Repetitive DNA content in the maize genome is uncoupled from population stratification at SNP loci

Mapping Intimacies ◽

10.1101/652859 ◽

2019 ◽

Author(s):

Simon Renny-Byfield ◽

Andy Baumgarten

Keyword(s):

Population Structure ◽

Repetitive Dna ◽

Population Stratification ◽

Gene Density ◽

Model Organisms ◽

Evolutionary Significance ◽

Chromosomal Distribution ◽

Repeat Content ◽

Content Variation ◽

Genome Wide

AbstractMotivationRepetitive DNA is a major component of plant genomes and is thought to be a driver of evolutionary novelty. Describing variation in repeat content among individuals and between populations is key to elucidating the evolutionary significance of repetitive DNA. However, the cost of producing references genomes has limited large-scale intraspecific comparisons to a handful of model organisms where multiple reference genomes are available.ResultsWe examine repeat content variation in the genomes of 94 elite inbred maize lines using graph-based repeat clustering, a reference-free and rapid assay of repeat content. We examine population structure using genome-wide repeat profiles and demonstrate the stiff-stalk and non-stiff-stalk heterotic populations are homogenous with regard to global repeat content. In contrast and similar to previously reported results, the same individuals show clear differentiation, and aggregate into two populations, when examining population structure using genome-wide SNPs. Additionally, we develop a novel kmer based technique to examine the chromosomal distribution of repeat clusters in silico and show a cluster dependent statistically significant association with gene density.ConclusionOur results indicate that repeat content variation in the heterotic populations of maize has not diverged and is uncoupled from population stratification at SNP loci. We also show that repeat families exhibit divergent patterns with regard to chromosomal distribution, some repeat clusters accumulate in regions of high gene density, whereas others aggregate in regions of low gene density.Author’s contributionsSRB and AB conceived the study, SRB performed the bioinformatic analysis, SRB wrote the paper with input from AB. email contacts: Simon Renny-Byfield: [email protected], Andy Baumgarten: [email protected]

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Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization

10.1101/19009605 ◽

2019 ◽

Cited By ~ 5

Author(s):

Qian Yang ◽

Eleanor Sanderson ◽

Kate Tilling ◽

M Carolina Borges ◽

Deborah A Lawlor

Keyword(s):

Population Structure ◽

Instrumental Variables ◽

Population Stratification ◽

Mendelian Randomization ◽

Mr Studies ◽

Sensitivity Analyses ◽

List Type ◽

Genome Wide Association Studies ◽

Inverse Association ◽

Genome Wide

AbstractBackgroundOur aim is to produce guidance on exploring and mitigating possible bias when genetic instrumental variables (IVs) associate with traits other than the exposure of interest in Mendelian randomization (MR) studies.MethodsWe use causal diagrams to illustrate scenarios that could result in IVs being related to (non-exposure) traits. We recommend that MR studies explore possible IV-non-exposure associations across a much wider range of traits than is usually the case. Where associations are found, confounding by population stratification should be assessed through adjusting for relevant population structure variables. To distinguish vertical from horizontal pleiotropy we suggest using bidirectional MR between the exposure and non-exposure traits and MR of the effect of the non-exposure traits on the outcome of interest. If vertical pleiotropy is plausible, standard MR methods should be unbiased. If horizontal pleiotropy is plausible, we recommend using multivariable MR to control for observed pleiotropic traits and conducting sensitivity analyses which do not require prior knowledge of specific invalid IVs or pleiotropic paths.ResultsWe applied our recommendations to an illustrative example of the effect of maternal insomnia on offspring birthweight in the UK Biobank. We found little evidence that unexpected IV-non-exposure associations were driven by population stratification. Three out of six observed non-exposure traits plausibly reflected horizontal pleiotropy. Multivariable MR and sensitivity analyses suggested an inverse association of insomnia with birthweight, but effects were imprecisely estimated in some of these analyses.ConclusionsWe provide guidance for MR studies where genetic IVs associate with non-exposure traits.Key messagesGenetic variants are increasingly found to associate with more than one social, behavioural or biological trait at genome-wide significance, which is a challenge in Mendelian randomization (MR) studies.Four broad scenarios (i.e. population stratification, vertical pleiotropy, horizontal pleiotropy and reverse causality) could result in an IV-non-exposure trait association.Population stratification can be assessed through adjusting for population structure with individual data, while two-sample MR studies should check whether the original genome-wide association studies have used robust methods to properly account for it.We apply currently available MR methods for discriminating between vertical and horizontal pleiotropy and mitigating against horizontal pleiotropy to an example exploring the effect of maternal insomnia on offspring birthweight.Our study highlights the pros and cons of relying more on sensitivity analyses without considering particular pleiotropic paths versus systematically exploring and controlling for potential pleiotropic paths via known characteristics.

