Two distinct genomic regions, harbouring the period and fruitless genes, affect male courtship song in Drosophila montana

Acoustic signals often have a significant role in pair formation and in species recognition. Determining the genetic basis of signal divergence will help to understand signal evolution by sexual selection and its role in the speciation process. An earlier study investigated quantitative trait locus for male courtship song carrier frequency (FRE) in Drosophila montana using microsatellite markers. We refined this study by adding to the linkage map markers for 10 candidate genes known to affect song production in Drosophila melanogaster. We also extended the analyses to additional song characters (pulse train length (PTL), pulse number (PN), interpulse interval, pulse length (PL) and cycle number (CN)). Our results indicate that loci in two different regions of the genome control distinct features of the courtship song. Pulse train traits (PTL and PN) mapped to the X chromosome, showing significant linkage with the period gene. In contrast, characters related to song pulse properties (PL, CN and carrier FRE) mapped to the region of chromosome 2 near the candidate gene fruitless, identifying these genes as suitable loci for further investigations. In previous studies, the pulse train traits have been found to vary substantially between Drosophila species, and so are potential species recognition signals, while the pulse traits may be more important in intra-specific mate choice.     

Quantitative trait loci mapping for fatty acid contents in the backfat on porcine chromosomes 1, 13, and 18

A partial genome scan using microsatellite markers was conducted in order to detect quantitative trait loci (QTLs) for 10 fatty acid contents of the backfat in a pig reference population. Two QTLs were found by studying SSC1, SSC13, and SSC18, where QTLs had already been identified for backfat thickness. A QTL was located between marker loci S0113 and SW974 on chromosome 1; this QTL was only significantly detected (P < 0.05) for linoleic acid. The other QTL was discovered between markers S0062 and S0120 on chromosome 18, and its significance only showed (P < 0.05) for myristic acid. The two QTLs mapped to the same location as the backfat thickness QTL. A third of the phenotypic variation was explained for linoleic acid by the QTL on chromosome 1, and a quarter for myristic acid by the QTL on chromosome 18. Further studies on fine mapping and positional comparative candidate gene analyses will be the next step toward a better understanding of the genetic architecture of fatty acid contents.     

Quantitative trait loci for yield components in oil palm (Elaeis guineensis Jacq.)

The development of an oil palm RFLP marker map has enabled marker-based QTL mapping studies to be undertaken. Information from 153 RFLP markers was used in combination with phenotypic data from an F₂ population to estimate the position and effects of quantitative trait loci (QTLs) for traits including yield of fruit and its components and measures of vegetative growth. The mapping population consisted of 84 palms segregating for the major gene influencing shell thickness. Marker data were analysed to produce a linkage map consisting of 22 linkage groups. The QTL mapping analysis was carried out by interval mapping and single-marker analysis for the unlinked markers; significance thresholds were generated by permutation. Using both single-marker and interval-mapping analysis significant marker associated QTL effects were found for 11 of the 13 traits analysed. The results of interval-mapping analysis of fruit weight, petiole cross section and rachis length, and ratios of shell:fruit, mesocarp:fruit and kernel:fruit indicated significant (P<0.05) QTLs at the genome-wide threshold. The putative QTLs were associated with between 8.2% and 44.0% of the phenotypic variation, with an average of 27% for the single-marker analysis and 19% for the interval-mapping analysis. The higher percentage of phenotypic variation explained in the single-marker analysis, when compared to the interval-mapping analysis, is likely to be due to the lower stringency associated with the single-marker analysis. Large dominance deviations were associated with a sizeable proportion of the putative QTLs. The ultimate objective of mapping QTLs in commercial populations is to utilise novel breeding strategies such as marker-assisted selection (MAS). The potential impact of MAS in oil palm breeding programmes is discussed. Received: 26 June 2000 / Accepted: 24 October 2000     

