基于小黑麦RIL群体的草产量相关性状QTL分析

为解析调控小黑麦(Triticosecale Wittmack)草产量的遗传机制,本研究以‘甘农7号’小黑麦与‘石大1号’小黑麦构建的F₆代重组自交系(RIL)群体为材料,利用小黑麦RIL群体分子图谱,结合株高、分蘖数、穗下节间长和单株鲜重的表型值,进行QTL分析。共检测到5个株高QTL,分布在连锁群LG1,LG3,LG4,LG6上,遗传贡献率为6.56%～12.86%;4个分蘖数QTL,分布在连锁群LG2,LG5,LG6,LG7上,遗传贡献率为7.11%～12.44%;3个穗下节间长QTL,分布在连锁群LG1,LG2,LG5上,遗传贡献率为7.69%～9.63%;4个单株鲜重QTL,分布在连锁群LG2,LG5,LG6,LG7上,遗传贡献率为9.65%～13.63%。其中,株高和分蘖数的主效位点为qPH6-1和qNT5-1,表型变异解释为12.86%和12.44%;单株鲜重的主效位点为qBS2-1和qBS6-1,表型变异解释率为12.17%和13.63%,二者累加对单株鲜重具有增加效应。本研究为饲用小黑麦生物产量的精细定位和分子辅助育种提供了重要理论支撑。     

四倍体彩色马铃薯花青素含量及产量性状的QTL定位

为确定彩色马铃薯薯块花青素含量、单株产量和商品薯率3个重要性状的QTL位点,以四倍体彩色马铃薯‘黑美人’בMIN-021’杂种F₁代分离群体的210个单株无性株系及其亲本为材料,通过对这3个重要性状进行两年一点的观测试验,以及亲本间和杂种株系间的差异显著性分析,用TetraploidMap软件在已构建出的2张双亲的高密度彩色马铃薯分子遗传连锁图谱上分别定位其QTL。结果显示,这3个性状在亲本间和杂种株系间差异显著,且F₁群体单株株系间各性状观测值均呈正态分布,适合QTL分析。在母本‘黑美人’的遗传连锁图谱上检测到13个QTL,其中控制花青素含量的有5个、单株产量和商品薯率各有4个,遗传贡献率变幅为7.98%~19.62%。在父本‘MIN-021’的遗传连锁图谱上检测到11个QTL,其中花青素含量和单株产量各有4个、商品薯率有3个,遗传贡献率范围在8.70%~21.62%之间。     

日本结缕草抗寒相关性状的QTL分析

为寻找与结缕草(Zoysia japonica steud.)抗寒性状相关的数量性状位点(QTLs),利用生态条件具明显差异的2个日本结缕草品系室兰(Zoysia japonsic cv. Muroran)和俵山北(Zoysia japonsic cv. Tawarayama Kita)及其假测交产生的F₁代86个个体为材料,研究其在人工低温胁迫条件下叶片的半致死温度(LT₅₀)及可溶性糖、可溶性蛋白含量及过氧化物歧化酶(SOD)活性的变化,并以447个SSR标记构建的日本结缕草遗传连锁图谱为基础,对抗寒相关的性状可溶性糖、可溶性蛋白含量及SOD活性进行了QTL定位分析。结果表明:F₁群体的可溶性蛋白含量与叶片LT₅₀呈显著负相关(P<0.05),可溶性糖含量及SOD活性与叶片LT₅₀呈极显著负相关(P<0.01)。分别定位到与叶片可溶性糖、可溶性蛋白含量和SOD活性相关的QTL各1个,分布于3个连锁群上,QTL的LOD值介于2.19~2.42之间,单个QTL可解释性状表型变异的范围在13.3%~13.8%。     

