Beth Bennett

The nature and identification of quantitative trait loci: a community’s view

This white paper by eighty members of the Complex Trait Consortium presents a communitys view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

Identification of peak bone mass QTL in a spontaneously osteoporotic mouse strain

Abstract. The whole genome scan for quantitative trait loci (QTLs) specifying peak bone mass was performed with the F2 intercrosses of SAMP6, an established murine model of senile osteoporosis, exhibiting a significantly lower peak bone mass, and SAMP2, exhibiting a higher peak bone mass. Cortical thickness index (CTI), a parameter of bone mass of femurs, was measured in 488 F2 progeny at 4 months of age, when the animals attained peak bone mass by microphotodensitometry. Genetic markers were typed at 90 loci spanning all chromosomes except the Y. By interval mapping of 246 male F2 mice, two loci were identified with significant linkage to peak bone mass, one on Chromosome (Chr) 11 and another on Chr 13, with a maximum lod score of 10.8 (22.2% of the total variance) and 5.8 (10.0%), respectively. Another locus on the X Chr was suggestive of a QTL associated oppositely with a low peak bone mass to the SAMP2 allele. This association was consistent with the distribution of peak bone mass in the F1 and F2. These findings should be useful to elucidate the genetics of osteoporosis.

Genetic structure of the LXS panel of recombinant inbred mouse strains: a powerful resource for complex trait analysis

The set of LXS recombinant inbred (RI) strains is a new and exceptionally large mapping panel that is suitable for the analysis of complex traits with comparatively high power. This panel consists of 77 strains—more than twice the size of other RI sets— and will typically provide sufficient statistical power (β = 0.8) to map quantitative trait loci (QTLs) that account for ∼25% of genetic variance with a genomewide p < 0.05. To characterize the genetic architecture of this new set of RI strains, we genotyped 330 MIT microsatellite markers distributed on all autosomes and the X Chromosome and assembled error-checked meiotic recombination maps that have an average F2-adjusted marker spacing of ∼4 cM. The LXS panel has a genetic structure consistent with random segregation and subsequent fixation of alleles, the expected 3–4 × map expansion, a low level of nonsyntenic association among loci, and complete independence among all 77 strains. Although the parental inbred strains—Inbred Long-Sleep (ILS) and Inbred Short-Sleep (ISS)—were derived originally by selection from an 8-way heterogeneous stock selected for differential sensitivity to sedative effects of ethanol, the LXS panel is also segregating for many other traits. Thus, the LXS panel provides a powerful new resource for mapping complex traits across many systems and disciplines and should prove to be of great utility in modeling the genetics of complex diseases in human populations.

Quantitative trait loci for ethanol sensitivity in the LS X SS recombinant inbred strains: Interval mapping

We are mapping the genes (quantitative trait loci or QTLs) that are responsible for individual differences in ethanol sensitivity, measured as the duration of loss of righting reflex (LORR) and blood ethanol concentrations upon recovery of the righting reflex (BEC). The Long-Sleep (LS) and Short-Sleep (SS) selected lines of mice manifest an 18-fold difference in LORR and serve as a rodent model for ethanol sensitivity. The LS x SS recombinant inbred (RI) series, developed from LS and SS lines, are an important resource for QTL mapping of ethanol-related responses. The current report summarizes the initial QTL analysis of LORR and BEC in the LS x SS strains and compares the results of correlational analysis with an interval-mapping approach. The data provide strong evidence for QTLs that influence ethanol sensitivity on mouse chromosomes 1 and 2 and possible QTLs on chromosomes 1, 3, 4, 5, 6, 7, 12, 13, 16, and 18. These results are compared to those from an F2 cross which confirms QTLs on chromsomes 1, 2, 4, and 18.

High-throughput sequence identification of gene coding variants within alcohol-related QTLs.

Low initial response to alcohol has been shown to be among the best predictors of development of alcoholism. A similar phenotypic measure, difference in initial sensitivity to ethanol, has been used for the genetic selection of two mouse strains, the Inbred Long-Sleep (ILS) and Inbred Short-Sleep (ISS) mice, and for the subsequent identification of four quantitative trait loci (QTLs) for alcohol sensitivity. We now report the application of high throughput comparative gene sequencing in the search for genes underlying these four QTLs. To carry out this search, over 1.7 million bases of comparative DNA sequence were generated from 68 candidate genes within the QTL intervals, corresponding to a survey of over 36,000 amino acids. Eight central nervous system genes, located within these QTLs, were identified that contain a total of 36 changes in protein coding sequence. Some of these coding variants are likely to contribute to the phenotypic variation between ILS/ISS animals, including sensitivity to alcohol, providing specific new genetic targets potentially important to the neuronal actions of alcohol.

Confirmation and Fine Mapping of Ethanol Sensitivity Quantitative Trait Loci, and Candidate Gene Testing in the LXS Recombinant Inbred Mice

In previous studies, we have mapped quantitative trait loci (QTLs) for hypnotic sensitivity to ethanol using a small recombinant inbred (RI) panel and a large F2 backcross. Alcohol sensitivity is a major predictor of long-term risk for alcoholism. We remapped hypnotic sensitivity using a new set of 75 RI strains, the LXS, derived from Inbred Long Sleep and Inbred Short Sleep strains. We expected to improve mapping resolution in the QTL regions and to identify novel QTLs for loss of the righting reflex due to ethanol. We used three common mapping algorithms (R/qtl, QTL Cartographer, and WebQTL) to map QTLs in the LXS, and we compared the results. Most mapping studies use only a single algorithm, an approach that may result in failure to identify minor QTLs. We confirmed most of our previously reported QTLs, although one major QTL from earlier work (Lore2) failed to replicate, possibly because it represented multiple linked genes separated by recombination in the RI strains. We also report narrowed confidence intervals, based on mapping with a new genetic resource of more than 4000 polymorphic single-nucleotide polymorphism markers. These narrowed confidence intervals will facilitate candidate gene identification and assessment of overlap with human regions specifying risk for alcoholism. Finally, we present an approach for using these RI strains to assess evidence for candidate genes in the narrowed intervals, and we apply this method to a strong candidate, the serotonin transporter.