植物QTL分析的理论研究进展

数量性状的表型是由数量性状基因座 ( Quantitative trait locus,QTL)和环境效应共同作用的结果。传统的数量遗传学采用统计学的方法由一级统计量和二级统计量描述处理 QTL的复合作用 ,估计各种遗传参数 (例如遗传力、遗传相关、遗传进度、有效因子数等 ) ,用于指导遗传育种实践。然而 ,在传统的数量遗传学分析中 ,往往假设数量性状受微效多基因控制 ,这些基因具有相同的并且是较微小的效应 ,所估计的遗传参数反映的是数量性状多基因系统的整体特征 ,其理论方法不能用于追踪研究和描述单个数量性状基因的作用。近年来 ,由于分子生物学技…     

QTL analysis for grain protein content using SSR markers and validation studies using NILs in bread wheat

QTL interval mapping for grain protein content (GPC) in bread wheat was conducted for the first time, using a framework map based on a mapping population, which was available in the form of 100 recombinant inbred lines (RILs). The data on GPC for QTL mapping was recorded by growing the RILs in five different environments representing three wheat growing locations from Northern India; one of these locations was repeated for 3 years. Distribution of GPC values followed normal distributions in all the environments, which could be explained by significant g x e interactions observed through analyses of variances, which also gave significant effects due to genotypes and environments. Thirteen (13) QTLs were identified in individual environments following three methods (single-marker analysis or SMA, simple interval mapping or SIM and composite interval mapping or CIM) and using LOD scores that ranged from 2.5 to 6.5. Threshold LOD scores (ranging from 3.05 to 3.57), worked out and used in each case, however, detected only seven of the above 13 QTLs. Only four (QGpc.ccsu-2B.1; QGpc.ccsu-2D.1; QGpc.ccsu-3D.1 and QGpc.ccsu-7A.1) of these QTLs were identified either in more than one location or following one more method other than CIM; another QTL (QGpc.ccsu-3D.2), which was identified using means for all the environments, was also considered to be important. These five QTLs have been recommended for marker-assisted selection (MAS). The QTLs identified as above were also validated using ten NILs derived from three crosses. Five of the ten NILs possessed 38 introgressed segments from 16 chromosomes and carried 42 of the 173 markers that were mapped. All the seven QTLs were associated with one or more of the markers carried by the above introgressed segments, thus validating the corresponding markers. More markers associated with many more QTLs to be identified should become available in the future by effective MAS for GPC improvement.     

A quantitative trait locus for oleic fatty acid content on Sus scrofa chromosome 7

A partial genome scan using microsatellite markers was conducted to detect quantitative trait loci (QTLs) for 10 fatty acid contents of backfat on 15 chromosomes in a porcine resource population. Two QTLs were discovered on Sus scrofa chromosome 4 (SSC4) and SSC7. The QTL on SSC4 was located between marker loci sw1336 and sw512, and this QTL was detected (P < 0.05) only for linoleic acid. Its position was in proximity of those mapped for linoleic acid content in previous studies. The QTL on SSC7 was mapped between markers swr1343 and sw2155, and it was significant (P < 0.05) only for oleic acid. A novelty of the QTL for oleic acid was suggested because the QTL was located far from any other QTLs previously mapped for fatness traits. The QTL on SSC7 explained 19% of phenotypic variation for oleic acid content. Further studies on fine mapping and positional comparative candidate gene analysis would be the next step toward better understanding of the genetic architecture of fatty acid contents.     

Localization of genes affecting species differences in male courtship song between Drosophila virilis and D. littoralis

The males of six species of the Drosophila virilis group (including D. virilis) keep their wings extended while producing a train of sound pulses, where the pulses follow each other without any pause. The males of the remaining five species of the group produce only one sound pulse during each wing extension/vibration, which results in species-specific songs with long pauses (in D. littoralis about 300 ms) between successive sound pulses. Genetic analyses of the differences between the songs of D. virilis and D. littoralis showed that species-specific song traits are affected by genes on the X chromosome, and for the length of pause, also by genes on chromosomes 3 and 4. The X chromosomal genes having a major impact on pulse and pause length were tightly linked with white, apricot and notched marker genes located at the proximal third of the chromosome. A large inversion in D. littoralis, marked by notched, prevents more precise localization of these genes by classical crossing methods.     

