QTL Analyses: Power, Precision, and Accuracy

Beavis, William D.

doi:10.1201/9780429117770-10

Cited by 238 publications

(227 citation statements)

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“…In addition, analyzing the collared flycatcher data allowed students to consider the importance of accounting for repeated phenotypic measurements when conducting a GWAS. Students were encouraged to critically evaluate effect size estimates from GWAS in light of the Beavis effect (Beavis, 1998), and the "winner's curse" (Kraft, 2008), which state that the effect sizes of loci passing a stringent statistical significance thresholds in QTL mapping or GWAS analyses are often upwardly biased, particularly in studies with low statistical power.…”

Section: Genomewide Association Studies (Marty Kardos)mentioning

confidence: 99%

Recent advances in conservation and population genomics data analysis

Hendricks

Anderson

Antão

et al. 2018

Evolutionary Applications

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New computational methods and next‐generation sequencing (NGS) approaches have enabled the use of thousands or hundreds of thousands of genetic markers to address previously intractable questions. The methods and massive marker sets present both new data analysis challenges and opportunities to visualize, understand, and apply population and conservation genomic data in novel ways. The large scale and complexity of NGS data also increases the expertise and effort required to thoroughly and thoughtfully analyze and interpret data. To aid in this endeavor, a recent workshop entitled “Population Genomic Data Analysis,” also known as “ConGen 2017,” was held at the University of Montana. The ConGen workshop brought 15 instructors together with knowledge in a wide range of topics including NGS data filtering, genome assembly, genomic monitoring of effective population size, migration modeling, detecting adaptive genomic variation, genomewide association analysis, inbreeding depression, and landscape genomics. Here, we summarize the major themes of the workshop and the important take‐home points that were offered to students throughout. We emphasize increasing participation by women in population and conservation genomics as a vital step for the advancement of science. Some important themes that emerged during the workshop included the need for data visualization and its importance in finding problematic data, the effects of data filtering choices on downstream population genomic analyses, the increasing availability of whole‐genome sequencing, and the new challenges it presents. Our goal here is to help motivate and educate a worldwide audience to improve population genomic data analysis and interpretation, and thereby advance the contribution of genomics to molecular ecology, evolutionary biology, and especially to the conservation of biodiversity.

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Section: Genomewide Association Studies (Marty Kardos)mentioning

confidence: 99%

Recent advances in conservation and population genomics data analysis

Hendricks

Anderson

Antão

et al. 2018

Evolutionary Applications

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show abstract

“…The Beavis effect is named after a series of papers by William Beavis focused on QTL mapping (Beavis, 1994(Beavis, , 1998. Beavis showed through a series of simulations that limited sample size in QTL mapping populations had several adverse consequences on the inferences one could make.…”

Section: Beavis Effectmentioning

confidence: 99%

What can genome‐wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

2017

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Contents 21I.21II.22III.24IV.25V.2930References30 Summary Understanding the evolutionary forces that shape genetic variation within species has long been a goal of evolutionary biology. Integrating data for the genetic architecture of traits from genome‐wide association mapping studies (GWAS) along with the development of new population genetic methods for identifying selection in sequence data may allow us to evaluate the roles of mutation–selection balance and balancing selection in shaping genetic variation at various scales. Here, we review the theoretical predictions for genetic architecture and additional signals of selection on genomic sequence for the loci that affect traits. Next, we review how plant GWAS have tested for the signatures of various selective scenarios. Limited evidence to date suggests that within‐population variation is maintained primarily by mutation–selection balance while variation across the landscape is the result of local adaptation. However, there are a number of inherent biases in these interpretations. We highlight these challenges and suggest ways forward to further understanding of the maintenance of variation.

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“…A total of twelve reproducible resistance QTL were mapped to ten different chromosomes (Table 3). Due to the relatively small mapping population size, it is very likely that QTL effects were overestimated (Beavis, 1998), and QTL with relatively high effects should therefore not necessarily be counted as major-effect QTL. Given that the FHB resistance levels in Bess were significantly greater than in NC-Neuse, we expected to find more QTL at which Bess alleles provided resistance compared with NC-Neuse alleles.…”

Section: Qtl Mappingmentioning

confidence: 99%

Validation of Fusarium Head Blight Resistance QTL in US Winter Wheat

et al. 2017

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Fusarium head blight (FHB), primarily caused by Fusarium graminearum Schwabe [telemorph: Gibberella zeae Schw. (Petch)], can significantly reduce the grain quality of wheat (Triticum aestivum L.) due to mycotoxin contamination. Two US soft red winter wheat cultivars, Bess and NC‐Neuse, have moderate resistance to FHB. The objective of this study was to validate genomic regions associated with FHB resistance identified in previous studies involving NC‐Neuse and the cultivar Truman, a full‐sib of Bess. A total of 98 doubled haploid lines derived from the cross Bess × NC‐Neuse were evaluated in inoculated, mist‐irrigated field nurseries. The lines were evaluated for FHB incidence, severity, Fusarium‐damaged kernels, and deoxynivalenol content in seven environments between 2011 and 2014. A 3338‐cM linkage map was developed based on 4014 simple sequence repeat and single nucleotide polymorphism markers. Twelve quantitative trait loci (QTL) associated with FHB resistance were identified. NC‐Neuse alleles provided resistance at QTL on five chromosomes and Bess alleles provided resistance at QTL on five other chromosomes. Alignment of linkage maps revealed that five of these QTL were overlapping with previously identified regions. Quantitative trait loci on chromosomes 1A, 4A, and 6A identified in this study overlapped with QTL regions identified in NC‐Neuse, and QTL identified on chromosomes 2B and 3B overlapped with QTL regions identified in Truman. A preliminary test using Kompetitive Allele‐Specific polymerase chain reaction assays on recent Uniform Southern Winter Wheat Scab Nursery entries showed that the assays developed for Qfhb.nc‐2B.1 may be good candidates for use in marker‐assisted selection.

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QTL Analyses: Power, Precision, and Accuracy

Cited by 238 publications

References 1 publication

Recent advances in conservation and population genomics data analysis

Recent advances in conservation and population genomics data analysis

What can genome‐wide association studies tell us about the evolutionary forces maintaining genetic variation for quantitative traits?

Validation of Fusarium Head Blight Resistance QTL in US Winter Wheat

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