Genome‐wide expression quantitative trait locus studies facilitate isolation of causal genes controlling panicle structure

The morphology of rice (Oryza sativa L.) panicles is an important determinant of grain yield, and elucidation of the genetic control of panicle structure is very important for fulfilling the demand for high yield in breeding programs. In a quantitative trait locus (QTL) study using 82 backcross inbred lines (BILs) derived from Koshihikari and Habataki, 68 QTLs for 25 panicle morphological traits were identified. Gene expression profiling from inflorescence meristems of BILs was obtained. A combination of phenotypic QTL (pQTL) and expression QTL (eQTL) analysis revealed co‐localization between pQTLs and eQTLs, consistent with significant correlations between phenotypic traits and gene expression levels. By combining pQTL and eQTL data, two genes were identified as controlling panicle structure: OsMADS18 modulates the average length of the primary rachis and OsFTL1 has pleiotropic effects on the total number of secondary rachides, number of grains per panicle, plant height and the length of flag leaves. Phenotypes were confirmed in RNA interference knocked‐down plants and overexpressor lines. The combination of pQTL and eQTL analysis could facilitate identification of genes involved in rice panicle formation.     

Genome-wide association for methamphetamine sensitivity in an advanced intercross mouse line

Sensitivity to the locomotor stimulant effects of methamphetamine (MA) is a heritable trait that utilizes neurocircuitry also associated with the rewarding effects of drugs. We used the power of a C57BL/6J × DBA/2J F(2) intercross (n = 676) and the precision of a C57BL/6J × DBA/2J F(8) advanced intercross line (Aap: B6, D2-G8; or F(8) AIL; n = 552) to identify and narrow quantitative trait loci (QTLs) associated with sensitivity to the locomotor stimulant effects of MA. We used the program QTLRel to simultaneously map QTL in the F(2) and F(8) AIL mice. We identified six genome-wide significant QTLs associated with locomotor activity at baseline and seven genome-wide significant QTLs associated with MA-induced locomotor activation. The average per cent decrease in QTL width between the F(2) and the integrated analysis was 65%. Additionally, these QTLs showed a distinct temporal specificity within each session that allowed us to further refine their locations, and identify one QTL with a 1.8-LOD support interval of 1.47 Mb. Next, we utilized publicly available bioinformatics resources to exploit strain-specific sequence data and strain- and region-specific expression data to identify candidate genes. These results illustrate the power of AILs in conjunction with sequence and gene expression data to investigate the genetic underpinnings of behavioral and other traits.     

A genetical genomics approach to genome scans increases power for QTL mapping

We describe a method for integrating gene expression information into genome scans and show that this can substantially increase the statistical power of QTL mapping. The method has three stages. First, standard clustering methods identify small (size 5-20) groups of genes with similar expression patterns. Second, each gene group is tested for a causative genetic locus shared with the clinical trait of interest. This is done using an EM algorithm approach that treats genotype at the putative causative locus as an unobserved variable and combines expression information from all of the genes in the group to infer genotype information at the locus. Finally, expression QTL (eQTL) are mapped for each gene group that shares a causative locus with the clinical trait. Such eQTL are candidates for the causative locus. Simulation results show that this method has far superior power to standard QTL mapping techniques in many circumstances. We applied this method to existing data on mouse obesity. Our method identified 27 putative body weight QTL, whereas standard QTL mapping produced only one. Furthermore, most gene groups with body weight QTL included cis genes, so candidate genes could be immediately identified. Eleven body weight QTL produced 16 candidate genes that have been previously associated with body weight or body weight-related traits, thus validating our method. In addition, 15 of the 16 other loci produced 32 candidate genes that have not been associated with body weight. Thus, this method shows great promise for finding new causative loci for complex traits.     

