scholarly journals Distinct characteristics of genes associated with phenome-wide variation in maize (Zea mays)

2019 ◽  
Author(s):  
Zhikai Liang ◽  
Yumou Qiu ◽  
James C. Schnable
Keyword(s):  
Natural Populations ◽  
Purifying Selection ◽  
Forward Genetics ◽  
Genome Wide Association ◽  
Phenotypic Traits ◽  
Functional Variation ◽  
Maize Diversity ◽  
Genome Wide ◽  
Gene Models ◽  
Using Data

ABSTRACTNaturally occurring functionally variable alleles in specific genes within a population allows the identification of which genes are involved in the determination of which phenotypes. The omnigenetic model proposes that essentially all genes which are expressed in relevant contexts likely play some role in determining phenotypic outcomes. Here, we develop an approach to identify genes where natural functional variation plays a role in shaping many phenotypic traits simultaneously. We demonstrate that this approach identifies a distinct set of genes relative to conventional genome wide association using data for 260 traits scored a maize diversity panel, and the genes identified using this approach are more likely to be independently validated than genes identified by convetional genome wide association. Genes identified by the new approach share a number of features with a gold standard set of genes characterized through forward genetics which separate them from both genes identified by conventional genome wide association and the overall population of annotated gene models. These features include evidence of significantly stronger purifying selection, positional conservation across the genomes of related species, greater length, and a scarcity of presence absence variation for these loci in natural populations. Genes identified by phenome-wide analyses also showed much stronger signals of GO enrichment and purification than genes identified by conventional genome wide association. Overall these findings are consistent with large subset of annotated gene models – despite support from transcriptional and homology evidence – being unlikely to play any role in determining organismal phenotypes.

Genome-wide association implicates numerous genes underlying ecological trait variation in natural populations ofPopulus trichocarpa

2014 ◽  
Vol 203 (2) ◽  
pp. 535-553 ◽  
Author(s):  
Athena D. McKown ◽  
Jaroslav Klápště ◽  
Robert D. Guy ◽  
Armando Geraldes ◽  
Ilga Porth ◽  
...  
Keyword(s):  
Natural Populations ◽  
Genome Wide Association ◽  
Trait Variation ◽  
Genome Wide

scholarly journals Genome-wide association analysis of insomnia using data from Partners Biobank

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Wenyu Song ◽  
John Torous ◽  
Joe Kossowsky ◽  
Chia-Yen Chen ◽  
Hailiang Huang ◽  
...  
Keyword(s):  
Association Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
Genome Wide ◽  
Using Data

Genome-wide association mapping of genomic regions associated with phenotypic traits in Canadian western spring wheat

2017 ◽  
Vol 37 (11) ◽  
Author(s):  
Hua Chen ◽  
Kassa Semagn ◽  
Muhammad Iqbal ◽  
Neshat Pazooki Moakhar ◽  
Teketel Haile ◽  
...  
Keyword(s):  
Association Mapping ◽  
Spring Wheat ◽  
Genome Wide Association ◽  
Phenotypic Traits ◽  
Genome Wide ◽  
Genomic Regions

scholarly journals Estimation of heritability for nine common cancers using data from genome-wide association studies in Chinese population

2016 ◽  
Vol 140 (2) ◽  
pp. 329-336 ◽  
Author(s):  
Juncheng Dai ◽  
Wei Shen ◽  
Wanqing Wen ◽  
Jiang Chang ◽  
Tongmin Wang ◽  
...  
Keyword(s):  
Chinese Population ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Using Data

scholarly journals Genome-wide association study of Alzheimer’s disease CSF biomarkers in the EMIF-AD Multimodal Biomarker Discovery dataset

2019 ◽  
Author(s):  
Shengjun Hong ◽  
Dmitry Prokopenko ◽  
Valerija Dobricic ◽  
Fabian Kilpert ◽  
Isabelle Bos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Biomarker Discovery ◽  
Case Control ◽  
Genome Wide Association ◽  
Great Promise ◽  
Phenotypic Traits ◽  
Genome Wide

AbstractAlzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Susceptibility to AD is considerably determined by genetic factors which hitherto were primarily identified using case-control designs. Elucidating the genetic architecture of additional AD-related phenotypic traits, ideally those linked to the underlying disease process, holds great promise in gaining deeper insights into the genetic basis of AD and in developing better clinical prediction models. To this end, we generated genome-wide single-nucleotide polymorphism (SNP) genotyping data in 931 participants of the European Medical Information Framework Alzheimer’s Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample to search for novel genetic determinants of AD biomarker variability. Specifically, we performed genome-wide association study (GWAS) analyses on 16 traits, including 14 measures of amyloid-beta (Aβ) and tau-protein species in the cerebrospinal fluid (CSF). In addition to confirming the well-established effects of apolipoprotein E (APOE) on diagnostic outcome and phenotypes related to Aβ42, we detected novel potential signals in the zinc finger homeobox 3 (ZFHX3) for CSF-Aβ38 and CSF-Aβ40 levels, and confirmed the previously described sex-specific association between SNPs in geminin coiled-coil domain containing (GMNC) and CSF-tau. Utilizing the results from independent case-control AD GWAS to construct polygenic risk scores (PRS) revealed that AD risk variants only explain a small fraction of CSF biomarker variability. In conclusion, our study represents a detailed first account of GWAS analyses on CSF-Aβ and -tau related traits in the EMIF-AD MBD dataset. In subsequent work, we will utilize the genomics data generated here in GWAS of other AD-relevant clinical outcomes ascertained in this unique dataset.

