Richard Mott

Mouse genomic variation and its effect on phenotypes and gene regulation.

We report genome sequences of 17 inbred strains of laboratory mice and identify almost ten times more variants than previously known. We use these genomes to explore the phylogenetic history of the laboratory mouse and to examine the functional consequences of allele-specific variation on transcript abundance, revealing that at least 12% of transcripts show a significant tissue-specific expression bias. By identifying candidate functional variants at 718 quantitative trait loci we show that the molecular nature of functional variants and their position relative to genes vary according to the effect size of the locus. These sequences provide a starting point for a new era in the functional analysis of a key model organism.

Strategies for mapping and cloning quantitative trait genes in rodents.

Over the past 15 years, more than 2,000 quantitative trait loci (QTLs) have been identified in crosses between inbred strains of mice and rats, but less than 1% have been characterized at a molecular level. However, new resources, such as chromosome substitution strains and the proposed Collaborative Cross, together with new analytical tools, including probabilistic ancestral haplotype reconstruction in outbred mice, Yin–Yang crosses and in silico analysis of sequence variants in many inbred strains, could make QTL cloning tractable. We review the potential of these strategies to identify genes that underlie QTLs in rodents.

Genome-wide genetic association of complex traits in heterogeneous stock mice

Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.

Multiple reference genomes and transcriptomes for Arabidopsis thaliana

Genetic differences between Arabidopsis thaliana accessions underlie the plant’s extensive phenotypic variation, and until now these have been interpreted largely in the context of the annotated reference accession Col-0. Here we report the sequencing, assembly and annotation of the genomes of 18 natural A. thaliana accessions, and their transcriptomes. When assessed on the basis of the reference annotation, one-third of protein-coding genes are predicted to be disrupted in at least one accession. However, re-annotation of each genome revealed that alternative gene models often restore coding potential. Gene expression in seedlings differed for nearly half of expressed genes and was frequently associated with cis variants within 5 kilobases, as were intron retention alternative splicing events. Sequence and expression variation is most pronounced in genes that respond to the biotic environment. Our data further promote evolutionary and functional studies in A. thaliana, especially the MAGIC genetic reference population descended from these accessions.

A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana

Identifying natural allelic variation that underlies quantitative trait variation remains a fundamental problem in genetics. Most studies have employed either simple synthetic populations with restricted allelic variation or performed association mapping on a sample of naturally occurring haplotypes. Both of these approaches have some limitations, therefore alternative resources for the genetic dissection of complex traits continue to be sought. Here we describe one such alternative, the Multiparent Advanced Generation Inter-Cross (MAGIC). This approach is expected to improve the precision with which QTL can be mapped, improving the outlook for QTL cloning. Here, we present the first panel of MAGIC lines developed: a set of 527 recombinant inbred lines (RILs) descended from a heterogeneous stock of 19 intermated accessions of the plant Arabidopsis thaliana. These lines and the 19 founders were genotyped with 1,260 single nucleotide polymorphisms and phenotyped for development-related traits. Analytical methods were developed to fine-map quantitative trait loci (QTL) in the MAGIC lines by reconstructing the genome of each line as a mosaic of the founders. We show by simulation that QTL explaining 10% of the phenotypic variance will be detected in most situations with an average mapping error of about 300 kb, and that if the number of lines were doubled the mapping error would be under 200 kb. We also show how the power to detect a QTL and the mapping accuracy vary, depending on QTL location. We demonstrate the utility of this new mapping population by mapping several known QTL with high precision and by finding novel QTL for germination data and bolting time. Our results provide strong support for similar ongoing efforts to produce MAGIC lines in other organisms.

Positional cloning of a quantitative trait locus on chromosome 13q14 that influences immunoglobulin E levels and asthma

Atopic or immunoglobulin E (IgE)-mediated diseases include the common disorders of asthma, atopic dermatitis and allergic rhinitis. Chromosome 13q14 shows consistent linkage to atopy and the total serum IgE concentration. We previously identified association between total serum IgE levels and a novel 13q14 microsatellite (USAT24G1; ref. 7) and have now localized the underlying quantitative-trait locus (QTL) in a comprehensive single-nucleotide polymorphism (SNP) map. We found replicated association to IgE levels that was attributed to several alleles in a single gene, PHF11. We also found association with these variants to severe clinical asthma. The gene product (PHF11) contains two PHD zinc fingers and probably regulates transcription. Distinctive splice variants were expressed in immune tissues and cells.