Quantitative trait loci for steady-state platelet count in mice

Platelet count in humans is a strongly genetically regulated trait, with approximately 85% of the interindividual variance in platelet numbers attributable to genetic factors. Inbred mouse strains also have strain-specific platelet count ranges. As part of a project to identify novel factors that regulate platelet count, we identified two inbred mouse strains, CBA/CaH and QSi5, with substantial differences in platelet count (mean values of 581 vs. 1062 × 10⁹/L). An F₂ intercross resource of 1126 animals was bred from these two parental strains for a genomewide scan for quantitative trait loci (QTL) for platelet count. QTL were identified on MMU1 (LOD 6.8, p < 0.0005) and MMU11 (LOD 11.2, p < 0.0005) by selectively genotyping animals from the extremes of the F₂ platelet count distribution. Three other QTL of suggestive statistical significance were also detected on MMU7, 13, and 17. It is noteworthy that no QTL were detected in the vicinity of the genes encoding thrombopoietin (Thpo), and its receptor (c-Mpl), both known to influence platelet production. Comparison of gene expression levels between the parental mouse strains by microarrays also showed little difference in the mRNA levels of these known candidate genes. These results represent the first published use of a genetic linkage-based approach in a mouse model toward the identification of genetic factors that regulate platelet count.     

Identification of candidate genes in the type 2 diabetes modifier locus using expression QTL

To identify new genetic determinants relevant to type 2 diabetes (T2D), diabetic F2 progeny were generated by intercrossing F1 mice obtained from a cross of BKS.Cg-Lepr(db)+/+m and DBA/2, and T2D-related phenotypes were measured. In the F2 population, increased susceptibility to diabetes and obesity was observed. We also detected the major quantitative trait loci (QTL) modifying the severity of diabetes on chromosome 9, where peaks of logarithm of odds (LOD) overlapped for three traits. To identify candidate genes in the QTL intervals, we combined "expression QTL" (eQTL), taking mRNA levels as quantitative traits, and "interstrain sequence variations, including cSNPs." As a result, four genes were identified from cosegregation of clinical QTL with eQTL and 13 genes were found from interstrain cSNPs as candidates in the T2D modifier QTL. Our combined approach shows the acceleration of the discovery of candidate genes in the QTL of interest, spanning several megabases.     

A major QTL on chromosome 11 influences psychostimulant and opioid sensitivity in mice

The identification of genes influencing sensitivity to stimulants and opioids is important for determining their mechanism of action and may provide fundamental insights into the genetics of drug abuse. We used a panel of C57BL/6J (B6; recipient)× A/J (donor) chromosome substitution strains (CSSs) to identify quantitative trait loci (QTL) for both open field activity and sensitivity to the locomotor stimulant response to methamphetamine (MA). Mice were injected with saline (days 1 and 2) and MA (day 3; 2 mg/kg i.p.). We analyzed the total distance traveled in the open field for 30 min following each injection. CSS-8, -11 and -16 showed reduced MA-induced locomotor activity relative to B6, whereas CSS-10 and -12 showed increased MA-induced locomotor activity. Further analysis focused on CSS-11 because it was robustly different from B6 following MA injection, but did not differ in activity following saline injection and because it also showed reduced locomotor activity in response to the mu-opioid receptor agonist fentanyl (0.2 mg/kg i.p.). Thus, CSS-11 captures QTLs for the response to both psychostimulants and opioids. Using a B6 × CSS-11 F₂ intercross, we identified a dominant QTL for the MA response on chromosome 11. We used haplotype association mapping of cis expression QTLs and bioinformatic resources to parse among genes within the 95% confidence interval of the chromosome 11 QTL. Identification of the genes underlying QTLs for response to psychostimulants and opioids may provide insights about genetic factors that modulate sensitivity to drugs of abuse.     

Quantitative trait loci and candidate genes associated with starch pasting viscosity characteristics in cassava (Manihot esculenta Crantz)

Starch pasting viscosity is an important quality trait in cassava (Manihot esculenta Crantz) cultivars. The aim here was to identify loci and candidate genes associated with the starch pasting viscosity. Quantitative trait loci (QTL) mapping for seven pasting viscosity parameters was carried out using 100 lines of an F₁ mapping population from a cross between two cassava cultivars Huay Bong 60 and Hanatee. Starch samples were obtained from roots of cassava grown in 2008 and 2009 at Rayong, and in 2009 at Lop Buri province, Thailand. The traits showed continuous distribution among the F₁ progeny with transgressive variation. Fifteen QTL were identified from mean trait data, with Logarithm of Odds (LOD) values from 2.77–13.01 and phenotype variations explained (PVE) from10.0–48.4%. In addition, 48 QTL were identified in separate environments. The LOD values ranged from 2.55–8.68 and explained 6.6–43.7% of phenotype variation. The loci were located on 19 linkage groups. The most important QTL for pasting temperature (PT) (qPT.1LG1) from mean trait values showed largest effect with highest LOD value (13.01) and PVE (48.4%). The QTL co‐localised with PT and pasting time (PTi) loci that were identified in separate environments. Candidate genes were identified within the QTL peak regions. However, the major genes of interest, encoding the family of glycosyl or glucosyl transferases and hydrolases, were located at the periphery of QTL peaks. The loci identified could be effectively applied in breeding programmes to improve cassava starch quality. Alleles of candidate genes should be further studied in order to better understand their effects on starch quality traits.     

