scholarly journals Genomic Insight into Differentiation and Selection Sweeps in the Improvement of Upland Cotton

Plants ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 711
Author(s):  
Mian Faisal Nazir ◽  
Yinhua Jia ◽  
Haris Ahmed ◽  
Shoupu He ◽  
Muhammad Shahid Iqbal ◽  
...  
Keyword(s):  
Upland Cotton ◽  
Population Genomics ◽  
Association Studies ◽  
Genome Structure ◽  
Morphological Characteristics ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
History Of ◽  
Fibre Yield ◽  
Insight Into

Upland cotton is the most economically important fibre crop. The human-mediated selection has resulted in modern upland cultivars with higher yield and better fibre quality. However, changes in genome structure resulted from human-mediated selection are poorly understood. Comparative population genomics offers us tools to dissect the genetic history of domestication and helps to understand the genome-wide effects of human-mediated selection. Hereby, we report a comprehensive assessment of Gossypium hirsutum landraces, obsolete cultivars and modern cultivars based on high throughput genome-wide sequencing of the core set of genotypes. As a result of the genome-wide scan, we identified 93 differential regions and 311 selection sweeps associated with domestication and improvement. Furthermore, we performed genome-wide association studies to identify traits associated with the differential regions and selection sweeps. Our study provides a genetic basis to understand the domestication process in Chinese cotton cultivars. It also provides a comprehensive insight into changes in genome structure due to selection and improvement during the last century. We also identified multiple genome-wide associations (GWAS associations) for fibre yield, quality and other morphological characteristics.

26-OR: Meta-analyses of Genome-Wide Association Studies for Proinsulin Provide Insight into Glycemic Trait Dysregulation

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 26-OR
Author(s):  
K. ALAINE BROADAWAY ◽  
XIANYONOG YIN ◽  
ALICE WILLIAMSON ◽  
EMMA WILSON ◽  
MAGIC INVESTIGATORS
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Meta Analyses ◽  
Insight Into

scholarly journals An approach to gene-based testing accounting for dependence of tests among nearby genes

2021 ◽  
Author(s):  
Ronald J Yurko ◽  
Kathryn Roeder ◽  
Bernie Devlin ◽  
Max G'Sell
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Autism Spectrum ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Strongly Correlated ◽  
Test Statistics ◽  
Test Statistic ◽  
Genome Wide ◽  
Insight Into

In genome-wide association studies (GWAS), it has become commonplace to test millions of SNPs for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive p-value thresholding (AdaPT), guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

Alzheimer's disease genetics: lessons to improve disease modelling

2011 ◽  
Vol 39 (4) ◽  
pp. 910-916 ◽  
Author(s):  
Rita J. Guerreiro ◽  
John Hardy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Current Knowledge ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Complete Understanding ◽  
Genome Wide ◽  
History Of ◽  
Ad Alzheimer’S Disease

In the present review, we look back at the recent history of GWAS (genome-wide association studies) in AD (Alzheimer's disease) and integrate the major findings with current knowledge of biological processes and pathways. These topics are essential for the development of animal models, which will be fundamental to our complete understanding of AD.

scholarly journals Proportion of idiopathic pulmonary fibrosis risk explained by known genetic loci

2020 ◽  
Author(s):  
Olivia C Leavy ◽  
Shwu-Fan Ma ◽  
Philip L Molyneaux ◽  
Toby M Maher ◽  
Justin M Oldham ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Genetic Architecture ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Risk Variants ◽  
Genome Wide ◽  
The Impact ◽  
Insight Into

Genome-wide association studies have identified 14 genetic loci associated with susceptibility to idiopathic pulmonary fibrosis (IPF), a devastating lung disease with poor prognosis. Of these, the variant with the strongest association, rs35705950, is located in the promoter region of the MUC5B gene and has a risk allele (T) frequency of 30-35% in IPF cases. Here we present estimates of the proportion of disease liability explained by each of the 14 IPF risk variants as well as estimates of the proportion of cases that can be attributed to each variant. We estimate that rs35705950 explains 5.9-9.4% of disease liability, which is much lower than previously reported estimates. Of every 100,000 individuals with the rs35705950_GG genotype we estimate 30 will have IPF, whereas for every 100,000 individuals with the rs35705950_GT genotype 152 will have IPF. Quantifying the impact of genetic risk factors on disease liability improves our understanding of the underlying genetic architecture of IPF and provides insight into the impact of genetic factors in risk prediction modelling.

