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Nucleotide sequence polymorphism within exon 4 of the bovine prolactin gene and its associations with milk performance traits.

Prolactin plays an important regulatory function in mammary gland development, milk secretion, and expression of milk protein genes. Hence the PRL gene is a potential quantitative trait locus and genetic marker of production traits in dairy cattle. We analysed the sequence of the PRL gene to investigate whether mutations in this sequence might be responsible for quantitative variations in milk yield and composition. Using SSCP and direct sequencing, we detected six single-nucleotide polymorphisms within a 294-bp prolactin gene fragment involving exon 4. All detected mutations were silent with respect to the amino acid sequence of the protein. PCR-RFLP genotyping of SNP 8398 R (RsaI) was used to assess allele frequencies in 186 Black-and-White cows (0.113 and 0.887 for A and G, respectively) and in 138 Jersey cows (0.706 and 0.294 for A and G, respectively). Black-and-White cows with genotype AG showed the highest milk yield, while cows with genotype GG showed the highest fat content.

Animals↗

A novel locus on the X chromosome regulates post-maturity bone density changes in mice.

Two mouse strains, AKR/J and SAMP6, were assessed longitudinally for bone mineral density of the spine. They displayed very different time courses of bone accrual, with the SAMP6 strain reaching a plateau for vertebral BMD at 3 months, whereas AKR/J mice continued to increase spine BMD for at least 8 months. Among 253 F(2) progeny of an AKR/JxSAMP6 cross, at 4 months of age, the BMD variance was 5-6% of the mean, vs. 15% for weight. Variance increased with age for every parameter measured, and was generally higher among males. The ratio of 6-month/4-month spine BMDs, termed DeltasBMD, had a normal distribution with 5.7% variance, and was largely independent of spine BMD (R=-0.23) or body weight (R=-0.12) at maturity. Heritability of the DeltasBMD trait was calculated at 0.59. Genetic mapping identified two significant loci, both distinct from those observed for BMD at maturity--implying that different genes regulate skeletal growth vs. remodeling. A locus on the X chromosome, replicated in two mouse F(2) populations (P<10(-4) for combined discovery and confirmation), affects age-dependent BMD change for both spine and the full skeleton. Its position agrees with a very narrow region identified by association mapping for effects on lumbar bone density in postmenopausal women [Parsons CA, Mroczkowski HJ, McGuigan FE, Albagha OM, Manolagas S, Reid DM, et al. Interspecies synteny mapping identifies a quantitative trait locus for bone mineral density on human chromosome Xp22. Hum Mol Genet 2005;14:3141-8]. A second locus, on chromosome 7, was observed in only one cross. Single-nucleotide polymorphisms (SNPs) are highly clustered near these loci, distinguishing the parental strains over only limited spans.

Absorptiometry, Photon↗

Genetic control of hippocampal neurogenesis.

Adult neurogenesis in the hippocampus is under complex genetic control. A recent comparative study of two inbred mouse strains using quantitative trait locus analysis has revealed that cell survival is most highly correlated with neurogenesis and identified candidate genes for further investigation.

Animals↗

Genetic loci contributing to age-related hippocampal lesions in mice.

C57BL/6J mice develop genetically determined age-related hippocampal granular deposits that have some similarities to lesions seen in the brains of human patients with tau protein related neurodegenerative disorders ("tauopathies"). We sought to identify the genetic loci responsible for these in an F2 intercross of inbred mouse strains C57BL/6J and DBA/2J, using quantitative trait locus (QTL) analysis. Hippocampal lesions were shown to be PAS positive, H and E negative, and immunoreactive for tau protein and alpha synuclein, but not to Abeta 1-40 or Abeta 1-42, or for ubiquitin. These were quantitated by histomorphometry, and QTL analysis revealed a locus on chromosome 7 with a lod score of 6.5 as well as two suggestive loci on chromosome 10. The genomic data indicate that the genetic basis is complex, but with one locus playing a major role in lesion formation. These lesions may represent a useful model for investigating dysregulation of tau protein in the hippocampus.

