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Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

Soybean is an important global crop for edible protein and oil, and plant height is a main breeding goal which is closely related to its plant shape and yield. In this research, a high-density genetic linkage map was constructed by 1996 SNP-bin markers on the basis of a recombinant inbred line population derived from Dongnong L13 × Henong 60. A total of 33 QTL related to plant height were identified, of which five were repeatedly detected in multiple environments. In addition, a 455-germplasm population with 63,306 SNP markers was used for multi-locus association analysis. A total of 62 plant height QTN were detected, of which 26 were detected repeatedly under multiple methods. Two candidate genes, Glyma.02G133000 and Glyma.05G240600, involving in plant height were predicted by pathway analysis in the regions identified by multiple environments and backgrounds, and validated by qRT-PCR. These results enriched the soybean plant height regulatory network and contributed to molecular selection-assisted breeding.

Soybean is an important global crop for edible protein and oil, and plant height is a main breeding goal which is closely related to its plant shape and yield. In this research, a high-density genetic linkage map was constructed by 1996 SNP-bin markers on the basis of a recombinant inbred line population derived from Dongnong L13 × Henong 60. A total of 33 QTL related to plant height were identified, of which five were repeatedly detected in multiple environments. In addition, a 455-germplasm population with 63,306 SNP markers was used for multi-locus association analysis. A total of 62 plant height QTN were detected, of which 26 were detected repeatedly under multiple methods. Two candidate genes, Glyma.02G133000 and Glyma.05G240600, involving in plant height were predicted by pathway analysis in the regions identified by multiple environments and backgrounds, and validated by qRT-PCR. These results enriched the soybean plant height regulatory network and contributed to molecular selection-assisted breeding.

Bacterial leaf streak (BLS) is a devastating rice disease caused by the bacterial pathogen, Xanthomonas oryzae pv. oryzicola (Xoc), which can result in severe damage to rice production worldwide. Based on a total of 510 rice accessions, trialed in two seasons and using six different multi-locus GWAS methods (mrMLM, ISIS EM-BLASSO, pLARmEB, FASTmrMLM, FASTmrEMMA and pKWmEB), 79 quantitative trait nucleotides (QTNs) reflecting 69 QTLs for BLS resistance were identified (LOD &gt, 3). The QTNs were distributed on all chromosomes, with the most distributed on chromosome 11, followed by chromosomes 1 and 5. Each QTN had an additive effect of 0.20 (cm) and explained, on average, 2.44% of the phenotypic variance, varying from 0.00–0.92 (cm) and from 0.00–9.86%, respectively. Twenty-five QTNs were detected by at least two methods. Among them, qnBLS11.17 was detected by as many as five methods. Most of the QTNs showed a significant interaction with their environment, but no QTNs were detected in both seasons. By defining the QTL range for each QTN according to the LD half-decay distance, a total of 848 candidate genes were found for nine top QTNs. Among them, more than 10% were annotated to be related to biotic stress resistance, and five showed a significant response to Xoc infection. Our results could facilitate the in-depth study and marker-assisted improvement of rice resistance to BLS.

Leaf rust caused by Puccinia triticina is the most widespread rust disease of wheat. As pathogen populations are constantly evolving, identification of novel sources of resistance is necessary to maintain disease resistance and stay ahead of this plant-pathogen evolutionary arms race. The wild genepool of wheat is a rich source of genetic diversity, accounting for 44% of the Lr genes identified. Here we performed a genome-wide association study (GWAS) on a diverse germplasm of 385 accessions, including 27 different Triticum and Aegilops species. Genetic characterization using the wheat 90 K array and subsequent filtering identified a set of 20,501 single nucleotide polymorphic (SNP) markers. Of those, 9,570 were validated using exome capture and mapped onto the Chinese Spring reference sequence v1.0. Phylogenetic analyses illustrated four major clades, clearly separating the wild species from the T. aestivum and T. turgidum species. GWAS was conducted using eight statistical models for infection types against six leaf rust isolates and leaf rust severity rated in field trials for 3-4 years at 2-3 locations in Canada. Functional annotation of genes containing significant quantitative trait nucleotides (QTNs) identified 96 disease-related loci associated with leaf rust resistance. A total of 21 QTNs were in haplotype blocks or within flanking markers of at least 16 known Lr genes. The remaining significant QTNs were considered loci that putatively harbor new Lr resistance genes. Isolation of these candidate genes will contribute to the elucidation of their role in leaf rust resistance and promote their usefulness in marker-assisted selection and introgression.

Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first need to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, ii) controlled by QTNs in various degrees of LD with each other, or iii) controlled by a single pleiotropic QTN. In addition to confirming that multivariate GWAS was either equal to or more powerful than univariate GWAS, we also showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments.

International audience; The budding yeast is certainly the prime industrial microorganism and is related to many biotechnological applications including food fermentations, biofuel production, green chemistry, and drug production. A noteworthy characteristic of this species is the existence of subgroups well adapted to specific processes with some individuals showing optimal technological traits. In the last 20 years, many studies have established a link between quantitative traits and single-nucleotide polymorphisms found in hundreds of genes. These natural variations constitute a pool of QTNs (quantitative trait nucleotides) that modulate yeast traits of economic interest for industry. By selecting a subset of genes functionally validated, a total of 284 QTNs were inventoried. Their distribution across pan and core genome and their frequency within the 1,011 genomes were analyzed. We found that 150 of the 284 QTNs have a frequency lower than 5%, meaning that these variants would be undetectable by genome-wide association studies (GWAS). This analysis also suggests that most of the functional variants are private to a subpopulation, possibly due to their adaptive role to specific industrial environment. In this review, we provide a literature survey of their phenotypic impact and discuss the opportunities and the limits of their use for industrial strain selection.

The budding yeast Saccharomyces cerevisiae is certainly the prime industrial microorganism and is related to many biotechnological applications including food fermentations, biofuel production, green chemistry, and drug production. A noteworthy characteristic of this species is the existence of subgroups well adapted to specific processes with some individuals showing optimal technological traits. In the last 20 years, many studies have established a link between quantitative traits and single-nucleotide polymorphisms found in hundreds of genes. These natural variations constitute a pool of QTNs (quantitative trait nucleotides) that modulate yeast traits of economic interest for industry. By selecting a subset of genes functionally validated, a total of 284 QTNs were inventoried. Their distribution across pan and core genome and their frequency within the 1,011 Saccharomyces cerevisiae genomes were analyzed. We found that 150 of the 284 QTNs have a frequency lower than 5%, meaning that these variants would be undetectable by genome-wide association studies (GWAS). This analysis also suggests that most of the functional variants are private to a subpopulation, possibly due to their adaptive role to specific industrial environment. In this review, we provide a literature survey of their phenotypic impact and discuss the opportunities and the limits of their use for industrial strain selection.

The budding yeast Saccharomyces cerevisiae is certainly the prime industrial microorganism and is related to many biotechnological applications including food fermentations, biofuel production, green chemistry, and drug production. A noteworthy characteristic of this species is the existence of subgroups well adapted to specific processes with some individuals showing optimal technological traits. In the last 20 years, many studies have established a link between quantitative traits and single-nucleotide polymorphisms found in hundreds of genes. These natural variations constitute a pool of QTNs (quantitative trait nucleotides) that modulate yeast traits of economic interest for industry. By selecting a subset of genes functionally validated, a total of 284 QTNs were inventoried. Their distribution across pan and core genome and their frequency within the 1,011 Saccharomyces cerevisiae genomes were analyzed. We found that 150 of the 284 QTNs have a frequency lower than 5%, meaning that these variants would be undetectable by genome-wide association studies (GWAS). This analysis also suggests that most of the functional variants are private to a subpopulation, possibly due to their adaptive role to specific industrial environment. In this review, we provide a literature survey of their phenotypic impact and discuss the opportunities and the limits of their use for industrial strain selection.

