan (cM) on the left, even if there were more markers co-segregating. Chromosomes are listed based on AD chromosome number.

38

Figure 6 – Inconsistencies between initial de novo inter-specific map and the intra-specific map. A.) Initial plots of inter-specific map order and correlation with intra-specific map show area of incorrect placement in center of the linkage group. B.) Corrected inter-specific linkage group and final plots. Figure 7 – Inter-specific linkage map of 26 allotetraploid cotton chromosomes. Map determined using 118 F2 individuals from G. hirsutum genetic standard line Texas Marker -1 by G. barbadense line 3-79 parents. One marker listed on the right per Kosambi centiMorgan (cM) on the left as in Figure 6. Chromosomes are listed based on AD chromosome number. Figure 8 – Frequency distribution of the number of crossovers. Numbers of crossovers detected for each F2 individual per chromosome (0 to >8) are displayed chromatically for each linkage group, which are organized by genetic size (longest at top, shortest at bottom). A.) Distribution of crossovers in the intra-specific mapping population. B.) Distribution of crossovers in the inter-specific mapping population. Figure 9 – Dot plot of the syntenic positions of SNP markers in the allotetraploid linkage maps versus the JGI G. raimondii reference genome. The 26 allotetraploid chromosomes are shown on the y-axis and the 13 chromosomes of G. raimondii are shown on the x-axis. Red arrows indicate translocation events relative to G. raimondii. A.) Intra-specific linkage map displaying positions of 4,521 mapped SNP in G. hirsutum with alignments to G. raimondii. B.) Inter-specific linkage map (G. hirsutum genetic standard line Texas Marker -1 by G. barbadense line 3-79) displaying positions of 12,027 mapped SNP with alignments to G. raimondii.

39

Legends for Supporting Tables & Figures Table S1 Excel spreadsheet listing all 70K markers and their descriptions on the CottonSNP63K. Table S2 Excel spreadsheet showing results of assay design of discovery group sets using Illumina’s Assay Design Tool. Figure S1 Dot plot of the syntenic positions of SNP markers in the allotetraploid linkage maps versus the BGI G. arboreum draft genome. The 26 allotetraploid chromosomes are shown on the y-axis and the 13 chromosomes of G. arboreum are shown on the x-axis. A.) Intra-specific linkage map displaying positions of 3,863 mapped SNP in G. hirsutum with alignments to G. arboreum. B.) Inter-specific linkage map (G. hirsutum genetic standard line Texas Marker -1 by G. barbadense line 3-79) displaying positions of 11,344 mapped SNP with alignments to G. arboreum.

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89.0 91.0 92.0 93.0

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101.0 102.0 103.0

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117.0 119.0 120.0

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124.0

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128.0

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130.0

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133.0 135.0 136.0 137.0

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142.0 144.0 145.0 146.0 148.0


22.0 23.0

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28.0

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33.0

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37.0 39.0 40.0 42.0

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37.0

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39.0 41.0 42.0

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49.0 50.0 51.0 52.0 53.0 56.0 57.0


61.0 62.0 63.0 64.0

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72.0

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80.0 81.0

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88.0 89.0 90.0

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101.0

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108.0 109.0

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48.0 50.0 51.0

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102.0

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113.0 114.0

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21.0 22.0

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35.0 36.0

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40.0

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43.0 44.0

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83.0 84.0

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i09756Gh

96.0 98.0 99.0

108.0 109.0

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92.0 93.0 94.0 95.0 96.0 97.0 99.0


104.0 105.0

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112.0 113.0 114.0 115.0 117.0

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162.0 163.0 165.0 167.0 168.0 170.0


172.0

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174.0

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0.0

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2.0

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5.0

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20.0 21.0

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31.0 32.0 33.0

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45.0 46.0

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52.0 53.0 54.0 56.0

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62.0

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101.0 102.0 103.0

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106.0

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0.0

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20.0

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22.0

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27.0

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30.0 31.0 33.0

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63.0 64.0 65.0 66.0

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99.0 100.0 102.0 103.0 104.0 105.0


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166.0 167.0 168.0 169.0 170.0


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AD Chr 22 AD Chr 23 AD Chr 24 AD Chr 25 AD Chr 26

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19.0 20.0

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31.0

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33.0

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35.0 37.0 38.0

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46.0 48.0 49.0 51.0 52.0 53.0 54.0 55.0

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0.0 1.0 3.0

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22.0 23.0

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26.0 27.0 28.0

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32.0

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30.0 32.0 33.0 35.0 36.0


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47.0 48.0 49.0 50.0 51.0 52.0 53.0

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40.0

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44.0 45.0 46.0 47.0 48.0 50.0 51.0


56.0

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72.0

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78.0

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80.0

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70.0 72.0 73.0 74.0 75.0 76.0


80.0

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93.0 94.0 95.0

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100.0

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102.0

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107.0 108.0

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110.0

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114.0 115.0 116.0

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121.0 122.0 123.0

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24.0 25.0 26.0 27.0 29.0


32.0

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40.0

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51.0 52.0 53.0 54.0 57.0 58.0 59.0 60.0 61.0 62.0 64.0 65.0 66.0 67.0 68.0 69.0 70.0 74.0 75.0 76.0 77.0 79.0