紫花苜蓿开花性状的遗传特性分析与QTL定位

通过构建紫花苜蓿杂交F1代遗传分离群体,研究紫花苜蓿早熟性状的遗传特性,确定始花期性状的最适遗传模型,同时定位始花期相关的QTL位点。以低产早熟紫花苜蓿(父本)和高产晚熟紫花苜蓿(母本)为亲本构建杂交群体,以亲本和二者杂交产生的152个F1代单株为研究对象。于2015和2016年调查始花期性状,运用主多基因遗传模型分析始花期性状的最适遗传模型。通过GBS测序技术对154个单株进行基因分型,利用测序产生的SNP标记构建连锁图谱,同时结合表型数据进行QTL定位。结果表明:2MG-A为始花期性状的最适遗传模型,2015年的主基因遗传率为99%,2016年的主基因遗传率为98.5%。父本连锁图覆盖图距为1386cM,平均标记密度3.2cM;母本连锁图覆盖图距为798.73cM,平均标记密度8.07cM。对两年的数据进行QTL定位分析得到2个主效QTL位点,表型贡献率分别为12.1334%和11.0157%。表明始花期主要受两对主效基因控制,同时具有加性作用。始花期性状主要由2个QTL位点控制。     

转紫花苜蓿MsLEA2基因提高拟南芥耐铝毒性研究

MsLEA2基因是从紫花苜蓿中克隆到的胚胎晚期富集蛋白基因,属于LEA_2家族。以转基因拟南芥T_3代植株的3个株系为材料,从表型、生理和分子生物学三个方面研究铝胁迫下转MsLEA2基因拟南芥的耐铝毒性能。结果表明:铝胁迫下转基因株系的脯氨酸含量高于对照(野生型),丙二醛含量和电导率则低于对照且差异显著(P0.05),CAT、POD和SOD活性显著高于对照。初步证明转MsLEA2基因拟南芥的耐铝毒性能明显高于对照,紫花苜蓿的MsLEA2基因具有提高植物耐铝毒胁迫的能力。     

用于鸡生长和肉质性状定位资源群体的构建

采用远交F-2设计,以杏花鸡(A系)为亲本,分别与隐性白洛克鸡(B系)、泰和丝羽乌骨鸡(C系)进行全同胞和半同胞的正反交,产生了包含A♂×B♀、B♂×A♀、A♂×C♀、C♂×A♀4个杂交组合的各两组F2群体,并进行详细的性状表型记录,建立了可用于定位生长和肉质的数量性状座位(quantitative trait loci,QTL)的4个资源群体。对F2群体的各种性状表型值进行分析,结果显示,F2群体各生长性状和肉质性状都分离得很好。资源群体有足够的群体规模,并有利于校正母体效应和环境影响,保证有效单核苷酸多态性(single nucleotide poly-morphism,SNP)检测数量和QTL定位的准确性,使资源群体的构建达到了预期的效果。     

芒属植物分蘖数性状的QTL定位

芒属植物是一种多年生C₄高大禾草,是一种重要的生物能源作物。分蘖是芒属植物重要的农艺性状之一,在调控其产量方面具有极其重要的作用。以五节芒和荻的种间杂交群体为材料,利用前期已构建的五节芒和荻的种间基因组遗传连锁图谱,结合2014年泰安、2015年泰安和东平3次重复调查的分蘖数表型数据,进行该重要性状的QTL遗传定位研究。结果表明:分蘖数频率分布呈现正态连续分布,符合数量性状遗传的特征;采用MQM复合区间作图法共定位到16个与分蘖数性状相关的QTL,单个QTL可解释的表型变异范围为11.4%~21.5%,LOD值为3.06~6.09。其中,3个QTL在3次定位分析中可重复检测到,qmfTI-2可分别解释12.7%、12.0%和15.5%的表型变异,qmsTI-1可分别解释12.0%、12.1%和19.8%的表型变异,qmsTI-2可分别解释21.5%、20.2%和13.4%的表型变异;3个QTL在2次定位分析中可重复检测到,qmfTI-1、qmfTI-3和qmsTI-4分别解释12.4%和11.4%、13.8%和13.2%、12.1%和14.3%的表型变异。通过对芒属植物分蘖数性状QTL分析,为芒属植物种质资源改良、分子标记辅助选择以及遗传学研究奠定基础。     

