Verification and fine-mapping of QTLs conferring days to flowering in soybean using residual heterozygous lines

Su, Chengfu; Lu, Weiguo; Zhao, Tuanjie; Gai, Junyi

doi:10.1007/s11434-010-0032-7

Cited by 31 publications

(14 citation statements)

References 23 publications

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“…Except for the D2 and E linkage groups, reported maturity-related QTLs were distributed across 18 additional linkage groups, predominantly the C2, L, and M linkage groups. Fine mapping and map-based cloning have been conducted for some QTLs (Yamanaka et al, 2005;Watanabe et al, 2009;Su et al, 2010). There are fewer studies on soybean branch trait loci, and the genetic contribution ratios of mapped soybean branch-related QTLs is low.…”

Section: Introductionmentioning

confidence: 99%

Analysis of quantitative trait loci for main plant traits in soybean

et al. 2015

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ABSTRACT. Plant traits are important indices for regulating and controlling yield ability in soybean varieties. It is important to comprehensively study the quantitative trait locus (QTL) mapping for soybean plant traits, cloning related genes, and marker assistant breeding. In this study, 236 F 2 generation plants and a derivative group were constructed by using Jiyu50 and Jinong18, obtained from Jilin Province. A total of 102 simple sequence repeat markers were used to construct a genetic linkage map. With 2 years of molecular and phenotypic data, QTL analyses and mapping were conducted for soybean maturity, plant height, main stem node, main stem branch, seed weight per plant, and more. Five main plant traits were analyzed via inclusive composite interval mapping using QTL IciMapping v2.2. Using one-dimensional scanning, a total of 30 QTLs were detected and distributed across 1 (A1), 4 (C2), and 12 (G). There were 9 linkage groups, including 16 major QTLs. Using two-dimensional scanning, 7 pairs of epistatic QTL interactions for maturity and plant height were detected in the soybean.

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Section: Introductionmentioning

confidence: 99%

Analysis of quantitative trait loci for main plant traits in soybean

et al. 2015

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“…Besides, the mapping method also intimately affects the mapping results, and an inappropriate mapping method may result in erroneous judgment or false-positive (Su et al, 2010a). Composite interval mapping (CIM) and multiple QTL mapping (MQM) are suitable to detect QTLs when the data fit the genetic model of y = μ + a 1 + e, and multiple interval mapping with regression forward selection (MIMR), multiple interval mapping with forward search (MIMF) and inclusive composite interval mapping (ICIM) are suitable to detect QTLs when the data fit the genetic models of y = μ + a 1 + e and y = μ + a 1 + a 1 + a 1 a 2 + e, while composite interval mapping based on mixed linear model (MCIM) fits all models (Su et al, 2010b).…”

mentioning

confidence: 99%

QTL Analysis for Seven Quality Traits of RIL Population in Japonica Rice Based on Three Genetic Statistical Models

et al. 2013

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QTL mapping for seven quality traits was conducted by using 254 recombinant inbred lines (RIL) derived from a japonica-japonica rice cross of Xiushui 79/C Bao. The seven traits investigated were grain length (GL), grain length to width ratio (LWR), chalk grain rate (CGR), chalkiness degree (CD), gelatinization temperature (GT), amylose content (AC) and gel consistency (GC) of head rice. Three mapping methods employed were composite interval mapping in QTLMapper 2.0 software based on mixed linear model (MCIM), inclusive composite interval mapping in QTL IciMapping 3.0 software based on stepwise regression linear model (ICIM) and multiple interval mapping with regression forward selection in Windows QTL Cartographer 2.5 based on multiple regression analysis (MIMR). Results showed that five QTLs with additive effect (A-QTLs) were detected by all the three methods simultaneously, two by two methods simultaneously, and 23 by only one method. Five A-QTLs were detected by MCIM, nine by ICIM and 28 by MIMR. The contribution rates of single A-QTL ranged from 0.89% to 38.07%. All the QTLs with epistatic effect (E-QTLs) detected by MIMR were not detected by the other two methods. Fourteen pairs of E-QTLs were detected by both MCIM and ICIM, and 142 pairs of E-QTLs were detected by only one method. Twenty-five pairs of E-QTLs were detected by MCIM, 141 pairs by ICIM and four pairs by MIMR. The contribution rates of single pair of E-QTL were from 2.60% to 23.78%. In the Xiu-Bao RIL population, epistatic effect played a major role in the variation of GL and CD, and additive effect was the dominant in the variation of LWR, while both epistatic effect and additive effect had equal importance in the variation of CGR, AC, GT and GC. QTLs detected by two or more methods simultaneously were highly reliable, and could be applied to improve the quality traits in japonica hybrid rice.

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“…QTL analysis has proven to be a powerful approach for novel gene and pathway discovery, especially for complex traits [15]. In the last decade, numerous disease-associated genetic components, such as HDL cholesterol [16−20] and hypertension [21] were successfully identified in mice using QTL mapping strategies.…”

Section: Mapping Results Associated With Genetic Modifiers Of the Scamentioning

confidence: 99%

Mapping of genetic modifiers of Plcd1 in scant hair mice (snthr 1Bao )

Zeng

Mao

et al. 2010

Chin. Sci. Bull.

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The scant hair mutant mouse (locus symbol: snthr 1Bao ) is a recessive mutation that originated in an ethylnitrosourea chemical carcinogenesis study using the DBA/2J inbred strain. The gene responsible for the mutation was previously determined to be phospholipase C, delta 1 (Plcd1; mutant allele symbol Plcd1 snthr1Bao ). To map the modifiers of Plcd1, an intercross (DBA/2J-snthr 1Bao / snthr 1Bao × C57BL/6J+/+) was conducted. The F2 mutant progeny exhibited a variety of alopecia phenotypes; all F2 mutants (n=507) were classified into 3 groups (mild, moderate, and severe alopecia) and genotyped based on 96 microsatellites. A major QTL was identified on mouse chromosome (mChr) 15 at 12 cM with an LOD score greater than 7 (P < 0.0001). Three minor QTLs were detected on mChr 2, 5, and 7 at 40, 84 and 48 cM, respectively. The QTLs on mChr 7 and 15 were associated with minor alopecia while the QTLs on mChr 2 and 5 were associated with moderate to severe alopecia. No antagonistic or synergistic effects among or between the 4 QTLs were found. Integrating the functions of the 4 potential regulatory QTLs and mutant Plcd1 snthr1Bao , we found that these QTLs might contribute to variations of scant hair severity by altering the Ca 2+ signal pathways in mouse skin. scant hair (locus symbol: snthr 1Bao ), phospholipase C, delta 1 (gene symbol: Plcd1), modifier genes, alopecia, QTL Citation: Wu B J, Zeng Y M, Mao H H, et al. Mapping of genetic modifiers of Plcd1 in scant hair mice (snthr 1Bao ).

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Verification and fine-mapping of QTLs conferring days to flowering in soybean using residual heterozygous lines

Cited by 31 publications

References 23 publications

Analysis of quantitative trait loci for main plant traits in soybean

Analysis of quantitative trait loci for main plant traits in soybean

QTL Analysis for Seven Quality Traits of RIL Population in Japonica Rice Based on Three Genetic Statistical Models

Mapping of genetic modifiers of Plcd1 in scant hair mice (snthr 1Bao )

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