Aging↗

Locus controlling LDL cholesterol response to dietary cholesterol is on baboon homologue of human chromosome 6.

OBJECTIVE: Cholesterolemic responses to dietary lipids are known to be heritable, but the genes that may affect this response have yet to be identified. Using segregation analysis, we previously detected a potential quantitative trait locus (QTL) in baboons that influenced low density lipoprotein cholesterol response to dietary cholesterol. We performed linkage analyses to locate this QTL by using data on the baboon genetic linkage map. METHODS AND RESULTS: We obtained evidence for linkage of this potential QTL to the same locus (D6S311) on the baboon homologue of human chromosome 6 by using variance components and parametric linkage analysis methods (2-point lod scores 4.17 [genomic probability value 0.008] and 2.81 [genomic P=0.10], respectively). Linkage analyses of serum levels of apolipoprotein B dietary response, a correlated trait, also gave weak suggestive evidence of linkage to this chromosomal region (maximum 2-point lod score 1.91). Although the LPA locus is nearby, we found no evidence of linkage with LPA. CONCLUSIONS: This report is the first to localize, in any primate species, a potential QTL that influences low density lipoprotein cholesterol response to dietary cholesterol.

Animals↗

A genome-wide search strategy for identifying quantitative trait loci involved in reading and spelling disability (developmental dyslexia).

Family and twin studies of developmental dyslexia have consistently shown that there is a significant heritable component for this disorder. However, any genetic basis for the trait is likely to be complex, involving reduced penetrance, phenocopy, heterogeneity and oligogenic inheritance. This complexity results in reduced power for traditional parametric linkage analysis, where specification of the correct genetic model is important. One strategy is to focus on large multigenerational pedigrees with severe phenotypes and/or apparent simple Mendelian inheritance, as has been successfully demonstrated for speech and language impairment. This approach is limited by the scarcity of such families. An alternative which has recently become feasible due to the development of high-throughput genotyping techniques is the analysis of large numbers of sib-pairs using allele-sharing methodology. This paper outlines our strategy for conducting a systematic genome-wide search for genes involved in dyslexia in a large number of affected sib-pair familites from the UK. We use a series of psychometric tests to obtain different quantitative measures of reading deficit, which should correlate with different components of the dyslexia phenotype, such as phonological awareness and orthographic coding ability. This enable us to use QTL (quantitative trait locus) mapping as a powerful tool for localising genes which may contribute to reading and spelling disability.

Alleles↗

Identification of a genetic locus modulating splenomegaly induced by granulocyte colony-stimulating factor in mice.

Clinically detectable splenomegaly and splenic rupture are uncommon but potentially life-threatening consequences of G-CSF administration. Increased spleen size in mice injected with G-CSF is a complex genetic trait amenable to investigation in experimental inter-strain crosses by quantitative trait analysis. A quantitative trait locus (QTL) with highly significant linkage (LOD 7.9) for splenomegaly was identified within a 22 centimorgan (cM) region on chromosome 1. Inheritance of a C57BL/6 haplotype in this region was associated with a greater spleen weight. The relevance of this locus was confirmed by analysing the responses of mice congenic for the distal 12 cM of this region (C57BL/6 and C57BL/6.SJL-Ptprc(a) Pep3(b)). Consistent with the QTL effect, mice lacking C57BL/6 alleles in this region had reduced splenomegaly induced by G-CSF. Intriguingly, peripheral blood neutrophilia and progenitor cell mobilisation responses to G-CSF were also significantly influenced.

Animals↗

Back to the future: genetic correlations, adaptation and speciation.

Genetic correlations can affect the course of phenotypic evolution. Although genetic correlations among traits are a common feature of quantitative genetic analyses, they have played a very minor role in recent linkage-map based analyses of the genetic architecture of quantitative traits. Here, we use our work on host-associated races in pea aphids to illustrate how quantitative trait locus (QTL) mapping can be used to test specific hypotheses about how genetic correlations may facilitate ecological specialization and speciation.