艾丁湖盐角草种群动态生命表及其对温度变化的响应

艾丁湖地处亚洲中部极端干旱区,夏季酷热,地势闭塞,环境条件独特。本研究在该地区选择3个具有不同水分特征和盐角草(Salicornia europaea)种群特征的样地(A样地:地形偏高,4月之后地表无积水,盐角草密度相对较低;B样地:位于地表径流覆盖范围内,地表长期有积水,种群密度相对中等;C样地:地表早期有积水,种群密度相对最大,盖度最高)进行调查,根据盐角草的生长情况,从3月至10月每月调查1~2次,共10次,每次统计样方内盐角草的株数。利用动态生命表以及相关性分析的方法来研究该地区盐角草种群变化及其与环境温度之间的关系。结果表明,艾丁湖盐角草种群密度在5月前基本呈增长状态,5月末至6月由于温度升高、水分下降和营养物质减少引发了种群自疏,出现第1次死亡率高峰期;7月至8月,A、B两个种群出现第2次死亡率高峰期,主要原因是连续长达34d的40℃以上的极端高温;9月A种群出现第3次死亡率高峰期,主要原因可能是高温以及高温加速土壤水分蒸发引起的土壤返盐、水分丧失,从而导致的高温胁迫和盐胁迫。相关性分析表明,A、B种群死亡率与高温显著相关(P0.05)。C种群仅有一次死亡率高峰期,且与温度相关不显著(P0.05),与A、B种群差异较大的原因可能是由于C样地水分条件好、种群密度高缓解了高温的胁迫作用。A、B两个种群存活曲线属于Deevy-Ⅲ型,即初期死亡率较高,9月之后趋于稳定;C种群属于Deevey-Ⅱ型存活曲线,呈对角线型,即各个龄级存活率基本一致。     

蒙古冰草×航道冰草杂种F4代株系同工酶分析

利用同工酶电泳技术研究蒙古冰草与航道冰草种间杂交F_{4代11个株系的遗传差异性。结果表明:在相同或不同生育阶段,供试材料的酯酶同工酶(EST)、过氧化物酶同工酶(POD)和超氧化物岐化酶(SOD)的位点、数目和强弱均存在差异,其表型差异是11个株系间及其与亲本间在蛋白质分子水平识别的重要遗传标记;选择抽穗期、分蘖期取样分析的同工酶酶谱特征,更能体现材料间酶谱表型的遗传差异性和提高鉴定结果的准确性;以遗传距离(GD)值0.40为基准,将13个材料聚类成6类:第1类株系6、7、9、8和10,第2类株系4和5,第3类株系1、3和2,第4类株系11,第5类父本航道冰草,第6类母本蒙古冰草;该研究结果对冰草杂交后代新品系的归类选育具有理论意义。}     

红三叶遗传图谱构建及抗白粉病基因QTL定位

本研究以感(岷山红三叶)、抗(澳大利亚红三叶品种♀Sensation×Renegade♂杂交新品系“甘农PR1”)白粉病红三叶材料为父母本杂交并种植成苗经人工接菌后筛选出抗、感白粉病的F₁群体为作图群体,利用AFLP标记构建红三叶高密度遗传图谱,并利用区间作图法对抗白粉病QTL进行了定位分析,可以为红三叶抗白粉病基因克隆和转基因等分子辅助育种奠定基础。结果表明,149个AFLP标记构建得到的遗传图谱包含7个连锁群(LG1,LG2,LG3,LG4,LG5,LG6和LG7),遗传图谱的总距离为640.5 cM。其中,LG1连锁群的遗传距离(140.6 cM)和标记间平均距离(9.4 cM)均最大;LG4连锁群的遗传距离(55.2 cM)和标记间平均距离(1.8 cM)最小。应用区间作图法对红三叶抗白粉病基因进行QTL分析定位,共检测到5个抗白粉病相关QTL位点(qrp-1,qrp-2,qrp-3,qrp-4和qrp-5),其中qrp-1、qrp-2、qrp-3和qrp-4位于LG4连锁群上,qrp-5位于LG5连锁群上。5个QTL位点对抗白粉病的贡献率为29%~90%,qrp-1对红三叶白粉病抗性的贡献率最大(90%),为主效QTL。     

