scholarly journals Genetic Mapping by Bulk Segregant Analysis in Drosophila: Experimental Design and Simulation-Based Inference

2016 ◽  
Author(s):  
John E Pool
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Bulk Segregant Analysis ◽  
Experimental Population ◽  
Modern Biology ◽  
Significant Qtls ◽  
Trait Mapping ◽  
Wide Range ◽  
Simulation Based ◽  
Trait Locus

AbstractIdentifying the genomic regions that underlie complex phenotypic variation is a key challenge in modern biology. Many approaches to quantitative trait locus mapping in animal and plant species suffer from limited power and genomic resolution. Here, I investigate whether bulk segregant analysis (BSA), which has been successfully applied for yeast, may have utility in the genomic era for trait mapping in Drosophila (and other organisms that can be experimentally bred in similar numbers). I perform simulations to investigate the statistical signal of a quantitative trait locus (QTL) in a wide range of BSA and introgression mapping (IM) experiments. BSA consistently provides more accurate mapping signals than IM (in addition to allowing the mapping of multiple traits from the same experimental population). The performance of BSA and IM is maximized by having multiple independent crosses, more generations of interbreeding, larger numbers of breeding individuals, and greater genotyping effort, but is less affected by the proportion of individuals selected for phenotypic extreme pools. I also introduce a prototype analysis method for Simulation-based Inference for BSA Mapping (SIBSAM). This method identifies significant QTLs and estimates their genomic confidence intervals and relative effect sizes. Importantly, it also tests whether overlapping peaks should be considered as two distinct QTLs. This approach will facilitate improved trait mapping in Drosophila and other species for which hundreds or thousands of offspring (but not millions) can be studied.

scholarly journals Quantitative Trait Locus Analysis in Avocado: The Challenge of a Slow-maturing Horticultural Tree Crop

2019 ◽  
Vol 144 (5) ◽  
pp. 352-362
Author(s):  
Vanessa E.T.M. Ashworth ◽  
Haofeng Chen ◽  
Carlos L. Calderón-Vázquez ◽  
Mary Lu Arpaia ◽  
David N. Kuhn ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Linkage Group ◽  
Linkage Map ◽  
Qtl Analysis ◽  
Quantitative Trait ◽  
Persea Americana ◽  
Phenotypic Traits ◽  
Experimental Population ◽  
Tree Crop ◽  
Trait Locus

The glossy, green-fleshed fruit of the avocado (Persea americana) has been the object of human selection for thousands of years. Recent interest in healthy nutrition has singled out the avocado as an excellent source of several phytonutrients. Yet as a sizeable, slow-maturing tree crop, it has been largely neglected by genetic studies, owing to a long breeding cycle and costly field trials. We use a small, replicated experimental population of 50 progeny, grown at two locations in two successive years, to explore the feasibility of developing a dense genetic linkage map and to implement quantitative trait locus (QTL) analysis for seven phenotypic traits. Additionally, we test the utility of candidate-gene single-nucleotide polymorphisms developed to genes from biosynthetic pathways of phytonutrients beneficial to human health. The resulting linkage map consisted of 1346 markers (1044.7 cM) distributed across 12 linkage groups. Numerous markers on Linkage Group 10 were associated with a QTL for flowering type. One marker on Linkage Group 1 tracked a QTL for β-sitosterol content of the fruit. A region on Linkage Group 3 tracked vitamin E (α-tocopherol) content of the fruit, and several markers were stable across both locations and study years. We argue that the pursuit of linkage mapping and QTL analysis is worthwhile, even when population size is small.

scholarly journals Quantitative trait locus mapping combined with variant and transcriptome analyses identifies a cluster of gene candidates underlying the variation in leaf wax between upland and lowland switchgrass ecotypes

2021 ◽  
Author(s):  
Peng Qi ◽  
Thomas H. Pendergast ◽  
Alex Johnson ◽  
Bochra A. Bahri ◽  
Soyeon Choi ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Acyl Carrier Protein ◽  
Energy Crop ◽  
Phenotypic Traits ◽  
Type Iii Polyketide Synthase ◽  
Complex Genetics ◽  
Trait Mapping ◽  
Leaf Wax ◽  
Trait Locus

