A mixed-model approach for genome-wide association studies of correlated traits in structured populations

Genome-wide association studies (GWAS) are a standard approach for studying the genetics of natural variation. A major concern in GWAS is the need to account for the complicated dependence structure of the data, both between loci as well as between individuals. Mixed models have emerged as a general and flexible approach for correcting for population structure in GWAS. Here, we extend this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-trait mixed model (MTMM) that considers both the within-trait and between-trait variance components simultaneously for multiple traits. We apply this to data from a human cohort for correlated blood lipid traits from the Northern Finland Birth Cohort 1966 and show greatly increased power to detect pleiotropic loci that affect more than one blood lipid trait. We also apply this approach to an Arabidopsis thaliana data set for flowering measurements in two different locations, identifying loci whose effect depends on the environment.     

Mouse ooplasm confers context-specific reprogramming capacity

Enucleated oocytes have the distinctive ability to reprogram somatic nuclei back to totipotency. Here, we investigate genome-scale DNA methylation patterns after nuclear transfer and compare them to the dynamics at fertilization. We identify specific targets for DNA demethylation after nuclear transfer, such as germline-associated promoters, as well as unique limitations that include certain repetitive element classes.     

Loss-of-function mutations in TGFB2 cause a syndromic presentation of thoracic aortic aneurysm

Loeys-Dietz syndrome (LDS) associates with a tissue signature for high transforming growth factor (TGF)-β signaling but is often caused by heterozygous mutations in genes encoding positive effectors of TGF-β signaling, including either subunit of the TGF-β receptor or SMAD3, thereby engendering controversy regarding the mechanism of disease. Here, we report heterozygous mutations or deletions in the gene encoding the TGF-β2 ligand for a phenotype within the LDS spectrum and show upregulation of TGF-β signaling in aortic tissue from affected individuals. Furthermore, haploinsufficient Tgfb2(+/-) mice have aortic root aneurysm and biochemical evidence of increased canonical and noncanonical TGF-β signaling. Mice that harbor both a mutant Marfan syndrome (MFS) allele (Fbn1(C1039G/+)) and Tgfb2 haploinsufficiency show increased TGF-β signaling and phenotypic worsening in association with normalization of TGF-β2 expression and high expression of TGF-β1. Taken together, these data support the hypothesis that compensatory autocrine and/or paracrine events contribute to the pathogenesis of TGF-β-mediated vasculopathies.     

Activation of the AXL kinase causes resistance to EGFR-targeted therapy in lung cancer

Human non-small cell lung cancers (NSCLCs) with activating mutations in EGFR frequently respond to treatment with EGFR-targeted tyrosine kinase inhibitors (TKIs), such as erlotinib, but responses are not durable, as tumors acquire resistance. Secondary mutations in EGFR (such as T790M) or upregulation of the MET kinase are found in over 50% of resistant tumors. Here, we report increased activation of AXL and evidence for epithelial-to-mesenchymal transition (EMT) in multiple in vitro and in vivo EGFR-mutant lung cancer models with acquired resistance to erlotinib in the absence of the EGFR p.Thr790Met alteration or MET activation. Genetic or pharmacological inhibition of AXL restored sensitivity to erlotinib in these tumor models. Increased expression of AXL and, in some cases, of its ligand GAS6 was found in EGFR-mutant lung cancers obtained from individuals with acquired resistance to TKIs. These data identify AXL as a promising therapeutic target whose inhibition could prevent or overcome acquired resistance to EGFR TKIs in individuals with EGFR-mutant lung cancer.     

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable.     

Genome stability, progressive kidney failure and aging

Two new studies report mutations in FAN1 and three other genome-stability genes that tie the DNA damage response to progressive kidney failure and the dysfunction of several other organs. These findings provide clues to the underlying causes of tissue decline and may add a series of genes to the growing list of genome maintenance factors that protect against premature aging.     

Maize HapMap2 identifies extant variation from a genome in flux

Whereas breeders have exploited diversity in maize for yield improvements, there has been limited progress in using beneficial alleles in undomesticated varieties. Characterizing standing variation in this complex genome has been challenging, with only a small fraction of it described to date. Using a population genetics scoring model, we identified 55 million SNPs in 103 lines across pre-domestication and domesticated Zea mays varieties, including a representative from the sister genus Tripsacum. We find that structural variations are pervasive in the Z. mays genome and are enriched at loci associated with important traits. By investigating the drivers of genome size variation, we find that the larger Tripsacum genome can be explained by transposable element abundance rather than an allopolyploid origin. In contrast, intraspecies genome size variation seems to be controlled by chromosomal knob content. There is tremendous overlap in key gene content in maize and Tripsacum, suggesting that adaptations from Tripsacum (for example, perennialism and frost and drought tolerance) can likely be integrated into maize.