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Analysis of Inter-Chromosomal Distribution of Disease-Related Genes in Human Genome

Current Protein and Peptide Science ◽

10.2174/1389203721666200426233158 ◽

2020 ◽

Vol 21 (11) ◽

pp. 1068-1077

Author(s):

Xiaochao Sun ◽

Bin Yang ◽

Qunye Zhang

Keyword(s):

Spatial Distribution ◽

Model Organisms ◽

Nucleotide Polymorphisms ◽

Chromosomal Distribution ◽

Single Nucleotide ◽

Protein Coding ◽

Single Chromosome ◽

Deletion Mutations ◽

Protein Coding Genes ◽

Disease Related Genes

: Many studies have shown that the spatial distribution of genes within a single chromosome exhibits distinct patterns. However, little is known about the characteristics of inter-chromosomal distribution of genes (including protein-coding genes, processed transcripts and pseudogenes) in different genomes. In this study, we explored these issues using the available genomic data of both human and model organisms. Moreover, we also analyzed the distribution pattern of protein-coding genes that have been associated with 14 common diseases and the insert/deletion mutations and single nucleotide polymorphisms detected by whole genome sequencing in an acute promyelocyte leukemia patient. We obtained the following novel findings. Firstly, inter-chromosomal distribution of genes displays a nonstochastic pattern and the gene densities in different chromosomes are heterogeneous. This kind of heterogeneity is observed in genomes of both lower and higher species. Secondly, protein-coding genes involved in certain biological processes tend to be enriched in one or a few chromosomes. Our findings have added new insights into our understanding of the spatial distribution of genome and disease- related genes across chromosomes. These results could be useful in improving the efficiency of disease-associated gene screening studies by targeting specific chromosomes.

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Genome-wide analyses reveal population structure and identify candidate genes associated with tail fatness in local sheep from a semi-arid area

animal ◽

10.1016/j.animal.2021.100193 ◽

2021 ◽

pp. 100193

Author(s):

I. Baazaoui ◽

S. Bedhiaf-Romdhani ◽

S. Mastrangelo ◽

E. Ciani

Keyword(s):

Population Structure ◽

Candidate Genes ◽

Arid Area ◽

Genome Wide ◽

Semi Arid

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Genetic diversity and population structure of popcorn germplasm resources using genome-wide SNPs through genotyping-by-sequencing

Genetic Resources and Crop Evolution ◽

10.1007/s10722-021-01137-0 ◽

2021 ◽

Author(s):

Diansi Yu ◽

Hui Wang ◽

Wei Gu ◽

Tao Qin ◽

Pingdong Sun ◽

...

Keyword(s):

Genetic Diversity ◽

Population Structure ◽

Genotyping By Sequencing ◽

Germplasm Resources ◽

Genome Wide

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Rapid and inexpensive preparation of genome-wide nucleosome footprints from model and non-model organisms

STAR Protocols ◽

10.1016/j.xpro.2021.100486 ◽

2021 ◽

Vol 2 (2) ◽

pp. 100486

Author(s):

Laura E. McKnight ◽

Johnathan G. Crandall ◽

Thomas B. Bailey ◽

Orion G.B. Banks ◽

Kona N. Orlandi ◽

...

Keyword(s):

Model Organisms ◽

Genome Wide

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Genome-wide characterization of 2-oxoglutarate and Fe(II)-dependent dioxygenase family genes in tomato during growth cycle and their roles in metabolism

BMC Genomics ◽

10.1186/s12864-021-07434-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Shuo Wei ◽

Wen Zhang ◽

Rao Fu ◽

Yang Zhang

Keyword(s):

