Chromosomes A07 and A05 associated with stable and major QTLs for pod weight and size in cultivated peanut (<Emphasis Type="Italic">Arachis hypogaea</Emphasis> L.)

Key message

Co-localized intervals and candidate genes were identified for major and stable QTLs controlling pod weight and size on chromosomes A07 and A05 in an RIL population across four environments.

Abstract

Cultivated peanut (Arachis hypogaea L.) is an important legume crops grown in > 100 countries. Hundred-pod weight (HPW) is an important yield trait in peanut, but its underlying genetic mechanism was not well studied. In this study, a mapping population (Xuhua 13 × Zhonghua 6) with 187 recombinant inbred lines (RILs) was developed to map quantitative trait loci (QTLs) for HPW together with pod length (PL) and pod width (PW) by both unconditional and conditional QTL analyses. A genetic map covering 1756.48 cM was constructed with 817 markers. Additive effects, epistatic interactions, and genotype-by-environment interactions were analyzed using the phenotyping data generated across four environments. Twelve additive QTLs were identified on chromosomes A05, A07, and A08 by unconditional analysis, and five of them (qPLA07, qPLA05.1, qPWA07, qHPWA07.1, and qHPWA05.2) showed major and stable expressions in all environments. Conditional QTL mapping found that PL had stronger influences on HPW than PW. Notably, qHPWA07.1, qPLA07, and qPWA07 that explained 17.93–43.63% of the phenotypic variations of the three traits were co-localized in a 5 cM interval (1.48 Mb in physical map) on chromosome A07 with 147 candidate genes related to catalytic activity and metabolic process. In addition, qHPWA05.2 and qPLA05.1 were co-localized with minor QTL qPWA05.2 to a 1.3 cM genetic interval (280 kb in physical map) on chromosome A05 with 12 candidate genes. This study provides a comprehensive characterization of the genetic components controlling pod weight and size as well as candidate QTLs and genes for improving pod yield in future peanut breeding.

     

Detecting the QTL-allele system conferring flowering date in a nested association mapping population of soybean using a novel procedure

Key message

The RTM-GWAS was chosen among five procedures to identify DTF QTL-allele constitution in a soybean NAM population; 139 QTLs with 496 alleles accounting for 81.7% of phenotypic variance were detected.

Abstract

Flowering date (days to flowering, DTF) is an ecological trait in soybean, closely related to its ability to adapt to areas. A nested association mapping (NAM) population consisting of four RIL populations (LM, ZM, MT and MW with M8206 as their common parent) was established and tested for their DTF under five environments. Using restriction-site-associated DNA sequencing the population was genotyped with SNP markers. The restricted two-stage multi-locus (RTM) genome-wide association study (GWAS) (RTM-GWAS) with SNP linkage disequilibrium block (SNPLDB) as multi-allele genomic markers performed the best among the five mapping procedures with software publicly available. It identified the greatest number of quantitative trait loci (QTLs) (139) and alleles (496) on 20 chromosomes covering almost all of the QTLs detected by four other mapping procedures. The RTM-GWAS provided the detected QTLs with highest genetic contribution but without overflowing and missing heritability problems (81.7% genetic contribution vs. heritability of 97.6%), while SNPLDB markers matched the NAM population property of multiple alleles per locus. The 139 QTLs with 496 alleles were organized into a QTL-allele matrix, showing the corresponding DTF genetic architecture of the five parents and the NAM population. All lines and parents comprised both positive and negative alleles, implying a great potential of recombination for early and late DTF improvement. From the detected QTL-allele system, 126 candidate genes were annotated and χ ² tested as a DTF candidate gene system involving nine biological processes, indicating the trait a complex, involving several biological processes rather than only a handful of major genes.

     

Confirmation and fine‐mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle

A genome‐wide association study of 2098 progeny‐tested Nordic Holstein bulls genotyped for 36 387 SNPs on 29 autosomes was conducted to confirm and fine‐map quantitative trait loci (QTL) for mastitis traits identified earlier using linkage analysis with sparse microsatellite markers in the same population. We used linear mixed model analysis where a polygenic genetic effect was fitted as a random effect and single SNPs were successively included as fixed effects in the model. We detected 143 SNP‐by‐trait significant associations (P < 0.0001) on 20 chromosomes affecting mastitis‐related traits. Among them, 21 SNP‐by‐trait combinations exceeded the genome‐wide significant threshold. For 12 chromosomes, both the present association study and the previous linkage study detected QTL, and of these, six were in the same chromosomal locations. Strong associations of SNPs with mastitis traits were observed on bovine autosomes 6, 13, 14 and 20. Possible candidate genes for these QTL were identified. Identification of SNPs in linkage disequilibrium with QTL will enable marker‐based selection for mastitis resistance. The candidate genes identified should be further studied to detect candidate polymorphisms underlying these QTL.     

