Variations in DNA elucidate molecular networks that cause disease

Abstract

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase beta (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors.

Figure 1. The distal half of chromosome 1 strongly influences metabolic and gene expression traits

a, Lod score curves for metabolic traits scored in the B × H cross demonstrate that they are all driven by one or more QTL on chromosome 1. b, Lod score curves for expression traits corresponding to genes mapped as QTGs for the metabolic traits in a (Apoa2 and Tnfs4) or to genes within ten-million base pairs of Apoa2 that give rise to strong, putative cis eQTL and that are significantly correlated with at least one of the metabolic traits depicted in a.

Figure 2. Genetic loci perturb molecular phenotypes that in turn lead to variations in disease-associated traits

a, Lod score plots for weight (solid black line), Apoa2 liver expression (solid red), Rgs5 liver expression (solid blue) and BB433460 liver expression (solid green) traits in the B × H cross. The dashed curves represent the lod score curves for weight conditional on the Apoa2 (dashed red), Rgs5 (dashed blue) and BB433460 (dashed green) liver gene expression traits. Conditioning on Apoa2 expression does not significantly reduce the weight lod score (independent relationship), whereas conditioning on Rgs5 or BB433460 does (causal relationship). b, Relationships supported between the expression and weight traits described in a: Apoa2 (top), Rgs5 (middle) and BB433460 (bottom) are predicted to be related to weight in an independent (Apoa2) and causal (Rgs5 and BB433460) way. Percentages represent the number of times the model shown was inferred out of 1,000 random samples drawn from the B × H cross. c, Generalization of the relationship discovered between BB433460 and weight, in which genetic loci (L_i) and environment perturb molecular networks of genes (G_i) that in turn leads to disease.

Figure 3. Genes in the MEM network validated as having a causal relationship with obesity traits

a, The MEMN is enriched for genes supported as having a causal relationship with disease traits in the B × H cross (red nodes). The black nodes represent genes in the MEMN not supported as causal for disease traits in the B × H cross. b, FMLM ratio curves for Lpl knockout (n = 25) and wild-type control (n = 23) mice (P = 1.09 × 10⁻⁵ that the difference at the last time point is significant). c, FMLM ratio curves for the Lactb transgenic (n = 36) and wild-type control (n = 27) mice (P = 4.48 × 10⁻⁵ that the difference at the last time point is significant). d, Weight curves for the Ppm1l^−/− (n = 18) and wild-type control (n = 18) mice (P = 1.93 × 10⁻¹¹ that the difference at the last time point is significant). Error bars in b–d represent ±1s.d. of the indicated measures based on replicates and signal-to-noise ratios derived from the model applied to the weight and fat mass differences.