不同水分环境下玉米叶面积QTL定位及候选基因分析

玉米叶面积的大小及分布特征不仅影响其光合效率、蒸腾速率,而且与其耐旱性、耐密性、抗倒伏性及产量形成紧密相关。深入剖析不同水旱环境下玉米不同生育时期不同叶位叶面积的分子遗传机理对玉米耐旱高产新品种的选育具有重要意义。本研究以构建的2套F_2∶3群体为试材,在8种水分环境下,采用复合区间作图法（CIM）和基于混合线性模型的复合区间作图法（MCIM）对玉米相应叶（V₁₈时期第10片叶、R₁时期穗三叶）叶面积进行单环境和多环境联合QTL分析;参考玉米基因组B73 RefGen_v3挖掘稳定表达的QTLs （sQTLs）区间内的候选基因,并对其进行功能分析。结果表明,采用CIM法,单环境下2个生育时期2套F_2∶3群体间总共定位到了7个玉米相应叶叶面积QTLs,主要受显性（81.0%）、部分显性（14.3%）和超显性（4.7%）等遗传效应的调控,其中在干旱环境下定位到了5个QTLs。采用MCIM法,在2套F_2∶3群体间总共检测到6个相应叶叶面积的联合QTLs,其中1个表现为显著的QTL与环境的互作（QTL×E, Bin 2.08~2.09）,1对QTLs （Bin 1.08~1.10与 Bin 2.08~2.09）参与了显著的加性与加性（AA）上位性互作。结合CIM和MCIM法进一步分析在2套F_2∶3群体间检测到了6个sQTLs,其分别位于Bin 1.08~1.10、Bin 2.08~2.09、Bin 4.08~4.09、Bin 6.05、Bin 8.03和Bin 10.03处,并在这些sQTLs区间内确定了12个玉米叶发育相关候选基因。采用生物信息学,总共收集了75个玉米叶发育相关候选基因,通过系统进化树分析表明,这些候选基因划分为3大进化分支,且上述检测到的12个候选基因分布于这3大进化分支上。这些结果为系统地解析玉米不同生育时期不同水旱环境下相应叶叶面积的分子遗传机理提供理论依据,检测到的sQTLs可作为叶面积改良的重要染色体区段,检测到的候选基因为其进一步克隆、功能分析及育种应用提供了信息参考。     

A whole-genome scan for quantitative trait loci affecting teat number in pigs

A whole-genome scan was conducted using 132 microsatellite markers to identify chromosomal regions that have an effect on teat number. For this purpose, an experimental cross between Chinese Meishan pigs and five commercial Dutch pig lines was used. Linkage analyses were performed using interval mapping by regression under line cross models including a test for imprinting effects. The whole-genome scan revealed highly significant evidence for three quantitative trait loci (QTL) affecting teat number, of which two were imprinted. Paternally expressed (i.e., maternally imprinted) QTL were found on chromosomes 2 and 12. A Mendelian expressed QTL was found on chromosome 10. The estimated additive effects showed that, for the QTL on chromosomes 10 and 12, the Meishan allele had a positive effect on teat number, but, for the QTL on chromosome 2, the Meishan allele had a negative effect on teat number. This study shows that imprinting may play an important role in the expression of teat number.     

Detection of quantitative trait loci for fat androstenone levels in pigs

A QTL analysis of fat androstenone levels from a three-generation experimental cross between Large White and Meishan pig breeds was carried out. A total of 485 F2 males grouped in 24 full-sib families, their 29 parents and 12 grandparents were typed for 137 markers distributed over the entire porcine genome. The F2 male population was measured for fat androstenone levels at 100, 120, 140, and 160 d of age and at slaughter around 80 kg liveweight. Statistical analyses were performed using two interval mapping methods: a line-cross (LC) regression method, which assumes alternative alleles are fixed in founder lines, and a half- full-sib (HFS) maximum likelihood method, where allele substitution effects were estimated within each half- and full-sib family. Both methods revealed genomewide significant gene effects on chromosomes 3, 7, and 14. The QTL explained, respectively, 7 to 11%, 11 to 15%, and 6 to 8% of phenotypic variance. Three additional significant QTL explaining 4 to 7% of variance were detected on chromosomes 4 and 9 using LC method and on chromosome 6 using HFS method. Suggestive QTL were also obtained on chromosomes 2, 10, 11, 13, and 18. Meishan alleles were associated with higher androstenone levels, except on chromosomes 7, 10, and 13, although 10 and 13 additive effects were near zero. The QTL had essentially additive effects, except on chromosomes 4, 10, and 13. No evidence of linked QTL or imprinting effects on androstenone concentration could be found across the entire porcine genome. The steroid chromosome P450 21-hydroxylase (CYP21) and cytochrome P450 cholesterol side chain cleavage subfamily XIA (CYP11A) loci were investigated as possible candidate genes for the chromosome 7 QTL. No mutation of coding sequence has been found for CYP21. Involvement of a candidate regulatory mutation of CYP11A gene proposed by others can be excluded in our animals.     

