Resolution Of Quantitative Trait Loci Research Articles

Enhanced recombination empowers the detection and mapping of Quantitative Trait Loci.

Modern plant breeding, such as genomic selection and gene editing, is based on the knowledge of the genetic architecture of desired traits. Quantitative trait loci (QTL) analysis, which combines high throughput phenotyping and genotyping of segregating populations, is a powerful tool to identify these genetic determinants and to decipher the underlying mechanisms. However, meiotic recombination, which shuffles genetic information between generations, is limited: Typically only one to two exchange points, called crossovers, occur between a pair of homologous chromosomes. Here we test the effect on QTL analysis of boosting recombination, by mutating the anti-crossover factors RECQ4 and FIGL1 in Arabidopsis thaliana full hybrids and lines in which a single chromosome is hybrid. We show that increasing recombination ~6-fold empowers the detection and resolution of QTLs, reaching the gene scale with only a few hundred plants. Further, enhanced recombination unmasks some secondary QTLs undetected under normal recombination. These results show the benefits of enhanced recombination to decipher the genetic bases of traits.

Highly complete long-read genomes reveal pangenomic variation underlying yeast phenotypic diversity.

Understanding the genetic causes of trait variation is a primary goal of genetic research. One way that individuals can vary genetically is through variable pangenomic genes: genes that are only present in some individuals in a population. The presence or absence of entire genes could have large effects on trait variation. However, variable pangenomic genes can be missed in standard genotyping workflows, owing to reliance on aligning short-read sequencing to reference genomes. A popular method for studying the genetic basis of trait variation is linkage mapping, which identifies quantitative trait loci (QTLs), regions of the genome that harbor causative genetic variants. Large-scale linkage mapping in the budding yeast Saccharomyces cerevisiae has found thousands of QTLs affecting myriad yeast phenotypes. To enable the resolution of QTLs caused by variable pangenomic genes, we used long-read sequencing to generate highly complete de novo genome assemblies of 16 diverse yeast isolates. With these assemblies, we resolved QTLs for growth on maltose, sucrose, raffinose, and oxidative stress to specific genes that are absent from the reference genome but present in the broader yeast population at appreciable frequency. Copies of genes also duplicate onto chromosomes where they are absent in the reference genome, and we found that these copies generate additional QTLs whose resolution requires pangenome characterization. Our findings show the need for highly complete genome assemblies to identify the genetic basis of trait variation.

Background controlled QTL mapping in pure-line genetic populations derived from four-way crosses

Pure lines derived from multiple parents are becoming more important because of the increased genetic diversity, the possibility to conduct replicated phenotyping trials in multiple environments and potentially high mapping resolution of quantitative trait loci (QTL). In this study, we proposed a new mapping method for QTL detection in pure-line populations derived from four-way crosses, which is able to control the background genetic variation through a two-stage mapping strategy. First, orthogonal variables were created for each marker and used in an inclusive linear model, so as to completely absorb the genetic variation in the mapping population. Second, inclusive composite interval mapping approach was implemented for one-dimensional scanning, during which the inclusive linear model was employed to control the background variation. Simulation studies using different genetic models demonstrated that the new method is efficient when considering high detection power, low false discovery rate and high accuracy in estimating quantitative trait loci locations and effects. For illustration, the proposed method was applied in a reported wheat four-way recombinant inbred line population.

Resolving the Complex Genetic Basis of Phenotypic Variation and Variability of Cellular Growth.

In all organisms, the majority of traits vary continuously between individuals. Explaining the genetic basis of quantitative trait variation requires comprehensively accounting for genetic and nongenetic factors as well as their interactions. The growth of microbial cells can be characterized by a lag duration, an exponential growth phase, and a stationary phase. Parameters that characterize these growth phases can vary among genotypes (phenotypic variation), environmental conditions (phenotypic plasticity), and among isogenic cells in a given environment (phenotypic variability). We used a high-throughput microscopy assay to map genetic loci determining variation in lag duration and exponential growth rate in growth rate-limiting and nonlimiting glucose concentrations, using segregants from a cross of two natural isolates of the budding yeast, Saccharomyces cerevisiae We find that some quantitative trait loci (QTL) are common between traits and environments whereas some are unique, exhibiting gene-by-environment interactions. Furthermore, whereas variation in the central tendency of growth rate or lag duration is explained by many additive loci, differences in phenotypic variability are primarily the result of genetic interactions. We used bulk segregant mapping to increase QTL resolution by performing whole-genome sequencing of complex mixtures of an advanced intercross mapping population grown in selective conditions using glucose-limited chemostats. We find that sequence variation in the high-affinity glucose transporter HXT7 contributes to variation in growth rate and lag duration. Allele replacements of the entire locus, as well as of a single polymorphic amino acid, reveal that the effect of variation in HXT7 depends on genetic, and allelic, background. Amplifications of HXT7 are frequently selected in experimental evolution in glucose-limited environments, but we find that HXT7 amplifications result in antagonistic pleiotropy that is absent in naturally occurring variants of HXT7 Our study highlights the complex nature of the genotype-to-phenotype map within and between environments.

