不同生长环境下水稻穗伸出度的QTL分析

以十和田/昆明小白谷225个F14家系为作图群体,在云南省弥勒县(正常生长环境)、嵩明县(自然低温胁迫环境)、丽江市(自然低温胁迫环境)等3个试点不同年份共5种不同生长环境下进行了水稻主穗和分蘖穗穗伸出度的异地鉴定,并利用SSR标记对水稻穗伸出度进行了QTL分析。检测结果表明,在5种不同的生长环境下共检测到12个与水稻穗伸出度相关的QTL,分别分布于第1(2个QTLs)、2、4、6(3个QTLs)、7(3个QTLs)、9(2个QTLs)号染色体,对表型的贡献率为3.72%~22.17%。其中与主穗穗伸出度相关的QTL共11个,与分蘖穗穗伸出度相关的QTL共7个,其中6个在主穗和分蘖穗上均检测到。在与主穗穗伸出度相关的11个QTL中,q PE-7-1在4种环境下均被检测到,解释的表型变异为9.49%~22.17%;q PE-1-1、q PE-1-2、q PE-6-1和q PE-9-2 4个QTL在2种环境下均被检测到。在与分蘖穗穗伸出度相关的7个QTL中,q PE-1-2、q PE-7-1和q PE-6-1 3个QTL在2种环境中均被检测到,解释的表型变异率分别为4.35%~12.64%、13.22%~20.89%和11.49%~15.73%。     

The correlation of intramuscular fat content between muscles of the lamb carcass and the use of computed tomography to predict intramuscular fat percentage in lambs

Intramuscular fat (IMF) % contributes positively to the juiciness and flavour of lamb and is therefore a useful indicator of eating quality. A rapid, non-destructive method of IMF determination like computed tomography (CT) would enable pre-sorting of carcasses based on IMF% and potential eating quality. Given the loin muscle (longissimus lumborum) is easy to sample, a single measurement at this site would be useful, providing is correlates well to other muscles. To determine the ability of CT to predict IMF%, this study used 400 animals and examined 5 muscles from three sections of the carcass: from the fore-section the m. supraspinatus and m. infraspinatus, from the saddle-section the m. longissimus lumborum and from the hind-section the m. semimembranosus and m. semitendinosus. The average CT pixel density of muscle was negatively associated with IMF% and can be used to predict IMF% although precision in this study was poor. The ability of CT to predict IMF% was greatest in the m. longissimus lumborum (slope −0.07) and smallest in the m. infraspinatus (slope −0.02). The correlation coefficients of IMF% between the five muscles were variable, with the highest correlation coefficients evident between muscles of the fore section (0.67 between the m. supraspinatus and the m. infraspinatus) and the weakest correlations were between the muscle of the fore and hind section. The correlation between the m. longissimus lumborum to the other muscles was fairly consistent with values ranging between 0.34 and 0.40 (partial correlation coefficient). The correlation between the proportion of carcass fat and the IMF% of the five muscles varied and was greatest in the m. longissimus lumborum (0.41).     

Mapping the genome of Miscanthus sinensis for QTL associated with biomass productivity

In light of rising energy costs, lignocellulosic ethanol has been identified as a renewable alternative to petroleum-based transportation fuels. In an attempt to reach government mandated ethanol production levels, potential plant biofeedstock candidates have been investigated, and cold-tolerant, perennial accessions within the C₄ grass genus Miscanthus have been identified as leading contenders in the Midwestern US. To facilitate the development of improved cultivars through marker-assisted breeding, a quantitative trait locus (QTL) study was conducted on a full-sib, F₁ mapping population segregating for flowering time, height, leaf width, and yield using a genetic map consisting of 846 segregating SNP and SSR markers. This was a 3 year study investigating the genetic architecture underlying traits important to biomass production in a population of 221 progeny from a cross between M. sinensis ‘Grosse Fountaine’ and M. sinensis ‘Undine’ established in the spring of 2010; 72 QTLs with LOD scores above the genome-wide, permuted threshold equivalent to a P-value of 0.05 were identified across 13 traits. Of the 36 QTLs identified in 2011, 22 were detected again the following year. Both the use of spring emergence and vigor rating as a covariate to account for variation related to differences in establishment increased the power to detect QTLs in the 2 year establishment period. Finally, a dry period in the middle of the 2012 growing season suggested that yield declines were due to a decrease in tiller diameter.     

