Carbohydrate metabolic pathway genes associated with quantitative trait loci (QTL) for obesity and type 2 diabetes: Identification by data mining

Increasing consumption of refined carbohydrates is now being recognized as a primary contributor to the development of nutritionally related chronic diseases such as obesity and type 2 diabetes mellitus (T2DM). A data mining approach was used to evaluate the role of carbohydrate metabolic pathway genes in the development of obesity and T2DM. Data from public databases were used to map the position of the carbohydrate metabolic pathway genes to known quantitative trait loci (QTL) for obesity and T2DM and for examining the pathway genes for the presence of sequence and structural genetic variants such as single nucleotide polymorphisms (SNPs) and copy number variants (CNS), respectively. The results demonstrated that a majority of the genes of the carbohydrate metabolic pathways are associated with QTL for obesity and many for T2DM. In addition, some key genes of the pathways also encode non-synonymous SNPs that exhibit significant differences in population frequencies. This study emphasizes the significance of the metabolic pathways genes in the development of disease phenotypes, its differential occurrence across populations and between individuals, and a strategy for interpreting an individuals' risk for disease.     

A genome-wide association search for type 2 diabetes genes in African Americans

African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10⁻⁸). SNP rs7560163 (P = 7.0×10⁻⁹, OR (95% CI) = 0.75 (0.67–0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10⁻⁵) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations.     

Vaspin gene expression in human adipose tissue: association with obesity and type 2 diabetes

Recently, vaspin was identified as an adipokine with insulin-sensitizing effects, which is predominantly secreted from visceral adipose tissue in a rat model of type 2 diabetes. In this study, we examined whether vaspin mRNA expression is a marker of visceral obesity and correlates with anthropometric and metabolic parameters in paired samples of visceral and subcutaneous adipose tissue from 196 subjects with a wide range of obesity, body fat distribution, insulin sensitivity, and glucose tolerance. Vaspin mRNA expression was only detectable in 23% of the visceral and in 15% of the subcutaneous (SC) adipose tissue samples. Vaspin mRNA expression was not detectable in lean subjects (BMI<25) and was more frequently detected in patients with type 2 diabetes. No significant correlations were found between visceral vaspin gene expression and visceral fat area or SC vaspin expression. However, visceral vaspin expression significantly correlates with BMI, % body fat, and 2 h OGTT plasma glucose. Subcutaneous vaspin mRNA expression is significantly correlated with WHR, fasting plasma insulin concentration, and glucose infusion rate during steady state of an euglycemic-hyperinsulinemic clamp. Multivariate linear regression analysis revealed % body fat as strongest predictor of visceral vaspin and insulin sensitivity as strongest determinant of SC vaspin mRNA expression. In conclusion, our data indicate that induction of human vaspin mRNA expression in adipose tissue is regulated in a fat depot-specific manner and could be associated with parameters of obesity, insulin resistance, and glucose metabolism.     

Heritability and tissue specificity of expression quantitative trait loci

Variation in gene expression is heritable and has been mapped to the genome in humans and model organisms as expression quantitative trait loci (eQTLs). We applied integrated genome-wide expression profiling and linkage analysis to the regulation of gene expression in fat, kidney, adrenal, and heart tissues using the BXH/HXB panel of rat recombinant inbred strains. Here, we report the influence of heritability and allelic effect of the quantitative trait locus on detection of cis- and trans-acting eQTLs and discuss how these factors operate in a tissue-specific context. We identified several hundred major eQTLs in each tissue and found that cis-acting eQTLs are highly heritable and easier to detect than trans-eQTLs. The proportion of heritable expression traits was similar in all tissues; however, heritability alone was not a reliable predictor of whether an eQTL will be detected. We empirically show how the use of heritability as a filter reduces the ability to discover trans-eQTLs, particularly for eQTLs with small effects. Only 3% of cis- and trans-eQTLs exhibited large allelic effects, explaining more than 40% of the phenotypic variance, suggestive of a highly polygenic control of gene expression. Power calculations indicated that, across tissues, minor differences in genetic effects are expected to have a significant impact on detection of trans-eQTLs. Trans-eQTLs generally show smaller effects than cis-eQTLs and have a higher false discovery rate, particularly in more heterogeneous tissues, suggesting that small biological variability, likely relating to tissue composition, may influence detection of trans-eQTLs in this system. We delineate the effects of genetic architecture on variation in gene expression and show the sensitivity of this experimental design to tissue sampling variability in large-scale eQTL studies.     

