Functional screening of an asthma QTL in YAC transgenic mice.

Many quantitative trait loci (QTLs) contributing to genetically complex conditions have been discovered, but few causative genes have been identified. This is mainly due to the large size of QTLs and the subtle connection between genotype and quantitative phenotype associated with these conditions. Transgenic mice have been successfully used to analyse well-characterized genes suspected of contributing to quantitative traits. Although this approach is powerful for examining one gene at a time, it can be impractical for surveying the large genomic intervals containing many genes that are typically associated with QTLs. To screen for genes contributing to an asthma QTL mapped to human chromosome 5q3 (refs 6,7), we characterized a panel of large-insert 5q31 transgenics based on studies demonstrating that altering gene dosage frequently affects quantitative phenotypes normally influenced by that gene. This panel of human YAC transgenics, propagating a 1-Mb interval of chromosome 5q31 containing 6 cytokine genes and 17 partially characterized genes, was screened for quantitative changes in several asthma-associated phenotypes. Multiple independent transgenic lines with altered IgE response to antigen treatment shared a 180-kb region containing 5 genes, including those encoding human interleukin 4 (IL4) and interleukin 13 (IL13 ), which induce IgE class switching in B cells. Further analysis of these mice and mice transgenic for mouse Il4 and Il13 demonstrated that moderate changes in Il4 and Il13 expression affect asthma-associated phenotypes in vivo. This functional screen of large-insert transgenics enabled us to identify genes that influence the QTL phenotype in vivo.     

High-resolution mapping of quantitative trait loci in outbred mice

Screening the whole genome of a cross between two inbred animal strains has proved to be a powerful method for detecting genetic loci underlying quantitative behavioural traits, but the level of resolution offered by quantitative trait loci (QTL) mapping is still too coarse to permit molecular cloning of the genetic determinants. To achieve high-resolution mapping, we used an outbred stock of mice for which the entire genealogy is known. The heterogeneous stock (HS) was established 30 years ago from an eight-way cross of C57BL/6, BALB/c, RIII, AKR, DBA/2, I, A/J and C3H inbred mouse strains. At the time of the experiment reported here, the HS mice were at generation 58, theoretically offering at least a 30-fold increase in resolution for QTL mapping compared with a backcross or an F2 intercross. Using the HS mice we have mapped a QTL influencing a psychological trait in mice to a 0.8-cM interval on chromosome 1. This method allows simultaneous fine mapping of multiple QTLs, as shown by our report of a second QTL on chromosome 12. The high resolution possible with this approach makes QTLs accessible to positional cloning.     

Analysing complex genetic traits with chromosome substitution strains

Many valuable animal models of human disease are known and new models are continually being generated in existing inbred strains,. Some disease models are simple mendelian traits, but most have a polygenic basis. The current approach to identifying quantitative trait loci (QTLs) that underlie such traits is to localize them in crosses, construct congenic strains carrying individual QTLs, and finally map and clone the genes. This process is time-consuming and expensive, requiring the genotyping of large crosses and many generations of breeding. Here we describe a different approach in which a panel of chromosome substitution strains (CSSs) is used for QTL mapping. Each of these strains has a single chromosome from the donor strain substituting for the corresponding chromosome in the host strain. We discuss the construction, applications and advantages of CSSs compared with conventional crosses for detecting and analysing QTLs, including those that have weak phenotypic effects.     

A rice quantitative trait locus for salt tolerance encodes a sodium transporter

Many important agronomic traits in crop plants, including stress tolerance, are complex traits controlled by quantitative trait loci (QTLs). Isolation of these QTLs holds great promise to improve world agriculture but is a challenging task. We previously mapped a rice QTL, SKC1, that maintained K(+) homeostasis in the salt-tolerant variety under salt stress, consistent with the earlier finding that K(+) homeostasis is important in salt tolerance. To understand the molecular basis of this QTL, we isolated the SKC1 gene by map-based cloning and found that it encoded a member of HKT-type transporters. SKC1 is preferentially expressed in the parenchyma cells surrounding the xylem vessels. Voltage-clamp analysis showed that SKC1 protein functions as a Na(+)-selective transporter. Physiological analysis suggested that SKC1 is involved in regulating K(+)/Na(+) homeostasis under salt stress, providing a potential tool for improving salt tolerance in crops.     