The practicality of detecting thermal Langmuir waves, charged particle impacts (i.e. ``shot noise”) and impacts from dust particles on a CubeSat in the Earth’s ionosphere are investigated in this thesis. The voltage power spectra predicted for thermal Langmuir waves (QTN) and particle shot noise are modelled for a dipole antenna, and shown to provide two good, independent, passive, in situ methods of measuring the plasma density and temperature in the ionosphere. If implemented, these data would be more frequent and cover a much greater domain than current ground-based methods, improving empirical ionospheric models. Antenna lengths 20 – 40 cm are found to be ideal for ionospheric conditions with QTN peaks and shot noise at the microvolt level, nicely matching CubeSat sizes. A monopole antenna response function is derived and QTN spectra are enhanced by an order of magnitude over the dipole. However, the function breaks down predicting shot noise and remains an area for future work. Using a NASA dust environment model, predictions for dust detection using the power spectrum show it useful only for large (approx. 4 microns or larger) single-particle impact events, which are rare. Assuming a CubeSat in a polar orbit at 300, 800, and 1,500 km altitude, the average dust spectrum is 3 times larger than the shot noise spectrum only once every 2,150, 110, and 225 min, respectively. Lastly, an amplifier circuit was built to detect the plasma wave signals inside a plasma chamber with conditions modelled on the lower ionosphere. An electron gun was used to produce the plasma and 3 exposed wires to simulate antennas. The amplifier was found to linearly amplify the weak signals inside the chamber across 0.1 – 1.5 MHz. Multiple peaks were measured and those at approx. 1.6 and 4 MHz were concluded to be the electron cyclotron frequency and Langmuir waves, respectively, albeit from the electron gun. Future work will require a less impactful plasma generator such as extreme UV light.

It is of great importance to identify quantitative trait loci (QTL) controlling fiber quality traits and yield components for future marker-assisted selection (MAS) and candidate gene function identifications. In this study, two kinds of traits in 231 F6:8 recombinant inbred lines (RILs), derived from an intraspecific cross between Xinluzao24, a cultivar with elite fiber quality, and Lumianyan28, a cultivar with wide adaptability and high yield potential, were measured in nine environments. This RIL population was genotyped by 122 SSR and 4729 SNP markers, which were also used to construct the genetic map. The map covered 2477.99 cM of hirsutum genome, with an average marker interval of 0.51 cM between adjacent markers. As a result, a total of 134 QTLs for fiber quality traits and 122 QTLs for yield components were detected, with 2.18–24.45 and 1.68–28.27% proportions of the phenotypic variance explained by each QTL, respectively. Among these QTLs, 57 were detected in at least two environments, named stable QTLs. A total of 209 and 139 quantitative trait nucleotides (QTNs) were associated with fiber quality traits and yield components by four multilocus genome-wide association studies methods, respectively. Among these QTNs, 74 were detected by at least two algorithms or in two environments. The candidate genes harbored by 57 stable QTLs were compared with the ones associated with QTN, and 35 common candidate genes were found. Among these common candidate genes, four were possibly “pleiotropic.” This study provided important information for MAS and candidate gene functional studies.

