Browsing by Subject "Population structure"

Now showing 1 - 3 of 3
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    Comparative analyses identify adaptive genetic variation in crops and crop wild relatives
    (2013-12) Fang, Zhou
    Comparative population genetic analyses provide a means of identifying adaptive genetic variation. In this dissertation, I apply population genetic approaches to identify putatively adaptive variants in the genomes of crops and crop wild relatives. These approaches have the potential to identify genetic variants that are under selection and thus potentially contributing to local adaptation. As a background to the dissertation, I present in Chapter 1 the state of research in this field at the time I started my PhD and give a brief introduction to the projects described in this dissertation. In Chapter 2, I report a ~50-Mb chromosomal inversion in the wild ancestor of maize - teosinte (Zea mays ssp. parviglumis) and characterized its distribution and abundance in natural populations using population genetic approaches. This is also the first study in plants to apply population genetic approaches to identify chromosomal structural variation. In Chapter 3, I used a population genetic approach to identify genomic regions that contain adaptive mutations resistant to Fusarium head blight in a barley experimental breeding population. The successful application of comparative population genetic approaches in this study suggests this approach can also be used to identify genomic regions that are under selection in other breeding populations. In Chapter 4, I studied the geographic differentiation in wild barley (Hordeum vulgare ssp. spontaneum). I found two genomic regions contribute disproportionately to the population structure in wild barley. These same regions, with reduced evidence of recombination, are strongly associated with environmental variables. Population genetic evidence and previous cytological and genetic studies suggest these two genomic regions may be chromosomal structural rearrangements.
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    Sunflower Evolution and Adaption to Climate Change
    (2021-04-30) Levi, Sophie J; Spear, Marissa M; Etterson, Julie R; Gross, Briana L
    Temperatures in Minnesota will rise drastically in the next several decades, and it is important to understand how plants will react to this climate change. Some researchers have studied these reactions with resurrection studies, in which modern and antecedent plant lines are grown in a common environment to monitor evolution thus far and possible adaption to new environments. However, few studies have investigated the hybrid progeny of these resurrected lines. The hybrids serve as a study system for understanding evolution and maternal effects. We investigated these hybrid progeny from a resurrection study of Helianthus annuus and found surprising phenotypic plasticity, decreased transgenerational plasticity in warmer temperatures, and introgression from cultivated varieties.
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    Two topics in association analysis of DNA sequencing data: population structure and multivariate traits
    (2013-08) Zhang, Yiwei
    As the next-generation sequencing technologies become mature and affordable, we now have access to massive data of single nucleotides variants (SNVs) with varying minor allele frequencies (MAFs). This poses new opportunities, as more information from the human genome is available. However, new challenges also show up, such as how to utilize those SNVs with low MAFs. With current intensive efforts in association testing to detect genetic loci associated with common diseases and complex traits, two issues are of primary interest: reducing spurious findings and increasing power for true discoveries. In association testing, a major cause to the elevated level of false positives is the confounding effect of population structure -- the so-called population stratification. As a remedy, one popular method is to add principal components (PCs) in a regression model, named principal component regression (PCR). Yet, it is not clear how PCR will work in testing rare variants (RVs, with MAF$<0.01$), or with population stratification in a fine scale. More questions arise, like what types and what sets of SNVs should be used to construct PCs, and whether there are other better methods than principal component analysis (PCA) for constructing PCs. Utilizing the DNA sequencing data from the 1000 Genomes project, we first investigate whether PCR is adequate in adjusting for population stratification while maintaining high power when testing low frequency variants (LFVs with 0.01&lq MAF<0.05) and RVs. Furthermore, we compare the performance of two dimension reduction methods, PCA and spectral dimension reduction (SDR), as well as twelve different types and sets of variants for constructing PCs. The comparison is conducted with respect to controlling population stratification in a fine scale. On the other hand, linear mixed models (LMM) have emerged with its superior performance in handling complex population structures. Herein, we examine the connection and difference between PCR and LMM based on the formulation of probabilistic PCA, and propose a hybrid method combining the two. Its outstanding performance in addressing both population structure and environmental confounders is established by simulations using the the Genetic Analysis Workshop (GAW) 18 data and the 1000 Genomes project data. Lastly, we consider boosting power for association analysis of multivariate traits. A new class of tests, the sum of powered score tests (SPU), and an adaptive SPU (aSPU) test are extended to the generalized estimation equations (GEE) framework. We apply the new and some existing methods to association testing on both CVs and RVs with an HIV/AIDS dataset and the GAW 18 data.