A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.     

Distribution and functional impact of DNA copy number variation in the rat

The abundance and dynamics of copy number variants (CNVs) in mammalian genomes poses new challenges in the identification of their impact on natural and disease phenotypes. We used computational and experimental methods to catalog CNVs in rat and found that they share important functional characteristics with those in human. In addition, 113 one-to-one orthologous genes overlap CNVs in both human and rat, 80 of which are implicated in human disease. CNVs are nonrandomly distributed throughout the genome. Chromosome 18 is a cold spot for CNVs as well as evolutionary rearrangements and segmental duplications, suggesting stringent selective mechanisms underlying CNV genesis or maintenance. By exploiting gene expression data available for rat recombinant inbred lines, we established the functional relationship of CNVs underlying 22 expression quantitative trait loci. These characteristics make the rat an excellent model for studying phenotypic effects of structural variation in relation to human complex traits and disease.     

Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population

Nested association mapping (NAM) offers power to resolve complex, quantitative traits to their causal loci. The maize NAM population, consisting of 5,000 recombinant inbred lines (RILs) from 25 families representing the global diversity of maize, was evaluated for resistance to southern leaf blight (SLB) disease. Joint-linkage analysis identified 32 quantitative trait loci (QTLs) with predominantly small, additive effects on SLB resistance. Genome-wide association tests of maize HapMap SNPs were conducted by imputing founder SNP genotypes onto the NAM RILs. SNPs both within and outside of QTL intervals were associated with variation for SLB resistance. Many of these SNPs were within or near sequences homologous to genes previously shown to be involved in plant disease resistance. Limited linkage disequilibrium was observed around some SNPs associated with SLB resistance, indicating that the maize NAM population enables high-resolution mapping of some genome regions.     

Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation

Allele-specific DNA methylation (ASM) is a hallmark of imprinted genes, but ASM in the larger nonimprinted fraction of the genome is less well characterized. Using methylation-sensitive SNP analysis (MSNP), we surveyed the human genome at 50K and 250K resolution, identifying ASM as recurrent genotype call conversions from heterozygosity to homozygosity when genomic DNAs were predigested with the methylation-sensitive restriction enzyme HpaII. Using independent assays, we confirmed ASM at 16 SNP-tagged loci distributed across various chromosomes. At 12 of these loci (75%), the ASM tracked strongly with the sequence of adjacent SNPs. Further analysis showed allele-specific mRNA expression at two loci from this methylation-based screen--the vanin and CYP2A6-CYP2A7 gene clusters--both implicated in traits of medical importance. This recurrent phenomenon of sequence-dependent ASM has practical implications for mapping and interpreting associations of noncoding SNPs and haplotypes with human phenotypes.     

The roads from phenotypic variation to gene discovery: mutagenesis versus QTLs

In model organisms, chemical mutagenesis provides a powerful alternative to natural, polygenic variation (for example, quantitative trait loci (QTLs)) for identifying functional pathways and complex disease genes. Despite recent progress in QTLs, we expect that mutagenesis is will ultimately prove more effective because the prospects of gene identification are high and every gene affecting a trait is potentially a target.     

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.     

Candidate lung tumor susceptibility genes identified through whole-genome association analyses in inbred mice

We performed a whole-genome association analysis of lung tumor susceptibility using dense SNP maps ( approximately 1 SNP per 20 kb) in inbred mice. We reproduced the pulmonary adenoma susceptibility 1 (Pas1) locus identified in previous linkage studies and further narrowed this quantitative trait locus (QTL) to a region of less than 0.5 Mb in which at least two genes, Kras2 (Kirsten rat sarcoma oncogene 2) and Casc1 (cancer susceptibility candidate 1; also known as Las1), are strong candidates. Casc1 knockout mouse tumor bioassays showed that Casc1-deficient mice were susceptible to chemical induction of lung tumors. We also found three more genetic loci for lung adenoma development. Analysis of one of these candidate loci identified a previously uncharacterized gene Lasc1, bearing a nonsynonymous substitution (D102E). We found that the Lasc1 Glu102 allele preferentially promotes lung tumor cell growth. Our findings demonstrate the prospects for using dense SNP maps in laboratory mice to refine previous QTL regions and identify genetic determinants of complex traits.     

Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease

Integration of genome-wide expression profiling with linkage analysis is a new approach to identifying genes underlying complex traits. We applied this approach to the regulation of gene expression in the BXH/HXB panel of rat recombinant inbred strains, one of the largest available rodent recombinant inbred panels and a leading resource for genetic analysis of the highly prevalent metabolic syndrome. In two tissues important to the pathogenesis of the metabolic syndrome, we mapped cis- and trans-regulatory control elements for expression of thousands of genes across the genome. Many of the most highly linked expression quantitative trait loci are regulated in cis, are inherited essentially as monogenic traits and are good candidate genes for previously mapped physiological quantitative trait loci in the rat. By comparative mapping we generated a data set of 73 candidate genes for hypertension that merit testing in human populations. Mining of this publicly available data set is expected to lead to new insights into the genes and regulatory pathways underlying the extensive range of metabolic and cardiovascular disease phenotypes that segregate in these recombinant inbred strains.     

Common genetic variants account for differences in gene expression among ethnic groups

Variation in DNA sequence contributes to individual differences in quantitative traits, but in humans the specific sequence variants are known for very few traits. We characterized variation in gene expression in cells from individuals belonging to three major population groups. This quantitative phenotype differs significantly between European-derived and Asian-derived populations for 1,097 of 4,197 genes tested. For the phenotypes with the strongest evidence of cis determinants, most of the variation is due to allele frequency differences at cis-linked regulators. The results show that specific genetic variation among populations contributes appreciably to differences in gene expression phenotypes. Populations differ in prevalence of many complex genetic diseases, such as diabetes and cardiovascular disease. As some of these are probably influenced by the level of gene expression, our results suggest that allele frequency differences at regulatory polymorphisms also account for some population differences in prevalence of complex diseases.     

Pleiotropic fitness effects of the Tre1-Gr5a region in Drosophila melanogaster

The abundance of transposable elements and DNA repeat sequences in mammalian genomes raises the question of whether such insertions represent passive evolutionary baggage or may influence the expression of complex traits. We addressed this question in Drosophila melanogaster, in which the effects of single transposable elements on complex traits can be assessed in genetically identical individuals reared in controlled environments. Here we demonstrate that single P-element insertions in the intergenic region between the gustatory receptor 5a (Gr5a, also known as Tre) and trapped in endoderm 1 (Tre1), which encodes an orphan receptor, exert complex pleiotropic effects on fitness traits, including selective nutrient intake, life span, and resistance to starvation and heat stress. Mutations in this region interact epistatically with downstream components of the insulin signaling pathway. Transposon-induced sex-specific and sex-antagonistic effects further accentuate the complex influences that intergenic transposable elements can contribute to quantitative trait phenotypes.     

Nuclear genetic control of mitochondrial DNA segregation

Mammalian mitochondrial DNA (mtDNA) is a high copy-number, maternally inherited genome that codes for a small number of essential proteins involved in oxidative phosphorylation. Mutations in mtDNA are responsible for a broad spectrum of clinical disorders. The segregation pattern of pathogenic mtDNA mutants is an important determinant of the nature and severity of mitochondrial disease, but it varies with the specific mutation, cell type and nuclear background and generally does not correlate well with mitochondrial dysfunction. To identify nuclear genes that modify the segregation behavior of mtDNA, we used a heteroplasmic mouse model derived from two inbred strains (BALB/c and NZB; ref. 12), in which we had previously demonstrated tissue-specific and age-dependent directional selection for different mtDNA genotypes in the same mouse. Here we show that this phenotype segregates in F2 mice from a genetic cross (BALB/c x CAST/Ei) and that it maps to at least three quantitative-trait loci (QTLs). Genome-wide scans showed linkage of the trait to loci on Chromosomes 2, 5 and 6, accounting for 16-35% of the variance in the trait, depending on the tissue and age of the mouse. This is the first genetic evidence for nuclear control of mammalian mtDNA segregation.