Finding the molecular basis of quantitative traits: successes and pitfalls.

Understanding the molecular basis of quantitative genetic variation is a principal goal for biomedicine. Although the complex genetic architecture of quantitative traits has so far largely frustrated attempts to identify genes in humans by standard linkage methodologies, quantitative trait loci (QTL) have been mapped in plants, insects and rodents. However, identifying the molecular bases of QTL remains a challenge. Here, we discuss why this is and how new experimental strategies and analytical techniques, combined with the fruits of the genome projects, are beginning to identify candidate genes for QTL studies in several model organisms.

Genetic dissection of a behavioral quantitative trait locus shows that Rgs2 modulates anxiety in mice

Here we present a strategy to determine the genetic basis of variance in complex phenotypes that arise from natural, as opposed to induced, genetic variation in mice. We show that a commercially available strain of outbred mice, MF1, can be treated as an ultrafine mosaic of standard inbred strains and accordingly used to dissect a known quantitative trait locus influencing anxiety. We also show that this locus can be subdivided into three regions, one of which contains Rgs2, which encodes a regulator of G protein signaling. We then use quantitative complementation to show that Rgs2 is a quantitative trait gene. This combined genetic and functional approach should be applicable to the analysis of any quantitative trait.

EST_GENOME: a program to align spliced DNA sequences to unspliced genomic DNA

This note describes the program EST_GENOME for aligning spliced DNA to unspliced genomic DNA. It is written in ANSI C and has been tested under Digital OSF3.2. The spurce code and documentation are available from ftp:// www.sanger.ac.uky ftp/pub/ badger/est_genome.2.tar.Z. The prediction of genes in uncharacterized genomic DNA sequence is currently one of the main problems facing sequence annotators. Methods based on de novo prediction, e.g. searching for motifs like the splice-site consensus, or on statistical properties such as biased codon usage, etc. (Solovyev et al., 1994; Hebsgaard et al., 1996) have been only partially successful, and investigators have often found that the surest way of predicting a gene is by alignment with a homologous protein sequence (Birney et al., 1996; Gelfand et al., 1996; Huang and Zhang, 1996), or a spliced gene product [an expressed sequence tag (EST), mRNA or cDNA], particularly now that a large number of ESTs are available (Hillier et al., 1996). Standard alignment tools are not ideal for finding the correct alignment of a spliced product to genomic DNA, because of the large introns which can occur in the genomic sequence and because the programs ignore the conserved sequences found at donor/acceptor splice sites (intron/exon boundaries). In addition, very large genomic DNA sequences can be hard to align using quadratic-space dynamic programming because they require too much memory. The program EST_GENOME addresses this problem. It allows large introns, can recognize splice sites and uses limited memory. This combination of features makes a powerful and useful tool. EST_GENOME is used routinely at the Sanger Centre to help annotate human genomic sequence. As it is slow compared with search methods like BLAST (Altschul et al., 1990), we first screen genomic DNA against dbEST using BLASTN. Any matching ESTs are realigned using EST_GENOME. The algorithm uses a modification of Smith and Waterman (1981). The penalty structure used to score an alignment is as follows (defaults are in parentheses). Aligned bases score +match (1) or cost —mismatch (1) as appropriate. An indel in

A high-resolution single nucleotide polymorphism genetic map of the mouse genome.

High-resolution genetic maps are required for mapping complex traits and for the study of recombination. We report the highest density genetic map yet created for any organism, except humans. Using more than 10,000 single nucleotide polymorphisms evenly spaced across the mouse genome, we have constructed genetic maps for both outbred and inbred mice, and separately for males and females. Recombination rates are highly correlated in outbred and inbred mice, but show relatively low correlation between males and females. Differences between male and female recombination maps and the sequence features associated with recombination are strikingly similar to those observed in humans. Genetic maps are available from http://gscan.well.ox.ac.uk/#genetic_map and as supporting information to this publication.