Adaptation, Biological↗

Sample-size guidelines for linkage analysis of a dominant locus for a quantitative trait by the method of lod scores.

Sample-size guidelines for linkage studies of quantitative traits partially determined by a dominant major locus are needed to provide a rough estimate of the amount of pedigree material that should be sampled to map the loci that influence such traits. After pedigrees are sampled, a specific power calculation can be carried out to evaluate the linkage information provided by the sampled pedigrees. Using computer simulation, I provide sample-size guidelines for linkage studies by the method of lod scores of quantitative traits partially determined by a dominant major locus. I consider the effects of a trait model, marker characteristics, and sampling strategy, with particular attention to sampling strategy because it is the one factor which the investigator can fully control. My results suggest that linkage studies of quantitative traits are practical, particularly if the investigator chooses an efficient sampling design and an efficient strategy to select pedigrees for linkage analysis.

Female↗

Integrating Genetics and Environment to Find Causal Mechanisms for Multiple Sclerosis.

Genome-wide association studies (GWAS) have identified hundreds of risk loci for multiple sclerosis (MS), but we have limited knowledge of the mechanisms through which genetic variants mediate risk. Similarly, epidemiological studies implicate numerous environmental risk factors in MS risk, but these cannot identify specific causal mechanisms. We review our current knowledge of genetic mechanisms in MS, including the critical role of expression quantitative trait locus (eQTL) mapping in translating genetic risk loci into causal mechanisms. Molecular and functional context has emerged as an important missing component of these studies, and we discuss how environmental risk factors can be modelled in a quantitative genetic context to identify disease mechanisms. In parallel, we highlight recent advances in which quantitative genetic methods establish a causal role for low vitamin D and obesity in MS, and to dissect the mechanisms through which these operate. As genetic, transcriptional, and epigenetic studies continue to expand, further mechanistic insights for MS are likely to come from the integration of genetic and environmental data.

Humans↗

Genetic analysis of personality traits and alcoholism using a mixed discrete continuous trait variance component model.

Bivariate analyses can improve power to detect linkage. This paper describes one application of a bivariate variance component method for estimating joint likelihoods of a continuous and a discrete trait. This method is applied to the Collaborative Study on the Genetics of Alcoholism data set to investigate the relationship between personality traits derived from the tridimensional personality questionnaire (TPQ) and alcoholism. The results indicate that the novelty-seeking subscale of the TPQ and alcoholism share a strong and significant genetic correlation (rho G = 0.83) and modest environmental correlation (rho E = 0.31). When both traits are considered jointly in a multipoint linkage model compared with the alcoholism trait alone, there is an improvement in the ability to detect and localize a quantitative trait locus on chromosome 4.

Age Factors↗

Multiple QTLs influence variation in paraoxonase 1 activity in Mexican Americans.

Paraoxonase 1 (PON1), a high-density-lipoprotein-associated enzyme known to protect against cellular damage from toxic agents, may also have antioxidant properties. Although the importance of the influence of the PON1 structural locus on chromosome 7q21-22 for variation in the concentration and activity of the enzyme is well-documented, the contribution of other loci is poorly understood. Based on the recent observations of at least one additional quantitative trait locus (QTL) for PON1 activity in pedigreed baboons, we conducted a whole-genome linkage screen for QTLs other than the PON1 structural locus that may influence PON1 activity in humans. We measured PON1 activity in frozen serum for 1,406 individuals in more than 40 extended pedigrees from the San Antonio Family Heart Study (SAFHS). We used a maximum-likelihood-based variance decomposition approach implemented in SOLAR to test for QTLs that may influence PON1 activity. In addition to a QTL for which we detected the strongest, significant evidence (LOD = 31.41) at or near the PON1 structural locus on chromosome 7q21-22, we also localized at least one additional significant QTL on chromosome 12 (LOD = 3.56). Furthermore, we detected suggestive evidence for two more PON-related QTLs on chromosomes 17 and 19. We have provided evidence that other genes, in addition to the well-known ones on chromosome 7, play a role in influencing normal variation in PON1 activity.