Mapping quantitative trait loci for binary trait in the F2:3 design

In the analysis of inheritance of quantitative traits with low heritability, an F_2:3 design that genotypes plants in F₂ and phenotypes plants in F_2:3 progeny is often used in plant genetics. Although statistical approaches for mapping quantitative trait loci (QTL) in the F_2:3 design have been well developed, those for binary traits of biological interest and economic importance are seldom addressed. In this study, an attempt was made to map binary trait loci (BTL) in the F_2:3 design. The fundamental idea was: the F₂ plants were genotyped, all phenotypic values of each F_2:3 progeny were measured for binary trait, and these binary trait values and the marker genotype informations were used to detect BTL under the penetrance and liability models. The proposed method was verified by a series of Monte-Carlo simulation experiments. These results showed that maximum likelihood approaches under the penetrance and liability models provide accurate estimates for the effects and the locations of BTL with high statistical power, even under of low heritability. Moreover, the penetrance model is as efficient as the liability model, and the F_2:3 design is more efficient than classical F₂ design, even though only a single progeny is collected from each F_2:3 family. With the maximum likelihood approaches under the penetrance and the liability models developed in this study, we can map binary traits as we can do for quantitative trait in the F_2:3 design.     

Mapping and quantitative trait loci analysis of verticillium wilt resistance genes in cotton

Verticillium wilt is one of the most serious constraints to cotton production in almost all of the cotton-growing countries. In this study, ＂XinLuZaol＂ （XLZl）, a susceptible cultivar Gossypium hirsutum L. and ＂Hai7124＂ （H7124）, a resistant line G. barbadense, and their F2：3 families were used to map and study the disease index induced by verticillium wilt. A total of 430 SSR loci were mapped into 41 linkage groups; the map spanned 3 745.9 cM and the average distance between adjacent loci was 8.71 cM. Four and five quantitative trait loci （QTLs） were detected based on the disease index investigated on July 22 and August 24 in 2004, respectively. These nine QTLs explained 10.63-28.83% of the phenotypic variance, six of them were located on the D sub-genome. Two QTLs located in the same marker intervals may partly explain the significant correlation of the two traits. QTLs explaining large phenotypic variation were identified in this study, which may be quite useful in cotton anti-disease breeding.     

Mapping quantitative trait loci for cytokines in the pig

Increased disease resistance through improved general immune capacity would be beneficial for the welfare and productivity of farm animals. Cytokines are essential diagnostic parameters in veterinary practice. To identify quantitative trait loci (QTL) for cytokine levels in serum in the pig, Interferon‐gamma (IFN‐γ) and Interleukin 10 (IL‐10) levels and the ratio of IFN‐γ to IL‐10 were measured in a composite pig population, before and after challenge with modified live CSF (classical swine fever) vaccine. Through interval mapping using the variance component approach and the permutation test, 11 QTL (five for IFN‐γ, two for IL‐10 and four for the ratio of IFN‐γ to IL‐10) with significance levels of P < 0.10 were identified, of which five were significant at the P < 0.05 level. The most significant QTL (P < 0.01) was found on chromosome 16, with effect on the ratio of IFN‐γ to IL‐10. Within these QTL regions, a number of known genes were revealed and their potential relationships to the studied traits were discussed. Some of these genes may serve as candidate genes for these traits in swine.     

Mapping quantitative traits and strategies to find quantitative trait genes

In 1999 a meeting took place at the Jackson Laboratory, a large mouse research centre in Bar Harbor, Maine, to consider the value of systematically collecting phenotypes on inbred strains of mice (Paigen and Eppig (2000) [1]). The group concluded that cataloguing the extensive phenotypic diversity present among laboratory mice, and in particular providing the research community with data from cohorts of animals, phenotyped according to standardized protocols, was essential if we were to take advantage of the possibilities of mouse genetics. Beginning with the collection of basic physiological, biochemical and behavioral data on nine commonly used inbred strains, the project has expanded so that by the beginning of 2010 data for 178 strains had been collected, with 105 phenotype projects yielding over 2000 different measurements (Bogue et al. (2007) [2].     

Validation of endogenous control genes in human adipose tissue: relevance to obesity and obesity-associated type 2 diabetes mellitus.