Genome-wide association links candidate genes to resistance to Plum Pox Virus in apricot (Prunus armeniaca).

In fruit tree species, many important traits have been characterized genetically by using single-family descent mapping in progenies segregating for the traits. However, most mapped loci have not been sufficiently resolved to the individual genes due to insufficient progeny sizes for high resolution mapping and the previous lack of whole-genome sequence resources of the study species. To address this problem for Plum Pox Virus (PPV) candidate resistance gene identification in Prunus species, we implemented a genome-wide association (GWA) approach in apricot. This study exploited the broad genetic diversity of the apricot (Prunus armeniaca) germplasm containing resistance to PPV, next-generation sequence-based genotyping, and the high-quality peach (Prunus persica) genome reference sequence for single nucleotide polymorphism (SNP) identification. The results of this GWA study validated previously reported PPV resistance quantitative trait loci (QTL) intervals, highlighted other potential resistance loci, and resolved each to a limited set of candidate genes for further study. This work substantiates the association genetics approach for resolution of QTL to candidate genes in apricot and suggests that this approach could simplify identification of other candidate genes for other marked trait intervals in this germplasm.

Linkage disequilibrium with linkage analysis of multiline crosses reveals different multiallelic QTL for hybrid performance in the flint and dent heterotic groups of maize.

Multiparental designs combined with dense genotyping of parents have been proposed as a way to increase the diversity and resolution of quantitative trait loci (QTL) mapping studies, using methods combining linkage disequilibrium information with linkage analysis (LDLA). Two new nested association mapping designs adapted to European conditions were derived from the complementary dent and flint heterotic groups of maize (Zea mays L.). Ten biparental dent families (N = 841) and 11 biparental flint families (N = 811) were genotyped with 56,110 single nucleotide polymorphism markers and evaluated as test crosses with the central line of the reciprocal design for biomass yield, plant height, and precocity. Alleles at candidate QTL were defined as (i) parental alleles, (ii) haplotypic identity by descent, and (iii) single-marker groupings. Between five and 16 QTL were detected depending on the model, trait, and genetic group considered. In the flint design, a major QTL (R(2) = 27%) with pleiotropic effects was detected on chromosome 10, whereas other QTL displayed milder effects (R(2) < 10%). On average, the LDLA models detected more QTL but generally explained lower percentages of variance, consistent with the fact that most QTL display complex allelic series. Only 15% of the QTL were common to the two designs. A joint analysis of the two designs detected between 15 and 21 QTL for the five traits. Of these, between 27 for silking date and 41% for tasseling date were significant in both groups. Favorable allelic effects detected in both groups open perspectives for improving biomass production.

Quantitative trait loci × environment interactions for plant morphology vary over ontogeny in Brassica rapa.

Growth in plants occurs via the addition of repeating modules, suggesting that the genetic architecture of similar subunits may vary between earlier- and later-developing modules. These complex environment × ontogeny interactions are not well elucidated, as studies examining quantitative trait loci (QTLs) expression over ontogeny have not included multiple environments. Here, we characterized the genetic architecture of vegetative traits and onset of reproduction over ontogeny in recombinant inbred lines of Brassica rapa in the field and glasshouse. The magnitude of genetic variation in plasticity of seedling internodes was greater than in those produced later in ontogeny. We correspondingly detected that QTLs for seedling internode length were environment-specific, whereas later in ontogeny the majority of QTLs affected internode lengths in all treatments. The relationship between internode traits and onset of reproduction varied with environment and ontogenetic stage. This relationship was observed only in the glasshouse environment and was largely attributable to one environment-specific QTL. Our results provide the first evidence of a QTL × environment × ontogeny interaction, and provide QTL resolution for differences between early- and later-stage plasticity for stem elongation. These results also suggest potential constraints on morphological evolution in early vs later modules as a result of associations with reproductive timing.