Adult↗

Genetically determined variation in the number of phenotypically defined hematopoietic progenitor and stem cells and in their response to early-acting cytokines.

Quantitative trait analysis may shed light on mechanisms regulating hematopoiesis in vivo. Strain-dependent variation existed among C57BL/6 (B6), DBA/2, and BXD recombinant inbred mice in the responsiveness of primitive progenitor cells to the early-acting cytokines kit ligand, flt3 ligand, and thrombopoietin. A significant quantitative trait locus was found on chromosome 2 that could not be confirmed in congenic mice, however, probably because of epistasis. Because it has been shown that alleles of unknown X-linked genes confer a selective advantage to hematopoietic stem cells in vivo in humans and in cats, we also analyzed reciprocal male D2B6F1 and B6D2F1 mice, revealing an X-linked locus regulating the responsiveness of progenitor and stem cells to early-acting factors. Among DBA/2, B6, and BXD recombinant inbred mice, correlating genetic variation was found in the absolute number and frequency of Lin(-)Sca1(++)kit(+) cells, which are highly enriched in hematopoietic progenitor and stem cells, and in the number of Lin(-)Sca1(++)kit(-) cells, a population whose biologic significance is unknown, suggesting that both populations are functionally related. Suggestive quantitative trait loci (QTLs) for the number of Lin(-)Sca1(++) cells on chromosomes 2, 4, and 7 were confirmed in successive rounds of mapping. The locus on chromosome 2 was confirmed in congenic mice. We thus demonstrated genetic variation in the response to cytokines critical for hematopoiesis in vivo and in the pool size of cells belonging to a phenotype used to isolate essentially pure primitive progenitor and stem cells, and we identified loci that may be relevant to the regulation of hematopoiesis in steady state.

Animals↗

QTL fine mapping by measuring and testing for Hardy-Weinberg and linkage disequilibrium at a series of linked marker loci in extreme samples of populations.

It has recently been demonstrated that fine-scale mapping of a susceptibility locus for a complex disease can be accomplished on the basis of deviations from Hardy-Weinberg (HW) equilibrium at closely linked marker loci among affected individuals. We extend this theory to fine-scale localization of a quantitative-trait locus (QTL) from extreme individuals in populations, by means of HW and linkage-disequilibrium (LD) analyses. QTL mapping and/or linkage analyses can establish a large genomic region ( approximately 30 cM) that contains a QTL. The QTL can be fine mapped by examination of the degree of deviation from HW and LD at a series of closely linked marker loci. The tests can be performed for samples of individuals belonging to either high or low percentiles of the phenotype distribution or for combined samples of these extreme individuals. The statistical properties (the power and the size) of the tests of this fine-mapping approach are investigated and are compared extensively, under various genetic models and parameters for the QTL and marker loci. On the basis of the results, a two-stage procedure that uses extreme samples and different tests (for HW and LD) is suggested for QTL fine mapping. This two-step procedure is economic and powerful and can accurately narrow a genomic region containing a QTL from approximately 30-1 cM, a range that renders physical mapping feasible for identification of the QTL. In addition, the relationship between parameterizations of complex diseases, by means of penetrance, and those of complex quantitative traits, by means of genotypic values, is outlined. This means that many statistical genetic methods developed for searching for susceptibility loci of complex diseases can be directly adopted and/or extended to QTL mapping for quantitative traits.

Alleles↗

Concordance of murine quantitative trait loci for salt-induced hypertension with rat and human loci.

