Quantitative trait locus mapping for atherosclerosis susceptibility

PURPOSE OF REVIEW: Atherosclerosis is a complex trait with both environmental and genetic aspects. Although some progress has been made in defining genes associated with atherosclerosis in humans, animal models have been useful in learning about pathways and genes involved in atherogenesis. This review describes an unbiased genetic mapping method called quantitative trait locus mapping and progress in using this method to identify genes that alter atherosclerosis susceptibility in mice. RECENT FINDINGS: Approximately 10 well defined genetic loci have been described that are associated with lesion severity in diet-induced or gene knockout mouse models of atherosclerosis. Recently, two of these genetic loci were narrowed considerably by analysis of genetic recombinants within these loci. In addition, a computational method to discover quantitative trait loci has been applied to atherosclerosis. However, none of the genes responsible for these atherosclerosis quantitative trait loci has been definitively identified. The recent completion of the mouse draft genome should facilitate the task of identifying these genes. SUMMARY: Quantitative trait locus mapping studies in mouse models of atherosclerosis have defined genetic regions that alter lesion severity. The identification of the responsible genes may lead to insights into the pathogenesis of atherosclerosis as well as to candidates for human genetic association studies.     

A Major Genetic Locus in Trypanosoma brucei Is a Determinant of Host Pathology

The progression and variation of pathology during infections can be due to components from both host or pathogen, and/or the interaction between them. The influence of host genetic variation on disease pathology during infections with trypanosomes has been well studied in recent years, but the role of parasite genetic variation has not been extensively studied. We have shown that there is parasite strain-specific variation in the level of splenomegaly and hepatomegaly in infected mice and used a forward genetic approach to identify the parasite loci that determine this variation. This approach allowed us to dissect and identify the parasite loci that determine the complex phenotypes induced by infection. Using the available trypanosome genetic map, a major quantitative trait locus (QTL) was identified on T. brucei chromosome 3 (LOD = 7.2) that accounted for approximately two thirds of the variance observed in each of two correlated phenotypes, splenomegaly and hepatomegaly, in the infected mice (named TbOrg1). In addition, a second locus was identified that contributed to splenomegaly, hepatomegaly and reticulocytosis (TbOrg2). This is the first use of quantitative trait locus mapping in a diploid protozoan and shows that there are trypanosome genes that directly contribute to the progression of pathology during infections and, therefore, that parasite genetic variation can be a critical factor in disease outcome. The identification of parasite loci is a first step towards identifying the genes that are responsible for these important traits and shows the power of genetic analysis as a tool for dissecting complex quantitative phenotypic traits.     

Detection of loci for allergic asthma using SMXA recombinant inbred strains of mice

Asthma is regarded as a multifactorial inflammatory disorder arising as a result of inappropriate immune responses in genetically susceptible individuals to common environmental antigens. However, the precise molecular basis is unknown. To identify genes for susceptibility to three asthma-related traits, airway hyperresponsiveness (AHR), eosinophil infiltration, and allergen-specific serum IgE levels, we conducted a genetic analysis using SMXA recombinant inbred (RI) strains of mice. Quantitative trait locus analysis detected a significant locus for AHR on chromosome 17. For eosinophil infiltration, significant loci were detected on chromosomes 9 and 16. Although we could not detect any significant loci for allergen-specific serum IgE, analysis of consomic strains showed that chromosomes 17 and 19 carried genes that affected this trait. We detected genetic susceptibility loci that separately regulated the three asthma-related phenotypes. Our results suggested that different genetic mechanisms regulate these asthma-related phenotypes. Genetic analyses using murine RI and consomic strains enhance understanding of the molecular mechanisms of asthma in human.     

Linkage analysis of quantitative trait loci in the presence of heterogeneity

Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.     