Abstract Key message Mapping combined with expression and variant analyses in switchgrass, a crop with complex genetics, identified a cluster of candidate genes for leaf wax in a fast-evolving region of chromosome 7K. Abstract Switchgrass (Panicum virgatum L.) is a promising warm-season candidate energy crop. It occurs in two ecotypes, upland and lowland, which vary in a number of phenotypic traits, including leaf glaucousness. To initiate trait mapping, two F2 mapping populations were developed by crossing two different F1 sibs derived from a cross between the tetraploid lowland genotype AP13 and the tetraploid upland genotype VS16, and high-density linkage maps were generated. Quantitative trait locus (QTL) analyses of visually scored leaf glaucousness and of hydrophobicity of the abaxial leaf surface measured using a drop shape analyzer identified highly significant colocalizing QTL on chromosome 7K (Chr07K). Using a multipronged approach, we identified a cluster of genes including Pavir.7KG077009, which encodes a Type III polyketide synthase-like protein, and Pavir.7KG013754 and Pavir.7KG030500, two highly similar genes that encode putative acyl-acyl carrier protein (ACP) thioesterases, as strong candidates underlying the QTL. The lack of homoeologs for any of the three genes on Chr07N, the relatively low level of identity with other switchgrass KCS proteins and thioesterases, as well as the organization of the surrounding region suggest that Pavir.7KG077009 and Pavir.7KG013754/Pavir.7KG030500 were duplicated into a fast-evolving chromosome region, which led to their neofunctionalization. Furthermore, sequence analyses showed all three genes to be absent in the two upland compared to the two lowland accessions analyzed. This study provides an example of and practical guide for trait mapping and candidate gene identification in a complex genetic system by combining QTL mapping, transcriptomics and variant analysis.

scholarly journals Using molecular markers to estimate quantitative trait locus parameters: power and genetic variances for unreplicated and replicated progeny.

Genetics ◽  
1990 ◽  
Vol 126 (3) ◽  
pp. 769-777 ◽  
Author(s):  
S J Knapp ◽  
W C Bridges
Keyword(s):  
Quantitative Trait Locus ◽  
Molecular Markers ◽  
Doubled Haploid ◽  
Quantitative Trait ◽  
Genetic Variance ◽  
Testing Hypotheses ◽  
Independent Variables ◽  
Wide Range ◽  
Trait Locus ◽  
Range Of Values

Abstract Many of the progeny types used to estimate quantitative trait locus (QTL) parameters can be replicated, e.g., recombinant inbred, doubled haploid, and F3 lines. These parameters are estimated using molecular markers or QTL genotypes estimated from molecular markers as independent variables. Experiment designs for replicated progeny are functions of the number of replications per line (r) and the number of replications per QTL genotype (n). The value of n is determined by the size of the progeny population (N), the progeny type, and the number of simultaneously estimated QTL parameters (q - 1). Power for testing hypotheses about means of QTL genotypes is increased by increasing r and n, but the effects of these factors have not been quantified. In this paper, we describe how power is affected by r, n, and other factors. The genetic variance between lines nested in QTL genotypes (sigma 2n:q) is the fraction of the genetic variance between lines (sigma 2n) which is not explained by simultaneously estimated intralocus and interlocus QTL parameters (phi 2Q); thus, sigma 2n:q = sigma 2n - phi 2Q. If sigma 2n:q not equal to 0, then power is not efficiently increased by increasing r and is maximized by maximizing n and using r = 1; however, if sigma 2n:q = 0, then r and n affect power equally and power is efficiently increased by increasing r and is maximized by maximizing N.r. Increasing n efficiently increases power for a wide range of values of sigma 2n:q.sigma 2n:q = 0 when the genetic variance between lines is fully explained by QTL parameters (sigma 2n = phi 2Q).(ABSTRACT TRUNCATED AT 250 WORDS)

Quantitative Trait Loci on Chromosomes 10 and 11 Influencing Mandible Size of SMXA RI Mouse Strains

2002 ◽  
Vol 81 (7) ◽  
pp. 501-504 ◽  
Author(s):  
A. Dohmoto ◽  
K. Shimizu ◽  
Y. Asada ◽  
T. Maeda
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Major Gene ◽  
Recombinant Inbred Strain ◽  
Mouse Strains ◽  
Chromosome 11 ◽  
Significant Qtls ◽  
Mandible Length ◽  
Trait Locus ◽  
Mandible Size

Predicting the mandible size before the termination of growth of the maxillofacial bones is essential in pedodontics as well as for the predictions needed for genetic analysis. Here, Quantitative Trait Locus (QTL) analysis was used to detect the chromosomal regions responsible for the mandible length between the menton and gonion in an SMXA recombinant inbred strain of mice. Around the region 60 cM from the centromere in chromosome 10, the logarithm of the odds score showed a higher than suggestive level. Around the regions 13 cM and 16 cM in chromosome 11, two significant QTLs were detected. Analysis of genotypes from loci corresponding to those QTLs revealed a large mandible when the region between the markers Hba and D11Mit163 and D10Mit70 and D10Mit136 indicated the genotype from the A/J and SM/J alleles, respectively. These results suggest that the major gene(s) responsible for mandible length are located in these regions.

scholarly journals A note on the theory of artificial selection in finite populations and application to QTL detection by bulk segregant analysis

