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We study variance estimation and associated confidence intervals for parameters characterizing genetic effects from genome-wide association studies (GWAS) in misspecified mixed model analysis. Previous studies have shown that, in spite of the model misspecification, certain quantities of genetic interests are consistently estimable, and consistent estimators of these quantities can be obtained using the restricted maximum likelihood (REML) method under a misspecified linear mixed model. However, the asymptotic variance of such a REML estimator is complicated and not ready to be implemented for practical use. In this paper, we develop practical and computationally convenient methods for estimating such asymptotic variances and constructing the associated confidence intervals. Performance of the proposed methods is evaluated empirically based on Monte-Carlo simulations and real-data application.

Large-scale structural variations, such as chromosomal translocations, can have profound effects on fitness and phenotype, but are difficult to identify and characterize. Here, we describe a simple and effective method aimed at identifying translocations using only the dosage of sequence reads mapped on the reference genome. We binned reads on genomic segments sized according to sequencing coverage and identified instances when copy number segregated in populations. For each dosage-polymorphic 1 Mb bin, we tested independence, effectively an apparent linkage disequilibrium (LD), with other variable bins. In nine potato (Solanum tuberosum) dihaploid families translocations affecting pericentromeric regions were common and in two cases were due to genomic misassembly. In two populations, we found evidence for translocation affecting euchromatic arms. In cv. PI 310467, a nonreciprocal translocation between chromosomes (chr.) 7 and 8 resulted in a 5-3 copy number change affecting several Mb at the respective chromosome tips. In cv. "Alca Tarma," the terminal arm of chr. 4 translocated to the tip of chr. 1. Using oligonucleotide-based fluorescent in situ hybridization painting probes (oligo-FISH), we tested and confirmed the predicted arrangement in PI 310467. In 192 natural accessions of Arabidopsis thaliana, dosage haplotypes tended to vary continuously and resulted in higher noise, while apparent LD between pericentromeric regions suggested the effect of repeats. This method, LD-CNV, should be useful in species where translocations are suspected because it tests linkage without the need for genotyping.

Long-term climate change and periodic environmental extremes threaten food and fuel security1 and global crop productivity2-4. Although molecular and adaptive breeding strategies can buffer the effects of climatic stress and improve crop resilience5, these approaches require sufficient knowledge of the genes that underlie productivity and adaptation6-knowledge that has been limited to a small number of well-studied model systems. Here we present the assembly and annotation of the large and complex genome of the polyploid bioenergy crop switchgrass (Panicum virgatum). Analysis of biomass and survival among 732 resequenced genotypes, which were grown across 10 common gardens that span 1,800 km of latitude, jointly revealed extensive genomic evidence of climate adaptation. Climate-gene-biomass associations were abundant but varied considerably among deeply diverged gene pools. Furthermore, we found that gene flow accelerated climate adaptation during the postglacial colonization of northern habitats through introgression of alleles from a pre-adapted northern gene pool. The polyploid nature of switchgrass also enhanced adaptive potential through the fractionation of gene function, as there was an increased level of heritable genetic diversity on the nondominant subgenome. In addition to investigating patterns of climate adaptation, the genome resources and gene-trait associations developed here provide breeders with the necessary tools to increase switchgrass yield for the sustainable production of bioenergy.

We discuss some fundamental issues regarding the roles that statistical models play in surveys. Many survey applications "borrow strength" from statistical models such as regression models and mixed effects models, even though these popular statistical models rarely hold exactly, if at all, in the real world. Yet, the idea of borrowing strength carries on, and the practice proves to be useful in many cases. We discuss basic ideas of borrowing strength via a statistical model and suggest practical criteria for evaluating a model, knowing that it is likely to be incorrect in a strict sense. Furthermore, we discuss a model selection strategy, known as the fence methods, which can incorporate practical interests into the selection criterion. Examples are used for illustration. Further remarks on challenging problems are offered. [ABSTRACT FROM AUTHOR]

In many practical problems, there is interest in the estimation of mixed effect projections for new data that are outside the range of the training data. Examples include predicting extreme small area means for rare populations or making treatment decisions for patients who do not fit typical risk profiles. Standard methods have long been known to struggle with such problems since the training data may not provide enough information about potential model changes for these new data values (extrapolation bias). We propose a new framework called Prediction Using Random-effect Extrapolation (PURE) which involves constructing a generalized independent variable hull (gIVH) to isolate a minority training set which is "close" to the prediction space, followed by a regrouping of the minority data according to the response variable which results in a new (but misspecified) random effect distribution. This misspecification reflects "extrapolated random effects" which prove vital to capture information that is needed for accurate model projections. Projections are then made using classified mixed model prediction (CMMP) (?) with the regrouped minority data. Comprehensive simulation studies and analysis of data from the National Longitudinal Mortality Study (NLMS) demonstrate superior predictive performance in these very challenging paradigms. An asymptotic analysis reveals why PURE results in more accurate projections. for this article are available online. [ABSTRACT FROM AUTHOR]

Centromeres in most higher eukaryotes are composed of long arrays of satellite repeats from a single satellite repeat family. Why centromeres are dominated by a single satellite repeat and how the satellite repeats originate and evolve are among the most intriguing and long-standing questions in centromere biology. We identified eight satellite repeats in the centromeres of tetraploid switchgrass (Panicum virgatum). Seven repeats showed characteristics associated with classical centromeric repeats with monomeric lengths ranging from 166 to 187 bp. Interestingly, these repeats share an 80-bp DNA motif. We demonstrate that this 80-bp motif may dictate translational and rotational phasing of the centromeric repeats with the cenH3 nucleosomes. The sequence of the last centromeric repeat, Pv156, is identical to the 5S ribosomal RNA genes. We demonstrate that a 5S ribosomal RNA gene array was recruited to be the functional centromere for one of the switchgrass chromosomes. Our findings reveal that certain types of satellite repeats, which are associated with unique sequence features and are composed of monomers in mono-nucleosomal length, are favorable for centromeres. Centromeric repeats may undergo dynamic amplification and adaptation before the centromeres in the same species become dominated by the best adapted satellite repeat.