70个水稻微卫星标记染色体位置的更正

微卫星标记(SSR)因其操作简单和稳定可靠的特点而成为一种重要的分子标记,被广泛应用于遗传作图和种质鉴定等方面。但其在染色体上位置的正确性将直接影响到基因定位的正确性和后续研究的方向。利用美国国家生物信息技术中心(NCBI)网站的Blast程序,将2740个SSR标记的前后引物序列与水稻粳稻品种日本晴基因组进行比对,共发现70个标记位于另一条染色体,对这70个标记重新锚定的染色体进行了更正。这将有助于今后水稻分子标记遗传连锁图的正确构建。     

Mapping of QTLs conferring resistance to bacterial leaf streak in rice

A large F₂ and a RI population were separately derived from a cross between two indica rice varieties, one of which was highly resistant to bacterial leaf streak (BLS) and the other highly susceptible. Following artificial inoculation of the RI population and over 2 years of testing, 11 QTLs were mapped by composite interval mapping (CIM) on six chromosomes. Six of the QTLs were detected in both seasons. Eight of the QTLs were significant following stepwise regression analysis, and of these, 5 with the largest effects were significant in both seasons. The detected QTLs explained 84.6% of the genetic variation in 1997. Bulked segregant analysis (BSA) of the extremes of the F₂ population identified 3 QTLs of large effect. The 3 QTLs were dentical to 3 of the 5 largest QTLs detected by CIM. The independent detection of the same QTLs using two methods of analysis in separate mapping populations verifies the existence of the QTLs for BLS and provides markers to ease their introduction into elite varieties. Received: 13 October 1999 / Accepted: 29 October 1999     

Quantitative trait loci associated with androgenic responsiveness in triticale (×Triticosecale Wittm.) anther culture

Quantitative trait loci (QTLs) associated with androgenic responsiveness in triticale were analyzed using a population of 90 DH lines derived from the F1 cross between inbred line ‘Saka 3006’ and cv. ‘Modus’, which was used in a number of earlier studies on molecular mapping in this crop. Using Windows QTL Cartographer and MapQTL 5.0, composite interval mapping (CIM) and association studies (Kruskal–Wallis test; K–W) for five androgenesis parameters (androgenic embryo induction, total regeneration and green plant regeneration ability, and two characteristics describing final androgenesis efficiency) were conducted. For the studied components of androgenic response, CIM detected in total 28 QTLs which were localized on 5 chromosomes from A and R genomes. Effects of all QTLs that were identified at 2.0 or above of the LOD score explained 5.1–21.7?% of the phenotypic variation. Androgenesis induction was associated with seven QTLs (LOD between 2.0 and 5.8) detected on chromosomes 5A, 4R, 5R and 7R, all of them confirmed by K–W test as regions containing the markers significantly linked to the studied trait. What is more, K–W test revealed additional markers on chromosomes: 5A, 2BL, 7B and 5R. Both total and green regeneration ability were controlled by genes localized on chromosome 4A. Some of the QTLs that affected final androgenesis efficiency were identical with those associated with androgenic embryo induction efficiency, suggesting that the observed correlation may be either due to tight linkage or to pleiotropy. Key message Five regions of the triticale genome were indicated as revealing significant marker/trait association. Markers located in these regions are potentially useful for triticale breeding through marker-assisted selection.     

Genetic dissection of vegetative propagation traits in Eucalyptus tereticornis and E. globulus

We have detected quantitative trait loci (QTLs) affecting vegetative propagation traits in Eucalyptus tereticornis and Eucalyptus globulus. Using amplified fragment length polymorphism (AFLP) genetic linkage maps, the inheritance of 199 markers was assessed in 94 F₁ individuals with extreme adventitious rooting response, and in 221 randomly chosen F₁ individuals. Phenotypes were scored in 1995 and 1996. QTL analyses were performed using chi-square tests (χ²), single-marker analysis (SMA), interval mapping (IM) and composite interval mapping (CIM). All approaches yielded similar QTL detection results. Three QTLs are hypothesized for mortality (MORT=% dead cuttings), nine for adventitious rooting (ROOT, RCT=% rooted cuttings relative to the surviving or total cuttings, respectively), four for petrification (PETR=% surviving unrooted cuttings), one for sprouting ability (SPR=number of stump sprout cuttings harvested in 1995) and four for the stability of adventitious rooting (STAB=absolute value of the difference ROOT95-ROOT96). All putative QTLs for MORT and PETR were located on the E. tereticornis map, and for SPR and STAB on the E. globulus map. We found different QTLs for MORT, ROOT, RCT, SPR and STAB. Putative QTLs accounted for 2.6–17.0% of the phenotypic variance of a trait (R²). Estimated standardized gene substitution effects varied between 0.13 and 0.49 phenotypic standard deviations (σ_p). These results indicate that the phenotypic variation in these traits has a meaningful genetic component and that stable QTLs can be found in a family of reasonable size where no previous knowledge of the trait was available. Received: 1 September 1998 / Accepted: 24 February 1999     

