Parker et al. GWAS approach in mice investigates genetic basis of complex traits

IBMS BoneKEy ◽  
2016 ◽  
Vol 13 ◽  
pp. 864
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Gwas Approach

scholarly journals Contextualizing genetic risk score for disease screening and rare variant discovery

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dan Zhou ◽  
Dongmei Yu ◽  
Jeremiah M. Scharf ◽  
Carol A. Mathews ◽  
Lauren McGrath ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Clinical Decision Making ◽  
Genetic Risk Score ◽  
Sufficient Conditions ◽  
Clinical Decision ◽  
Generative Models ◽  
Variant Discovery ◽  
Models Of Disease ◽  
The Uk

AbstractStudies of the genetic basis of complex traits have demonstrated a substantial role for common, small-effect variant polygenic burden (PB) as well as large-effect variants (LEV, primarily rare). We identify sufficient conditions in which GWAS-derived PB may be used for well-powered rare pathogenic variant discovery or as a sample prioritization tool for whole-genome or exome sequencing. Through extensive simulations of genetic architectures and generative models of disease liability with parameters informed by empirical data, we quantify the power to detect, among cases, a lower PB in LEV carriers than in non-carriers. Furthermore, we uncover clinically useful conditions wherein the risk derived from the PB is comparable to the LEV-derived risk. The resulting summary-statistics-based methodology (with publicly available software, PB-LEV-SCAN) makes predictions on PB-based LEV screening for 36 complex traits, which we confirm in several disease datasets with available LEV information in the UK Biobank, with important implications on clinical decision-making.

scholarly journals Meta-QTL analysis and identification of candidate genes for quality, abiotic and biotic stress in durum wheat

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jose Miguel Soriano ◽  
Pasqualina Colasuonno ◽  
Ilaria Marcotuli ◽  
Agata Gadaleta
Keyword(s):  
Molecular Markers ◽  
Abiotic Stress ◽  
Durum Wheat ◽  
Candidate Genes ◽  
Biotic Stress ◽  
Qtl Analysis ◽  
Complex Traits ◽  
Genetic Basis ◽  
Agronomic Traits ◽  
Plant Performance

AbstractThe genetic improvement of durum wheat and enhancement of plant performance often depend on the identification of stable quantitative trait loci (QTL) and closely linked molecular markers. This is essential for better understanding the genetic basis of important agronomic traits and identifying an effective method for improving selection efficiency in breeding programmes. Meta-QTL analysis is a useful approach for dissecting the genetic basis of complex traits, providing broader allelic coverage and higher mapping resolution for the identification of putative molecular markers to be used in marker-assisted selection. In the present study, extensive QTL meta-analysis was conducted on 45 traits of durum wheat, including quality and biotic and abiotic stress-related traits. A total of 368 QTL distributed on all 14 chromosomes of genomes A and B were projected: 171 corresponded to quality-related traits, 127 to abiotic stress and 71 to biotic stress, of which 318 were grouped in 85 meta-QTL (MQTL), 24 remained as single QTL and 26 were not assigned to any MQTL. The number of MQTL per chromosome ranged from 4 in chromosomes 1A and 6A to 9 in chromosome 7B; chromosomes 3A and 7A showed the highest number of individual QTL (4), and chromosome 7B the highest number of undefined QTL (4). The recently published genome sequence of durum wheat was used to search for candidate genes within the MQTL peaks. This work will facilitate cloning and pyramiding of QTL to develop new cultivars with specific quantitative traits and speed up breeding programs.

scholarly journals Epistasis for Three Grain Yield Components in Rice (Oryxa sativa L.)

Genetics ◽  
1997 ◽  
Vol 145 (2) ◽  
pp. 453-465 ◽  
Author(s):  
Zhikang Li ◽  
Shannon R M Pinson ◽  
William D Park ◽  
Andrew H Paterson ◽  
James W Stansel
Keyword(s):  
Grain Yield ◽  
Complex Traits ◽  
Yield Components ◽  
Genetic Basis ◽  
Kernel Weight ◽  
Progeny Testing ◽  
Grain Yield Components ◽  
Main Effects ◽  
Grain Number Per Panicle

The genetic basis for three grain yield components of rice, 1000 kernel weight (KW), grain number per panicle (GN), and grain weight per panicle (GWP), was investigated using restriction fragment length polymorphism markers and F4 progeny testing from a cross between rice subspecies japonica (cultivar Lemont from USA) and indica (cv. Teqing from China). Following identification of 19 QTL affecting these traits, we investigated the role of epistasis in genetic control of these phenotypes. Among 63 markers distributed throughout the genome that appeared to be involved in 79 highly significant (P < 0.001) interactions, most (46 or 73%) did not appear to have “main” effects on the relevant traits, but influenced the trait(s) predominantly through interactions. These results indicate that epistasis is an important genetic basis for complex traits such as yield components, especially traits of low heritability such as GN and GWP. The identification of epistatic loci is an important step toward resolution of discrepancies between quantitative trait loci mapping and classical genetic dogma, contributes to better understanding of the persistence of quantitative genetic variation in populations, and impels reconsideration of optimal mapping methodology and marker-assisted breeding strategies for improvement of complex traits.