遗传与基因表达数据的整合—— eQTL的方法及应用

高通量的基因型分析和芯片技术的发展使人们能够进一步研究哪些遗传差异最终影响基因的表达。通过表达数量性状座位(eQTL)作图方法可对基因表达水平的遗传基础进行解析。与传统的QTL分析方法一样, eQTL的主要目标是鉴别表达性状座位所在的染色体区域。但由于表达谱数据成千上万, 而传统的QTL分析方法最多分析几十个性状, 因此需要考虑这类实验设计的特点以及统计分析方法。本文详细介绍了eQTL定位过程及其研究方法, 重点从个体选择、基因芯片实验设计、基因表达数据的获得与标准化、作图方法及结果分析等方面进行了综述, 指出了当前eQTL研究存在的问题和局限性。最后介绍了eQTL研究在估计基因表达遗传率、挖掘候选基因、构建基因调控网络、理解基因间及基因与环境的互作的应用进展。     

Genetic Regulation of Bone Metabolism in the Chicken: Similarities and Differences to Mammalian Systems

Birds have a unique bone physiology, due to the demands placed on them through egg production. In particular their medullary bone serves as a source of calcium for eggshell production during lay and undergoes continuous and rapid remodelling. We take advantage of the fact that bone traits have diverged massively during chicken domestication to map the genetic basis of bone metabolism in the chicken. We performed a quantitative trait locus (QTL) and expression QTL (eQTL) mapping study in an advanced intercross based on Red Junglefowl (the wild progenitor of the modern domestic chicken) and White Leghorn chickens. We measured femoral bone traits in 456 chickens by peripheral computerised tomography and femoral gene expression in a subset of 125 females from the cross with microarrays. This resulted in 25 loci for female bone traits, 26 loci for male bone traits and 6318 local eQTL loci. We then overlapped bone and gene expression loci, before checking for an association between gene expression and trait values to identify candidate quantitative trait genes for bone traits. A handful of our candidates have been previously associated with bone traits in mice, but our results also implicate unexpected and largely unknown genes in bone metabolism. In summary, by utilising the unique bone metabolism of an avian species, we have identified a number of candidate genes affecting bone allocation and metabolism. These findings can have ramifications not only for the understanding of bone metabolism genetics in general, but could also be used as a potential model for osteoporosis as well as revealing new aspects of vertebrate bone regulation or features that distinguish avian and mammalian bone.     

遗传基因组学(Genetical genomics)的研究进展

遗传基因组学(geneticalgenomics)是将微阵列技术和数量性状座位(QTL)分析结合起来,全基因组水平上定位基因表达的QTL(eQTL).它为研究复杂性状的分子机理和调控网络提供全新的手段.遗传基因组这个概念和研究策略在2001年由Janson和Nap首先提出,到目前为止,遗传基因组学已应用于酵母、老鼠、人以及玉米等植物.研究结果表明:基因表达水平的差异是可遗传的复杂性状;eQTL可以分为顺式作用eQTL和反式作用eQTL,顺式作用eQTL就是某个基因的eQTL定位到该基因所在的基因组区域,表明可能是该基因本身的差别引起mRNA水平的差别,反式作用就是eQTL定位到其他基因组区域,表明其他基因的差别控制该基因mRNA水平的差异.将eQTL结果、基因功能注解以及多种统计分析方法相结合,不仅能更准确地鉴别控制复杂性状及其相关基因表达的候选基因,而且能构建相应的基因调控网络.