MVQTLCIM: composite interval mapping of multivariate traits in a hybrid F1 population of outbred species

Abstract Using time-related phenotypic data, methods of composite interval mapping and multiple-trait composite interval mapping based on least squares were applied to map quantitative trait loci (QTL) underlying the development of tiller number in rice. A recombinant inbred population and a corresponding saturated molecular marker linkage map were constructed for the study. Tiller number was recorded every 4 or 5 days for a total of seven times starting at 20 days after sowing. Five QTL were detected on chromosomes 1, 3, and 5. These QTL explained more than half of the genetic variance at the final observation. All the QTL displayed an S-shaped expression curve. Three QTL reached their highest expression rates during active tillering stage, while the other two QTL achieved this either before or after the active tillering stage.

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Bayesian Mapping of Multiple Quantitative Trait Loci From Incomplete Inbred Line Cross Data

Genetics ◽

10.1093/genetics/148.3.1373 ◽

1998 ◽

Vol 148 (3) ◽

pp. 1373-1388

Author(s):

Mikko J Sillanpää ◽

Elja Arjas

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Composite Interval Mapping ◽

Backcross Progeny ◽

Random Variable ◽

Interval Mapping ◽

Mapping Method ◽

Structure Mapping ◽

Standard Interval ◽

Trait Loci

Abstract A novel fine structure mapping method for quantitative traits is presented. It is based on Bayesian modeling and inference, treating the number of quantitative trait loci (QTLs) as an unobserved random variable and using ideas similar to composite interval mapping to account for the effects of QTLs in other chromosomes. The method is introduced for inbred lines and it can be applied also in situations involving frequent missing genotypes. We propose that two new probabilistic measures be used to summarize the results from the statistical analysis: (1) the (posterior) QTL-intensity, for estimating the number of QTLs in a chromosome and for localizing them into some particular chromosomal regions, and (2) the location wise (posterior) distributions of the phenotypic effects of the QTLs. Both these measures will be viewed as functions of the putative QTL locus, over the marker range in the linkage group. The method is tested and compared with standard interval and composite interval mapping techniques by using simulated backcross progeny data. It is implemented as a software package. Its initial version is freely available for research purposes under the name Multimapper at URL http://www.rni.helsinki.fi/~mjs.

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Interval and Composite Interval Mapping of Somatic Cell Score, Yield, and Components of Milk in Dairy Cattle

Journal of Dairy Science ◽

10.3168/jds.s0022-0302(02)74395-6 ◽

2002 ◽

Vol 85 (11) ◽

pp. 3081-3091 ◽

Cited By ~ 44

Author(s):

S.L. Rodriguez-Zas ◽

B.R. Southey ◽

D.W. Heyen ◽

H.A. Lewin

Keyword(s):

Dairy Cattle ◽

Somatic Cell ◽

Composite Interval Mapping ◽

Interval Mapping ◽

Somatic Cell Score

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High-Resolution Mapping in Two RIL Populations Refines Major “QTL Hotspot” Regions for Seed Size and Shape in Soybean (Glycine max L.)

International Journal of Molecular Sciences ◽

10.3390/ijms21031040 ◽

2020 ◽

Vol 21 (3) ◽

pp. 1040 ◽

Cited By ~ 3

Author(s):

Aiman Hina ◽

Yongce Cao ◽

Shiyu Song ◽

Shuguang Li ◽

Ripa Akter Sharmin ◽

...

Keyword(s):

Seed Size ◽

Composite Interval Mapping ◽

Mixed Model ◽

Gene Annotation ◽

Soybean Seed ◽

Interval Mapping ◽

Epistatic Effect ◽

Size And Shape ◽

Network Analyses ◽

Major Qtl

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., “QTL Hotspot A”, “QTL Hotspot B”, “QTL Hotspot C”, and “QTL Hotspot D” located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four “QTL Hotspots” were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

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Genome-Wide Composite Interval Mapping (GCIM) of Expressional Quantitative Trait Loci in Backcross Population

Methods in Molecular Biology - eQTL Analysis ◽

10.1007/978-1-0716-0026-9_5 ◽

2019 ◽

pp. 63-71

Author(s):

Yuan-Ming Zhang

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Composite Interval Mapping ◽

Interval Mapping ◽

Backcross Population ◽

Genome Wide ◽

Trait Loci

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Multi‐locus GWAS and genome‐wide composite interval mapping ( GCIM )

The Model Legume Medicago truncatula ◽

10.1002/9781119409144.ch108 ◽

2019 ◽

pp. 863-869

Author(s):

Yuan‐Ming Zhang

Keyword(s):