A complete genetic linkage map and QTL analyses for bast fibre quality traits, yield and yield components in jute (Corchorus olitorius L.)

We report the first complete microsatellite genetic map of jute (Corchorus olitorius L.; 2n = 2 × = 14) using an F₆ recombinant inbred population. Of the 403 microsatellite markers screened, 82 were mapped on the seven linkage groups (LGs) that covered a total genetic distance of 799.9 cM, with an average marker interval of 10.7 cM. LG5 had the longest and LG7 the shortest genetic lengths, whereas LG1 had the maximum and LG7 the minimum number of markers. Segregation distortion of microsatellite loci was high (61%), with the majority of them (76%) skewed towards the female parent. Genomewide non-parametric single-marker analysis in combination with multiple quantitative trait loci (QTL)-models (MQM) mapping detected 26 definitive QTLs for bast fibre quality, yield and yield-related traits. These were unevenly distributed on six LGs, as co-localized clusters, at genomic sectors marked by 15 microsatellite loci. LG1 was the QTL-richest map sector, with the densest co-localized clusters of QTLs governing fibre yield, yield-related traits and tensile strength. Expectedly, favorable QTLs were derived from the desirable parents, except for nearly all of those for fibre fineness, which might be due to the creation of new gene combinations. Our results will be a good starting point for further genome analyses in jute.     

Searching for novel sources of field resistance to Ug99 and Ethiopian stem rust races in durum wheat via association mapping

Puccinia graminis f. sp. tritici, the causative agent of stem rust in wheat, is a devastating disease of durum wheat. While more than 50 stem rust resistance (Sr) loci have been identified in wheat, only a few of them have remained effective against Ug99 (TTKSK race) and other durum-specific Ethiopian races. An association mapping (AM) approach based on 183 diverse durum wheat accessions was utilized to identify resistance loci for stem rust response in Ethiopia over four field-evaluation seasons and artificial inoculation with Ug99 and a mixture of durum-specific races. The panel was profiled with simple sequence repeat, Diversity Arrays Technology and sequence-tagged site markers (1,253 in total). The resistance turned out to be oligogenic, with twelve QTL-tagging markers that were significant (P < 0.05) across three or four seasons. R ² values ranged from 1.1 to 11.3 %.Twenty-four additional single-marker/QTL regions were found to be significant over two seasons. The AM results confirmed the role of Sr13, previously described in bi-parental mapping studies, and the role of chromosome regions putatively harbouring Sr9, Sr14, Sr17 and Sr28. Three minor QTLs were coincident with those reported in hexaploid wheat and five overlapped with those recently reported in the Sebatel × Kristal durum mapping population. Thirteen single-marker/QTL regions were located in chromosome regions where no Sr genes/QTLs have been previously reported. The allelic variation identified in this study is readily available and can be exploited for marker-assisted selection, thus providing additional opportunities for a more durable stem rust resistance under field conditions.     