Genetic basis of natural variation in body pigmentation in Drosophila melanogaster

Body pigmentation in insects and other organisms is typically variable within and between species and is often associated with fitness. Regulatory variants with large effects at bab1, t and e affect variation in abdominal pigmentation in several populations of Drosophila melanogaster. Recently, we performed a genome wide association (GWA) analysis of variation in abdominal pigmentation using the inbred, sequenced lines of the Drosophila Genetic Reference Panel (DGRP). We confirmed the large effects of regulatory variants in bab1, t and e; identified 81 additional candidate genes; and validated 17 candidate genes (out of 28 tested) using RNAi knockdown of gene expression and mutant alleles. However, these analyses are imperfect proxies for the effects of segregating variants. Here, we describe the results of an extreme quantitative trait locus (xQTL) GWA analysis of female body pigmentation in an outbred population derived from light and dark DGRP lines. We replicated the effects on pigmentation of 28 genes implicated by the DGRP GWA study, including bab1, t and e and 7 genes previously validated by RNAi and/or mutant analyses. We also identified many additional loci. The genetic architecture of Drosophila pigmentation is complex, with a few major genes and many other loci with smaller effects.     

Developments from analysis of variance through to generalized linear models and beyond

The collaborations between statisticians and biologists during the 100 years since AAB was founded have led to a very impressive list of statistical techniques, whose use now goes well beyond agriculture and biology. One example is the maximum likelihood methodology for probit analysis, arising from the collaboration between Sir Ronald Fisher and Chester Bliss. Others include analysis of variance, design of experiments, generalized linear models and the residual, or restricted, maximum likelihood (REML) algorithm for fitting unbalanced linear mixed models.     

An Expression QTL of Closely Linked Candidate Genes Affects pH of Meat in Chickens

Genetical genomics has been suggested as a powerful approach to study the genotype–phenotype gap. However, the relatively low power of these experiments (usually related to the high cost) has hindered fulfillment of its promise, especially for loci (QTL) of moderate effects.One strategy with which to overcome the issue is to use a targeted approach. It has two clear advantages: (i) it reduces the problem to a simple comparison between different genotypic groups at the QTL and (ii) it is a good starting point from which to investigate downstream effects of the QTL. In this study, from 698 F2 birds used for QTL mapping, gene expression profiles of 24 birds with divergent homozygous QTL genotypes were investigated. The targeted QTL was on chromosome 1 and affected initial pH of breast muscle. The biological mechanisms controlling this trait can be similar to those affecting malignant hyperthermia or muscle fatigue in humans. The gene expression study identified 10 strong local signals that were markedly more significant compared to any genes on the rest of the genome. The differentially expressed genes all mapped to a region <1 Mb, suggesting a remarkable reduction of the QTL interval. These results, combined with analysis of downstream effect of the QTL using gene network analysis, suggest that the QTL is controlling pH by governing oxidative stress. The results were reproducible with use of as few as four microarrays on pooled samples (with lower significance level). The results demonstrate that this cost-effective approach is promising for characterization of QTL.     

A New Simple Method for Improving QTL Mapping Under Selective Genotyping

The selective genotyping approach, where only individuals from the high and low extremes of the trait distribution are selected for genotyping and the remaining individuals are not genotyped, has been known as a cost-saving strategy to reduce genotyping work and can still maintain nearly equivalent efficiency to complete genotyping in QTL mapping. We propose a novel and simple statistical method based on the normal mixture model for selective genotyping when both genotyped and ungenotyped individuals are fitted in the model for QTL analysis. Compared to the existing methods, the main feature of our model is that we first provide a simple way for obtaining the distribution of QTL genotypes for the ungenotyped individuals and then use it, rather than the population distribution of QTL genotypes as in the existing methods, to fit the ungenotyped individuals in model construction. Another feature is that the proposed method is developed on the basis of a multiple-QTL model and has a simple estimation procedure similar to that for complete genotyping. As a result, the proposed method has the ability to provide better QTL resolution, analyze QTL epistasis, and tackle multiple QTL problem under selective genotyping. In addition, a truncated normal mixture model based on a multiple-QTL model is developed when only the genotyped individuals are considered in the analysis, so that the two different types of models can be compared and investigated in selective genotyping. The issue in determining threshold values for selective genotyping in QTL mapping is also discussed. Simulation studies are performed to evaluate the proposed methods, compare the different models, and study the QTL mapping properties in selective genotyping. The results show that the proposed method can provide greater QTL detection power and facilitate QTL mapping for selective genotyping. Also, selective genotyping using larger genotyping proportions may provide roughly equivalent power to complete genotyping and that using smaller genotyping proportions has difficulties doing so. The R code of our proposed method is available on http://www.stat.sinica.edu.tw/chkao/.     

