QTL Effects Research Articles

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data.

The main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

Identification of seed protein and oil related QTL in 944 RILs from a diallel of early-maturing European soybean

Soybean [Glycine max (L.) Merr.] is a global protein source and is currently expanding in Central and Northern Europe. Protein and oil content are two important quality traits that have been studied in different germplasm, however, their genetic architecture in early-maturing European soybean has not been investigated yet. In this study, we therefore performed QTL mapping for both traits using 944 recombinant inbred lines derived from eight families from a half-diallel crossing design. We identified five QTL for each trait, with the QTL on chromosomes 8, 15, and 20 being identified for both protein content and oil content. The known major QTL on chromosome 20 was detected in four families whereas the other QTL were only found in single families. Further analyses revealed the QTL to have pleiotropic but inverse effects on both traits. The effect of the major QTL was comparable between families, illustrating that it is largely independent from the genetic background. Collectively, our results illustrate the quantitative nature of protein and oil content in early European soybean. Marker-assisted selection for the QTL is possible, but the inverse effect on protein and oil content should be kept in mind.

Purebred and Crossbred Genomic Evaluation and Mate Allocation Strategies To Exploit Dominance in Pig Crossbreeding Schemes.

We investigated the effectiveness of mate allocation strategies accounting for non-additive genetic effects to improve crossbred performance in a two-way crossbreeding scheme. We did this by computer simulation of 10 generations of evaluation and selection. QTL effects were simulated as correlated across purebreds and crossbreds, and (positive) heterosis was simulated as directional dominance. The purebred-crossbred correlation was 0.30 or 0.68 depending on the genetic variance component used. Dominance and additive marker effects were estimated simultaneously for purebreds and crossbreds by multiple trait genomic BLUP. Four scenarios that differ in the sources of information (only purebred data, or purebred and crossbred data) and mate allocation strategies (mating at random, minimizing expected future inbreeding, or maximizing the expected total genetic value of crossbred animals) were evaluated under different cases of genetic variance components. Selecting purebred animals for purebred performance yielded a response of 0.2 genetic standard deviations of the trait “crossbred performance” per generation, whereas selecting purebred animals for crossbred performance doubled the genetic response. Mate allocation strategy to maximize the expected total genetic value of crossbred descendants resulted in a slight increase (0.8%, 4% and 0.5% depending on the genetic variance components) of the crossbred performance. Purebred populations increased homozygosity, but the heterozygosity of the crossbreds remained constant. When purebred-crossbred genetic correlation is low, selecting purebred animals for crossbred performance using crossbred information is a more efficient strategy to exploit heterosis and increase performance at the crossbred commercial level, whereas mate allocation did not improve crossbred performance.

Detection of quantitative genes controlling of chlorophyll content in rice seedling under normal and salinity stress and comparison of different QTL mapping methods

شوری یک محدودیت عمده در توسعه کشت برنج می‌باشد. بهبود بخشیدن به تحمل به شوری در برنج ازنظر ژنتیکی یک مسئله بسیار مهم در برنامه‌های اصلاحی است. به‌منظور مکان یابی ژن های کنترل کننده محتوای کلروفیل، 96 لاین خالص نوترکیب برنج ایرانی حاصل تلاقی ارقام ندا × اهلمی-طارم تحت تنش شوری در مرحله گیاهچه‌ای آزمایشی به‌صورت مرکب در قالب طرح کاملا تصادفی با سه تکرار و دو شرایط کشت نرمال و تنش شوری در دانشگاه گنبد‌کاووس در سال 1395 در شرایط گیاهچه کشت شدند. مکان‌یابی ژن‌های کنترل‌کننده محتوای کلروفیل با استفاده از روش‌های مختلف مکان‌یابی شامل SIM-MEL، SIM، CIM، MIM، PMLE، ICIM و STSIM انجام گرفت و با استفاده از هر کدام از این روش‌ها QTL‌های مشابه و متفاوتی ردیابی شد. 40 نشانگر SSR و 16 نشانگر ISSR (76 آلل تکثیر‌شده چند شکل)، 2 نشانگر IRAP (7 آلل تکثیر‌شده چند شکل) و یک نشانگر iPBS (3 آلل تکثیر‌شده چند شکل) بر روی 12 کروموزوم برنج توزیع شدند. روش ICIM، CIM و SIM در شرایط نرمال و تنش شوری بیشترین تشابه مکان‌های ژنی ردیابی شده را دارا بودند. qCHL-6 در شش روش مکان‌یابی در موقعیت 52 سانتی‌مورگان از کروموزوم 6 شناسایی شد. بنابراین با استفاده از QTL‌های شناسایی شده می‌توان پس از تعیین اعتبار QTLها، ژنوتیپ‌های برتر از نظر محتوای کلروفیل برای برنامه‌های انتخاب به کمک نشانگر را شناسایی کرد.