Quantitative trait loci for directional but not fluctuating asymmetry of mandible characters in mice

Non-directional variation in right minus left differences in bilateral characters, referred to as fluctuating asymmetry (FA), often has been assumed to be largely or entirely environmental in origin. FA increasingly has been used as a measure of developmental stability, and its presumed environmental origin has facilitated the comparisons of populations believed to differ in their levels of stability. Directional asymmetry (DA), in which one side is consistently larger than the other, has been assumed to be at least partially heritable. Both these assumptions were tested with interval mapping techniques designed to detect any quantitative trait loci (QTLs) affecting FA or DA in 15 bilateral mandible characters in house mice resulting from a cross of the F1 between CAST/Ei (wild strain) and M16i (selected for rapid growth rate) back to M16i. For purposes of the analysis, all mandibles were triply measured and 92 microsatellite markers were scored in a total of 350 mice. No significant QTLs were found for FA, but three QTLs significantly affected DA in several characters, confirming both assumptions. The QTLs for DA were similar in location to those affecting the size of several of the mandible characters, although they accounted for an average of only 1% of the total phenotypic variation in DA.     

Fine mapping quantitative trait loci affecting milk production traits on bovine chromosome 6 in a Chinese Holstein population

To fine map the previously detected quantitative trait loci （QTLs） affecting milk production traits on bovine chromosome 6 （BTA6）, 15 microsatellite markers situated within an interval of 14.3 cM spanning from BMS690 to BM4528 were selected and 918 daughters of 8 sires were genotyped. Two mapping approaches, haplotype sharing based LD mapping and single marker regression mapping, were used to analyze the data. Both approaches revealed a quantitative trait locus （QTL） with significant effects on milk yield, fat yield and protein yield located in the segment flanked by markers BMS483 and MNB209, which spans a genetic distance of 0.6 cM and a physical distance of 1.5 Mb. In addition, the single marker regression mapping also revealed a QTL affecting fat percentage and protein percentage at marker DIK2291. Our fine mapping work will facilitate the cloning of candidate genes underlying the QTLs for milk production traits.     

Detection of Fusarium head blight resistance QTL in a wheat population using bulked segregant analysis

A population of 218 recombinant inbred lines (RILs) was developed from the cross of two wheat (Triticum aestivum L.) cultivars, 'Ning 894037' and 'Alondra'. Ning 894037 has resistance to Fusarium head blight (FHB) and Alondra is moderately susceptible. Response of the RILs and their parental lines to FHB infection was evaluated with point inoculation in four experiments both in greenhouse and in field conditions. Distribution of disease severity in the population is continuous, indicating quantitative inheritance of resistance to FHB. Bulked segregant analysis and QTL mapping based on simple sequence repeat (SSR) markers revealed three chromosome regions that are responsible for FHB resistance. A chromosome region on 3BS accounted for 42.5% of the phenotypic variation for FHB resistance. Additional QTLs were located on chromosomes 2D and 6B. These three QTLs jointly accounted for 51.6% of the phenotypic variation. SSR markers linked to the QTLs influencing resistance to FHB have potential for use in breeding programs.     

Mapping quantitative trait loci associated with aluminum toxin tolerance in NJRIKY recombinant inbred line population of soybean (Glycine max)

To investigate the genetic mechanism of AI-tolerance in soybean,a recombinant inbred line population (RIL) with 184 F2:7:11 lines derived from the cross of Kefeng No.1 x Nannong 1138-2 (AI-tolerant x AI-sensitive) were tested in pot experimentwith sand culture medium in net room in Nanjing.Four traits,i.e.plant height,number of leaves,shoot dry weight and root dry weight at seedling stage,were evaluated and used to calculate the average membership index (FAi) as the indicator of AI-tolerance.The composite interval mapping (ClM) under WinQTL Cartographer v.2.5 detected five QTLs (i.e.qFAiol,qFAi-2,qFAi-3,qFAi-4 and qFAi-5),explaining 5.20%-9.07% of the total phenotypic variation individually.While with the multiple interval mapping (MIM) of the same software,five QTLs (qFAi-1,qFAi-5,qFAi-6,qFAi-7,and qFAi-8) explaining 5.7%-24.60% of the total phenotypic variation individually were mapped.Here qFAi-1 and qFAi-5 were detected by both CIM and MIM with the locations in a same flanking marker region,GMKF046-GMKF080 on B1 and satt278-sat_95 on L,respectively.While qFAi-2 under CIM and qFAi-6 under MIM both on D1b2 were located in neighboring regions with their confidence intervals overlapped and might be the same locus.Segregation analysis under major gene plus polygene inheritance model showed that Al-tolerance was controlled by two major genes (h2mg =33.05%) plus polygenes (h2pg=52.73%).Both QTL mapping and segregation analysis confirmed two QTLs responsible for Al-tolerance with relatively low heritability,and there might be a third QTL,confounded with the polygenes in segregation analysis.     

