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1. Structure-informed clustering for population stratification in association studies

   https://doi.org/10.1186/s12859-023-05511-w  

   Bose, Aritra ; Burch, Myson ; Chowdhury, Agniva ; Paschou, Peristera ; Drineas, Petros ( October 2023 , BMC Bioinformatics)

   Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants.

   To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans.

   CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

    

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2. Genome-Wide Association Analyses in the Model Rhizobium Ensifer meliloti

   https://doi.org/10.1128/mSphere.00386-18  

   Epstein, Brendan ; Abou-Shanab, Reda A. ; Shamseldin, Abdelaal ; Taylor, Margaret R. ; Guhlin, Joseph ; Burghardt, Liana T. ; Nelson, Matthew ; Sadowsky, Michael J. ; Tiffin, Peter ; Oh, Julia ( October 2018 , mSphere)

   ABSTRACT Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Association studies therefore provide a powerful complement to approaches that rely on de novo mutations for characterizing gene function. Although bacteria should be amenable to GWAS, few GWAS have been conducted on bacteria, and the extent to which nonindependence among genomic variants (e.g., linkage disequilibrium [LD]) and the genetic architecture of phenotypic traits will affect GWAS performance is unclear. We apply association analyses to identify candidate genes underlying variation in 20 biochemical, growth, and symbiotic phenotypes among 153 strains of Ensifer meliloti . For 11 traits, we find genotype-phenotype associations that are stronger than expected by chance, with the candidates in relatively small linkage groups, indicating that LD does not preclude resolving association candidates to relatively small genomic regions. The significant candidates show an enrichment for nucleotide polymorphisms (SNPs) over gene presence-absence variation (PAV), and for five traits, candidates are enriched in large linkage groups, a possible signature of epistasis. Many of the variants most strongly associated with symbiosis phenotypes were in genes previously identified as being involved in nitrogen fixation or nodulation. For other traits, apparently strong associations were not stronger than the range of associations detected in permuted data. In sum, our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. However, careful evaluation of candidates is necessary to avoid false signals of association. IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in Ensifer meliloti , an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations. 

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3. A review of statistical methods in imaging genetics

   https://doi.org/10.1002/cjs.11487  

   Nathoo, Farouk S. ; Kong, Linglong ; Zhu, Hongtu ; for the Alzheimer's Disease Neuroimaging Initiative ( February 2019 , Canadian Journal of Statistics)

   With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi‐modality imaging, genetic, neurocognitive and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function and brain‐related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention and treatment of numerous complex brain‐related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high‐dimensional imaging phenotypes and high‐dimensional genetic data, a big data squared (BD²) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high‐dimensional nature of BD², various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel‐wise approaches, reduced rank regression, mixture models and group sparse multi‐task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research.The Canadian Journal of Statistics47: 108–131; 2019 © 2019 Statistical Society of Canada

    

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4. Adaptive evolution in a conifer hybrid zone is driven by a mosaic of recently introgressed and background genetic variants

   https://doi.org/10.1038/s42003-020-01632-7  

   Menon, Mitra ; Bagley, Justin C. ; Page, Gerald F. M. ; Whipple, Amy V. ; Schoettle, Anna W. ; Still, Christopher J. ; Wehenkel, Christian ; Waring, Kristen M. ; Flores-Renteria, Lluvia ; Cushman, Samuel A. ; et al ( February 2021 , Communications Biology)

   Extant conifer species may be susceptible to rapid environmental change owing to their long generation times, but could also be resilient due to high levels of standing genetic diversity. Hybridisation between closely related species can increase genetic diversity and generate novel allelic combinations capable of fuelling adaptive evolution. Our study unravelled the genetic architecture of adaptive evolution in a conifer hybrid zone formed betweenPinus strobiformisandP. flexilis. Using a multifaceted approach emphasising the spatial and environmental patterns of linkage disequilibrium and ancestry enrichment, we identified recently introgressed and background genetic variants to be driving adaptive evolution along different environmental gradients. Specifically, recently introgressed variants fromP. flexiliswere favoured along freeze-related environmental gradients, while background variants were favoured along water availability-related gradients. We posit that such mosaics of allelic variants within conifer hybrid zones will confer upon them greater resilience to ongoing and future environmental change and can be a key resource for conservation efforts.

    

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5. Are rare variants really independent?

   https://doi.org/10.1002/gepi.22039  

   Turkmen, Asuman ; Lin, Shili ( March 2017 , Genetic Epidemiology)

   Recent advances in genotyping with high‐density markers allow researchers access to genomic variants including rare ones. Linkage disequilibrium (LD) is widely used to provide insight into evolutionary history. It is also the basis for association mapping in humans and other species. Better understanding of the genomic LD structure may lead to better‐informed statistical tests that can improve the power of association studies. Although rare variant associations with common diseases (RVCD) have been extensively studied recently, there is very limited understanding, and even controversial view of LD structures among rare variants and between rare and common variants. In fact, many popular RVCD tests make the assumptions that rare variants are independent. In this report, we show that two commonly used LD measures are not capable of detecting LD when rare variants are involved. We present this argument from two perspectives, both the LD measures themselves and the computational issues associated with them. To address these issues, we propose an alternative LD measure, the polychoric correlation, that was originally designed for detecting associations among categorical variables. Using simulated as well as the 1000 Genomes data, we explore the performances of LD measures in detail and discuss their implications in association studies.

    

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