Using genetic markers for disease resistance to improve production under constant infection pressure

Animals will show reduced production when exposed to a constant infection pressure unless they are fully resistant, the size of the reduction depending on the degree of resistance and the severity of infection. In this article, the use of QTL for disease resistance for improving productivity under constant infection pressure is investigated using stochastic simulation. A previously published model was used with two thresholds for resistance: a threshold below which production is not possible and a threshold above which production is not affected by the infection. Between thresholds, observed production under constant infection is a multiplicative function of underlying potential production and level of resistance. Some simplifications of reality were adopted in the model, such as no genetic correlation between potential production and resistance, the absence of influence of lack of resistance on reproductive capacity, and the availability of phenotypes in both sexes. Marker-assisted selection was incorporated by assuming a proportion of the genetic variance to be explained by the QTL, which thus is defined as a continuous trait. Phenotypes were available for production, not for resistance. The infection pressure may vary across time. Results were compared to mass selection on production under constant as well as intermittent infection pressure, where the infection pressure varied between but not within years. Selection started in a population with a very poor level of resistance. Incorporation of QTL information is valuable (i.e., the increase in observed production relative to mass selection) when a large proportion of the additive genetic variance is explained by the QTL (50% genetic variance explained) and when the heritability for resistance is low (h2R = 0.1). Under constant infection pressure, incorporating QTL information does not increase selection responses in observed production when the QTL effect explains less than 25% of the genetic variance. Under intermittent selection pressure, the use of QTL information gives a slightly greater increase in observed production in early generations, relative to mass selection on observed production, but still only when the QTL effect is large or the heritability for resistance is low. The additional advantage of incorporating QTL information is that use of (preventive) medical treatment is possible, or animals may be evaluated in uninfected environments.     

Detection of single nucleotide polymorphisms associated with ultrasonic backfat depth in a segregating Meishan x White Composite population

Multiple genomic scans have identified QTL for backfat deposition across the porcine genome. The objective of this study was to detect SNP and genomic regions associated with ultrasonic backfat. A total of 74 SNP across 5 chromosomes (SSC 1, 3, 7, 8, and 10) were selected based on their proximity to backfat QTL or to QTL for other traits of interest in the experimental population. Gilts were also genotyped for a SNP thought to influence backfat in the thyroxine-binding globulin gene (TBG) on SSC X. Genotypic data were collected on 298 gilts, divided between the F8 and F10 generations of the US Meat Animal Research Center Meishan resource population (composition, one-quarter Meishan). Backfat depths were recorded by ultrasound from 3 locations along the back at approximately 210 and 235 d of age in the F8 and F10 generations, respectively. Ultrasound measures were averaged for association analyses. Regressors for additive, dominant, and parent-of-origin effects of each SNP were calculated using genotypic probabilities computed by allelic peeling algorithms in GenoProb. The association model included the fixed effects of scan date and TBG genotype, the covariates of weight and SNP regressors, and random additive polygenic effects to account for genetic similarities between animals not explained by known genotypes. Variance components for polygenic effects and error were estimated using MTDFREML. Initially, each SNP was fitted (once with and once without parent-of-origin effects) separately due to potential multi-collinearity between regressions of closely linked markers. To form a final model, all significant SNP across chromosomes were included in a common model and were individually removed in successive iterations based on their significance. Across all analyses, TBG was significant, with an additive effect of approximately 1.2 to 1.6 mm of backfat. Three SNP on SSC3 remained in the final model even though few studies have identified QTL for backfat on this chromosome. Two of these SNP exhibited irregular parent-of-origin effects and may not have been detected in other genome scans. One significant SNP on SSC7 remained in the final, backward-selected model; the estimated effect of this marker was similar in magnitude and direction to previously identified QTL. This SNP can potentially be used to introgress the leaner Meishan allele into commercial swine populations.     