Multi-parent multi-environment QTL analysis: an illustration with the EU-NAM Flint population

Key messageMulti-parent populations multi-environment QTL experiments data should be analysed jointly to estimate the QTL effect variation within the population and between environments.Commonly, QTL detection in multi-parent populations (MPPs) data measured in multiple environments (ME) is done by analyzing genotypic values ‘averaged’ across environments. This method ignores the environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects, but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyse MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariance. Using data from the EU-NAM Flint population, we show that these methods estimate the QTLxE effects and that they can improve the quality of the QTL detection. Those methods also have a larger inference power. For example, they can be extended to integrate environmental indices like temperature or precipitation to better understand the mechanisms behind the QTLxE effects. Therefore, our methodology allows the exploitation of the full MPP-ME data potential: to estimate QTL effect variation (a) within the MPP between sub-populations due to different genetic backgrounds and (b) between environments.

Multiparental QTL analysis: can we do it in polyploids?

Many ornamental crops are polyploid or even exist at different ploidy levels. Polyploid QTL analysis tools have been developed in recent years, yet they are limited in the population types they accept. Biparental populations are nowadays being regarded as a limited tool for QTL discovery, as only a limited number of QTLs occurs in an experimental cross and their effects might not be stable across genetic backgrounds. Genome-Wide Association Studies include more genetic diversity but suffer from (hidden) genetic structure and low frequency of QTL alleles. Both factors influence QTL detection and effect estimation, decreasing the sensitivity of QTL analysis. Alternatively, multiparental populations (MPP) can be used, potentially combining multiple QTLs and QTL alleles with known population structure and balanced allele frequencies. Breeding populations of interconnected crosses also constitute a form of MPP and QTLs identified in them might be more applicable to commercial cultivars. To perform QTL analysis in polyploids, mixed models or Bayesian approaches that consider pedigree information are recommended. During the analysis, QTL effects are ideally estimated using Identity by Descent (IBD) alleles (genomic regions that originate from the same ancestor) which can be obtained through haplotype estimation. Although MPPs could thus be a powerful set-up to estimate polyploid haplotypes, a software gap was identified as no current polyploid haplotyping tools are able to utilize MPP pedigree information to obtain haplotypes across an MPP. In order to utilize MPPs to their full extent and expand polyploid QTL analyses to encompass typical breeding populations, new haplotyping tools must be developed.

Utilization of genetically diverse mouse model identifies genetic loci that correlate to response to immune checkpoint inhibitors to B16F0 melanoma

Abstract Historically metastatic melanoma has been associated with poor prognosis. The advent of immune checkpoint inhibitors targeting CTLA-4 and PD-1 have greatly improved patient survival in metastatic melanoma, but a significant proportion of patients fail to respond to therapy. To explore the effect of genetic background on immune checkpoint inhibitor efficacy, we have developed a tumor immunotherapy model using genetically heterogeneous Diversity Outbred (DO) mice and the C57BL/6 syngeneic B16F0 melanoma model. To implement genetic diversity, DO mice were crossed with C57BL/6 to generate (C57BL/6xDO)F1 mice. Untreated (C57BL/6xDO)F1 mice (n=34) reliably develop B16F0 tumors after subcutaneous inoculation, with some variation in tumor latency. To test the role of genetics in response to ICI, (C57BL/6xDO)F1 mice (n=142) were treated with combined anti-PD1/anti-CTLA-4 on days 3, 6, and 10 after inoculation with 2×105 B16F0 cells. Mice receiving therapy show wide variation in tumor development of up to 65 days (mean 20.86 +/− 11.04, CV 52.94%), with 19 never developing tumor by day 88. Re-challenge of tumor free mice confirmed adaptive response to tumor. Preliminary Genome Wide Association shows suggestive associations on chromosomes 8, 12, and 13. The locus on chromosome 13 contains a family of related genes with known function in autoimmune conditions and expression in several immune cell types. QTL effects show the NZO genotype is a positive driver at this locus and C57BL/6 is a negative driver, reflecting known ICI resistance in the C57BL/6 strain. These results demonstrate host genetic background significantly contributes to response to ICI and identifies genomic loci associating with primary resistance. NIH R37 CA220482.

Identification and Characterization of a Large Effect QTL from Oryza glumaepatula Revealed Pi68(t) as Putative Candidate Gene for Rice Blast Resistance

BackgroundField resistance is often effective and durable as compared to vertical resistance. The introgression line (INGR15002) derived from O. glumaepatula has proven broad spectrum field resistance for both leaf and neck blast.ResultsQuantitative Trait Loci (QTL) analysis of INGR15002, led to the identification of two major QTL - qBL3 contributing about 34% and 32% phenotypic variance towards leaf and neck blast resistance, respectively and qBL7 contributing about 25% of phenotypic variance for leaf blast. Further, qBL3 was fine mapped, narrowed down to 300 kb region and a linked SNP maker was identified. By combining mapping with microarray analysis, a candidate gene, Os03g0281466 (malectin-serine threonine kinase), was identified in the fine mapped region and named as Pi68(t). The nucleotide variations in the coding as well as upstream region of the gene was identified through cloning and sequence analysis of Pi68(t) alleles. These significant variations led to the non-synonymous changes in the protein as well as variations (presence/absence) in four important motifs (W-box element; MYC element; TCP element; BIHD1OS) at promoter region those are associated with resistance and susceptible reactions. The effect of qBL3 was validated by its introgression into BPT5204 (susceptible variety) through marker-assisted selection and progeny exhibiting resistance to both leaf and neck blast was identified. Further, the utility of linked markers of Pi68(t) in the blast breeding programs was demonstrated in elite germplasm lines.ConclusionsThis is the first report on the identification and characterization of major effect QTL from O. glumaepatula, which led to the identification of a putative candidate gene, Pi68(t), which confers field resistance to leaf as well as neck blast in rice.