Quantitative trait locus on 15q for a metabolic syndrome variable derived from factor analysis

The metabolic syndrome represents a cluster of cardiovascular risk factors co-occurring in the same individual. The aim of this study was to identify chromosomal regions encoding genes predisposing to the metabolic syndrome using composite factors derived from maximum likelihood-based factor analysis. Genetic data were obtained from the Quebec Family Study and included 707 subjects from 264 nuclear families. Factor analyses were performed on eight metabolic syndrome-related phenotypes including waist circumference; BMI; systolic and diastolic blood pressure; and plasma insulin, glucose, triglyceride, and high-density lipoprotein-cholesterol levels. Three factors were identified and interpreted as general metabolic syndrome, blood pressure, and blood lipids, respectively. The general metabolic syndrome factor had high factor loadings (>0.4) for all phenotypes and explained 42% of the total variance, and family membership accounted for 45.6% of the factor variance. A genome-wide linkage scan performed with this first factor revealed the existence of a quantitative trait locus on chromosome 15 (86 cM) with a logarithm of odds score of 3.15. Suggestive evidence of linkage (logarithm of odds > 1.75) was also observed on chromosomes 1p, 3p, 3q, 6q, 7p, 19q, and 21q. These quantitative trait loci may harbor genes contributing to the clustering of the metabolic syndrome-related phenotypes.     

Genome-wide association: a promising start to a long race

A recent study by Cheung et al. demonstrates how to identify expression quantitative trait loci (eQTLs) underlying gene expression phenotypes through a combination of genome-wide linkage analysis and subsequent fine mapping or by genome-wide association (GWA) analysis. This study emphasizes the complexity of human traits, highlighting the challenges faced by investigators--in particular, insufficient linkage disequilibrium between the trait and marker variant, genetic heterogeneity and correcting for multiple testing will all adversely impact the power to detect loci by association. These issues must be considered carefully if the GWA approach is to succeed in mapping complex phenotypes.     

Susceptibility to neoplastic and non-neoplastic pulmonary diseases in mice: genetic similarities

Chronic inflammation predisposes toward many types of cancer. Chronic bronchitis and asthma, for example, heighten the risk of lung cancer. Exactly which inflammatory mediators (e.g., oxidant species and growth factors) and lung wound repair processes (e.g., proangiogenic factors) enhance pulmonary neoplastic development is not clear. One approach to uncover the most relevant biochemical and physiological pathways is to identify genes underlying susceptibilities to inflammation and to cancer development at the same anatomic site. Mice develop lung adenocarcinomas similar in histology, molecular characteristics, and histogenesis to this most common human lung cancer subtype. Over two dozen loci, called Pas or pulmonary adenoma susceptibility, Par or pulmonary adenoma resistance, and Sluc or susceptibility to lung cancer genes, regulate differential lung tumor susceptibility among inbred mouse strains as assigned by QTL (quantitative trait locus) mapping. Chromosomal sites that determine responsiveness to proinflammatory pneumotoxicants such as ozone (O3), particulates, and hyperoxia have also been mapped in mice. For example, susceptibility QTLs have been identified on chromosomes 17 and 11 for O3-induced inflammation (Inf1, Inf2), O3-induced acute lung injury (Aliq3, Aliq1), and sulfate-associated particulates. Sites within the human and mouse genomes for asthma and COPD phenotypes have also been delineated. It is of great interest that several susceptibility loci for mouse lung neoplasia also contain susceptibility genes for toxicant-induced lung injury and inflammation and are homologous to several human asthma loci. These QTLs are described herein, candidate genes are suggested within these sites, and experimental evidence that inflammation enhances lung tumor development is provided.     

Subsets of Finns with high HDL to total cholesterol ratio show evidence for linkage to type 2 diabetes on chromosome 6q

OBJECTIVES: The purpose of this study was to examine carefully heterogeneity underlying evidence for linkage to type 2 diabetes (T2DM) on chromosome 6q from two sets of FUSION families. METHODS: Ordered subsets analysis (OSA) was performed on two sets of FUSION families. For OSA results showing significant improvement in evidence for linkage, T2DM-related phenotypes were compared between individuals with T2DM within the subset versus the complement. RESULTS: OSA analysis revealed 105 families with the highest average HDL to total cholesterol ratio (HDL ratio) that had strongly increased evidence for linkage (MLS = 7.91 at 78.0 cM; uncorrected p = 0.00002). Subjects with T2DM within this subset were significantly leaner, had lower fasting glucose, insulin, and C-peptide, and more favorable cardiovascular risk profile compared to the complement set of subjects with T2DM. OSA also revealed 33 families with the lowest average fasting insulin that had increased evidence for linkage at a second locus (MLS = 3.45 at 128 cM; uncorrected p = 0.017) coincident with quantitative trait locus linkage analysis results for fasting and 2-hour insulin in subjects without T2DM. CONCLUSIONS: These results suggest two diabetes susceptibility loci on chromosome 6q that may affect subsets of individuals with a milder form of T2DM.     