Modular Skeletal Evolution in Sticklebacks Is Controlled by Additive and Clustered Quantitative Trait Loci

Understanding the genetic architecture of evolutionary change remains a long-standing goal in biology. In vertebrates, skeletal evolution has contributed greatly to adaptation in body form and function in response to changing ecological variables like diet and predation. Here we use genome-wide linkage mapping in threespine stickleback fish to investigate the genetic architecture of evolved changes in many armor and trophic traits. We identify >100 quantitative trait loci (QTL) controlling the pattern of serially repeating skeletal elements, including gill rakers, teeth, branchial bones, jaws, median fin spines, and vertebrae. We use this large collection of QTL to address long-standing questions about the anatomical specificity, genetic dominance, and genomic clustering of loci controlling skeletal differences in evolving populations. We find that most QTL (76%) that influence serially repeating skeletal elements have anatomically regional effects. In addition, most QTL (71%) have at least partially additive effects, regardless of whether the QTL controls evolved loss or gain of skeletal elements. Finally, many QTL with high LOD scores cluster on chromosomes 4, 20, and 21. These results identify a modular system that can control highly specific aspects of skeletal form. Because of the general additivity and genomic clustering of major QTL, concerted changes in both protective armor and trophic traits may occur when sticklebacks inherit either marine or freshwater alleles at linked or possible “supergene” regions of the stickleback genome. Further study of these regions will help identify the molecular basis of both modular and coordinated changes in the vertebrate skeleton.     

K-rasG12V mediated lung tumor models identified three new quantitative trait loci modifying events post-K-ras mutation

A high incidence of oncogenic K-ras mutations is observed in lung adenocarcinoma of human cases and carcinogen-induced animal models. The process of oncogenic K-ras-mediated lung adenocarcinogenesis can be dissected into two parts: pre- and post-K-ras mutation. Adoption of transgenic lines containing a flox-K-rasG12V transgene eliminates the use of chemical carcinogens and enables us to study directly crucial events post-K-ras mutation without considering the cellular events involved with oncogenic K-ras mutation, e.g., distribution and metabolism of chemical carcinogens, DNA repair, and somatic recombination by host factors. We generated two mouse strains C57BL/6J-Ryr2^tm1Nobs and A/J-Ryr2^tm1Nobs in which K-rasG12V can be transcribed from the cytomegalovirus early enhancer/chicken beta actin promoter in virtually any tissue. Upon K-rasG12V induction in lung epithelial cells by an adenovirus expressing the Cre recombinase, the number of tumors in the C57BL/6J-Ryr2^tm1Nobs/+ mouse line was 12.5 times that in the A/J-Ryr2^tm1Nobs/+ mouse line. Quantitative trait locus (QTL) analysis revealed that new three modifier loci, D3Mit19, D3Mit45 and D11Mit20, were involved in the differential susceptibility between the two lines. In addition, we found that differential expression of the wild-type K-ras gene, which was genetically turn out to be anti-oncogenic activity on K-rasG12V, could not account for the different susceptibility in our two K-rasG12V-mediated lung tumor models. Thus, we provide a genetic system that enables us to explore new downstream modifiers post-K-ras mutation.     

Identification of quantitative trait loci for shank length and growth at different development stages in chicken

Shank length affects chicken leg health and longer shanks are a source of leg problems in heavy-bodied chickens. Identification of quantitative trait loci (QTL) affecting shank length traits may be of value to genetic improvement of these traits in chickens. A genome scan was conducted on 238 F₂ chickens from a reciprocal cross between the Silky Fowl and the White Plymouth Rock breeds using 125 microsatellite markers to detect static and developmental QTL affecting weekly shank length and growth (from 1 to 12 weeks) in chickens. Static QTL affected shank length from birth to time t , while developmental QTL affected shank growth from time t− 1 to time t . Seven static QTL on six chromosomes (GGA2, GGA3, GGA4, GGA7, GGA9 and GGA23) were detected at ages of 2, 3, 4, 5, 6, 7, 9 and 12 weeks, and six developmental QTL on five chromosomes (GGA1, GGA2, GGA4, GGA5 and GGA23) were detected for five shank growth periods, weeks 2–3, 4–5, 5–6, 10–11 and 11–12. A static QTL and a developmental QTL ( SQSL1 and DQSL2 ) were identified at GGA2 (between ADL0190 and ADL0152 ). SQSL1 explained 2.87–5.30% of the phenotypic variation in shank length from 3 to 7 weeks. DQSL2 explained 2.70% of the phenotypic variance of shank growth between 2 and 3 weeks. Two static and two developmental QTL were involved chromosome 4 and chromosome 23. Two chromosomes (GGA7 and GGA9) had static QTL but no developmental QTL and another two chromosomes (GGA1 and GGA5) had developmental QTL but no static QTL. The results of this study show that shank length and shank growth at different developmental stages involve different QTL.