Composite Interval Mapping ◽

Interval Mapping ◽

Genome Wide

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Mapping Quantitative Trait Loci for Resistance to Rice Blast

Phytopathology ◽

10.1094/phyto-06-10-0151 ◽

2011 ◽

Vol 101 (2) ◽

pp. 176-181 ◽

Cited By ~ 19

Author(s):

Y. Jia ◽

G. Liu

Keyword(s):

Quantitative Trait Loci ◽

Phenotypic Variation ◽

Quantitative Trait ◽

Composite Interval Mapping ◽

Rice Blast ◽

Ssr Marker ◽

Interval Mapping ◽

Chromosome 8 ◽

Chromosome 12 ◽

Trait Loci

Quantitative trait loci (QTLs) conferring resistance to rice blast, caused by Magnaporthe oryzae, have been under-explored. In the present study, composite interval mapping was used to identify the QTLs that condition resistance to the 6 out of the 12 common races (IB1, IB45, IB49, IB54, IC17, and ID1) of M. oryzae using a recombinant inbred line (RIL) population derived from a cross of the moderately susceptible japonica cultivar Lemont with the moderately resistant indica cultivar Jasmine 85. Disease reactions of 227 F7 RILs were determined using a category scale of ratings from 0, representing the most resistant, to 5, representing the most susceptible. A total of nine QTLs responsive to different degrees of phenotypic variation ranging from 5.17 to 26.53% were mapped on chromosomes 3, 8, 9, 11, and 12: qBLAST3 at 1.9 centimorgans (cM) to simple sequence repeat (SSR) marker RM282 on chromosome 3 to IB45 accounting for 5.17%; qBLAST8.1 co-segregated with SSR marker RM1148 to IB49 accounting for 6.69%, qBLAST8.2 at 0.1 cM to SSR marker RM72 to IC17 on chromosome 8 accounting for 7.22%; qBLAST9.1 at 0.1 cM to SSR marker RM257 to IB54, qBLAST9.2 at 2.1 cM to SSR marker RM108, and qBLAST9.3 at 0.1 cM to SSR marker RM215 to IC17 on chromosome 9 accounting for 4.64, 7.62, and 4.49%; qBLAST11 at 2.2 cM to SSR marker RM244 to IB45 and IB54 on chromosome 11 accounting for 26.53 and 19.60%; qBLAST12.1 at 0.3 cM to SSR marker OSM89 to IB1 on chromosome 12 accounting for 5.44%; and qBLAST12.2 at 0.3 and 0.1 cM to SSR marker OSM89 to IB49 and ID1 on chromosome 12 accounting for 9.7 and 10.18% of phenotypic variation, respectively. This study demonstrates the usefulness of tagging blast QTLs using physiological races by composite interval mapping.

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Identification of QTL/segments related to seed-quality traits in G. soja using chromosome segment substitution lines

Plant Genetic Resources ◽

10.1017/s147926211400029x ◽

2014 ◽

Vol 12 (S1) ◽

pp. S65-S69 ◽

Cited By ~ 4

Author(s):

Wubin Wang ◽

Qingyuan He ◽

Hongyan Yang ◽

Shihua Xiang ◽

Guangnan Xing ◽

...

Keyword(s):

Composite Interval Mapping ◽

Seed Quality ◽

Interval Mapping ◽

Chromosome Segment ◽

Quality Traits ◽

Negative Effects ◽

Positive Effects ◽

Segment Substitution ◽

Cultivated Soybean ◽

Seed Quality Traits

Annual wild soybean characterized by low 100-seed weight (100SW), high protein content (PRC) and low oil content (OIC) may have favourable exotic genes/alleles for broadening the genetic base of the cultivated soybean. To evaluate the wild alleles/segments, a chromosome segment substitution line population comprising 151 lines with N24852 (wild) as the donor and NN1138-2 (cultivated) as the recurrent parent was analysed using single-marker analysis, interval mapping, inclusive composite interval mapping and mixed linear composite interval mapping. On 14 segments of ten chromosomes, 17 quantitative trait loci (QTL) were identified, with two segments each containing two QTL for 100SW and OIC and one segment containing two QTL for PRC and OIC, respectively. All the seven wild alleles/segments for 100SW were associated with negative effects and three were associated with positive effects, but one was associated with a negative effect for PRC, and five were associated with negative effects, but one was associated with a positive effect for OIC. Except Satt216 and Sat_224 for 100SW, the identified QTL/segments have been reported from cultivated soybean mapping populations. The detected wild segments may provide materials for further characterization, cloning and pyramiding of the alleles conferring the seed-quality traits.

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