SNP markers‐based map construction and genome‐wide linkage analysis in Brassica napus

An Illumina Infinium array comprising 5306 single nucleotide polymorphism (SNP) markers was used to genotype 175 individuals of a doubled haploid population derived from a cross between Skipton and Ag‐Spectrum, two Australian cultivars of rapeseed (Brassica napus L.). A genetic linkage map based on 613 SNP and 228 non‐SNP (DArT, SSR, SRAP and candidate gene markers) covering 2514.8 cM was constructed and further utilized to identify loci associated with flowering time and resistance to blackleg, a disease caused by the fungus Leptosphaeria maculans. Comparison between genetic map positions of SNP markers and the sequenced Brassica rapa (A) and Brassica oleracea (C) genome scaffolds showed several genomic rearrangements in the B. napus genome. A major locus controlling resistance to L. maculans was identified at both seedling and adult plant stages on chromosome A07. QTL analyses revealed that up to 40.2% of genetic variation for flowering time was accounted for by loci having quantitative effects. Comparative mapping showed Arabidopsis and Brassica flowering genes such as Phytochrome A/D, Flowering Locus C and agamous‐Like MADS box gene AGL1 map within marker intervals associated with flowering time in a DH population from Skipton/Ag‐Spectrum. Genomic regions associated with flowering time and resistance to L. maculans had several SNP markers mapped within 10 cM. Our results suggest that SNP markers will be suitable for various applications such as trait introgression, comparative mapping and high‐resolution mapping of loci in B. napus.     

Quantitative Trait Loci Mapping for ChlorophyⅡ Fluorescence and Associated Traits in Wheat （ Triticum aestivum）

Parameters of chlorophyll fluorescence kinetics （PCFKs） under drought stress condition are generally used to characterize instincts for dehydration tolerance in wheat （Triticum aestivum L.）. Therefore, it is important to map quantitative trait loci （QTLs） for PCFKs in wheat genetic improvement for drought tolerance. A doubled haploid （DH） population with 150 lines, derived from a cross between two common wheat varieties, Hanxuan 10 and Lumai 14, was used to analyze the correlation between PCFKs and chlorophyll content （CHIC） and to map QTLs at the grainfilling stage under conditions of both rainfed （drought stress, DS） and well-watered （WW）, respectively. QTLs for these traits were detected by QTLMapper version 1.0 based on the composite Interval mapping method of the mixed-linear model. The results showed a very significant positive correlation between Fv, Fm, Fv/Fm and Fv/Fo. The correlation coefficients were generally higher under WW than under DS. Also, there was a significant or a highly significant positive correlation between Fv, Fm, Fv/Fm, Fv/Fo and CHIC. The correlation coefficients were higher in the DS group than the WW group. A total of 14 additive QTLs （nine QTLs detected under DS and five QTLs under WW） and 25 pairs of eplstatlc QTLs （15 pairs detected under DS and 10 pairs under WW） for PCFKs were mapped on chromosomes 6A, 7A, 1B, 3B, 4D and 7D. The contributions of additive QTLs for PCFKs to phenotype variation were from 8.40% to 72.72%. Four additive QTLs （two QTLs detected under DS and WW apiece） controlling Chic were mapped on chromosomes 1A, 5A and 7A. The contributions of these QTLs for ChIC to phenotype variation were from 7.27% to 11.68%. Several QTL clusters were detected on chromosomes 1B, 7A and 7D, but no shared chromosomal regions for them were identified under different water regimes, indicating that these QTLs performed different expression patterns under rainfed and well-watered conditions.     

Fine mapping of qAHPS07 and functional studies of AhRUVBL2 controlling pod size in peanut (Arachis hypogaea L.)