scholarly journals Host genetics and microbiome associations from the lens of genome wide association studies

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Daphna Rothschild ◽  
Elad Barkan ◽  
Eran Segal
Keyword(s):  
Gut Microbiome ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Lessons Learned ◽  
Host Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
History Of

Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotypes associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: (a) Adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; (b) enforcing stringent statistical criteria to reduce the number of false positive findings; and (c) considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.

scholarly journals Detecting polygenic adaptation in admixture graphs

2017 ◽  
Author(s):  
Fernando Racimo ◽  
Jeremy J. Berg ◽  
Joseph K. Pickrell
Keyword(s):  
Human Evolution ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Mcmc Algorithm ◽  
Genome Wide ◽  
Polygenic Adaptation ◽  
History Of ◽  
Different Populations ◽  
The Mean ◽  
Open Question

AbstractAn open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred. To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain Monte Carlo (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation. We show via simulations that this method - which we call PolyGraph - has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different populations during human evolution.

scholarly journals Predicting Parkinson’s: Using Neural Networks to Evaluate the Genetic Risk Factors Behind the Disease

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Andrew Yuan ◽  
Isha Jagadish ◽  
Trisha Gongalore ◽  
Joseph Alzagatiti
Keyword(s):  
Risk Factors ◽  
Dopaminergic Neurons ◽  
Association Studies ◽  
Substantia Nigra Pars Compacta ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Predictive Diagnosis ◽  
Genome Wide ◽  
Significant Research ◽  
History Of

To date, researchers do not know the exact reasons for the loss of dopaminergic neurons in the substantia nigra pars compacta that leads to Parkinson’s Disease (PD). Thus, it is extremely difficult to predict whether or not a patient is likely to develop the disease later on, as their risk increases with age. However, once patients present with the common symptoms indicative of the illness, a substantial amount of dopaminergic neurons are already lost. Seeing as there are no current avenues of replacing those neurons, predictive diagnosis and preventive measures could be of extraordinary help in devising treatments. Our aim was to use the significant research into possible high-risk genetic factors from genome-wide association studies (GWAS) to formulate a predictive neural network model for Parkinson’s. We analyzed patient genomes for mutations in the top 20 genes associated with PD, as well as 21 genes implicated in axon guidance pathways, to determine whether the patients were at high or low risk for Parkinson’s. Our model produced an accuracy and AUROC of 94%. We found this significant because it showed a strong correlation between the single nucleotide polymorphisms (SNPs) we analyzed and PD. We believe our model can be further improved upon by adding considerations for other investigated risk factors, such as patient age, familial history of disease, or gut microbiota inconsistencies among others.

Prostate Cancer

2017 ◽  
Author(s):  
Catherine M. Tangen ◽  
Marian L. Neuhouser ◽  
Janet L. Stanford
Keyword(s):  
Prostate Cancer ◽  
Cancer Susceptibility ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Rare Mutations ◽  
Genome Wide ◽  
Sequencing Studies ◽  
Distinct Disease ◽  
History Of ◽  
Family Based

Prostate cancer is the most common solid tumor and the second leading cause of cancer-related mortality in American men. Worldwide, prostate cancer ranks second and fifth as a cause of cancer and cancer deaths, respectively. Despite the international burden of disease due to prostate cancer, its etiology is unclear in most cases. Established risk factors include age, race/ancestry, and family history of the disease. Prostate cancer has a strong heritable component, and genome-wide association studies have identified over 110 common risk-associated genetic variants. Family-based sequencing studies have also found rare mutations (e.g., HOXB13) that contribute to prostate cancer susceptibility. Numerous environmental and lifestyle factors (e.g., obesity, diet) have been examined in relation to prostate cancer incidence, but few modifiable exposures have been consistently associated with risk. Some of the variability in results may be related to etiological heterogeneity, with different causes underlying the development of distinct disease subgroups.

Sign in / Sign up

Export Citation Format

Share Document