QTL alleles on chromosome 7 from fatty Meishan pigs reduce fat deposition

~~QTL alleles on chromosome 7 from fatty Meishan pigs reduce fat deposition@岳根华$Department of Animal Breeding and Biotechnology,University of Hohenheim!Garbenstr.17,70593 Stutt-gart,GermanyCurrent address:Institute of Molecular Agrobiology,national University of Singapore @Petra Beeckmann$Department of Animal Breeding and Biotechnology,University of Hohenheim!Garbenstr.17,70593 Stutt-gart,Germany @Gerhard Moser$Department of Animal Breeding and Biotechnology,University…     

Mapping of quantitative trait loci affecting behaviour in swine

Behavioural indices in vertebrates are under genetic control at least to some extent. In spite of significant behavioural problems in farm animals, information on the genetic background of behaviour is sparse. The aim of this study was to map QTL for behavioural indices in swine under healthy conditions and after infection with Sarcocystis miescheriana , as behaviour can be significantly influenced by disease . This well-described parasite model subsequently leads to acute (day 14 p.i.), subclinical (day 28 p.i.) and chronic disease (day 42 p.i.), allowing the study and comparison of the behaviour of pigs under four different states of health or disease. The study was based on a well-described Pietrain/Meishan F₂ family that has recently allowed the detection of QTL for disease resistance. We have mapped six genome-wide significant and 24 chromosome-wide significant QTL for six behavioural indices in swine. Six of these QTL (i.e. 20% of total QTL) showed effects on behavioural traits of the healthy pigs (day 0). Some of them (QTL on SSC11 and 18) lost influence on behavioural activities during disease, while the effects of others (QTL on SSC5, SSC8) partly remained during the whole experiment, although with different effects on the distinct behavioural indices. The disease model has been of high relevance to detect effects of gene loci on behavioural indices. Considering the importance of segregating alleles and environmental conditions that allow the identification of the phenotype, we conclude that there are indeed QTL with interesting effects on behavioural indices in swine.     

QTL alleles on chromosome 7 from fatty Meishan pigs reduce fat deposition

For detecting QTL in the whole swine genome, 1068 pigs from three F₂ populations constructed by crossing European Wild boar and Pietrain (W×P), Meishan and Pietrain (M×P), and Wild Boar and Meishan (W × M) were genotyped for genetic markers evenly spaced at approximately 20 cM intervals. AQTL analysis was performed using a least-squares method. Here the results of the QTL analysis on the porcine chromosome 7 are presented. QTL for carcass composition (e.g. head weight, carcass length, backfat depth, abdominal fat and bacon meat) were mapped in the chromosomal region CYPA/CYPD-TNFB-S0102 in M×P and W×M, but not in W×P. The QTL explained 5.3%–27.2% of the F₂ phenotypic variance in the two F₂ populations. Most traits affected by the mapped QTL were related to carcass fatness. The mode of gene action of QTL was additive. Surprisingly, in contrast to the parental phenotype, the QTL alleles from fatty Meishan were associated with thinner backfat than Pietrain and Wild Boar alleles, suggesting that the genome of the fatty Meishan pig contains genes which can reduce fat content of carcass substantially.     

Detection of novel SNPs and mapping of the fatness QTL on pig chromosome 7q1.1-1.4 region

Many QTLs for fatness traits have been mapped on pig chromosome 7q1.1-1.4 in various pig resource populations. Eight novel markers, including seven SNPs and one insertion or deletion within BTNL1, COL21A1, PPARD, GLP1R, MDFI, GNMT, ABCC10, and PLA2G7 genes, as well as two previously reported SNPs in SLC39A7 and HMGA1 genes, were genotyped in Large White and Meishan pig breeds. Except for two SNPs in HMGA1 and ABCC10 genes, allele frequencies of the other eight markers are highly significant different between Chinese indigenous Meishan breeds and Large White pig breeds. Eight polymorphic sites were then used for linkage and QTL mapping to refine the fatness QTL in a Large White × Meishan F(2) resource population. Five chromosome-wise significant QTLs were detected, of which the QTLs for leaf fat weight, backfat thickness at 6-7th rib and rump, and mean backfat thickness were narrowed to the interval between PPARD and GLP1R genes and the QTL for backfat thickness at thorax-waist between GNMT and PLA2G7 genes on SSC7p1.1-q1.4.     

