Domestication‐related variation in social preferences in chickens is affected by genotype on a growth QTL

A growth‐related QTL on chicken chromosome 1 has previously been shown to influence domestication behaviour in chickens. In this study, we used Red Junglefowl (RJF) and White Leghorn (WL) as well as the intercross between them to investigate whether stress affects the way birds allocate their time between familiar and unfamiliar conspecifics in a social preference test (‘social support seeking’), and how this is related to genotype at specific loci within the growth QTL. Red Junglefowl males spent more time with unfamiliar chickens before the stressful event compared to the other birds, whereas all birds except WL males tended to spend less time with unfamiliar ones after stress. A significant QTL locus was found to influence both social preference under undisturbed circumstances and social support seeking. The WL allele at this QTL was associated not only with a preference for unfamiliar individuals but also with a shift towards familiar ones in response to stress (social support seeking). A second, suggestive QTL also affected social support seeking, but in the opposite direction; the WL allele was associated with increased time spent with unfamiliar individuals. The region contains several possible candidate genes, and gene expression analysis of a number of them showed differential expression between RJF and WL of AVPR2 (receptor for vasotocin), and possibly AVPR1a (another vasotocin receptor) and NRCAM (involved in neural development) in the lower frontal lobes of the brains of RJF and WL animals. These three genes continue to be interesting candidates for the observed behavioural effects .     

Disentangling Two QTL on Porcine Chromosome 12 for Backfat Fatty Acid Composition

A previous study allowed the identification of two QTL regions at positions 11–34 cM (QTL1) and 68–76 cM (QTL2) on porcine chromosome SSC12 affecting several backfat fatty acids in an Iberian x Landrace F2 intercross. In the current study, different approaches were performed in order to better delimit the quoted QTL regions and analyze candidate genes. A new chromosome scan, using 81 SNPs selected from the Porcine 60KBeadChip and six previously genotyped microsatellites have refined the QTL positions. Three new functional candidate genes (ACOX1, ACLY, and SREBF1) have been characterized. Moreover, two putative promoters of porcine ACACA gene have also been investigated. New isoforms and 24 SNPs were detected in the four candidate genes, 19 of which were genotyped in the population. ACOX1 and ACLY SNPs failed to explain the effects of QTL1 on palmitic and gadoleic fatty acids. QTL2, affecting palmitoleic, stearic, and vaccenic fatty acids, maps close to the ACACA gene location. The most significant associations have been detected between one intronic (g.53840T > C) and one synonymous (c.5634T > C) ACACA SNPs and these fatty acids. Complementary analyses including ACACA gene expression quantification and association studies in other porcine genetic types do not support the expected causal effect of ACACA SNPs.     

Discovery of genomic regions and candidate genes controlling shelling percentage using QTL‐seq approach in cultivated peanut (Arachis hypogaea L.)

Cultivated peanut (Arachis hypogaea L.) is an important grain legume providing high‐quality cooking oil, rich proteins and other nutrients. Shelling percentage (SP) is the 2nd most important agronomic trait after pod yield and this trait significantly affects the economic value of peanut in the market. Deployment of diagnostic markers through genomics‐assisted breeding (GAB) can accelerate the process of developing improved varieties with enhanced SP. In this context, we deployed the QTL‐seq approach to identify genomic regions and candidate genes controlling SP in a recombinant inbred line population (Yuanza 9102 × Xuzhou 68‐4). Four libraries (two parents and two extreme bulks) were constructed and sequenced, generating 456.89–790.32 million reads and achieving 91.85%–93.18% genome coverage and 14.04–21.37 mean read depth. Comprehensive analysis of two sets of data (Yuanza 9102/two bulks and Xuzhou 68‐4/two bulks) using the QTL‐seq pipeline resulted in discovery of two overlapped genomic regions (2.75 Mb on A09 and 1.1 Mb on B02). Nine candidate genes affected by 10 SNPs with non‐synonymous effects or in UTRs were identified in these regions for SP. Cost‐effective KASP (Kompetitive Allele‐Specific PCR) markers were developed for one SNP from A09 and three SNPs from B02 chromosome. Genotyping of the mapping population with these newly developed KASP markers confirmed the major control and stable expressions of these genomic regions across five environments. The identified candidate genomic regions and genes for SP further provide opportunity for gene cloning and deployment of diagnostic markers in molecular breeding for achieving high SP in improved varieties.     

Mapping quantitative trait loci associated with southern leaf blight resistance in maize (Zea mays L.)

Southern leaf blight (SLB) caused by the fungus Cochliobolus heterostrophus (Drechs.) Drechs. is a major foliar disease of maize worldwide. Our objectives were to identify quantitative trait loci (QTL) for resistance to SLB and flowering traits in recombinant inbred line (RIL) population derived from the cross of inbred lines LM5 (resistant) and CM140 (susceptible). A set of 207 RILs were phenotyped for resistance to SLB at three time intervals for two consecutive years. Four putative QTL for SLB resistance were detected on chromosomes 3, 8 and 9 that accounted for 54% of the total phenotypic variation. Days to silking and anthesis–silking interval (ASI) QTL were located on chromosomes 6, 7 and 9. A comparison of the obtained results with the published SLB resistance QTL studies suggested that the detected bins 9.03/02 and 8.03/8.02 are the hot spots for SLB resistance whereas novel QTL were identified in bins 3.08 and 8.01/8.04. The linked markers are being utilized for marker‐assisted mobilization of QTL conferring resistance to SLB in elite maize backgrounds. Fine mapping of identified QTL will facilitate identification of candidate genes underlying SLB resistance.     