scholarly journals Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Ana Viñuela ◽  
Arushi Varshney ◽  
Martijn van de Bunt ◽  
Rashmi B. Prasad ◽  
Olof Asplund ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association ◽  
Regulatory Sequences ◽  
Genome Wide Association Studies ◽  
Risk Variants ◽  
Genome Wide ◽  
Gwas Signal ◽  
Using Data

Abstract Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.

scholarly journals Genome-Wide Association Study towards Genomic Predictive Power for High Production and Quality of Milk in American Alpine Goats

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Y. Tilahun ◽  
T. A. Gipson ◽  
T. Alexander ◽  
M. L. McCallum ◽  
P. R. Hoyt
Keyword(s):  
Association Study ◽  
Candidate Genes ◽  
Predictive Power ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Breeding Value ◽  
Phenotypic Traits ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Dairy Traits

This paper reports an exploratory study based on quantitative genomic analysis in dairy traits of American Alpine goats. The dairy traits are quality-determining components in goat milk, cheese, ice cream, etc. Alpine goat phenotypes for quality components have been routinely recorded for many years and deposited in the Council on Dairy Cattle Breeding (CDCB) repository. The data collected were used to conduct an exploratory genome-wide association study (GWAS) from 72 female Alpine goats originating from locations throughout the U.S. Genotypes were identified with the Illumina Goat 50K single-nucleotide polymorphisms (SNP) BeadChip. The analysis used a polygenic model where the dropping criterion was a call rate≥0.95. The initial dataset was composed of ~60,000 rows of SNPs and 21 columns of phenotypic traits and composed of 53,384 scaffolds containing other informative data points used for genomic predictive power. Phenotypic association with the 50K BeadChip revealed 26,074 reads of candidate genes. These candidate genes segregated as separate novel SNPs and were identified as statistically significant regions for genome and chromosome level trait associations. Candidate genes associated differently for each of the following phenotypic traits: test day milk yield (13,469 candidate genes), test day protein yield (25,690 candidate genes), test day fat yield (25,690 candidate genes), percentage protein (25,690 candidate genes), percentage fat (25,690 candidate genes), and percentage lactose content (25,690 candidate genes). The outcome of this study supports elucidation of novel genes that are important for livestock species in association to key phenotypic traits. Validation towards the development of marker-based selection that provides precision breeding methods will thereby increase the breeding value.

scholarly journals The probability of genetic parallelism and convergence in natural populations

2012 ◽  
Vol 279 (1749) ◽  
pp. 5039-5047 ◽  
Author(s):  
Gina L. Conte ◽  
Matthew E. Arnegard ◽  
Catherine L. Peichel ◽  
Dolph Schluter
Keyword(s):  
Adaptive Evolution ◽  
Natural Populations ◽  
Small Subset ◽  
Phenotypic Traits ◽  
Phenotypic Evolution ◽  
Genomic Studies ◽  
Genetic Level ◽  
Candidate Gene Studies ◽  
Different Populations ◽  
Using Data

Genomic and genetic methods allow investigation of how frequently the same genes are used by different populations during adaptive evolution, yielding insights into the predictability of evolution at the genetic level. We estimated the probability of gene reuse in parallel and convergent phenotypic evolution in nature using data from published studies. The estimates are surprisingly high, with mean probabilities of 0.32 for genetic mapping studies and 0.55 for candidate gene studies. The probability declines with increasing age of the common ancestor of compared taxa, from about 0.8 for young nodes to 0.1–0.4 for the oldest nodes in our study. Probability of gene reuse is higher when populations begin from the same ancestor (genetic parallelism) than when they begin from divergent ancestors (genetic convergence). Our estimates are broadly consistent with genomic estimates of gene reuse during repeated adaptation to similar environments, but most genomic studies lack data on phenotypic traits affected. Frequent reuse of the same genes during repeated phenotypic evolution suggests that strong biases and constraints affect adaptive evolution, resulting in changes at a relatively small subset of available genes. Declines in the probability of gene reuse with increasing age suggest that these biases diverge with time.

scholarly journals Univariate/Multivariate Genome-Wide Association Scans Using Data from Families and Unrelated Samples

PLoS ONE ◽  
2009 ◽  
Vol 4 (8) ◽  
pp. e6502 ◽  
Author(s):  
Lei Zhang ◽  
Yu-Fang Pei ◽  
Jian Li ◽  
Christopher J. Papasian ◽  
Hong-Wen Deng
Keyword(s):  
Genome Wide Association ◽  
Genome Wide ◽  
Using Data
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