1998 ◽  
Vol 72 (1) ◽  
pp. 55-58 ◽  
Author(s):  
WILLIAM G. HILL
Keyword(s):  
Quantitative Trait Locus ◽  
Artificial Selection ◽  
Quantitative Trait ◽  
Gene Frequency ◽  
Bulk Segregant Analysis ◽  
Finite Population ◽  
Qtl Detection ◽  
Selection For ◽  
Trait Locus ◽  
Almost All

Formulae are given for computing the distribution of numbers of selected individuals of each genotype and thus change in gene frequency at a locus with a large effect on a quantitative trait under truncation selection in a finite population. Results are illustrated with respect to use of selection for quantitative trait locus (QTL) detection, specifically by bulk segregant analysis with linked markers, for which probabilities that selected samples will comprise almost all one genotypic class are computed.

scholarly journals Selective DNA pooling for determination of linkage between a molecular marker and a quantitative trait locus.

Genetics ◽  
1994 ◽  
Vol 138 (4) ◽  
pp. 1365-1373 ◽  
Author(s):  
A Darvasi ◽  
M Soller
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Allele Frequencies ◽  
Selective Genotyping ◽  
Technical Error ◽  
Dna Pooling ◽  
Wide Range ◽  
Trait Locus ◽  
Pooled Samples ◽  
Selective Dna Pooling

Abstract Selective genotyping is a method to reduce costs in marker-quantitative trait locus (QTL) linkage determination by genotyping only those individuals with extreme, and hence most informative, quantitative trait values. The DNA pooling strategy (termed: "selective DNA pooling") takes this one step further by pooling DNA from the selected individuals at each of the two phenotypic extremes, and basing the test for linkage on marker allele frequencies as estimated from the pooled samples only. This can reduce genotyping costs of marker-QTL linkage determination by up to two orders of magnitude. Theoretical analysis of selective DNA pooling shows that for experiments involving backcross, F2 and half-sib designs, the power of selective DNA pooling for detecting genes with large effect, can be the same as that obtained by individual selective genotyping. Power for detecting genes with small effect, however, was found to decrease strongly with increase in the technical error of estimating allele frequencies in the pooled samples. The effect of technical error, however, can be markedly reduced by replication of technical procedures. It is also shown that a proportion selected of 0.1 at each tail will be appropriate for a wide range of experimental conditions.

scholarly journals A Major and Stable Quantitative Trait Locus qSS2 for Seed Size and Shape Traits in a Soybean RIL Population

2021 ◽  
Vol 12 ◽  
Author(s):  
Giriraj Kumawat ◽  
Donghe Xu
Keyword(s):  
Quantitative Trait Locus ◽  
Seed Size ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Seed Shape ◽  
Significant Qtls ◽  
Size And Shape ◽  
Growing Seasons ◽  
Ril Population ◽  
Trait Locus

Seed size and shape traits are important determinants of seed yield and appearance quality in soybean [Glycine max (L.) Merr.]. Understanding the genetic architecture of these traits is important to enable their genetic improvement through efficient and targeted selection in soybean breeding, and for the identification of underlying causal genes. To map seed size and shape traits in soybean, a recombinant inbred line (RIL) population developed from K099 (small seed size) × Fendou 16 (large seed size), was phenotyped in three growing seasons. A genetic map of the RIL population was developed using 1,485 genotyping by random amplicon sequencing-direct (GRAS-Di) and 177 SSR markers. Quantitative trait locus (QTL) mapping was conducted by inclusive composite interval mapping. As a result, 53 significant QTLs for seed size traits and 27 significant QTLs for seed shape traits were identified. Six of these QTLs (qSW8.1, qSW16.1, qSLW2.1, qSLT2.1, qSWT1.2, and qSWT4.3) were identified with LOD scores of 3.80–14.0 and R2 of 2.36%–39.49% in at least two growing seasons. Among the above significant QTLs, 24 QTLs were grouped into 11 QTL clusters, such as, three major QTLs (qSL2.3, qSLW2.1, and qSLT2.1) were clustered into a major QTL on Chr.02, named as qSS2. The effect of qSS2 was validated in a pair of near isogenic lines, and its candidate genes (Glyma.02G269400, Glyma.02G272100, Glyma.02G274900, Glyma.02G277200, and Glyma.02G277600) were mined. The results of this study will assist in the breeding programs aiming at improvement of seed size and shape traits in soybean.

scholarly journals BSA4Yeast: Web-based quantitative trait locus linkage analysis and bulk segregant analysis of yeast sequencing data

GigaScience ◽  
2019 ◽  
Vol 8 (6) ◽  
Author(s):  
Zhi Zhang ◽  
Paul P Jung ◽  
Valentin Grouès ◽  
Patrick May ◽  
Carole Linster ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Linkage Analysis ◽  
Quantitative Trait ◽  
Bulk Segregant Analysis ◽  
Sequencing Data ◽  
Web Based ◽  
Trait Locus
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