Utilization of F1 information in estimating QTL effects in F2 crosses between outbred lines.

Quantitative trait loci (QTL) analysis in designed experiments is investigated using a mixed model framework through the modification of segment mapping techniques. Allele effects are modelled in the F1 generation allowing the estimation of additive substitution effects while accounting for QTL segregation within lines and differences in mean QTL effects between lines. The resulting approach is called F1 segment mapping. Simulation is used to illustrate the method and its properties. F1 segment mapping has advantages over F2 segment mapping in the derivation of exact additive genetic covariances and in the computation time for variance component estimation.     

Detection of quantitative trait loci for growth and carcass composition in cattle

The objective of the present study was to detect quantitative trait loci for economically important traits in a family from a Bos indicus x Bos taurus sire. A Brahman x Hereford sire was used to develop a half-sib family (n = 547). The sire was mated to Bos taurus cows. Traits analyzed were birth (kg) and weaning weights (kg); hot carcass weight (kg); marbling score; longissimus area (cm2); USDA yield grade; estimated kidney, pelvic, and heart fat (%); fat thickness (cm); fat yield (%); and retail product yield (%). Meat tenderness was measured as Warner-Bratzler shear force (kg) at 3 and 14 d postmortem. Two hundred and thirty-eight markers were genotyped in 185 offspring. One hundred and thirty markers were used to genotype the remaining 362 offspring. A total of 312 markers were used in the final analysis. Seventy-four markers were common to both groups. Significant QTL (expected number of false-positives < 0.05) were observed for birth weight and longissimus area on chromosome 5, for longissimus area on chromosome 6, for retail product yield on chromosome 9, for birth weight on chromosome 21, and for marbling score on chromosome 23. Evidence suggesting (expected number of false-positives < 1) the presence of QTL was detected for several traits. Putative QTL for birth weight were detected on chromosomes 1, 2, and 3, and for weaning weight on chromosome 29. For hot carcass weight, QTL were detected on chromosomes 10, 18, and 29. Four QTL for yield grade were identified on chromosomes 2, 11, 14, and 19. Three QTL for fat thickness were detected on chromosomes 2, 3, 7, and 14. For marbling score, QTL were identified on chromosomes 3, 10, 14, and 27. Four QTL were identified for retail product yield on chromosomes 12, 18, 19, and 29. A QTL for estimated kidney, pelvic, and heart fat was detected on chromosome 15, and a QTL for meat tenderness measured as Warner-Bratzler shear force at 3 d postmortem was identified on chromosome 20. Two QTL were detected for meat tenderness measured as Warner-Bratzler shear force at 14 d postmortem on chromosomes 20 and 29. These results present a complete scan in all available progeny in this family. Regions underlying QTL need to be assessed in other populations.     

Effects of quantitative trait loci on chromosomes 1, 2, 4, and 7 on growth, carcass, and meat quality traits in backcross Meishan x Large White pigs