Genetic studies in rat models: insights into cardiovascular disease

PURPOSE OF REVIEW: Limited to 2003-2004 publications, this review focuses on 'big picture' concepts learned from rat genetic studies of cardiovascular disease. RECENT DEVELOPMENTS: Analysis reveals insights into pathogenic paradigms, as well as experimental perspectives into rat-based systems of analyses of complex cardiovascular disease. Key concepts are forwarded. Multiple susceptibility genes underlie several quantitative trait loci for blood pressure suggesting a 'quantitative trait loci cluster' concept; hypertension end-organ disease quantitative trait loci are distinct from blood pressure quantitative trait loci indicating differential susceptibility paradigms for hypertension and each complication (stroke, renal disease, cardiac hypertrophy); distinct blood pressure quantitative trait loci are found in males and females indicating gender-specific susceptibility; and genetic subtypes comprise polygenic hypertension in rat models suggesting a genetic basis for clinical heterogeneity of human essential hypertension. Gender specific genetic susceptibility plays a key role in coronary artery disease susceptibility; multiple distinct quantitative trait loci underlie hyperlipidemia and type-2 diabetes, indicating multiple susceptibilities in risk factors for cardiovascular disease. Studies in transgenic inbred rat-strain models demonstrate value for serial, complex, cardiovascular pathophysiological analyses within a genetic context. SUMMARY: Cognizant of the limitations of animal model studies, observations from rat genetic studies provide insight into respective modeled human cardiovascular diseases and risk factor susceptibility, as well as systematically dissect the multifaceted complexities apparent in human complex cardiovascular disease. Given the recapitulation of many features of human cardiovascular disease, the value of rat model-based genetic studies for complex cardiovascular disease is unequivocal, thus mandating the expansion of resources for maximization of rat-based genetic studies.     

Phenotypic variability in X-linked ocular albinism: relationship to linkage genotypes.

One hundred nineteen individuals from 11 families with X-linked ocular albinism (OA1) were studied with respect to both their clinical phenotypes and their linkage genotypes. In a four-generation Australian family, two affected males and an obligatory carrier lacked cutaneous melanin macroglobules (MMGs); ocular features were identical to those of Nettleship-Falls OA1. Four other families had more unusual phenotypic features in addition to OA1. All OA1 families were genotyped at DXS16, DXS85, DXS143, STS, and DXS452 and for a CA-repeat polymorphism at the Kallmann syndrome locus (KAL). Separate two-point linkage analyses were performed for the following: group A, six families with biopsy-proved MMGs in at least one affected male; group B, four families whose biopsy status was not known; and group C, OA-9 only (16 samples), the family without MMGs. At the set of loci closest to OA1, there is no clear evidence in our data set for locus heterogeneity between groups A and C or among the four other families with complex phenotypes. Combined multipoint analysis (LINKMAP) in the 11 families and analysis of individual recombination events confirms that the major locus for OA1 resides within the DXS85-DXS143 interval. We suggest that more detailed clinical evaluations of OA1 individuals and families should be performed for future correlation with specific mutations in candidate OA1 genes.     

A major susceptibility gene for asthma maps to chromosome 14q24

Asthma is a complex genetic disorder with a heterogeneous phenotype, largely attributed to the interactions among many genes and between these genes and the environment. Numerous loci and candidate genes have been reported to show linkage and association to asthma and atopy. Although some studies reporting these observations are compelling, no gene has been mapped that confers a sufficiently high risk of asthma to meet the stringent criteria for genomewide significance. Using 175 extended Icelandic families that included 596 patients with asthma, we performed a genomewide scan with 976 microsatellite markers. The families were identified by cross-matching a list of patients with asthma from the Department of Allergy/Pulmonary Medicine of the National University Hospital of Iceland with a genealogy database of the entire Icelandic nation. We detected linkage of asthma to chromosome 14q24, with an allele-sharing LOD score of 2.66. After we increased the marker density within the locus to an average of one microsatellite every 0.2 cM, the LOD score rose to 4.00. We designate this locus "asthma locus one" (AS1). Taken together, these results provide evidence of a novel susceptibility gene for asthma on chromosome 14q24.     