Mapping of quantitative trait loci for clinical–chemical traits in swine

Clinical–chemical traits are diagnostic parameters essential for characterization of health and disease in veterinary practice. The traits show significant variability and are under genetic control, but little is known about the fundamental genetic architecture of this variability, especially in swine. We have identified QTL for alkaline phosphatase (ALP), lactate (LAC), bilirubin (BIL), creatinine (CRE) and ionized sodium (Na⁺), potassium (K⁺) and calcium (Ca⁺⁺) from the serum of 139 F₂ pigs from a Meishan/Pietrain family before and after challenge with Sarcocystis miescheriana , a protozoan parasite of muscle. After infection, the pigs passed through three stages representing acute disease, subclinical disease and chronic disease. Forty-two QTL influencing clinical–chemical traits during these different stages were identified on 15 chromosomes. Eleven of the QTL were significant on a genome-wide level; 31 QTL were chromosome-wide significant. QTL showed specific health/disease patterns with respect to the baseline values of the traits as well as the values obtained through the different stages of disease. QTL influencing different traits at different times were found primarily on chromosomes 1, 3, 7 and 14. The most prominent QTL for the investigated clinical–chemical traits mapped to SSC3 and 7. Baseline traits of ALP, LAC, BIL, Ca⁺⁺ and K⁺ were influenced by QTL regions on SSC3, 6, 7, 8 and 13. Single QTL explained up to 21.7% of F₂ phenotypic variance. Our analysis confirms that variation of clinical–chemical traits is associated with multiple chromosomal regions.     

Heritabilities and quantitative trait loci for blood gases and blood pH in swine

Maintaining pH and blood gases in a narrow range is essential to sustain normal biochemical reactions. Decreased oxygenation, poor tissue perfusion, disturbance to CO₂ expiration, and shortage of HCO₃⁻ can lead to metabolic acidosis. This is a common situation in swine, and originates from a broad range of medical conditions. pH and blood gases appear to be under genetic control, and populations with physiological traits closer to the pathological thresholds may be more susceptible to developing pathological conditions. However, little is known about the genetic basis of such traits. We have therefore estimated phenotypic and genetic variability and identified quantitative trait loci (QTL) for pH and blood gases in blood samples from 139 F₂ pigs from the Meishan/Pietrain family. Samples were taken before and after challenge with Sarcocystis miescheriana , a protozoan parasite of muscle. Twenty-seven QTL influencing pH and blood gases were identified on nine chromosomes. Five of the QTL were significant on a genome-wide level; 22 QTL were significant on a chromosome-wide level. QTL for pH-associated traits have been mapped to SSC3, 18 and X. QTL associated with CO₂ have been detected on SSC6, 7, 8 and 9, and QTL associated with O₂ on SSC2 and SSC8. QTL showed specific health/disease patterns that were related to the physiological state of the pigs from day 0, to acute disease (day 14), convalescence (day 28) and chronic disease (day 42). The results demonstrate that pH and blood gases are influenced by multiple chromosomal areas, each with relatively small effects.     

Major-effect QTLs for stem and foliage resistance to late blight in the wild potato relatives <Emphasis Type="Italic">Solanum sparsipilum</Emphasis> and <Emphasis Type="Italic">S. spegazzinii</Emphasis> are mapped to chromosome X

To find out new resistance sources to late blight in the wild germplasm for potato breeding, we examined the polygenic resistance of Solanum sparsipilum and S. spegazzinii by a quantitative trait locus (QTL) analysis. We performed stem and foliage tests under controlled conditions in two diploid mapping progenies. Four traits were selected for QTL detection. A total of 30 QTLs were mapped, with a large-effect QTL region on chromosome X detected in both potato relatives. The mapping of literature-derived markers highlighted colinearities with published late blight QTLs or R-genes. Results showed (a) the resistance potential of S. sparsipilum and S. spegazzinii for late blight control, and (b) the efficacy of the stem test as a complement to the foliage test to break down the complex late blight resistance into elementary components. The relationships of late blight resistance QTLs with R-genes and maturity QTLs are discussed.     