To investigate the genetic control of salt-induced hypertension, we performed a quantitative trait locus analysis on male mice from a reciprocal backcross between the salt-sensitive C57BL/6J and the normotensive A/J inbred mouse strains after they were provided with water containing 1% salt for 2 weeks. Genome-wide scans performed on these mice and analyzed with a combination of conventional marker-based regressions and a novel simultaneous search for pairs revealed six significant quantitative trait loci associated with salt-induced blood pressure, two of which were interacting loci. These six loci, named Bpq1-6 for blood pressure quantitative trait loci, mapped to D1Mit334, D1Mit14, D4Mit164, D5Mit31, D6Mit15, and D15Mit13. Furthermore, five of these six loci were concordant with hypertension loci in rats, and four were concordant with hypertension loci in humans, suggesting that quantitative trait loci mapping in model organisms can be used to guide the search for human blood pressure genes.

Analysis of Variance↗

Integrating QTL and high-density SNP analyses in mice to identify Insig2 as a susceptibility gene for plasma cholesterol levels.

The use of inbred strains of mice to dissect the genetic complexity of common diseases offers a viable alternative to human studies, given the control over experimental parameters that can be exercised. Central to efforts to map susceptibility loci for common diseases in mice is a comprehensive map of DNA variation among the common inbred strains of mice. Here we present one of the most comprehensive high-density, single nucleotide polymorphism (SNP) maps of mice constructed to date. This map consists of 10,350 SNPs genotyped in 62 strains of inbred mice. We demonstrate the utility of these data via a novel integrative genomics approach to mapping susceptibility loci for complex traits. By integrating in silico quantitative trait locus (QTL) mapping with progressive QTL mapping strategies in segregating mouse populations that leverage large-scale mapping of the genetic determinants of gene expression traits, we not only facilitate identification of candidate quantitative trait genes, but also protect against spurious associations that can arise in genetic association studies due to allelic association among unlinked markers. Application of this approach to our high-density SNP map and two previously described F2 crosses between strains C57BL/6J (B6) and DBA/2J and between B6 ApoE(-/-) and C3H/HeJ ApoE(-/-) results in the identification of Insig2 as a strong candidate susceptibility gene for total plasma cholesterol levels.

Animals↗

Identification of novel genetic loci for intraocular pressure: a genomewide scan of the Beaver Dam Eye Study.

OBJECTIVE: To identify genetic loci that control intraocular pressure (IOP). METHODS: We performed a genomewide scan of IOP, using 486 pedigrees ascertained through a population-based cohort, the Beaver Dam Eye Study. Linkage analysis was performed using the modified Haseman-Elston regression models and variance components linkage analysis. RESULTS: Seven regions of interest were identified on chromosomes 2, 5, 6, 7, 12, 15, and 19. The novel linkage region on chromosome 19p had an empirical multipoint P value of 6.1 x 10(-5). Two of the regions (2 and 19) were especially interesting since each has been identified as a potential linkage region for blood pressure. CONCLUSIONS: The results of this genomewide scan provide evidence that a quantitative trait locus may influence elevated IOP and may colocalize with blood pressure loci. These loci may control systemic pressure reflected in the eye and vascular system. CLINICAL RELEVANCE: Glaucoma is a leading cause of blindness in the world, and the identification of genes that contribute to this disease is essential. Elevated IOP is a principal risk factor for primary open-angle glaucoma and an intriguing quantitative trait that may strongly influence the development of disease.

Adult↗

Chipping away at complex behavior: transcriptome/phenotype correlations in the mouse brain.

Highly parallel gene expression profiling has the potential to provide new insight into the molecular mechanisms of complex brain diseases and behavioral traits. We review how gene expression profiling in various brain regions of inbred mouse strains has been used to identify genes that may contribute to strain-specific phenotypes. New data, which demonstrate the use of gene expression profiling in combination with behavioral testing to identify candidate genes involved in mediating variation in running wheel activity, are also presented. These and other studies suggest that a combination of gene expression profiling and more traditional genetic approaches, such as quantitative trait locus analysis, can be used to identify genes responsible for specific neurobehavioral phenotypes.

Animals↗