A stochastic expectation and maximization algorithm for detecting quantitative trait-associated genes

MOTIVATION: Most biological traits may be correlated with the underlying gene expression patterns that are partially determined by DNA sequence variation. The correlations between gene expressions and quantitative traits are essential for understanding the functions of genes and dissecting gene regulatory networks. RESULTS: In the present study, we adopted a novel statistical method, called the stochastic expectation and maximization (SEM) algorithm, to analyze the associations between gene expression levels and quantitative trait values and identify genetic loci controlling the gene expression variations. In the first step, gene expression levels measured from microarray experiments were assigned to two different clusters based on the strengths of their association with the phenotypes of a quantitative trait under investigation. In the second step, genes associated with the trait were mapped to genetic loci of the genome. Because gene expressions are quantitative, the genetic loci controlling the expression traits are called expression quantitative trait loci. We applied the same SEM algorithm to a real dataset collected from a barley genetic experiment with both quantitative traits and gene expression traits. For the first time, we identified genes associated with eight agronomy traits of barley. These genes were then mapped to seven chromosomes of the barley genome. The SEM algorithm and the result of the barley data analysis are useful to scientists in the areas of bioinformatics and plant breeding. Availability and implementation: The R program for the SEM algorithm can be downloaded from our website: http://www.statgen.ucr.edu.     

A score test for the linkage analysis of qualitative and quantitative traits based on identity by descent data from sib-pairs

We propose a general likelihood-based approach to the linkage analysis of qualitative and quantitative traits using identity by descent (IBD) data from sib-pairs. We consider the likelihood of IBD data conditional on phenotypes and test the null hypothesis of no linkage between a marker locus and a gene influencing the trait using a score test in the recombination fraction theta between the two loci. This method unifies the linkage analysis of qualitative and quantitative traits into a single inferential framework, yielding a simple and intuitive test statistic. Conditioning on phenotypes avoids unrealistic random sampling assumptions and allows sib-pairs from differing ascertainment mechanisms to be incorporated into a single likelihood analysis. In particular, it allows the selection of sib-pairs based on their trait values and the analysis of only those pairs having the most informative phenotypes. The score test is based on the full likelihood, i.e. the likelihood based on all phenotype data rather than just differences of sib-pair phenotypes. Considering only phenotype differences, as in Haseman and Elston (1972) and Kruglyak and Lander (1995), may result in important losses in power. The linkage score test is derived under general genetic models for the trait, which may include multiple unlinked genes. Population genetic assumptions, such as random mating or linkage equilibrium at the trait loci, are not required. This score test is thus particularly promising for the analysis of complex human traits. The score statistic readily extends to accommodate incomplete IBD data at the test locus, by using the hidden Markov model implemented in the programs MAPMAKER/SIBS and GENEHUNTER (Kruglyak and Lander, 1995; Kruglyak et al., 1996). Preliminary simulation studies indicate that the linkage score test generally matches or outperforms the Haseman-Elston test, the largest gains in power being for selected samples of sib-pairs with extreme phenotypes.     

The use of mouse models to unravel genetic architecture of physical activity: a review

The discovery of genetic variants that underlie a complex phenotype is challenging. One possible approach to facilitate this endeavor is to identify quantitative trait loci (QTL) that contribute to the phenotype and consequently unravel the candidate genes within these loci. Each proposed candidate locus contains multiple genes and, therefore, further analysis is required to choose plausible candidate genes. One of such methods is to use comparative genomics in order to narrow down the QTL to a region containing only a few genes. We illustrate this strategy by applying it to genetic findings regarding physical activity (PA) in mice and human. Here, we show that PA is a complex phenotype with a strong biological basis and complex genetic architecture. Furthermore, we provide considerations for the translatability of this phenotype between species. Finally, we review studies which point to candidate genetic regions for PA in humans (genetic association and linkage studies) or use mouse models of PA (QTL studies) and we identify candidate genetic regions that overlap between species. On the basis of a large variety of studies in mice and human, statistical analysis reveals that the number of overlapping regions is not higher than expected on a chance level. We conclude that the discovery of new candidate genes for complex phenotypes, such as PA levels, is hampered by various factors, including genetic background differences, phenotype definition and a wide variety of methodological differences between studies .     