Identification of pleiotropic genes and gene sets underlying growth and immunity traits: a case study on Meishan pigs

Both growth and immune capacity are important traits in animal breeding. The animal quantitative trait loci (QTL) database is a valuable resource and can be used for interpreting the genetic mechanisms that underlie growth and immune traits. However, QTL intervals often involve too many candidate genes to find the true causal genes. Therefore, the aim of this study was to provide an effective annotation pipeline that can make full use of the information of Gene Ontology terms annotation, linkage gene blocks and pathways to further identify pleiotropic genes and gene sets in the overlapping intervals of growth-related and immunity-related QTLs. In total, 55 non-redundant QTL overlapping intervals were identified, 1893 growth-related genes and 713 immunity-related genes were further classified into overlapping intervals and 405 pleiotropic genes shared by the two gene sets were determined. In addition, 19 pleiotropic gene linkage blocks and 67 pathways related to immunity and growth traits were discovered. A total of 343 growth-related genes and 144 immunity-related genes involved in pleiotropic pathways were also identified, respectively. We also sequenced and genotyped 284 individuals from Chinese Meishan pigs and European pigs and mapped the single nucleotide polymorphisms (SNPs) to the pleiotropic genes and gene sets that we identified. A total of 971 high-confidence SNPs were mapped to the pleiotropic genes and gene sets that we identified, and among them 743 SNPs were statistically significant in allele frequency between Meishan and European pigs. This study explores the relationship between growth and immunity traits from the view of QTL overlapping intervals and can be generalized to explore the relationships between other traits.     

The IGF2-intron3-G3072A substitution explains a major imprinted QTL effect on backfat thickness in a Meishan x European white pig intercross

A paternally expressed QTL for muscle growth and backfat thickness (BFT) has previously been identified near the IGF2 locus on the distal tip of pig chromosome 2 (SSC2p) in three experimental F2 populations. Recently, a mutation in a regulatory element of the IGF2 gene was identified as the quantitative trait nucleotide (QTN) underlying the major QTL effect on muscle growth and BFT in crosses between Large White and Wild Boar or Pietrain. This study demonstrates that the IGF2 mutation also controls the paternally expressed QTL for backfat thickness in a cross between Meishan and European Whites. In addition, a comparison of QTL of backfat thickness measured by Hennessy grading probe (HGP) and by ultrasound measurement (USM) was made. In the USM analyses, the IFG2 mutation explains the entire QTL effect on SSC2p, whereas in the HGP analysis the presence of a second minor QTL can not be excluded. Finally, this study shows that this particular IGF2 mutation does not cause the paternally expressed QTL for teat number mapping to the same region of SSC2p as the BFT QTL.     

Quantitative trait loci for electrical seizure threshold mapped in C57BLKS/J and C57BL/10SnJ mice

We mapped the quantitative trait loci (QTL) that contribute to the robust difference in maximal electroshock seizure threshold (MEST) between C57BLKS/J (BKS) and C57BL10S/J (B10S) mice. BKS, B10S, BKS × B10S F1 and BKS × B10S F2 intercross mice were tested for MEST at 8-9 weeks of age. Results of F2 testing showed that, in this cross, MEST is a continuously distributed trait determined by polygenic inheritance. Mice from the extremes of the trait distribution were genotyped using microarray technology. MEST correlated significantly with body weight and sex; however, because of the high correlation between these factors, the QTL mapping was conditioned on sex alone. A sequential series of statistical analyses was used to map QTLs including single-point, multipoint and multilocus methods. Two QTLs reached genome-wide levels of significance based upon an empirically determined permutation threshold: chromosome 6 (LOD = 6.0 at ～69 cM) and chromosome 8 (LOD = 5.7 at ～27 cM). Two additional QTLs were retained in a multilocus regression model: chromosome 3 (LOD = 2.1 at ～68 cM) and chromosome 5 (LOD = 2.7 at ～73 cM). Together the four QTLs explain one third of the total phenotypic variance in the mapping population. Lack of overlap between the major MEST QTLs mapped here in BKS and B10S mice and those mapped previously in C57BL/6J and DBA/2J mice (strains that are closely related to BKS and B10S) suggest that BKS and B10S represent a new polygenic mouse model for investigating susceptibility to seizures.     

