Exploring the genetic patterns of complex diseases via the integrative genome-wide approach

Ben Teng, Can Yang, Jiming Liu, Zhipeng Cai, Xiang Wan
(Submitted on 26 Jan 2015)

Motivation: Genome-wide association studies (GWASs), which assay more than a million single nucleotide polymorphisms (SNPs) in thousands of individuals, have been widely used to identify genetic risk variants for complex diseases. However, most of the variants that have been identified contribute relatively small increments of risk and only explain a small portion of the genetic variation in complex diseases. This is the so-called missing heritability problem. Evidence has indicated that many complex diseases are genetically related, meaning these diseases share common genetic risk variants. Therefore, exploring the genetic correlations across multiple related studies could be a promising strategy for removing spurious associations and identifying underlying genetic risk variants, and thereby uncovering the mystery of missing heritability in complex diseases. Results: We present a general and robust method to identify genetic patterns from multiple large-scale genomic datasets. We treat the summary statistics as a matrix and demonstrate that genetic patterns will form a low-rank matrix plus a sparse component. Hence, we formulate the problem as a matrix recovering problem, where we aim to discover risk variants shared by multiple diseases/traits and those for each individual disease/trait. We propose a convex formulation for matrix recovery and an efficient algorithm to solve the problem. We demonstrate the advantages of our method using both synthesized datasets and real datasets. The experimental results show that our method can successfully reconstruct both the shared and the individual genetic patterns from summary statistics and achieve better performance compared with alternative methods under a wide range of scenarios.

Ancestry specific association mapping in admixed populations

Line Skotte, Thorfinn Sand S Korneliussen, Ida Moltke, Anders Albrechtsen
doi: http://dx.doi.org/10.1101/014001

As recently demonstrated in several genetic association studies, historically small and isolated populations can offer increased statistical power due to extended link- age equilibrium and increased genetic drift over many generations. However, many such populations, like the Greenlandic Inuit population, have recently experienced substantial admixture with other populations, which can complicate the association studies. One important complication is that most current methods for performing association testing are based on the assumption that the effect of the tested ge- netic marker is the same regardless of ancestry. This is a reasonable assumption for a causal variant, but may not hold for the genetic markers that are tested in association studies, which are usually not causal. The effects of non-causal genetic markers depend on how strongly their presence correlate with the presence of the causal marker, and this may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies. Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect sizes are allowed to depend on the ancestry of the allele.Our method does not rely on accurate inference of local ancestry, yet using simulations we show that in some scenarios it gives a dramatic increase in statistical power to detect associations. In addition, the method allows for testing for difference in effect size between ancestral populations, which can be used to determine if a SNP is causal. We demonstrate the usefulness of the method on data from the Greenlandic population.

Integrating crop growth models with whole genome prediction through approximate Bayesian computation

Frank Technow, Carlos D. Messina, L. Radu Totir, Mark Cooper
doi: http://dx.doi.org/10.1101/014100

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (GxE), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of GxE and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with a simulated data set. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising novel approach to improving prediction accuracy for some of the most challenging scenarios of interest to applied geneticists.