Mapping of QTL on chromosome X for fat deposition, muscling and growth traits in a wild boar × Meishan F2 family using a high‐density gene map

Quantitative trait loci (QTL) for fat deposition, growth and muscling traits have been previously mapped on the basis of low-density linkage maps in a wild boar x Meishan F2family to the chromosome X region flanked by SW2456 and SW1943. Improved QTL resolution was possible using data for F2 animals with a marker density of 2.7 cM distance in the SW2456 to SW1943 region, including AR, SERPINA7 and ACSL4 as candidate genes. The resolution of the QTL scan was increased substantially, as evidenced by the higher F-ratio values for all QTL. Maxima of F-ratio values for fat deposition, muscling and growth traits were 28.6, 18.2 and 16.5 respectively, and those QTL positions accounted for 7.9%, 5.0% and 4.5% of the F2 phenotypic variance (VF2) respectively. QTL for fatness and growth and for most muscling traits mapped near ACSL4, with the exception of the QTL for ham traits that mapped proximally, in the vicinity of AR. An analysis performed separately for F2 male animals showed the predominant QTL affecting fat deposition traits (up to 13.6% VF2) near AR and two QTL for muscling traits (up to 9.9% VF2) mapped close to ACSL4. In the F2 female animals, QTL affecting muscling (up to 12.1% VF2) mapped at ACSL4 and SW2456, and QTL for fat deposition (10% VF2) and growth (up to 10.5% VF2) mapped at ACSL4.

Positional Cloning of Quantitative Trait Loci for Blood Pressure: How Close Are We?

Blood pressure (BP) in any human population exhibits as a continuous variable that fits a bell-shaped curve. Hypertensive individuals are those whose BP is maintained at one extreme of the curve and above a defined cutoff. Despite progress made in identifying the mechanisms underlying certain rare monogenic forms of hypertension,1,2 the etiology and pathogenesis of essential hypertension remain poorly understood. Because existing human populations are genetically heterogeneous, and because environmental factors impacting on the pathogenesis of hypertension cannot be controlled in a given population, it is difficult to identify the molecular mechanisms that transduce the sequela of essential hypertension via direct human studies.3 To alleviate the drawbacks of human investigations, animal models, especially inbred rodents, have been developed and experimentally manipulated to identify quantitative trait loci (QTLs) for BP, because major confounding environmental factors, such as diet and genetic background, can be systematically controlled. Once identified in animal models, the molecular basis may be translated into physiological understandings of essential hypertension in humans. It is with this expectation that efforts have been launched to identify the molecular basis of BP QTLs in animal models. Because the identification of individual QTLs is primarily based on their chromosome locations unbiased by, or unrestricted to, their physiological roles, positional cloning is believed to be the most efficient strategy. Before we embark on discussions regarding QTL discovery, a definition is in order. Semantic arguments abound as to exactly what a QTL, that is, a locus,4 entails. Is it 1 gene or a collection of genes? As genetic mapping progresses from a large chromosome segment to an interval of submegabase, several regions initially thought to contain 1 BP QTL5 appear to harbor >1 in each of them,6–10 whereas several other regions turned out to harbor 1 QTL as expected.11,12 …

Identification of Additional Quantitative Trait Loci (QTL) Responsible for Susceptibility to Infectious Pancreatic Necrosis Virus in Rainbow Trout

Previous typing of microsatellite markers in a BC1F1 backcross rainbow trout Oncorhynchus mykiss revealed two quantitative trait loci (QTL) with a significant impact on suscep- tibility to infectious pancreatic necrosis. In the present study, additional genetic markers were mapped using the same strain in order to increase QTL resolution. A total of 199 individuals were screened at 226 marker loci (106 microsatellites, 116 amplified fragment length polymorphism markers, classical major histocompatibility complex molecules (MHC class I, nonclassical MHC class I, MHC class II) and tyrosinase) and the QTL were determined by single-point and interval mappings. Seven QTL were distinguished in the linkage groups RT-11, 12, 17, 23, 26, 29 and 31, in addition to the two already known in RT-3 and 22. The major QTL were those in RT-3, 12 and 22; whereas the classical MHC class I locus Onmy-UBA and the MHC class II locus Onmy-DAB were not associated with any of the QTL, a nonclassical MHC class I region with Onmy-UCA, Onmy-UDA, and Onmy-UEA mapped to the major QTL on RT-3.