Metabolic Pathways ◽

Developmental Stages ◽

Expression Patterns ◽

Growth Cycle ◽

Model Organisms ◽

Gibberellin Biosynthesis ◽

Type I ◽

Multiple Model ◽

Genome Wide ◽

Tomato Growth

Abstract Background 2-Oxoglutarate and Fe(II)-dependent dioxygenases (2ODDs) belong to the 2-oxoglutarate-dependent dioxygenase (2OGD) superfamily and are involved in various vital metabolic pathways of plants at different developmental stages. These proteins have been extensively investigated in multiple model organisms. However, these enzymes have not been systematically analyzed in tomato. In addition, type I flavone synthase (FNSI) belongs to the 2ODD family and contributes to the biosynthesis of flavones, but this protein has not been characterized in tomato. Results A total of 131 2ODDs from tomato were identified and divided into seven clades by phylogenetic classification. The Sl2ODDs in the same clade showed similar intron/exon distributions and conserved motifs. The Sl2ODDs were unevenly distributed across the 12 chromosomes, with different expression patterns among major tissues and at different developmental stages of the tomato growth cycle. We characterized several Sl2ODDs and their expression patterns involved in various metabolic pathways, such as gibberellin biosynthesis and catabolism, ethylene biosynthesis, steroidal glycoalkaloid biosynthesis, and flavonoid metabolism. We found that the Sl2ODD expression patterns were consistent with their functions during the tomato growth cycle. These results indicated the significance of Sl2ODDs in tomato growth and metabolism. Based on this genome-wide analysis of Sl2ODDs, we screened six potential FNSI genes using a phylogenetic tree and coexpression analysis. However, none of them exhibited FNSI activity. Conclusions Our study provided a comprehensive understanding of the tomato 2ODD family and demonstrated the significant roles of these family members in plant metabolism. We also suggest that no FNSI genes in tomato contribute to the biosynthesis of flavones.

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Single Nucleotide Polymorphism Discovery and Genetic Differentiation Analysis of Geese Bred in Poland, Using Genotyping-by-Sequencing (GBS)

Genes ◽

10.3390/genes12071074 ◽

2021 ◽

Vol 12 (7) ◽

pp. 1074

Author(s):

Joanna Grzegorczyk ◽

Artur Gurgul ◽

Maria Oczkowicz ◽

Tomasz Szmatoła ◽

Agnieszka Fornal ◽

...

Keyword(s):

Genotyping By Sequencing ◽

Read Depth ◽

Model Organisms ◽

Single Nucleotide Polymorphism Discovery ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Polymorphism Discovery ◽

Genome Wide ◽

Plumage Development ◽

Edar Gene

Poland is the largest European producer of goose, while goose breeding has become an essential and still increasing branch of the poultry industry. The most frequently bred goose is the White Kołuda® breed, constituting 95% of the country’s population, whereas geese of regional varieties are bred in smaller, conservation flocks. However, a goose’s genetic diversity is inaccurately explored, mainly because the advantages of the most commonly used tools are strongly limited in non-model organisms. One of the most accurate used markers for population genetics is single nucleotide polymorphisms (SNP). A highly efficient strategy for genome-wide SNP detection is genotyping-by-sequencing (GBS), which has been already widely applied in many organisms. This study attempts to use GBS in 12 conservative goose breeds and the White Kołuda® breed maintained in Poland. The GBS method allowed for the detection of 3833 common raw SNPs. Nevertheless, after filtering for read depth and alleles characters, we obtained the final markers panel used for a differentiation analysis that comprised 791 SNPs. These variants were located within 11 different genes, and one of the most diversified variants was associated with the EDAR gene, which is especially interesting as it participates in the plumage development, which plays a crucial role in goose breeding.

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Humans and other commonly used model organisms are resistant to cycloheximide-mediated biases in ribosome profiling experiments

Nature Communications ◽

10.1038/s41467-021-25411-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Puneet Sharma ◽

Jie Wu ◽

Benedikt S. Nilges ◽

Sebastian A. Leidel

Keyword(s):

Human Cells ◽

Ribosome Profiling ◽

Model Organisms ◽

Treatment Conditions ◽

Genome Wide ◽

Optimized Protocol ◽

Gene Level ◽

Translation Inhibitor ◽

Species Specific ◽

Conformational Restrictions

AbstractRibosome profiling measures genome-wide translation dynamics at sub-codon resolution. Cycloheximide (CHX), a widely used translation inhibitor to arrest ribosomes in these experiments, has been shown to induce biases in yeast, questioning its use. However, whether such biases are present in datasets of other organisms including humans is unknown. Here we compare different CHX-treatment conditions in human cells and yeast in parallel experiments using an optimized protocol. We find that human ribosomes are not susceptible to conformational restrictions by CHX, nor does it distort gene-level measurements of ribosome occupancy, measured decoding speed or the translational ramp. Furthermore, CHX-induced codon-specific biases on ribosome occupancy are not detectable in human cells or other model organisms. This shows that reported biases of CHX are species-specific and that CHX does not affect the outcome of ribosome profiling experiments in most settings. Our findings provide a solid framework to conduct and analyze ribosome profiling experiments.

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