Cultivated peanut (Arachis hypogaea L.) is an important oil and cash crop. Pod size is one of the major traits determining yield and commodity characteristic of peanut. Fine mapping of quantitative trait locus (QTL) and identification of candidate genes associated with pod size are essential for genetic improvement and molecular breeding of peanut varieties. In this study, a major QTL related to pod size, qAHPS07, was fine mapped to a 36.46 kb interval on chromosome A07 using F₂, recombinant inbred line (RIL) and secondary F₂ populations. qAHPS07 explained 38.6%, 23.35%, 37.48%, 25.94% of the phenotypic variation for single pod weight (SPW), pod length (PL), pod width (PW) and pod shell thickness (PST), respectively. Whole genome resequencing and gene expression analysis revealed that a RuvB-like 2 protein coding gene AhRUVBL2 was the most likely candidate for qAHPS07. Overexpression of AhRUVBL2 in Arabidopsis led to larger seeds and plants than the wild type. AhRUVBL2-silenced peanut seedlings represented small leaves and shorter main stems. Three haplotypes were identified according to three SNPs in the promoter of AhRUVBL2 among 119 peanut accessions. Among them, SPW, PW and PST of accessions carrying Hap_ATT represent 17.6%, 11.2% and 26.3% higher than those carrying Hap_GAC，respectively. In addition, a functional marker of AhRUVBL2 was developed. Taken together, our study identified a key functional gene of peanut pod size, which provides new insights into peanut pod size regulation mechanism and offers practicable markers for the genetic improvement of pod size-related traits in peanut breeding.     

Genotyping‐by‐sequencing‐based genome‐wide association studies on Verticillium wilt resistance in autotetraploid alfalfa (Medicago sativa L.)

Verticillium wilt (VW) is a fungal disease that causes severe yield losses in alfalfa. The most effective method to control the disease is through the development and use of resistant varieties. The identification of marker loci linked to VW resistance can facilitate breeding for disease‐resistant alfalfa. In the present investigation, we applied an integrated framework of genome‐wide association with genotyping‐by‐sequencing (GBS) to identify VW resistance loci in a panel of elite alfalfa breeding lines. Phenotyping was performed by manual inoculation of the pathogen to healthy seedlings, and scoring for disease resistance was carried out according to the standard test of the North America Alfalfa Improvement Conference (NAAIC). Marker–trait association by linkage disequilibrium identified 10 single nucleotide polymorphism (SNP) markers significantly associated with VW resistance. Alignment of the SNP marker sequences to the M. truncatula genome revealed multiple quantitative trait loci (QTLs). Three, two, one and five markers were located on chromosomes 5, 6, 7 and 8, respectively. Resistance loci found on chromosomes 7 and 8 in the present study co‐localized with the QTLs reported previously. A pairwise alignment (blastn ) using the flanking sequences of the resistance loci against the M. truncatula genome identified potential candidate genes with putative disease resistance function. With further investigation, these markers may be implemented into breeding programmes using marker‐assisted selection, ultimately leading to improved VW resistance in alfalfa.     

Functional mapping of N deficiency‐induced response in wheat yield‐component traits by implementing high‐throughput phenotyping

As overfertilization leads to environmental concerns and the cost of N fertilizer increases, the issue of how to select crop cultivars that can produce high yields on N‐deficient soils has become crucially important. However, little information is known about the genetic mechanisms by which crops respond to environmental changes induced by N signaling. Here, we dissected the genetic architecture of N‐induced phenotypic plasticity in bread wheat (Triticum aestivum L.) by integrating functional mapping and semiautomatic high‐throughput phenotyping data of yield‐related canopy architecture. We identified a set of quantitative trait loci (QTLs) that determined the pattern and magnitude of how wheat cultivars responded to low N stress from normal N supply throughout the wheat life cycle. This analysis highlighted the phenological landscape of genetic effects exerted by individual QTLs, as well as their interactions with N‐induced signals and with canopy measurement angles. This information may shed light on our mechanistic understanding of plant adaptation and provide valuable information for the breeding of N‐deficiency tolerant wheat varieties.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

A first step toward the development of a barley NAM population and its utilization to detect QTLs conferring leaf rust seedling resistance

Key message

We suggest multi-parental nested association mapping as a valuable innovation in barley genetics, which increases the power to map quantitative trait loci and assists in extending genetic diversity of the elite barley gene pool.

