In the present work, we characterized the impact of the entire common genetic variability of the LXRβ
gene and searched for associations between 5 tagSNPs and various metabolic phenotypes in 3 independent samples. We report a suggestive association between the rs17373080 minor G allele (representing a 5.7 kbp haplotype block) in LXRβ
and an approximately 20% lower risk of T2DM in the HUNT2 study. The MONICA study containing only 209 individuals with type 2 diabetes and the HELENA study (adolescents) none, these population samples are not suitable for replication of the association between SNPs and diabetes risk found in HUNT2. However, this result is in line with recent work by Dahlman et al
., who showed that this allele was associated with a 30% lower risk of T2DM in a study comprising 988 cases and 941 controls [31
]. This protective effect can be explained by the lower plasma glucose levels observed in the non-diabetic subjects of the HUNT2 study carrying the G allele. Moreover, we found two LXRβ
SNPs (rs17373080 and rs2695121) associated with insulin levels and HOMA indexes in adolescents. These associations could reflect LXRβ's role in insulin secretion, since it has been shown that LXRβ activation in pancreatic beta cells increases insulin expression and secretion via
the SREBP-1 regulated pathway [5
We also showed that the G allele of rs17373080 was associated with higher risk of obesity or overweight in the MONICA and the HELENA studies, respectively. In line with our results, Dahlman et al
. reported a marginal association (p
= 0.06) between rs17373080 and the risk of obesity in a study of 559 obese and 438 non-obese individuals [32
]. However, they detected no association between rs17373080 and BMI as a continuous trait in 1721 adults [31
]. We confirmed this absence of association in a larger sample (n = 5448). Only rs2303044 was significantly associated with BMI when combining the 2 adult and the adolescent studies. The presence of many confounding factors and compensation mechanisms may hide the impact of LXRβ on fat mass.
The opposing effects of the rs17373080 G allele on obesity (deleterious) and T2DM (protective) may appear to be contradictory. However, LXRβ seems to play opposing roles in fat metabolism and glucose homeostasis. Lxrβ-/-
mice display lower amounts of adipose tissue on one hand and glucose intolerance (due to impaired glucose-induced insulin secretion) on the other [11
]. Furthermore, the effects of the rs17373080 G allele on obesity and T2DM seem to be the opposite of what would be expected from the Lxrβ-/-
mouse phenotype [11
]. However, this may reflect species differences, as discussed by Dahlman et al
]. Also, this may reveal differences between a complete gene knock-out and subtle changes like SNPs. The SNPs may induce the recruitment of different co-factors that modify the effect of LXRβ on target genes. For example, if the disease-associated allele creates a binding site for a transactivator, the disease association would be opposite to the effect observed in the Lxrβ-/-
mice. Furthermore, epigenetic changes may be involved. Lastly, the fact that LXRβ is expressed in several organs and tissues with different regulatory mechanisms adds to the complexity of the association. Indeed, as shown in our transient transfection experiments, HNF4α activated LXRβ in HepG2 but repressed it in JEG3 cells. The full mechanistic and physiological relevance of the statistical associations found in this and other previous studies should be elucidated in other cell lines expressing the LXRβ
gene in its various allelic forms.
To the best of our knowledge, no LXRβ SNPs have been significantly associated with obesity or T2DM in genome-wide association studies (GWAS). However, due to the very low p-value threshold required in GWAS (<10-8), nominal associations with LXRβ SNPs gene (albeit weak) may have gone unreported. The associations in our present study were probably overestimated, since the number of subjects was lower than in GWAS. Nevertheless, GWAS do not cover the entire genetic variability of each gene. Thus, a candidate gene approach, as in the present study, may help detect associations between SNPs and disorders.
While performing the present study, Dahlman et al
] found a potential NF1 binding site overlapping rs17373080 using the transcription element search system and showed that NF1 could bind and regulate the expression of the LXRβ
gene, whatever the allele. In our study, a MatInspector analysis revealed that the minor C allele of rs28514894 (in perfect LD with the minor G allele of rs17373080) could create binding sites for either HNF4α or NF1. We showed that the C allele of rs28514894 was associated with higher LXRβ
basal promoter activity, which suggested that this allele was associated with higher LXRβ
mRNA levels. However, we did not observe major difference between the two alleles in terms of DNA binding and transactivation by HNF4α or NF1s. Nevertheless, the inter-allele differences in TF binding and transcriptional activity may be too small to be detected by these methods but can still make a difference in the whole organism over the years - especially if (as would be expected) there are tissue-specific effects.