No abstract

Major depressive disorder (MDD), one of the most frequently encountered forms of mental illness and a leading cause of disability worldwide1, poses a major challenge to genetic analysis. To date no robustly replicated genetic loci have been identified 2, despite analysis of more than 9,000 cases3. Using low coverage genome sequence of 5,303 Chinese women with recurrent MDD selected to reduce phenotypic heterogeneity, and 5,337 controls screened to exclude MDD, we identified and replicated two genome-wide significant loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene (P-value = 2.53×10−10) the other in an intron of the LHPP gene (P = 6.45×10−12). Analysis of 4,509 cases with a severe subtype of MDD, melancholia, yielded an increased genetic signal at the SIRT1 locus. We attribute our success to the recruitment of relatively homogeneous cases with severe illness.

It is evident that epigenetic factors, especially DnA methylation, have essential roles in obesity development. Here, using pig as a model, we investigate the systematic association between DnA methylation and obesity. We sample eight variant adipose and two distinct skeletal muscle tissues from three pig breeds living within comparable environments but displaying distinct fat level. We generate 1,381 Gb of sequence data from 180 methylated DnA immunoprecipitation libraries, and provide a genome-wide DnA methylation map as well as a gene expression map for adipose and muscle studies. The analysis shows global similarity and difference among breeds, sexes and anatomic locations, and identifies the differentially methylated regions. The differentially methylated regions in promoters are highly associated with obesity development via expression repression of both known obesity-related genes and novel genes. This comprehensive map provides a solid basis for exploring epigenetic mechanisms of adipose deposition and muscle growth.

Ferredoxin reductase (FDXR), a target of p53, modulates p53-dependent apoptosis and is necessary for steroidogenesis and biogenesis of iron-sulfur clusters. To determine the biological function of FDXR, we generated a Fdxrdeficient mouse model and found that loss of Fdxr led to embryonic lethality potentially due to iron overload in developing embryos. Interestingly, mice heterozygous in Fdxr had a short life span and were prone to spontaneous tumors and liver abnormalities, including steatosis, hepatitis, and hepatocellular carcinoma. We also found that FDXR was necessary for mitochondrial iron homeostasis and proper expression of several master regulators of iron metabolism, including iron regulatory protein 2 (IRP2). Surprisingly, we found that p53 mRNA translation was suppressed by FDXR deficiency via IRP2. Moreover, we found that the signal from FDXR to iron homeostasis and the p53 pathway was transduced by ferredoxin 2, a substrate of FDXR. Finally, we found that p53 played a role in iron homeostasis and was required for FDXR-mediated iron metabolism. Together, we conclude that FDXR and p53 are mutually regulated and that the FDXR-p53 loop is critical for tumor suppression via iron homeostasis.

Rapid increase in the availability of fine-scale genetic marker maps has led to the intensive use of QTL mapping in the genetic study of quantitative traits. Composite interval mapping (CIM) is one of the most commonly used methods for QTL mapping with populations derived from biparental crosses. However, the algorithm used in CIM cannot completely ensure that the effect of QTL at current testing interval is not absorbed by the background marker variables, and may result in biased estimation of QTL effect. We proposed a statistical method for QTL mapping, which was called inclusive composite interval mapping (ICIM). Two steps were included in ICIM. In the first step, stepwise regression was applied to identify the most significant regression variables. In the second step, a one-dimensional scanning or interval mapping was conducted for detecting additive (and dominance) QTL and a two-dimensional scanning was conducted for detecting digenic epistasis. ICIM provides intuitive statistics for testing additive, dominance and epistasis, and can be used for most experimental populations derived from two inbred parental lines. The EM algorithm used in ICIM has a fast convergence speed and is therefore less computing intensive. ICIM retains all advantages of CIM over interval mapping, and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. A doubled haploid (DH) population in barley was used to demonstrate the application of ICIM in mapping additive QTL and additive by additive interacting QTL.