Abstract

Plant genetic resources are a key asset to further improve crop species. The nested association mapping (NAM) approach was introduced to identify favorable genes in multi-parental populations. Here, we report toward the development of the first explorative barley NAM population and demonstrate its usefulness in a study on mapping quantitative trait loci (QTLs) for leaf rust resistance. The NAM population HEB-5 was developed from crossing and backcrossing five exotic barley donors with the elite barley cultivar ‘Barke,’ resulting in 295 NAM lines in generation BC₁S₁. HEB-5 was genetically characterized with 1,536 barley SNPs. Across HEB-5 and within the NAM families, no deviation from the expected genotype and allele frequencies was detected. Genetic similarity between ‘Barke’ and the NAM families ranged from 78.6 to 83.1 %, confirming the backcrossing step during population development. To explore its usefulness, a screen for leaf rust (Puccinia hordei) seedling resistance was conducted. Resistance QTLs were mapped to six barley chromosomes, applying a mixed model genome-wide association study. In total, four leaf rust QTLs were detected across HEB-5 and four QTLs within family HEB-F23. Favorable exotic QTL alleles reduced leaf rust symptoms on two chromosomes by 33.3 and 36.2 %, respectively. The located QTLs may represent new resistance loci or correspond to new alleles of known resistance genes. We conclude that the exploratory population HEB-5 can be applied to mapping and utilizing exotic QTL alleles of agronomic importance. The NAM concept will foster the evaluation of the genetic diversity, which is present in our primary barley gene pool.     

QTL mapping for some grain traits in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Grain traits are important agronomic attributes with the market value as well as milling yield of bread wheat. In the present study, quantitative trait loci (QTL) regulating grain traits in wheat were identified. Data for grain area size (GAS), grain width (GWid), factor form density (FFD), grain length-width ratio (GLWR), thousand grain weight (TGW), grain perimeter length (GPL) and grain length (GL) were recorded on a recombinant inbred line derived from the cross of NW1014?×?HUW468 at Meerut and Varanasi locations. A linkage map of 55 simple sequence repeat markers for 8 wheat chromosomes was used for QTL analysis by Composite interval mapping. Eighteen QTLs distributed on 8 chromosomes were identified for seven grain traits. Of these, five QTLs for GLWR were found on chromosomes 1A, 6A, 2B, and 7B, three QTLs for GPL were located on chromosomes 4A, 5A and 7B and three QTLs for GAS were mapped on 5D and 7D. Two QTLs were identified on chromosomes 4A and 5A for GL and two QTLs for GWid were identified on chromosomes 7D and 6A. Similarly, two QTLs for FFD were found on chromosomes 1A and 5D. A solitary QTL for TGW was identified on chromosome 2B. For several traits, QTLs were also co-localized on chromosomes 2B, 4A, 5A, 6A, 5D, 7B and 7D. The QTLs detected in the present study may be validated for specific crosses and then used for marker-assisted selection to improve grain quality in bread wheat.     

Linkage and association mapping reveals the genetic basis of brown fibre (Gossypium hirsutum)

Brown fibre cotton is an environmental‐friendly resource that plays a key role in the textile industry. However, the fibre quality and yield of natural brown cotton are poor, and fundamental research on brown cotton is relatively scarce. To understand the genetic basis of brown fibre cotton, we constructed linkage and association populations to systematically examine brown fibre accessions. We fine‐mapped the brown fibre region, Lc₁, and dissected it into 2 loci, qBF‐A07‐1 and qBF‐A07‐2. The qBF‐A07‐1 locus mediates the initiation of brown fibre production, whereas the shade of the brown fibre is affected by the interaction between qBF‐A07‐1 and qBF‐A07‐2. Gh_A07G2341 and Gh_A07G0100 were identified as candidate genes for qBF‐A07‐1 and qBF‐A07‐2, respectively. Haploid analysis of the signals significantly associated with these two loci showed that most tetraploid modern brown cotton accessions exhibit the introgression signature of Gossypium barbadense. We identified 10 quantitative trait loci (QTLs) for fibre yield and 19 QTLs for fibre quality through a genome‐wide association study (GWAS) and found that qBF‐A07‐2 negatively affects fibre yield and quality through an epistatic interaction with qBF‐A07‐1. This study sheds light on the genetics of fibre colour and lint‐related traits in brown fibre cotton, which will guide the elite cultivars breeding of brown fibre cotton.     