Further studies on using multiple-cross mapping (MCM) to map quantitative trait loci

We have completed whole-genome scans for quantitative trait loci (QTLs) associated with acute ethanol-induced activation in the six F(2) intercrosses that can be formed from the C57BL/6J (B6), DBA/2J (D2) , BALB/cJ (C), and LP/J (LP) inbred strains. The goal was to test the hypothesis that given the relatively simple structure of the laboratory mouse genome, the same QTLs will be detected in multiple crosses which in turn will provide support for the strategy of multiple-cross mapping (MCM). QTLs with LOD scores greater than 4 were detected on Chrs 1, 2, 3, 8, 9, 13, 14, and 16. Only for the QTL on distal Chr 1 was there convincing evidence that the same or at least a very similar QTL was detected in multiple crosses. We also mapped the Chr 2 QTL directly in heterogeneous stock (HS) animals derived from the four inbred strains. At G(19) the QTL was mapped to an approximately 3-Mbp interval and this interval was associated with a haplotype block with a largely biallelic structure: B6-L:C-D2. We conclude that mapping in HS animals not only provides significantly greater QTL resolution, at least in some cases it provides significantly more information about the QTL haplotype structure.

An improved genetic model generates high-resolution mapping of QTL for protein quality in maize endosperm.

The genetic mapping of polymorphic markers in a cross between two inbred plant lines has proven to be a powerful method for detecting quantitative trait loci (QTL) underlying complex traits. However, existing methods of QTL mapping were developed for disomic inheritance of both marker and QTL loci in a diploid population. To map QTL influencing traits expressed in the endosperm, a triploid embryo-nourishing tissue resulting from double fertilization, existing QTL mapping models require modification to consider the trisomic inheritance of the endosperm and the generation difference between the mapping population and the endosperm. Such a model requires simultaneous use of two successive generations, which theoretically can lead to an increase in resolution for QTL mapping compared with the use of a single backcross or F(2) generation. Using a newly developed model based on these considerations, we demonstrate the improved resolution of QTL, influencing protein quality traits in maize endosperm. The increased resolution made possible with this approach makes identified QTL accessible to positional cloning.

Resolution of quantitative trait loci for mechanical measures accounting for genetic variation in fruit texture of apple (Malus pumila Mill.)

Different attributes of fruit flesh texture contribute to consumer preference for eating quality in apple. We extended previous studies in order to determine whether a range of mechanical measures could resolve further the genetic contribution to variation in the physical attributes of apple fruit flesh. In particular we were interested in accounting for variation of the sensory traits crispness and juiciness. A quantitative genetic analysis of mechanical measures derived from compression and wedge fracture tests was carried out. This was based on segregation in an unselected mapping population which had previously been used to identify QTLs associated with penetrometer readings, stiffness by acoustic resonance, and a range of sensory descriptors. For wedge fracture tests significant QTLs were detected on L16 and L01. Those on L16 corresponded with positions previously determined for sensory measures of crispness and juiciness. Distance at maximum force was accounted for by a single QTL on L16 and correlated well with crispness and juiciness, suggesting that it may be appropriate for the selection of genotypes with fruit posessing desirable texture attributes. We established that the association of the sensory texture QTL on L16 is unlikely to be due to perceptual interactions with the Ma acidity locus. For compression measures, QTLs were detected on L01, L06, L08 and L15. Specific gravity is well-correlated with compression stiffness modulus, and both have a significant QTL on L06. Measures of cell size and shape determined across the population failed to detect any significant QTLs.

A porcine BAC library with tenfold genome coverage: a resource for physical and genetic map integration.

A porcine BAC library with tenfold genome coverage: a resource for physical and genetic map integration.

High resolution mapping and identification of new quantitative trait loci (QTL) affecting susceptibility to Marek's disease.

Marek's disease (MD) is a lymphoproliferative disease of chickens that costs the poultry industry approximately $1 billion annually. Genetic resistance to MD is gaining increased attention to augment vaccinal control as disease outbreaks occur more frequently. Previously, analysis of a 272 F2 White Leghorn resource population measured for many MD traits and genotyped for 78 microsatellite markers revealed two and four quantitative trait loci (QTL) with significant and suggestive association, respectively, to one or more MD associated traits. Additional genetic markers have since been scored on the MD resource population to increase QTL resolution and genome coverage. Saturation of four of the QTL regions with 17 markers revealed five new QTL while 32 markers extended the genome coverage by 400 + CM and uncovered three more QTL. QTL analysis by single-point and interval mapping algorithms agreed well when marker saturation was approximately 20 CM or less. Currently 127 genetic markers cover approximately 68% of the genome that contain up to 14 MD QTL associated to one or more MD trait; seven at the significant level and seven at the suggestive level. Individually each QTL accounts for 2-10% of the variation and, in general, resistance was dominant although the resistant allele may come from either parental line. This study suggests that a limited number of genomic regions play a major role in the genetic control of MD resistance. Markers linked to these loci may be useful for selection of MD resistant stock by the poultry industry following verification of the association within their breeding populations.