Robust estimation of experimentwise P values applied to a genome scan of multiple asthma traits identifies a new region of significant linkage on chromosome 20q13

Over 30 genomic regions show linkage to asthma traits. Six asthma genes have been cloned, but the putative loci in many linked regions have not been identified. To search for asthma susceptibility loci, we performed genomewide univariate linkage analyses of seven asthma traits, using 202 Australian families ascertained through a twin proband. House-dust mite sensitivity (Dpter) exceeded the empirical threshold for significant linkage at 102 cM on chromosome 20q13, near marker D20S173 (empirical pointwise P = .00001 and genomewide P = .005, both uncorrected for multiple-trait testing). Atopy, bronchial hyperresponsiveness (BHR), and forced expiratory volume in 1 s (FEV1) were also linked to this region. In addition, 16 regions were linked to at least one trait at the suggestive level, including 12q24, which has consistently shown linkage to asthma traits in other studies. Some regions were expected to be false-positives arising from multiple-trait testing. To address this, we developed a new approach to estimate genomewide significance that accounts for multiple-trait testing and for correlation between traits and that does not require a Bonferroni correction. With this approach, Dpter remained significantly linked to 20q13 (empirical genomewide P = .042), and airway obstruction remained linked to 12q24 at the suggestive level. Finally, we extended this method to show that the linkage of Dpter, atopy, BHR, FEV1, asthma, and airway obstruction to chromosome 20q13 is unlikely to be due to chance and may result from a quantitative trait locus in this region that affects several of these traits.     

Linkage of interleukin 6 locus to human osteopenia by sibling pair analysis

Osteopenia and osteoporosis are common human conditions considered to result from the interplay of multiple genetic and environmental factors. Twin and family studies have yielded strong correlations between levels of bone mass and a number of genetic factors. The genes involved could regulate metabolism, formation and resorption of bone, all processes that determine bone mass. We tested 192 sibling pairs of adult Japanese women from 136 families for genetic linkage between osteopenia and allelic variants of four candidate genes (interleukin-6, interleukin-6 receptor, calcium-sensing receptor, and matrix gla protein) using qualitative and quantitative methods, and using as genetic markers dinucleotide-repeat polymorphisms present in or near each of those loci. The interleukin-6 locus showed evidence of linkage to osteopenia analyzed as a qualitative trait, with mean allele sharing of 0.40 (P=0.0001) in discordant pairs and 0.55 (P=0.04) in concordant affected pairs. Variation at this locus was also linked to decreased bone mineral density measured as a quantitative trait (P=0.02). Analyses limited only to the post-menopausal women showed similar or even stronger results. No other locus among those tested showed any evidence of linkage by either method. The results provided strong evidence that genetic variation at the interleukin-6 locus affects regulation of bone mineral metabolism and confers risk for osteopenia and osteoporosis in adult women.     

Dissecting complex physiological functions through the use of molecular quantitative genetics

Testing possible associations between physiological and biochemicaltraits by comparing plant phenotypes and looking for correlationsbetween them is unreliable. The development of molecular markertechnologies offers powerful alternative methods to examinethe relationships between traits. This review describes thegenetical methods required to analyse possible associationsbetween traits that are inherited in a quantitative manner usingquantitative trait locus (QTL) analysis. The regulation of carbohydratemetabolism is chosen as an example of how QTL analysis can beused to identify key control factors in a series of processes,by identifying possible candidate genes for QTL effects on sucroseand starch metabolism. Methods are also described to study theassociation between physiological traits such as abscislc acidconcentrations and stomata1 conductance. Advantages and somelimitations of QTL analysis over other methods currently inuse by physiologists to test associations between traits arediscussed. Key words: Candidate genes, genetic maps, molecular markers, quantitative trait locus (QTL) analysis, physiological traits     

Identification of RAPD markers linked to genetic factors controlling the milling energy requirement of barley

Doubled haploid (DH) populations of barley have been used in combination with PCR-based polymorphic-assay procedures to identify molecular markers linked to genes controlling the milling energy requirement of the grain. Milling energy (ME) is a quantitative trait and locating individual quantitative trait loci (QTLs) involved the construction of bulks by combining DNA from DH families representing the extreme members of the distribution for ME. In addition, the individuals had alternative alleles at theRrn2 locus that has previously been shown to be linked to an ME QTL. The DNA bulks were screened with Randomly Amplified Polymorphic DNA (RAPD) markers and polymorphic amplification products tested for linkage to genes influencing the expression of ME in a DH population. Several markers were identified which are linked to a QTL controlling ME and the recombination fraction determined by maximum likelihood procedures. The results indicate that DHs in combination with RAPDs and bulked segregant analysis provide an efficient method for locating QTLs in barely. Furthermore, this approach is applicable to mapping other QTLs in a range of organisms from which DH or recombinant inbred lines can be extracted.