Quantitative trait locus mapping reveals complex genetic architecture of quantitative virulence in the wheat pathogen Zymoseptoria tritici

We conducted a comprehensive analysis of virulence in the fungal wheat pathogen Zymoseptoria tritici using quantitative trait locus (QTL) mapping. High‐throughput phenotyping based on automated image analysis allowed the measurement of pathogen virulence on a scale and with a precision that was not previously possible. Across two mapping populations encompassing more than 520 progeny, 540 710 pycnidia were counted and their sizes and grey values were measured. A significant correlation was found between pycnidia size and both spore size and number. Precise measurements of percentage leaf area covered by lesions provided a quantitative measure of host damage. Combining these large and accurate phenotypic datasets with a dense panel of restriction site‐associated DNA sequencing (RADseq) genetic markers enabled us to genetically dissect pathogen virulence into components related to host damage and those related to pathogen reproduction. We showed that different components of virulence can be under separate genetic control. Large‐ and small‐effect QTLs were identified for all traits, with some QTLs specific to mapping populations, cultivars and traits and other QTLs shared among traits within the same mapping population. We associated the presence of four accessory chromosomes with small, but significant, increases in several virulence traits, providing the first evidence for a meaningful function associated with accessory chromosomes in this organism. A large‐effect QTL involved in host specialization was identified on chromosome 7, leading to the identification of candidate genes having a large effect on virulence.     

Fifteen years of quantitative trait loci studies in fish: challenges and future directions

Understanding the genetic basis of phenotypic variation is a major challenge in biology. Here, we systematically evaluate 146 quantitative trait loci (QTL) studies on teleost fish over the last 15 years to investigate (i) temporal trends and (ii) factors affecting QTL detection and fine‐mapping. The number of fish QTL studies per year increased over the review period and identified a cumulative number of 3632 putative QTLs. Most studies used linkage‐based mapping approaches and were conducted on nonmodel species with limited genomic resources. A gradual and moderate increase in the size of the mapping population and a sharp increase in marker density from 2011 onwards were observed; however, the number of QTLs and variance explained by QTLs changed only minimally over the review period. Based on these findings, we discuss the causative factors and outline how larger sample sizes, phenomics, comparative genomics, epigenetics and software development could improve both the quantity and quality of QTLs in future genotype–phenotype studies. Given that the technical limitations on DNA sequencing have mostly been overcome in recent years, a renewed focus on these and other study design factors will likely lead to significant improvements in QTL studies in the future.     

Beyond pathways: genetic dissection of tocopherol content in maize kernels by combining linkage and association analyses

Although tocopherols play an important role in plants and animals, the genetic architecture of tocopherol content in maize kernels has remained largely unknown. In this study, linkage and association analyses were conducted to examine the genetic architecture of tocopherol content in maize kernels. Forty‐one unique quantitative trait loci (QTLs) were identified by linkage mapping in six populations of recombinant inbred lines (RILs). In addition, 32 significant loci were detected via genome‐wide association study (GWAS), 18 of which colocalized with the QTLs identified by linkage mapping. Fine mapping of a major QTL validated the accuracy of GWAS and QTL mapping results and suggested a role for nontocopherol pathway genes in the modulation of natural tocopherol variation. We provided genome‐wide evidence that genes involved in fatty acid metabolism, chlorophyll metabolism and chloroplast function may affect natural variation in tocopherols. These findings were confirmed through mutant analysis of a particular gene from the fatty acid pathway. In addition, the favourable alleles for many of the significant SNPs/QTLs represented rare alleles in natural populations. Together, our results revealed many novel genes that are potentially involved in the variation of tocopherol content in maize kernels. Pyramiding of the favourable alleles of the newly elucidated genes and the well‐known tocopherol pathway genes would greatly improve tocopherol content in maize.     

Loci and candidate gene identification for resistance to Sclerotinia sclerotiorum in soybean (Glycine max L. Merr.) via association and linkage maps

Soybean white mold (SWM), caused by Sclerotinia sclerotiorum ((Lib.) W. Phillips), is currently considered to be the second most important cause of soybean yield loss due to disease. Research is needed to identify SWM‐resistant germplasm and gain a better understanding of the genetic and molecular basis of SWM resistance in soybean. Stem pigmentation after treatment with oxaloacetic acid is an effective indicator of resistance to SWM. A total of 128 recombinant inbred lines (RILs) derived from a cross of ‘Maple Arrow’ (partial resistant to SWM) and ‘Hefeng 25’ (susceptible) and 330 diverse soybean cultivars were screened for the soluble pigment concentration of their stems, which were treated with oxalic acid. Four quantitative trait loci (QTLs) underlying soluble pigment concentration were detected by linkage mapping of the RILs. Three hundred and thirty soybean cultivars were sequenced using the whole‐genome encompassing approach and 25 179 single‐nucleotide polymorphisms (SNPs) were detected for the fine mapping of SWM resistance genes by genome‐wide association studies. Three out of five SNP markers representing a linkage disequilibrium (LD) block and a single locus on chromosome 13 (Gm13) were significantly associated with the soluble pigment content of stems. Three more SNPs that represented three minor QTLs for the soluble pigment content of stems were identified on another three chromosomes by association mapping. A major locus with the largest effect on Gm13 was found both by linkage and association mapping. Four potential candidate genes involved in disease response or the anthocyanin biosynthesis pathway were identified at the locus near the significant SNPs (<60 kbp). The beneficial allele and candidate genes should be useful in soybean breeding for improving resistance to SWM.     