The aim of the present study was to test the influence of obesity and the presence of type 2 diabetes mellitus (T2DM) on the expression of ten housekeeping genes and of the 18S rRNA in a group of human adipose tissue samples from the omental and subcutaneous depot. Adipose tissue biopsies were obtained by laparoscopic surgery from lean and obese patients. After the extraction, mRNA levels in adipose tissue samples were quantified by real-time PCR using the commercial HUMAN ENDOGENOUS CONTROL PLATES. From the genes analyzed, 18S rRNA exhibited the most stable expression levels in both depots regardless of the pathophysiological conditions of obesity and obesity-associated T2DM. Contrarily, GAPD was the gene with the highest variation in its expression levels, being upregulated (8.0-fold) in the obese group and downregulated (3.5-fold) in obesity-associated T2DM. Our results show that 18S rRNA may be the most suitable gene for normalization in expression studies performed in human adipose tissue samples obtained from patients suffering from obesity and/or obesity-associated T2DM, whereas GAPD is less appropriate for comparison purposes under these circumstances.     

Mapping quantitative trait loci for longitudinal traits in line crosses

Quantitative traits whose phenotypic values change over time are called longitudinal traits. Genetic analyses of longitudinal traits can be conducted using any of the following approaches: (1) treating the phenotypic values at different time points as repeated measurements of the same trait and analyzing the trait under the repeated measurements framework, (2) treating the phenotypes measured from different time points as different traits and analyzing the traits jointly on the basis of the theory of multivariate analysis, and (3) fitting a growth curve to the phenotypic values across time points and analyzing the fitted parameters of the growth trajectory under the theory of multivariate analysis. The third approach has been used in QTL mapping for longitudinal traits by fitting the data to a logistic growth trajectory. This approach applies only to the particular S-shaped growth process. In practice, a longitudinal trait may show a trajectory of any shape. We demonstrate that one can describe a longitudinal trait with orthogonal polynomials, which are sufficiently general for fitting any shaped curve. We develop a mixed-model methodology for QTL mapping of longitudinal traits and a maximum-likelihood method for parameter estimation and statistical tests. The expectation-maximization (EM) algorithm is applied to search for the maximum-likelihood estimates of parameters. The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees.     

Mapping of quantitative trait loci associated with protein expression variation in barley grains

Barley (Hordeum vulgare) is an important cereal crop grown for both the feed and malting industries. Hence, there is great interest to gain deeper insight into the determinants of grain nutritional quality in order to improve the assessment of new traits. Two-dimensional gel electrophoresis was employed for the characterization of the grain proteome of doubled-haploid introgression lines (IL) representing a wild barley genome (Hordeum spontaneum Hs213) within a modern cultivar background (H. vulgare cv. Brenda). Proteome maps were subjected to differential cluster analysis and revealed ILs with similar or different protein expression patterns compared to the Brenda parent. A total of 51 quantitative trait loci for protein expression (pQTL) were detected, and proteins underlying these pQTL were further examined by mass spectrometry. Identification was successful for 49 of the segregating spots and functional annotation of proteins revealed that most proteins are involved in metabolism and disease/defence-related processes. Among those, multigene families of glyceraldehyde-3-phosphate dehydrogenases, heat shock proteins, peroxidases, and serpins were identified. Overall, eight pQTL signals were discovered in two independently grown sets of plants. The mapped spots included protein disulfide isomerase, α-amylase inhibitor BDAI, NADP malic enzyme, adenosine kinase and peroxidase BP1. Specific marker information of proteins involved in developmental events and protein storage as well as in disease- and defence-related processes now allows for targeted breeding approaches to improve the grain quality in barley.     

Mapping and analysis of quantitative trait loci in experimental populations

Simple statistical methods for the study of quantitative trait loci (QTL), such as analysis of variance, have given way to methods that involve several markers and high-resolution genetic maps. As a result, the mapping community has been provided with statistical and computational tools that have much greater power than ever before for studying and locating multiple and interacting QTL. Apart from their immediate practical applications, the lessons learnt from this evolution of QTL methodology might also be generally relevant to other types of functional genomics approach that are aimed at the dissection of complex phenotypes, such as microarray assessment of gene expression.     