在猪12号染色体上定位数量性状位点

为了找出猪１２号染色体上的数量性状位点,在２个家系中测定了１０个遗传标记,并记录了５１个重要经济性状。该２家系,一个由欧洲野猪与皮特兰杂交而成,另一个由梅山与皮特兰杂交建成。应用最小二乘法进行了数量性状位点定位,同时进行了生长激素基因型与性状间的相关分析。应用最小二乘法并未发现数量性状位点,而应用相关分析发现生长激素基因型与某些膘情性状高度相关。带有基因型Ｃ１Ａ２／Ｃ４Ａ２的猪比带有基因型Ｃ２Ａ２     

QTL sequencing strategy to map genomic regions associated with resistance to ascochyta blight in chickpea

Whole‐genome sequencing‐based bulked segregant analysis (BSA) for mapping quantitative trait loci (QTL) provides an efficient alternative approach to conventional QTL analysis as it significantly reduces the scale and cost of analysis with comparable power to QTL detection using full mapping population. We tested the application of next‐generation sequencing (NGS)‐based BSA approach for mapping QTLs for ascochyta blight resistance in chickpea using two recombinant inbred line populations CPR‐01 and CPR‐02. Eleven QTLs in CPR‐01 and six QTLs in CPR‐02 populations were mapped on chromosomes Ca1, Ca2, Ca4, Ca6 and Ca7. The QTLs identified in CPR‐01 using conventional biparental mapping approach were used to compare the efficiency of NGS‐based BSA in detecting QTLs for ascochyta blight resistance. The QTLs on chromosomes Ca1, Ca4, Ca6 and Ca7 overlapped with the QTLs previously detected in CPR‐01 using conventional QTL mapping method. The QTLs on chromosome Ca4 were detected in both populations and overlapped with the previously reported QTLs indicating conserved region for ascochyta blight resistance across different chickpea genotypes. Six candidate genes in the QTL regions identified using NGS‐based BSA on chromosomes Ca2 and Ca4 were validated for their association with ascochyta blight resistance in the CPR‐02 population. This study demonstrated the efficiency of NGS‐based BSA as a rapid and cost‐effective method to identify QTLs associated with ascochyta blight in chickpea.     

Quantitative trait loci for red blood cell traits in swine

Haematological traits are essential diagnostic parameters in veterinary practice but knowledge on the genetic architecture controlling variability of erythroid traits is sparse, especially in swine. To identify QTL for erythroid traits in the pig, haematocrit (HCT), haemoglobin (HB), erythrocyte counts (RBC) and mean corpuscular haemoglobin content (MCHC) were measured in 139 F₂ pigs from a Meishan/Pietrain family, before and after challenge with the protozoan pathogen Sarcocystis miescheriana . The pigs passed through three stages representing acute disease, reconvalescence and chronic disease. Forty-three single QTL controlling erythroid traits were identified on 16 chromosomes. Twelve of the QTL were significant at the genome-wide level while 31 were significant at a chromosome-wide level. Because erythroid traits varied with health and disease status, QTL influencing the erythroid phenotypes showed specific health/disease patterns. Regions on SSC5, 7, 8, 12 and 13 contained QTL for baseline erythroid traits, while the other QTL regions affected distinct stages of the disease model. Single QTL explained 9–17% of the phenotypic variance in the F₂ animals. Related traits were partly under common genetic influence. Our analysis confirms that erythroid trait variation differs between Meishan and Pietrain breeds and that this variation is associated with multiple chromosomal regions.     

Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

A mapping population of 104 F(3) lines of pearl millet, derived from a cross between two inbred lines H 77/833-2 x PRLT 2/89-33, was evaluated, as testcrosses on a common tester, for traits determining grain and stover yield in seven different field trials, distributed over 3 years and two seasons. The total genetic variation was partitioned into effects due to season (S), genotype (G), genotype x season interaction (G x S), and genotype x environment-within-season interaction [G x E(S)]. QTLs were determined for traits for their G, G x S, and G x E(S) effects, to assess the magnitude and the nature (cross over/non-crossover) of environmental interaction effects on individual QTLs. QTLs for some traits were associated with G effects only, while others were associated with the effects of both G and G x S and/or G, G x S and G x E(S) effects. The major G x S QTLs detected were for flowering time (on LG 4 and LG 6), and mapped to the same intervals as G x S QTLs for several other traits (including stover yield, harvest index, biomass yield and panicle number m(-2)). All three QTLs detected for grain yield were unaffected by G x S interaction however. All three QTLs for stover yield (mapping on LG 2, LG 4 and LG 6) and one of the three QTLs for grain yield (mapping on LG 4) were also free of QTL x E(S) interactions. The grain yield QTLs that were affected by QTL x E(S) interactions (mapping on LG 2 and LG 6), appeared to be linked to parallel QTL x E(S) interactions of the QTLs for panicle number m(-2) on (LG 2) and of QTLs for both panicle number m(-2) and harvest index (LG 6). In general, QTL x E(S) interactions were more frequently observed for component traits of grain and stover yield, than for grain or stover yield per se.     

Combined analyses of data from quantitative trait loci mapping studies. Chromosome 4 effects on porcine growth and fatness

For many species several similar QTL mapping populations have been produced and analyzed independently. Joint analysis of such data could be used to increase power to detect QTL and evaluate population differences. In this study, data were collated on almost 3000 pigs from seven different F(2) crosses between Western commercial breeds and either the European wild boar or the Chinese Meishan breed. Genotypes were available for 31 markers on chromosome 4 (on average 8.3 markers per population). Data from three traits common to all populations (birth weight, mean backfat depth at slaughter or end of test, and growth rate from birth to slaughter or end of test) were analyzed for individual populations and jointly. A QTL influencing birth weight was detected in one individual population and in the combined data, with no significant interaction of the QTL effect with population. A QTL affecting backfat that had a significantly greater effect in wild boar than in Meishan crosses was detected. Some evidence for a QTL affecting growth rate was detected in all populations, with no significant differences between populations. This study is the largest F(2) QTL analysis achieved in a livestock species and demonstrates the potential of joint analysis.     

陆地棉产量性状QTLs的分子标记及定位

用我国的高产栽培品种泗棉3号和美国栽培品种TM-1为材料,构建F₂和F_2∶3作图群体,应用301对SSR引物和1040个RAPD引物,对产量性状QTLs进行了分子标记筛选,结果共筛选出了37对SSR多态性引物和10个RAPD多态性引物的49个位点,鉴定出了控制产量性状变异的主效QTLs。定位于第9染色体的连锁群,分别具有控制铃重、衣分和籽指的主效QTLs,铃重的2个QTLs分别解释F_2∶3群体表型变异的18.2%和21.0%;在F₂群体检测到的1个衣分QTL解释表型变异的25%,另一个衣分QTL在F₂群体和F_2∶3群体都检测到,解释F₂群体衣分的24.9%的表型变异,解释F_2∶3群体衣分的5.9%的表型变异;在F_2∶3群体铃重的一个QTL的同一位置同时检测到一个籽指QTL,它解释15.6%的表型变异,是一因多效或是紧密连锁的两个QTLs,有待进一步研究。本研究标记的产量性状主效QTLs可用于棉花产量性状的标记辅助选择。     

QTL mapping analysis of plant height and ear height of maize (Zea mays L.)

Genetic map containing 103 microsatellite loci obtained on 200 F2 plants derived from the cross R15 x 478 was used for quantitative trait loci (QTL) mapping in maize. QTL were characterized in a population of 200 F2:4 lines, derived from selfing the F2 plants, and were evaluated with two replications in two environments. QTL determinations were made from the mean of these two environments. Plant height (PH) and ear height (EH) were measured. Using composite interval mapping (CIM) method, a total of 14 distinct QTLs were identified: nine for PH and five for EH. Additive, partial dominance, dominance, and overdominance actions existed among all detected QTL affecting plant height and ear height. The QTL explained 78.27% of the phenotypic variance of PH and 41.50% of EH. The 14 QTLs displayed mostly dominance or partial dominance gene action and mapped to chromosomes 2, 3, 4, 8 and 9.     