小麦幼苗耐热性的QTL定位分析

以小麦DH群体(‘旱选10号’ב鲁麦14’)为材料,在高温(热胁迫)及常温(对照)两种条件下考察小麦幼苗的根干重、苗干重、幼苗生物量、叶片叶绿素含量、叶绿素荧光参数及其耐热指数,并应用基于混合线性模型的复合区间作图法分析幼苗性状及其耐热指数QTL的数量、染色体分布及表达情况,以及QTL与环境的互作效应。结果显示:(1)亲本‘旱选10号’的耐热性明显优于‘鲁麦14’,且杂交后代的耐热性出现超亲分离。(2)控制幼苗耐热相关性状的QTL位点在染色体2D、6B、3A、4A、5A和7A上分布较多,而控制幼苗性状耐热指数的QTL在染色体6A、6B、3A、2D、5A和7A上分布较多,QTL位点在染色体上的分布有区域化的趋势。(3)控制幼苗性状的单个加性QTL和上位性QTL解释的表型变异分别平均为2.48%和2.65%;而控制耐热指数的单个加性QTL和上位性QTL解释的表型变异分别平均为8.84%和1.98%。(4)在热胁迫和对照条件下共检测到与幼苗性状及其耐热指数有关的加性效应QTL 13个和上位性效应QTL 28对,分布在除4D和6D以外的19条染色体上。研究表明,控制幼苗性状的QTL以上位性效应为主,而其耐热指数的QTL以加性效应为主。     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

A unified DNA sequence and non‐DNA sequence mapping model of complex traits

Increasing evidence shows that quantitative inheritance is based on both DNA sequence and non‐DNA sequence variants. However, how to simultaneously detect these variants from a mapping study has been unexplored, hampering our effort to illustrate the detailed genetic architecture of complex traits. We address this issue by developing a unified model of quantitative trait locus (QTL) mapping based on an open‐pollinated design composed of randomly sampling maternal plants from a natural population and their half‐sib seeds. This design forms a two‐level hierarchical platform for a joint linkage‐linkage disequilibrium analysis of population structure. The EM algorithm was implemented to estimate and test DNA sequence‐based effects and non‐DNA sequence‐based effects of QTLs. We applied this model to analyze genetic mapping data from the OP design of a gymnosperm coniferous species, Torreya grandis, identifying 25 significant DNA sequence and non‐DNA sequence QTLs for seedling height and diameter growth in different years. Results from computer simulation show that the unified model has good statistical properties and is powerful for QTL detection. Our model enables the tests of how a complex trait is affected differently by DNA‐based effects and non‐DNA sequence‐based transgenerational effects, thus allowing a more comprehensive picture of genetic architecture to be charted and quantified.     

Genome‐wide association mapping for female fertility traits in Danish and Swedish Holstein cattle

A genome‐wide association study was conducted using a mixed model analysis for QTL for fertility traits in Danish and Swedish Holstein cattle. The analysis incorporated 2,531 progeny tested bulls, and a total of 36 387 SNP markers on 29 bovine autosomes were used. Eleven fertility traits were analyzed for SNP association. Furthermore, mixed model analysis was used for association analyses where a polygenic effect was fitted as a random effect, and genotypes at single SNPs were successively included as a fixed effect in the model. The Bonferroni correction for multiple testing was applied to adjust the significance threshold. Seventy‐four SNP‐trait combinations showed chromosome‐wide significance, and five of these were significant genome‐wide. Twenty‐four QTL regions on 14 chromosomes were detected. Strong evidence for the presence of QTL that affect fertility traits were observed on chromosomes 3, 5, 10, 13, 19, 20, and 24. The QTL intervals were generally smaller than those described in earlier linkage studies. The identification of fertility trait‐associated SNPs and mapping of the corresponding QTL in small chromosomal regions reported here will facilitate searches for candidate genes and candidate polymorphisms.