Mapping of quantitative trait loci for oil content in cottonseed kernel

Oil content in cottonseed is a major quality trait which when improved through breeding could enhance the competitiveness of cottonseed oil among other vegetable oils. Cottonseed oil content is a quantitative trait controlled by genes in the tetraploid embryo and tetraploid maternal plant genomes, and the knowledge of quantitative trait loci (QTLs) and the genetic effects related to oil content in both genomes could facilitate the improvement in its quality and quantity. However, till date, QTL mapping and genetic analysis related to this trait in cotton have only been conducted in the tetraploid embryo genome. In the current experiment, an IF₂ population of cottonseed kernels from the random crossing of 188 intraspecific recombinant inbred lines which were derived from the hybrid of two parents, HS46 and MARCABUCAG8US-1-88, were used to simultaneously locate QTLs for oil content in the embryo and maternal plant genomes. The four QTLs found to be associated with oil content in cottonseed were: qOC-18-1 on chromosome 18; qOC-LG-11 on linkage group 11; qOC-18-2 on chromosome 18; and qOC-22 on chromosome 22. At a high selection threshold of 0.05, there was strong evidence linking the QTLs above the oil content in cottonseed. Embryo additive and dominant effects from the tetraploid embryo genome, as well as maternal additive effects from the tetraploid maternal plant genome were found to be significant contributors to genetic variation in cottonseed oil content.     

Mapping quantitative trait loci for downy mildew resistance in pearl millet

Quantitative trait loci (QTLs) for resistance to pathogen populations of Scelerospora graminicola from India, Nigeria, Niger and Senegal were mapped using a resistant x susceptible pearl millet cross. An RFLP map constructed using F₂ plants was used to map QTLs for traits scored on F₄ families. QTL analysis was carried out using the interval mapping programme Mapmaker/QTL. Independent inheritance of resistance to pathogen populations from India, Senegal, and populations from Niger and Nigeria was shown. These results demonstrate the existence of differing virulences in the pathogen populations from within Africa and between Africa and India. QTLs of large effect, contributing towards a large porportion of the variation in resistance, were consistently detected in repeated screens. QTLs of smaller and more variable effect were also detected. There was no QTLs that were effective against all four pathogen populations, demonstrating that pathotype-specific resistance is a major mechanism of downy mildew resistance in this cross. For all but one of the QTLs, resistance was inherited from the resistant parent and the inheritance of resistance tended to be the result of dominance or over-dominance. The implications of this research for pearl millet breeding are discussed.     

Mapping quantitative trait loci for preharvest sprouting resistance in white wheat

The premature germination of seeds before harvest, known as preharvest sprouting (PHS), is a serious problem in all wheat growing regions of the world. In order to determine genetic control of PHS resistance in white wheat from the relatively uncharacterized North American germplasm, a doubled haploid population consisting of 209 lines from a cross between the PHS resistant variety Cayuga and the PHS susceptible variety Caledonia was used for QTL mapping. A total of 16 environments were used to detect 15 different PHS QTL including a major QTL, QPhs.cnl-2B.1, that was significant in all environments tested and explained from 5 to 31% of the trait variation in a given environment. Three other QTL QPhs.cnl-2D.1, QPhs.cnl-3D.1, and QPhs.cnl-6D.1 were detected in six, four, and ten environments, respectively. The potentially related traits of heading date (HD), plant height (HT), seed dormancy (DOR), and rate of germination (ROG) were also recorded in a limited number of environments. HD was found to be significantly negatively correlated with PHS score in most environments, likely due to a major HD QTL, QHd.cnl-2B.1, found to be tightly linked to the PHS QTL QPhs.cnl-2B.1. Using greenhouse grown material no overlap was found between seed dormancy and the four most consistent PHS QTL, suggesting that greenhouse environments are not representative of field environments. This study provides valuable information for marker-assisted breeding for PHS resistance, future haplotyping studies, and research into seed dormancy.     