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101.0 102.0 103.0 104.0

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11.0

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14.0

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20.0

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25.0

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38.0 39.0

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44.0 45.0 46.0 48.0

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66.0

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88.0

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102.0 103.0 105.0 106.0 108.0 110.0 112.0 114.0 115.0 116.0 117.0 118.0 120.0 121.0 122.0 123.0 125.0

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115.0 116.0 118.0 119.0 120.0


128.0 129.0 131.0 132.0 134.0


136.0

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143.0

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130.0

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134.0

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137.0

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143.0

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36.0 38.0 39.0 41.0

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45.0 46.0

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51.0 52.0 55.0 56.0 57.0 58.0 59.0 60.0 61.0 63.0 64.0 66.0 67.0 68.0 69.0 71.0 72.0 73.0 74.0 75.0 79.0 80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 89.0 90.0 91.0 92.0 93.0 94.0 95.0 96.0 97.0 98.0 100.0 101.0 103.0 104.0 105.0 106.0 107.0 108.0 109.0 110.0 113.0 115.0 116.0 117.0 118.0 119.0 122.0 124.0 125.0 127.0 131.0 134.0 137.0 140.0 141.0 142.0 143.0 144.0

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0.0 1.0 2.0 3.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 15.0 16.0 17.0 20.0 21.0

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27.0

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30.0 31.0 32.0 33.0

i52694Gb i17005Gh i27883Gh i10337Gh

37.0 38.0 39.0 42.0 43.0 44.0 47.0 48.0 50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0 60.0 61.0 62.0 63.0 66.0 67.0 70.0 72.0 74.0 75.0 77.0 78.0 79.0 81.0 82.0 83.0 85.0 86.0 87.0 89.0 90.0 91.0 92.0 93.0 95.0 97.0 99.0 101.0 102.0 105.0 106.0 108.0 109.0

i49381Gh i40677Gh i53503Gb i37354Gh i20784Gh i16945Gh i40592Gh i44983Gh i52156Gb i10355Gh i56757Gb i49528Gh i38788Gh i50985Gb i54939Gb i51801Gb i12736Gh i52838Gb i54584Gb i42782Gh i12752Gh i52702Gb i51675Gb i20464Gh i17832Gh i51473Gb i12784Gh i00134Gh i12817Gh i01127Gh i12834Gh i17844Gh i12849Gh i51902Gb i22515Gh i12861Gh i12863Gh i12875Gh i49530Gh i12885Gh i43998Gh i58751Gb i49147Gh i12912Gh i53248Gb i35007Gh i51441Gb i60947Gt i12933Gh i54029Gb i50976Gb

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AD Chr 06 AD Chr 07

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85.0 86.0 87.0 88.0 89.0

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158.0 159.0 160.0

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Development and bin mapping of gene-associated interspecific SNPs for cotton (Gossypium hirsutum L.) introgression breeding efforts.

Development of a SNP array and its application to genetic mapping and diversity assessment in pepper (Capsicum spp.).

A comparative meta-analysis of QTL between intraspecific Gossypium hirsutum and interspecific G. hirsutum × G. barbadense populations.

An updated felCat5 SNP manifest for the Illumina Feline 63k SNP genotyping array.

In vitro induction of genetic variability in cotton (Gossypium spp.).

BAC-End Sequence-Based SNP Mining in Allotetraploid Cotton (Gossypium) Utilizing Resequencing Data, Phylogenetic Inferences, and Perspectives for Genetic Mapping.

Development of EST-based SNP and InDel markers and their utilization in tetraploid cotton genetic mapping.

Mining and Analysis of SNP in Response to Salinity Stress in Upland Cotton (Gossypium hirsutum L.).

Genome-wide identification of multifunctional laccase gene family in cotton (Gossypium spp.); expression and biochemical analysis during fiber development.

Changes in the non-structural carbohydrate content of cotton (Gossypium spp.) fibres at different stages of development.

Characterization of the global transcriptome for cotton (Gossypium hirsutum L.) anther and development of SSR marker.

Quantitative trait loci analysis of Verticillium wilt resistance in interspecific backcross populations of Gossypium hirsutum × Gossypium barbadense.

A High-Density Consensus Map of Common Wheat Integrating Four Mapping Populations Scanned by the 90K SNP Array.

Mapping genomic loci for cotton plant architecture, yield components, and fiber properties in an interspecific (Gossypium hirsutum L. × G. barbadense L.) RIL population.

Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array.

Development and validation of a high density SNP genotyping array for Atlantic salmon (Salmo salar).

Breeding Potential of Introgression Lines Developed from Interspecific Crossing between Upland Cotton (Gossypium hirsutum) and Gossypium barbadense: Heterosis, Combining Ability and Genetic Effects.

Evaluation of cotton leaf curl virus resistance in BC1, BC2, and BC3 progenies from an interspecific cross between Gossypium arboreum and Gossypium hirsutum.

SNP Marker Discovery in Pima Cotton (Gossypium barbadense L.) Leaf Transcriptomes.

Comparison of intraspecific, interspecific and intergeneric chloroplast diversity in Cycads.

Cotton QTLdb: a cotton QTL database for QTL analysis, visualization, and comparison between Gossypium hirsutum and G. hirsutum × G. barbadense populations.

Proteomics profiling of fiber development and domestication in upland cotton (Gossypium hirsutum L.).

Comprehensive analysis of TCP transcription factors and their expression during cotton (Gossypium arboreum) fiber early development.

Distribution and evolution of cotton fiber development genes in the fibreless Gossypium raimondii genome.