A major breeding target in Upland cotton (Gossypium hirsutum L.) is to improve the fiber quality. To address this issue, 169 diverse accessions, genotyped by 53,848 high-quality single-nucleotide polymorphisms (SNPs) and phenotyped in four environments, were used to conduct genome-wide association studies (GWASs) for fiber quality traits using three single-locus and three multi-locus models. As a result, 342 quantitative trait nucleotides (QTNs) controlling fiber quality traits were detected. Of the 342 QTNs, 84 were simultaneously detected in at least two environments or by at least two models, which include 29 for fiber length, 22 for fiber strength, 11 for fiber micronaire, 12 for fiber uniformity, and 10 for fiber elongation. Meanwhile, nine QTNs with 10% greater sizes (R2) were simultaneously detected in at least two environments and between single- and multi-locus models, which include TM80185 (D13) for fiber length, TM1386 (A1) and TM14462 (A6) for fiber strength, TM18616 (A7), TM54735 (D3), and TM79518 (D12) for fiber micronaire, TM77489 (D12) and TM81448 (D13) for fiber uniformity, and TM47772 (D1) for fiber elongation. This indicates the possibility of marker-assisted selection in future breeding programs. Among 455 genes within the linkage disequilibrium regions of the nine QTNs, 113 are potential candidate genes and four are promising candidate genes. These findings reveal the genetic control underlying fiber quality traits and provide insights into possible genetic improvements in Upland cotton fiber quality.

A major breeding target in Upland cotton (Gossypium hirsutum L.) is to improve the fiber quality. To address this issue, 169 diverse accessions, genotyped by 53,848 high-quality single-nucleotide polymorphisms (SNPs) and phenotyped in four environments, were used to conduct genome-wide association studies (GWASs) for fiber quality traits using three single-locus and three multi-locus models. As a result, 342 quantitative trait nucleotides (QTNs) controlling fiber quality traits were detected. Of the 342 QTNs, 84 were simultaneously detected in at least two environments or by at least two models, which include 29 for fiber length, 22 for fiber strength, 11 for fiber micronaire, 12 for fiber uniformity, and 10 for fiber elongation. Meanwhile, nine QTNs with 10% greater sizes (R2) were simultaneously detected in at least two environments and between single- and multi-locus models, which include TM80185 (D13) for fiber length, TM1386 (A1) and TM14462 (A6) for fiber strength, TM18616 (A7), TM54735 (D3), and TM79518 (D12) for fiber micronaire, TM77489 (D12) and TM81448 (D13) for fiber uniformity, and TM47772 (D1) for fiber elongation. This indicates the possibility of marker-assisted selection in future breeding programs. Among 455 genes within the linkage disequilibrium regions of the nine QTNs, 113 are potential candidate genes and four are promising candidate genes. These findings reveal the genetic control underlying fiber quality traits and provide insights into possible genetic improvements in Upland cotton fiber quality.

Background High-resolution mapping of the loci (QTN) responsible for genetic variation in quantitative traits is essential for positional cloning of candidate genes, and for effective marker assisted selection. The confidence interval (QTL) flanking the point estimate of QTN-location is proportional to the number of individuals in the mapping population carrying chromosomes recombinant in the given interval. Consequently, many designs for high resolution QTN mapping are based on increasing the proportion of recombinants in the mapping population. The "Targeted Recombinant Progeny" (TRP) design is a new design for high resolution mapping of a target QTN in crosses between pure, or inbred lines. It is a three-generation procedure generating a large number of recombinant individuals within a QTL previously shown to contain a QTN. This is achieved by having individuals that carry chromosomes recombinant across the target QTL interval as parents of a large mapping population; most of whom will therefore carry recombinant chromosomes targeted to the given QTL. The TRP design is particularly useful for high resolution mapping of QTN that differentiate inbred or pure lines, and hence are not amenable to high resolution mapping by genome-wide association tests. Results In the absence of residual polygenic variation, population sizes required for achieving given mapping resolution by the TRP-F2 design relative to a standard F2 design ranged from 0.289 for a QTN with standardized allele substitution effect = 0.2, mapped to an initial QTL of 0.2 Morgan to 0.041 for equivalent QTN mapped to an initial QTL of 0.02 M. In the presence of residual polygenic variation, the relative effectiveness of the TRP design ranges from 1.068 to 0.151 for the same initial QTL intervals and QTN effect. Thus even in the presence of polygenic variation, the TRP can still provide major savings. Simulation showed that mapping by TRP should be based on 30-50 markers spanning the initial interval; and on at least 50 or more G2 families representing this number of recombination points,. Conclusions The TRP design can be an effective procedure for achieving high and ultra-high mapping resolution of a target QTN previously mapped to a known confidence interval (QTL).