Mapping quantitative trait loci for seedling vigor in rice using RFLPs

Improving seedling vigor is an important objective of modern rice (Oryza saliva L.) breeding programs. The purpose of this study was to identify and map quantitative trait loci (QTL) underlying seedling vigor-related traits using restriction fragment length polymorphisms (RFLPs). An F₂ population of 204 plants was developed from a cross between a low-vigor japonica cultivar Labelle (LBL) and a high-vigor indica cultivar Black Gora (BG). A linkage map was constructed of 117 markers spanning 1496 Haldane cM and encompassing the 12 rice chromosomes with an average marker spacing of 14 cM. The length of the shoots, roots, coleoptile and mesocotyl were measured on F₃ families in slantboard tests conducted at two temperatures (18° and 25°C). By means of interval analysis, 13 QTLs, each accounting for 7% to 38% of the phenotypic variance, were identified and mapped in the two temperature regimes at a log-likelihood (LOD) threshold of 2.5. Four QTLs controlled shoot length, 2 each controlled root and coleoptile lengths and 5 influenced mesocotyl length. Single-point analysis confirmed the presence of these QTLs and detected additional loci for shoot, root and coleoptile lengths, these latter usually accounting for less than 5% of the phenotypic variation. Only 3 QTLs detected both by interval and singlepoint analyses were expressed under both temperature regimes. Additive, dominant and overdominant modes of gene action were observed. Contrary to what was predicted from parental phenotype, the low-vigor LBL contributed 46% of the positive alleles for shoot, root and coleoptile lengths. Positive alleles from the high-vigor parent BG were identified for increased root, coleoptile and mesocotyl lengths. However, BG contributed alleles with only minor effects for shoot length, the most important determinant of seedling vigor in water-seeded rice, suggesting that it would not be an ideal donor parent for introducing faster shoot growth alleles into temperate japonica cultivars.     

Mapping quantitative trait loci for tissue culture response in VCS3M-DH population of Brassica rapa

Key message

Quantitative trait loci (QTL) controlling callus induction and plant regeneration were identified in the VCS3M-DH population of Brassica rapa.

Abstract

Quantitative trait loci (QTL) controlling callus induction and plant regeneration were identified in the VCS3M-DH population of Brassica rapa. The VCS3M-DH population showed wide and continuous variation in callus induction and shoot regeneration. Significant coefficient correlations were detected between these two parameters. Broad-sense heritability (h ²) for the two traits was around 0.7, indicating genetic regulation of regeneration ability in this population. In the composite interval mapping analysis, two QTLs for callus induction ability, qCi2 and qCi7, were mapped on chromosome A02 and A07, explaining 28.6 % of phenotypic variation. For plant regeneration, four QTLs, qPr6-1 qPr6-2, qPr7, and qPr9 were identified on chromosome A06, A07, and A09, which in total explained 50.1 % of phenotypic variation. Furthermore, 15 putative candidate genes were found on the interval of the six QTLs, which were related to various plant hormones, MADS-box genes, and serine/threonine related genes. These results provide important information to identify genes related to tissue culture ability in B. rapa.     

Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis

Background  

The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci (eQTL). To date, most studies have searched for eQTL by analyzing gene expression traits one at a time. As thousands of expression traits are typically analyzed, this can reduce power because of the need to correct for the number of hypothesis tests performed. In addition, gene expression traits exhibit a complex correlation structure, which is ignored when analyzing traits individually.     

Mapping quantitative trait loci in F2 incorporating phenotypes of F3 progeny

In plants and laboratory animals, QTL mapping is commonly performed using F(2) or BC individuals derived from the cross of two inbred lines. Typical QTL mapping statistics assume that each F(2) individual is genotyped for the markers and phenotyped for the trait. For plant traits with low heritability, it has been suggested to use the average phenotypic values of F(3) progeny derived from selfing F(2) plants in place of the F(2) phenotype itself. All F(3) progeny derived from the same F(2) plant belong to the same F(2:3) family, denoted by F(2:3). If the size of each F(2:3) family (the number of F(3) progeny) is sufficiently large, the average value of the family will represent the genotypic value of the F(2) plant, and thus the power of QTL mapping may be significantly increased. The strategy of using F(2) marker genotypes and F(3) average phenotypes for QTL mapping in plants is quite similar to the daughter design of QTL mapping in dairy cattle. We study the fundamental principle of the plant version of the daughter design and develop a new statistical method to map QTL under this F(2:3) strategy. We also propose to combine both the F(2) phenotypes and the F(2:3) average phenotypes to further increase the power of QTL mapping. The statistical method developed in this study differs from published ones in that the new method fully takes advantage of the mixture distribution for F(2:3) families of heterozygous F(2) plants. Incorporation of this new information has significantly increased the statistical power of QTL detection relative to the classical F(2) design, even if only a single F(3) progeny is collected from each F(2:3) family. The mixture model is developed on the basis of a single-QTL model and implemented via the EM algorithm. Substantial computer simulation was conducted to demonstrate the improved efficiency of the mixture model. Extension of the mixture model to multiple QTL analysis is developed using a Bayesian approach. The computer program performing the Bayesian analysis of the simulated data is available to users for real data analysis.