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

scholarly journals Self-sensitisation of Staphylococcus aureus to the antimicrobial factors found in human blood.

2021 ◽  
Author(s):  
Edward Douglas ◽  
Tarcisio Brignoli ◽  
Mario Recker ◽  
Eoin O'Brien ◽  
Rachel McLoughlin ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Genetic Basis ◽  
Functional Verification ◽  
Invasive Infection ◽  
Opportunistic Pathogens ◽  
Dead End ◽  
Wall Teichoic Acids ◽  
Respiratory Microbiome ◽  
Gwas Approach

For opportunistic pathogens, the switch from a commensal to an invasive lifestyle is often considered an accidental event. But with plentiful opportunity, what leads one accidental event to result in an invasive infection, and another not to? And how much of this apparent stochasticity is driven by bacterial factors? To answer these questions, here we focussed on the major human pathogen Staphylococcus aureus, which can both reside asymptomatically as a member of our respiratory microbiome, or become invasive and cause infections as severe as bacteraemia. Survival upon exposure to the antibacterial factors found in serum is a critical aspect of their ability to cause bacteraemia, and across a collection of 300 clinical isolates we found there to be significant variability in this capability. Utilising a GWAS approach we have uncovered the genetic basis of much of this variability through the identification and functional verification of a number of new polymorphic loci that affect serum survival: tcaA, tarK, gntR, ilvC, arsB, yfhO, and pdhD. The expression of one of these genes, tcaA, was found to be induced upon exposure to serum, while simultaneously enhancing the sensitivity of S. aureus to serum through a process involving the ligation of wall teichoic acids into the cell wall. As blood-stage infections are a transmission dead-end for the bacteria, that S. aureus actively responds to serum to produce a protein which specifically limits their ability to survive in this environment demonstrates that the switch from the commensal to the invasive lifestyle is complex, and that TcaA may contribute to the long-term success of S. aureus by restricting the bacteria to their more readily transmissible commensal state.

scholarly journals Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies

2019 ◽  
Vol 36 (12) ◽  
pp. 2890-2905 ◽  
Author(s):  
Christos Vlachos ◽  
Robert Kofler
Keyword(s):  
Complex Traits ◽  
Quantitative Traits ◽  
Genetic Basis ◽  
Genome Wide Association Study ◽  
Selection Regime ◽  
Genome Wide ◽  
A Genome ◽  
Higher Power ◽  
Powerful Approach ◽  
Next Generation Sequencing Ngs

Abstract Evolve and resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and nonselected populations using next-generation sequencing (NGS), the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations has been examined in previous works. However, the influence of the selection regime, that is the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a genome-wide association study, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.

scholarly journals Genome-wide association study of biologically informed periodontal complex traits offers novel insights into the genetic basis of periodontal disease

2016 ◽  
Vol 25 (10) ◽  
pp. 2113-2129 ◽  
Author(s):  
Steven Offenbacher ◽  
Kimon Divaris ◽  
Silvana P. Barros ◽  
Kevin L. Moss ◽  
Julie T. Marchesan ◽  
...  
Keyword(s):  
Periodontal Disease ◽  
Association Study ◽  
Complex Traits ◽  
Genetic Basis ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Genome Wide

scholarly journals Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

2021 ◽  
Author(s):  
Alex N. Nguyen Ba ◽  
Katherine R. Lawrence ◽  
Artur Rego-Costa ◽  
Shreyas Gopalakrishnan ◽  
Daniel Temko ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Basis ◽  
Association Studies ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Trait Locus

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.Significance statementUnderstanding the genetic basis of important phenotypes is a central goal of genetics. However, the highly polygenic architectures of complex traits inferred by large-scale genome-wide association studies (GWAS) in humans stand in contrast to the results of quantitative trait locus (QTL) mapping studies in model organisms. Here, we use a barcoding approach to conduct QTL mapping in budding yeast at a scale two orders of magnitude larger than the previous state of the art. The resulting increase in power reveals the polygenic nature of complex traits in yeast, and offers insight into widespread patterns of pleiotropy and epistasis. Our data and analysis methods offer opportunities for future work in systems biology, and have implications for large-scale GWAS in human populations.

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