Mapping of quantitative trait loci controlling broomrape (Orobanche crenata Forsk.) resistance in faba bean (Vicia faba L.).

Orobanche crenata Forsk. is a root parasite that produces devastating effects on many crop legumes and has become a limiting factor for faba bean production in the Mediterranean region. The efficacy of available control methods is minimal and breeding for broomrape resistance remains the most promising method of control. Resistance seems to be scarce and complex in nature, being a quantitative characteristic difficult to manage in breeding programmes. To identify and map the QTLs (quantitative trait loci) controlling the trait, 196 F2 plants derived from the cross between a susceptible and a resistant parent were analysed using isozymes, RAPD, seed protein genes, and microsatellites. F2-derived F3 lines were studied for broomrape resistance under field conditions. Of the 130 marker loci segregating in the F2 population, 121 could be mapped into 16 linkage groups. Simple interval mapping (SIM) and composite interval mapping (CIM) were performed using QTL Cartographer. Composite interval mapping using the maximum number of markers as cofactors was clearly the most efficient way to locate putative QTLs. Three QTLs for broomrape resistance were detected. One of the three QTLs explained more than 35% of the phenotypic variance, whereas the others accounted for 11.2 and 25.5%, respectively. This result suggests that broomrape resistance in faba bean can be considered a polygenic trait with major effects of a few single genes.     

大豆昆虫抗性相关QTLs的元分析

大豆虫害严重危害大豆生产。虽然大豆抗虫相关QTLs研究增多, 但由于作图群体不同、同种昆虫抗性QTL的调查性状不同以及数据分析方法存在差异等原因, 使QTL精确性和有效性被降低。因此, 获得相对真实且有效的QTLs位点对于促进分子标记辅助选择有重要意义。文章通过搜集已报道的81个与大豆昆虫抗性相关的QTL, 提取相对有效且可靠的QTLs标记信息, 利用元分析软件BioMercator2.1将这些QTLs映射到大豆公共遗传连锁图谱Soymap2上, 通过单独与联合的两种元分析途径, 利用QTLs的95%的置信区间来推断“真实QTLs”的位置。文章不仅构建了一张大豆昆虫抗性一致性图谱, 而且通过两种元分析途径分别得到12个和14个QTLs位点, 且其中有6个位点QTL的位置一致。它们被定位在9个连锁群上, 主要成簇分布在E、F、H、M等4个连锁群上, 图距由原来平均15 cM缩减到平均3.67 cM。除了一个与大豆食心虫抗性相关的位点外, 其余QTLs都与多种昆虫抗性相关。研究结果明显缩短了原来已报道的QTL置信区间, 为大豆抗虫相关QTL的精细定位以及抗虫相关基因挖掘提供了依据。     

Genomic regions determining resistance to leaf stripe (Pyrenophora graminea) in barley.

Leaf stripe is a seed-borne disease of barley (Hordeum vulgare) caused by Pyrenophora graminea. Little is known about the genetics of resistance to this pathogen. In the present work, QTL analysis was applied on two recombinant inbred line (RIL) populations derived from two- and six-rowed barley genotypes with different levels of partial resistance to barley leaf stripe. Quantitative trait loci for partial resistance were identified using the composite interval mapping (CIM) method of PLABQTL software, using the putative QTL markers as cofactors. In the L94 x 'Vada' mapping population, one QTL for resistance was detected on chromosome 2H; the same location as the leaf-stripe resistance gene Rdg1 mapped earlier in 'Alf', where it confers complete resistance to the pathogen. An additional minor-effect QTL was identified by further analyses in this segregating population on chromosome 7H. In L94 x C123, two QTLs for resistance were mapped, one each on chromosomes 7H and 2H.