Congenic strains developed for alcohol- and drug-related phenotypes

Quantitative trait loci (QTLs) for many alcohol- and drug-related traits have been mapped using well-accepted mapping techniques. The ultimate goal of gene identification necessitates confirmation of the QTL and reduction of the interval surrounding the QTL; both can be accomplished in congenic strains. These strains carry a chromosomal region introgressed from a donor strain onto the genetic background of a second, recipient strain. Multiple generations of backcrossing reduce the unlinked donor genome to less than 0.1%. Then, phenotypic comparisons between mice congenic for the donor region and controls from the recipient strain allow confirmation of the QTL effect. Animals with recombinations in the donor region can be used to generate interval-specific congenic recombinant lines. Numerous congenic strains are currently being developed in which chromosomal regions carrying QTLs for alcohol- and drug-related traits have been transferred from one strain onto a second strain. The purpose of this review is to summarize the chromosomal regions, donor and recipient strains, and results obtained from these congenics. Most researchers developing such strains are willing to share these resources to facilitate localization of the genetic bases of other phenotypes.

Phenotype and Genetics of Progressive Sensorineural Hearing Loss (Snhl1) in the LXS Set of Recombinant Inbred Strains of Mice

Progressive sensorineural hearing loss is the most common form of acquired hearing impairment in the human population. It is also highly prevalent in inbred strains of mice, providing an experimental avenue to systematically map genetic risk factors and to dissect the molecular pathways that orchestrate hearing in peripheral sensory hair cells. Therefore, we ascertained hearing function in the inbred long sleep (ILS) and inbred short sleep (ISS) strains. Using auditory-evoked brain stem response (ABR) and distortion product otoacoustic emission (DPOAE) measurements, we found that ISS mice developed a high-frequency hearing loss at twelve weeks of age that progressed to lower frequencies by 26 weeks of age in the presence of normal endocochlear potentials and unremarkable inner ear histology. ILS mice exhibited milder hearing loss, showing elevated thresholds and reduced DPOAEs at the higher frequencies by 26 weeks of age. To map the genetic variants that underlie this hearing loss we computed ABR thresholds of 63 recombinant inbred stains derived from the ISS and ILS founder strains. A single locus was linked to markers associated with ISS alleles on chromosome 10 with a highly significant logarithm of odds (LOD) score of 15.8. The 2-LOD confidence interval spans ∼4 Megabases located at position 54–60 Mb. This locus, termed sensorineural hearing loss 1 (Snhl1), accounts for approximately 82% of the phenotypic variation. In summary, this study identifies a novel hearing loss locus on chromosome 10 and attests to the prevalence and genetic heterogeneity of progressive hearing loss in common mouse strains.

Expression profiling identifies novel candidate genes for ethanol sensitivity QTLs.

The Inbred Long Sleep (ILS) and Inbred Short Sleep (ISS) mouse strains have a 16-fold difference in duration of loss of the righting response (LORR) following administration of a sedative dose of ethanol. Four quantitative trait loci (QTLs) have been mapped in these strains for this trait. Underlying each of these QTLs must be one or more genetic differences (polymorphisms in either gene coding or regulatory regions) influencing ethanol sensitivity. Because prior studies have tended to focus on differences in coding regions, genome-wide expression profiling in cerebellum was used here to identify candidate genes for regulatory region differences in these two strains. Fifteen differentially expressed genes were found that map to the QTL regions and polymorphisms were identified in the promoter regions of four of these genes by direct sequencing of ILS and ISS genomic DNA. Polymorphisms in the promoters of three of these genes, Slc22a4, Rassf2, and Tax1bp3, disrupt putative transcription factor binding sites. Slc22a4 and another candidate, Xrcc5, have human orthologs that map to genomic regions associated with human ethanol sensitivity in genetic linkage studies. These genes represent novel candidates for the LORR phenotype and provide new targets for future studies into the neuronal processes underlying ethanol sensitivity.

Genetics of body weight in the LXS recombinant inbred mouse strains

This is the first phenotypic analysis of 75 new recombinant inbred (RI) strains derived from ILS and ISS progenitors. We analyzed body weight in two independent cohorts of female mice at various ages and in males at 60 days. Body weight is a complex trait which has been mapped in numerous crosses in rodents. The LXS RI strains displayed a large range of weights, transgressing those of the inbred progenitors, supporting the utility of this large panel for mapping traits not selected in the progenitors. Numerous QTLs for body weight mapped in single- and multilocus scans. We assessed replication between these and previously reported QTLs based on overlapping confidence intervals of published QTLs for body weight at 60 days and used meta-analyses to determine combined p values for three QTL regions located on Chromosomes 4, 5, and 11. Strain distribution patterns of microsatellite marker genotypes, weight, and other phenotypes are available on WebQTL (http://www.webqtl.org/search.html) and allow genetic mapping of any heritable quantitative phenotype measured in these strains. We report one such analysis, correlating brain and body weights. Large reference panels of RI strains, such as the LXS, are invaluable for identifying genetic correlations, GXE (Gene X Environment) interactions, and replicating previously identified QTLs.