Identification of Related QTLs at Late Developmental Stage in Rice (Oryza sativa L.) Under Two Nitrogen Levels

QTL underlying related traits at the late developmental stage under two different nitrogen levels were investigated in rice using a population of chromosome segment substitution lines (CSSL) derived from a cross between Teqing and Lemont. A total of 31 QTLs referring 5 traits, that is, plant height (PH), panicle number per plant (PN), chlorophyll content (CC), shoot dry weight (SDW) and grain yield per plant (YD), were detected. Under normal N level, 3 QTLs were detected for each trait, while under low N level, 5,4, 5 and 2 QTLs were detected for PH, PN, CC and SDW respectively. Most of the QTLs were located on chromosome 2, 3, 7, 11 and 12. QTLs controlling different traits or the same trait under different N levels were mapped on the same or adjacent intervals, forming several clusters in rice chromosomes. More than two traits were controlled by QTLs on one of four intervals (RM30-RM439, RM18-RM478, RM309-RM270, and RM235-RM17), suggesting that there were some pleiotropic effects. It was supposed that some QTLs only detected at low N level might be associated with the ability to tolerate the low N stress in rice.     

Shrinkage Estimation Method for Mapping Multiple Quantitative Trait Loci

In this article, shrinkage estimation method for multiple-marker analysis and for mapping multiple quantitative trait loci (QTL) was reviewed. For multiple-marker analysis, Xu (Genetics, 2003, 163:789-801) developed a Bayesian shrinkage estimation (BSE) method. The key to the success of this method is to allow each marker effect have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, a large number of markers can be handled although most of them may have negligible effects. Under epistatic genetic model, however, the running time is very long. To overcome this problem, a novel method of incorporating the idea described above into maximum likelihood, known as penalized likelihood method, was proposed. A simulated study showed that this method can handle a model with multiple effects, which are ten times larger than the sample size. For multiple QTL analysis, two modified versions for the BSE method were introduced: one is the fixed-interval method and another is the variable-interval method. The former deals with markers with intermediate density, and the latter can handle markers with extremely high density as well as model with epistatic effects. For the detection of epistatic effects, penalized likelihood method and the variable-interval approach of the BSE method are available.     

Prediction of IBD based on population history for fine gene mapping

A novel multiple regression method (RM) is developed to predict identity-by-descent probabilities at a locus L (IBD_L), among individuals without pedigree, given information on surrounding markers and population history. These IBD_L probabilities are a function of the increase in linkage disequilibrium (LD) generated by drift in a homogeneous population over generations. Three parameters are sufficient to describe population history: effective population size (Ne), number of generations since foundation (T), and marker allele frequencies among founders (p). IBD_L are used in a simulation study to map a quantitative trait locus (QTL) via variance component estimation. RM is compared to a coalescent method (CM) in terms of power and robustness of QTL detection. Differences between RM and CM are small but significant. For example, RM is more powerful than CM in dioecious populations, but not in monoecious populations. Moreover, RM is more robust than CM when marker phases are unknown or when there is complete LD among founders or Ne is wrong, and less robust when p is wrong. CM utilises all marker haplotype information, whereas RM utilises information contained in each individual marker and all possible marker pairs but not in higher order interactions. RM consists of a family of models encompassing four different population structures, and two ways of using marker information, which contrasts with the single model that must cater for all possible evolutionary scenarios in CM.     

利用重组自交系和SSR标记进行陆地棉株型QTL的鉴定和定位

通过中棉所12与8891的杂交及多代自交,获得由180个家系构成的重组自交系F8、F9群体。重组自交系群体、两亲本及F1于2002、2003两年种植;对株型性状进行了研究,两年共调查了10个株型形状。利用该重组自交系群体,采用SSR为主体的分子标记构建了遗传连锁图,并对株型性状进行了单位点和双位点水平的QTL定位。结果表明,QTL加性效应和上位性互作效应作为棉花重组自交系株型性状的遗传基础起着重要作用;中棉所12与8891间多态性位点偏少,而表型差异较大且其杂交种湘杂棉二号有很强的杂种优势,QTL互作可部分解释这一现象：结合对产量品质性状的研究结果,认为上位性可能是湘杂棉二号杂种优势的重要遗传基础。     

两种供氮水平下水稻生长后期相关性状的QTL定位

以特青为母本与Lemont杂交,然后用特青为轮回亲本回交,建立特青背景下的染色体片段置换系（CSSL）群体。在正常和低氮条件下分别在生长后期对株高（PH）、单株穗数（PN）、叶绿素含量（CC）、地上部干物重（SDW）和单株籽粒产量（YD）等性状进行了QTL分析,共检测到31个QTL。其中在正常供氮水平下控制PH、PN、CC、SDW和YD的QTL数目均为3个;在低氮水平下检测到5、4、5和2个影响PH、PN、CC和SDW的QTL,在低氮水平下没有检测到控制YD的位点。大部分QTL集中在第2、3、7、11和12染色体上,影响不同性状或在两种供氮水平下影响同一性状的QTL在染色体上成串或成簇分布。其中RM30-RM439、RM18-RM478、RM309-RM270、RM235-RM17等区域同时检测到控制两个以上性状的QTL,表现出明显的一因多效现象。推测仅在低氮水平下检测到的QTL可能跟水稻对低氮胁迫耐性有一定的关联。