Identifying quantitative trait nucleotides (QTNs), the genetic polymorphisms linked to phenotypic variation, has become a goal for many plant ecologists and evolutionary biologists in recent years. But what is the true value of this potentially expensive and labor intensive programme of research? In this review we discuss the ways by which the QTN programme can offer unique insight into the ecology and evolution of adaptation in plants. We cite recent noteworthy examples of QTN work and provide recommendations for refocusing efforts to identify and study the genes underlying ecologically important traits., The goal of identifying the genes or even nucleotides underlying quantitative and adaptive traits has been characterized as the ‘QTN programme’ and has recently come under severe criticism. Part of the reason for this criticism is that much of the QTN programme has asserted that finding the genes and nucleotides for adaptive and quantitative traits is a fundamental goal, without explaining why it is such a hallowed goal. Here we outline motivations for the QTN programme that offer general insight, regardless of whether QTNs are of large or small effect, and that aid our understanding of the mechanistic dynamics of adaptive evolution. We focus on five areas: (i) vertical integration of insight across different levels of biological organization, (ii) genetic parallelism and the role of pleiotropy in shaping evolutionary dynamics, (iii) understanding the forces maintaining genetic variation in populations, (iv) distinguishing between adaptation from standing variation and new mutation, and (v) the role of genomic architecture in facilitating adaptation. We argue that rather than abandoning the QTN programme, we should refocus our efforts on topics where molecular data will be the most effective for testing hypotheses about phenotypic evolution.

The noise in the Langmuir Probe and Plasma Wave Instrument (LP-PWI) on board ESA:s future Jupiter satellite JUICE (Jupiter ICy Moons Explorer) was investigated. Thermal Johnson-Nyquist noise and shot noise, caused by fluctuations in the probe-plasma currents, were combined with the quasi-thermal noise (QTN) due to thermal fluctuations in the electric field in the plasma, using a small signal equivalent circuit model. The contributions and effects of each of the considered noise sources were examined and compared for a number of representative space plasma conditions, including the cold dense plasma of Ganymede's ionosphere and the hot tenuous plasma out in the Jovian magnetosphere. The results showed that in the cold dense plasma of Ganymede's ionosphere, the antenna was long compared to the Debye length and the quasi-thermal noise had a clearly pronounced peak and a steep high-frequency cut-off. For an antenna biased to 1 V with respect to the plasma, the shot noise due to the ambient plasma was the dominant source of noise. For a an antenna at the floating potential the photoelectron shot noise coalesced with the shot and Nyquist noises of the ambient plasma to form almost a single curve. In the hot tenuous plasma out in Jupiter's magnetosphere, the antenna was short compared to the Debye length and the QTN spectrum was much flatter, with little or no peak at the plasma frequency and a very weak high-frequency cut-off. For an antenna biased to 1 V, the shot noise due to photoelectron emission dominated at Callisto's orbital position whereas at Ganymede's and Europa's orbital positions the Nyquist and shot noises of the ambient plasma particles were the dominant noise components. For an antenna at the floating potential, the shot and Nyquist noises of the ambient plasma also dominated the output noise, except at Europa's orbital position, where the quasi-thermal noise was the largest noise component for frequencies at and above the plasma frequency. The numerical calculations were performed using MATLAB. The code was made available in a Git repository at https://github.com/eliasodelstad/irfuproj_JUICE_noise.

