Experiment
Marfan syndrome (MFS) is an autosomal dominant disease of the connective tissue, affecting mostly the skeletal, ocular and cardiovascular systems, caused by mutations in the
FBN1 gene.
Here, a new mouse model, Fbn1tm1Lper, which presents different phenotype severity dependent on the genetic backgrounds, was used to identify genes involved in modulating MFS phenotype. By analysis of F1 and F2 crosses between C57BL/6J (B6) and 129/Sv (129) heterozygous for the Fbn1 mutation each affected system revealed its own set of modifier genes.
The mapping population comprised 82 3-month-old (129(CD1)-Fbn1tm1Lper C57BL/6J) F2 heterozygous animals produced by crossing a wild-type B6 male and a heterozygous 129 female to generate F1 animals, then crossing wild-type and heterozygous F1 animals. From the F2 generation, a set of 46 animals exhibiting phenotypic extremes (skeletal, cardiovascular, or pulmonary manifestation) were obtained.
DNA was extracted from a 0.5-cm piece of tail and each sample underwent two independent PCR amplifications to identify the presence of the Fbn1tm1Lper allele and the normal allele, which served as an internal reaction control.
The quantifying phenotypes were:
Skeletal (KR phenotype): A full body x-ray of each mouse was digitized and
cervical-thoracic segment length and the straight-line distance of the same segment were measured. The measurements established a kyphosis ratio (segment length/straight distance; KR), which was used to score the severity of the skeletal manifestation of MFS.
Cardiovascular (AWT phenotype): Histological samples were photographed and the lengths of the inner and outer perimeters of the aorta were measured to estimate the aortic wall thickness.
Pulmonary (Lm phenotype): The size of alveolar airways was determined by measuring the mean chord length on H&E-stained lungs.
From the 82 animals 10 animals were selected with extreme phenotypes to represent each tail of each phenotypic distribution, a total of 46 different animals, were genotyped with 7851 SNP microarrays. All statistical analysis were conducted in R version 2.12, with significance set at p=0.05. The suggestiveness (p<0.63) and significance threshold (p<
0.05) were defined by 1024 stratified permutations due to the selective genotyping used to generate the data.
A significant QTL for the KR phenotype, Krq1 (kyphosis ratio QTL 1) mapped to Chromosome 6, p<0.05, with a LOD score of 6.12 nearest marker UNC060308920 (48.85 cM). The confidence interval spanned from 41.0 to 49.1 cM.
Two suggestive QTL for the KR phenotype were also identified:
Krq2 (kyphosis ratio QTL 2) mapped to Chromosome 3, p<0.63, with a LOD score of 3.85 nearest marker UNC030244865 (64.49 cM). The confidence interval spanned from 56.1 to 68.4 cM.
Krq3 (kyphosis ratio QTL 3) mapped to Chromosome X, p<0.63, with a LOD score of 3.84 nearest marker JAX00177060 (5.91 cM). The confidence interval spanned from 2.4 to 20.2 cM.
Two suggestive QTL were also identified for the cardiovascular phenotype:
QTL Awtq1 (aortic wall thickness QTL 1) mapped to Chromosome 4, p<0.63, with a LOD score of 7.19 nearest marker UNC04036624 (68.74 cM). The confidence interval spanned from 66.8 to 70.6 cM.
QTL Awtq2 (aortic wall thickness QTL 2) mapped to Chromosome 13, p<0.63 with a LOD score of 7.19 nearest marker UNC130158595 (47.79 cM). The confidence interval spanned from 44.4 to 52.1 cM.
Based on the genotypes of the closest SNP to the estimated position of each QTL,
QTL Krq1 and Awtq2 displayed a dominant effect with the 129 allele dominating the B6 allele and the B6 allele dominating the 129 allele in Krq2 (Fig. 4); conversely, Awtq1 demonstrated an additive effect. The effect of Krq3 could not be precisely identified.
Epistatic effects on the phenotypic traits were also identified. Krq1 and Krq2 interacted such that homozygosity for the B6 allele at Krq1 caused the KR phenotype to manifest in its most severe form only when a mouse was also homozygous for the 129 allele at the Krq2 locus (Fig. 4F). Awtq1 and Awtq2 also exhibited epistatic interactions: the additive behavior of Awtq1 could only be identified when the B6 allele was homozygous at the Awtq2 locus (Fig. 4I).
Krq1 and Krq2 and their interaction accounted for 47.4% of the trait's variability (p<0.001). For the AWT phenotype, the full model was composed of Awtq1, Awtq2, their interaction, the animals sex as a covariate, and the interaction of sex with both QTLs; this model explained 53.5% of the traits variability (p <0.001).
Candidate genes within each QTL were listed for each QTL in Supplementary Tables 1-5.
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