The aim of this work was to estimate whether genetic dissection of QTL on chromosomes 1, 2, 4, and 7, detected in an F2 Meishan x Large White population, can be achieved with a recombinant back-cross progeny test approach. For this purpose, a first generation of backcross (BC1) was produced by using frozen semen of F1 Large White x Meishan boars with Large White females. Four BC1 boars were selected because of their heterozygosity for at least 1 of the 4 regions. The BC1 boars were crossed with Large White sows, and the resulting BC2 offspring were measured for several growth and body composition traits. Contrary to the F2 animals, BC2 animals were also measured for meat quality traits in adductor, gluteus superficialis (GS), longissimus dorsi, and biceps femoris (BF) muscles. Each BC1 boar was tested for a total of 39 traits and for the 4 regions with statistical interval mapping analyses. The QTL effects obtained in BC1 families showed some differences compared with those described in F1 families. However, we confirmed QTL effects for growth in the SW1301-SW2512 markers interval on chromosome 1 and also for body composition in the SW1828-SW2512 markers interval on chromosome 1, in the SW2443-SWR783 markers interval on chromosome 2, and in the SW1369-SW632 markers interval on chromosome 7. In addition, we detected new QTL for growth traits on chromosome 2 and for meat quality traits on chromosomes 1 and 2. Growth of animals from weaning to the end of the test was influenced by the IGF2 gene region on chromosome 2. Concerning meat quality, ultimate pH of adductor, longissimus dorsi, and BF were affected by the interval delimited by UMNP3000 and SW2512 markers on chromosome 1, and a* of GS, L* of BF, and water-holding capacity of GS were affected by QTL located between marker loci SW2443 and SWR783 on chromosome 2. Recombinant progeny testing appeared to be a suitable strategy for the genetic dissection of the QTL investigated.     

A QTL on pig chromosome 4 affects fatty acid metabolism: evidence from an Iberian by Landrace intercross

Three Iberian boars were bred to 31 Landrace sows to produce 79 F1 pigs. Six F1 boars were mated to 73 F1 sows. The F2 progeny from 33 full-sib families (250 individuals) were genotyped for seven microsatellites spanning the length of chromosome 4. Least squares procedures for interval mapping were used to detect quantitative trait loci (QTL). A permutation test was used to establish nominal significance levels associated with QTL effects, and resulting probability levels were corrected to a genomewide basis. Observed QTL effects were (genomewide significance, position of maximum significance in centimorgans): percentage of linoleic acid in subcutaneous adipose tissue (< 0.01, 81); backfat thickness (< 0.01, 83); backfat weight (< 0.01, 80); longissimus muscle area (0.02, 83); live weight (0.19, 88); and percentage of oleic acid in subcutaneous adipose tissue (0.25, 81). Gene action was primarily additive. The Iberian genotypes were fatter, slower growing, and had lower linoleic and higher oleic acid contents than Landrace genotypes. The interval from 80 to 83 cM contains the FAT1 and A-FABP loci that have been shown previously to affect fat deposition in pigs. This is the first report of a QTL affecting fatty acid composition of subcutaneous adipose tissue in pigs and provides a guide for the metabolic pathways affected by candidate genes described in this region of chromosome 4.     

Quantitative trait loci that control plasma lipid levels in an F2 intercross between C57BL/6J and DDD.Cg-A(y) inbred mouse strains

The objectives of this study were to characterize plasma lipid phenotypes and dissect the genetic basis of plasma lipid levels in an obese DDD.Cg-A(y) mouse strain. Plasma triglyceride (TG) levels were significantly higher in the DDD.Cg-A(y) strain than in the B6.Cg-A(y) strain. In contrast, plasma total-cholesterol (CHO) levels did not substantially differ between the two strains. As a rule, the A(y) allele significantly increased TG levels, but did not increase CHO levels. Quantitative trait locus (QTL) analyses for plasma TG and CHO levels were performed in two types of F(2) female mice [F(2)A(y) (F(2) mice carrying the A(y) allele) and F(2) non- A(y) mice (F(2) mice without the A(y) allele)] produced by crossing C57BL/6J females and DDD.Cg-A(y) males. Single QTL scan identified one significant QTL for TG levels on chromosome 1, and two significant QTLs for CHO levels on chromosomes 1 and 8. When the marker nearest to the QTL on chromosome 1 was used as covariates, four additional significant QTLs for CHO levels were identified on chromosomes 5, 6, and 17 (two loci). In contrast, consideration of the agouti locus genotype as covariates did not detect additional QTLs. DDD.Cg-A(y) showed a low CHO level, although it had Apoa2(b), which was a CHO-increasing allele at the Apoa2 locus. This may have been partly due to the presence of multiple QTLs, which were associated with decreased CHO levels, on chromosome 8.