The aim of this study was to test for the existence of causative genetic variation affecting body composition traits within or adjacent to the porcine insulin-like growth factor 2 (IGF2) gene beyond the known IGF2-intron3-G3072A mutation. A focussed quantitative trait loci analysis using four microsatellite markers within the telomeric region of porcine chromosome 2p was conducted in a large resource population comprising 2741 F2 offspring. The analysis of two subsets of animals that were alternatively homozygous for the in3G3072A mutation provides evidence for additional genetic variation significantly contributing to the overall quantitative trait nucleotide variance within our population.

Domestic animals have become very different from their wild ancestors during domestication and animal breeding. This provides a good model to unravel the molecular mechanisms underlying phenotypic variation. In my thesis I have studied genes affecting two important traits, leanness in pigs and hair greying-associated melanoma in horses. In the first part of the thesis, I focused on an intronic mutation leading to more muscle growth and less fat deposition in domestic pigs to identify a transcription factor (TF) that binds to the regulatory element overlapping with the mutation. The aim has been to further study the function of the previously unknown TF in mouse myoblast cells and in insulin-producing cells (Paper I-III). We discovered a new TF ZBED6 binding to intron 3 of the IGF2 gene, in which a single nucleotide substitution in pigs abrogates the binding and causes increased leanness in domestic pigs. Silencing of ZBED6 expression in mouse myoblasts increased Igf2 expression, cell proliferation and migration, and myotube formation. This result is in line with the increased leanness phenotype in mutant pigs. Chromatin Immunoprecipitation-sequencing (ChIP-seq) using an anti-ZBED6 antibody identified 1200 ZBED6 target genes besides IGF2 and many are TFs controlling fundamental biological processes. In the first follow-up study we found ZBED6 mainly affected the expression of muscle protein genes by directly regulating Igf2 and Twist2 expression, in agreement with our previous observation of faster myotube formation in ZBED6-silenced cells. ChIP-seq with antibodies against six different histone modifications revealed that ZBED6 preferentially binds to active promoters and modulates transcriptional activity by a novel mechanism rather than by recruiting repressive histone modifications. The second follow-up study revealed that ZBED6 affects the morphology and insulin content and release in pancreatic ß cells. In the second part (Paper IV), we investigate the functional significance of an intronic duplication in the Syntaxin 17 (STX17) gene causing hair greying and melanoma in horses. We found two Microphtalmia-associated transcription factor (MITF) binding sites within the duplication and showed that the duplicated sequence up-regulates reporter gene expression in a melanocyte-specific manner both by reporter assays in mouse melanocytes and in transgenic zebrafish. These results established that the intronic duplication acts as a melanocyte-specific enhancer that becomes much stronger when it is duplicated.

A domesticated transposon mediates the effects of a single-nucleotide polymorphism responsible for enhanced muscle growth Using a highly specific and quantitative protein-DNA interaction screen based on the SILAC technology the Mann group identifies a protein termed muscle growth regulator (MGR). MGR represents a domesticated transposon and is the long sought repressor at the G3072A IGF2 single nucleotide polymorphism that regulates muscle growth and confers a lean phenotype to European domestic pigs., Single-nucleotide polymorphisms (SNPs) in the regulatory regions of the genome can have a profound impact on phenotype. The G3072A polymorphism in intron 3 of insulin-like growth factor 2 (IGF2) is implicated in higher muscle content and reduced fat in European pigs and is bound by a putative repressor. Here, we identify this repressor—which we call muscle growth regulator (MGR)—by using a DNA protein interaction screen based on quantitative mass spectrometry. MGR has a bipartite nuclear localization signal, two BED-type zinc fingers and is highly conserved between placental mammals. Surprisingly, the gene is located in an intron and belongs to the hobo-Ac-Tam3 transposase superfamily, suggesting regulatory use of a formerly parasitic element. In transactivation assays, MGR differentially represses the expression of the two SNP variants. Knockdown of MGR in C2C12 myoblast cells upregulates Igf2 expression and mild overexpression retards growth. Thus, MGR is the repressor responsible for enhanced muscle growth in the IGF2 G3072A polymorphism in commercially bred pigs.