Quantitative trait loci for low aflatoxin production in two related maize populations

Aflatoxin B(1) formed by Aspergillus flavus Fr:Link has been associated with animal disease and liver cancer in humans. We performed genetic studies in progenies derived from maize inbred Tex6, associated with relatively low levels of aflatoxin production, crossed with the historically important inbred B73. (Tex6 x B73) x B73 BC(1)S(1) and Tex6 x B73 F(2:3) mapping populations were produced and evaluated in 1996 and 1997 in Champaign, Ill. Ears were inoculated 20 to 24 days after midsilk using a pinboard method and a mixture of conidia of A. flavus Link:Fr. isolates. Aflatoxin B(1) levels in harvested ears were determined using an indirect competitive ELISA. Molecular markers were assayed on the populations and used to generate maps. Molecular marker - QTL associations for lower levels of aflatoxin production were determined using multiple regression (MR) and composite interval analysis with multiple regression (CIM MR). MR revealed sets of markers associated with lower aflatoxin production in 1996 and 1997, and CIM MR detected a smaller subset of loci significant in 1997. QTLs for lower aflatoxin were attributed to both Tex6 and B73 parental sources. Environment strongly influenced the detection of QTLs for lower aflatoxin production in different years. There were very few chromosome regions associated with QTLs in more than 1 year or population with MR analysis, and none with CIM MR analysis. In 1997, QTLs for lower aflatoxin were detected with CIM MR in bins 5.01-2 and 5.04-5 in the BC(1)S(1) population, and in bins 3.05-6, 4.07-8 and 10.05-10.07 in the F(2:3) population. These QTL associations appear the most promising for further study.     

Quantitative trait loci mapping of adult-plant resistance to powdery mildew in triticale

Powdery mildew (PM) is a common disease caused by Blumeria graminis, which affects cereals and has recently adapted to triticale. Adult-plant resistance (APR) genes provide durable protection of crops from the disease. Quantitative trait loci corresponding to the APR effects were mapped in an F₂ population of “Lamberto” (susceptible) × “Moderto” (resistant). A genetic map of winter triticale was constructed based on the segregation of 863 DArT, 38 microsatellite and 10 resistance gene analogue markers. Composite interval mapping (CIM) was applied to identify three QTLs for maximum disease severity (MDS) and two for the area under disease progress curve (AUDPC) conferring resistance to the powdery mildew on chromosomes: 6A, 7A, 1B and 4R. The 39% variation in AUDPC was explained by the main QTL localised on chromosome 4R. Genes coding TRIUR3 proteins, serine/threonine protein kinase and cell wall associated kinases were localised in silico within the QTL and alternative DNA markers were proposed for flexible use in laboratories of diversified throughput.     

Inferences regarding the numbers and locations of QTLs under multiple-QTL models using interval mapping and composite interval mapping

This paper examines the properties of likelihood maps generated by interval mapping (IM) and composite interval mapping (CIM), two widely used methods for detecting quantitative trait loci (QTLs). We evaluate the usefulness of interpretations of entire maps, rather than only evaluating summary statistics that consider isolated features of maps. A simulation study was performed in which traits with varying genetic architectures, including 20-40 QTLs per chromosome, were examined with both IM and CIM under different marker densities and sample sizes. IM was found to be an unreliable tool for precise estimation of the number and locations of individual QTLs, although it has greater power for simply detecting the presence of QTLs than CIM. The ability of CIM to resolve the correct number of QTLs and to estimate their locations correctly is good if there are three or fewer QTLs per 100 centiMorgans, but can lead to erroneous inferences for more complex architectures. When the underlying genetic architecture of a trait consists of several QTLs with randomly distributed effects and locations likelihood profiles were often indicative of a few underlying genes of large effect. Studies that have detected more than a few QTLs per chromosome should be interpreted with caution.