Identification of novel functional CpG-SNPs associated with type 2 diabetes and coronary artery disease.

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D) and coronary artery disease (CAD), respectively. Nevertheless, these studies were generally performed for single-trait/disease and failed to assess the pleiotropic role of the identified variants. To identify novel functional loci and the pleiotropic relationship between CAD and T2D, the targeted cFDR analysis on CpG-SNPs was performed by integrating two independent large and multi-centered GWASs with summary statistics of T2D (26,676 cases and 132,532 controls) and CAD (60,801 cases and 123,504 controls). Applying the cFDR significance threshold of 0.05, we observed a pleiotropic enrichment between T2D and CAD by incorporating pleiotropic effects into a conditional analysis framework. We identified 79 novel CpG-SNPs for T2D, 61 novel CpG-SNPs for CAD, and 18 novel pleiotropic loci for both traits. Among these novel CpG-SNPs, 33 of them were annotated as methylation quantitative trait locus (meQTL) in whole blood, and ten of them showed expression QTL (eQTL), meQTL, and metabolic QTL (metaQTL) effects simultaneously. To the best of our knowledge, we performed the first targeted cFDR analysis on CpG-SNPs, and our findings provided novel insights into the shared biological mechanisms and overlapped genetic heritability between T2D and CAD.

Simulation of QTL by sequencing for agronomic quantitative trait loci detection in small to medium population size in soybean

Quantitative Trait Loci by sequencing (QTL-seq) is a QTL detection method that utilizes the principles of Bulked Segregants Analysis. It detects alleles with extreme frequencies in whole-genome sequence data from two bulked populations with contrasting phenotypes. This approach is less laborious than QTL detection using linkage mapping, and the result had been shown to be comparable in the same mapping population. However, since the genomes of the two bulked populations are completely sequenced, it can facilitate further characterization of the QTL segment and the genes underlying the QTLs. In this study, QTL-seq was simulated using high-density SNP genotyping data from a recombinant inbred population consisting of 188 individuals. The genomes of both parents had been sequenced, and the SNP genotyping identified 2,207 SNP markers that were polymorphic and segregating in the population. Since the markers are dense enough and well distributed across the genome, they can be used to represent the alleles that can be obtained from whole genome resequencing of bulked individuals. The availability of genotype data for each individual in the mapping population also enabled the detection of QTL via linkage mapping. Using data generated from both approaches, various simulations were conducted to compare the results that could be obtained under ideal conditions, as well as less ideal ones such as when the QTL effects are small, the presence of skewed phenotype distribution, and a small number of bulked samples.

Identification of potential genomic regions for egg weight by a haplotype-based genome-wide association study using Bayesian methods

ABSTRACT 1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes as they are usually better at capturing multi-allelic QTL effects, compared to individual SNP genotypes in genome-wide association studies. The objectives of this study were to identify genomic regions related to egg weight traits by Bayesian methods (BayesA, BayesB, and BayesN) that fit fixed-length haplotypes using GenSel software. 2. Genotypes at 294,705 SNPs, that were common on a 600K Affymetrix chip, were phased for an egg-laying hen population of 1,063 birds. Recorded traits included first egg weight (FEW) and average egg weight at 28, 36, 56, 66, 72 and 80 weeks of age. 2. Fitting 1Mb haplotypes from BayesB resulted in the highest proportion of genetic variance explained for the egg weight traits. Based on the trait, the genetic variance explained by each marker ranged from 27% to 76%. 3. Different haplotype windows associated with egg weight traits only explained a small percentage of the genetic variance. 4. The top one 1-Mb window on GGA1 explained approximately 4.05% of total genetic variance for the FEW. Candidate genes, including PRKAR2B, HMGA2, LEMD3, GRIP1, EHBP1, MAP3K7, and MYH were identified for egg weight traits. 5. Several genomic regions, potentially associated with egg weight traits, were identified, some of which overlapped with known genes and previously reported QTL regions for egg production traits.

Intraspecific Genetic Variation Underlying Postmating Reproductive Barriers between Species in the Wild Tomato Clade (Solanum sect. Lycopersicon)

A goal of speciation genetics is to understand how the genetic components underlying interspecific reproductive barriers originate within species. Unilateral incompatibility (UI) is a postmating prezygotic barrier in which pollen rejection in the female reproductive tract (style) occurs in only one direction of an interspecific cross. Natural variation in the strength of UI has been observed among populations within species in the wild tomato clade. In some cases, molecular loci underlying self-incompatibility (SI) are associated with this variation in UI, but the mechanistic connection between these intra- and inter-specific pollen rejection behaviors is poorly understood in most instances. We generated an F2 population between SI and SC genotypes of a single species, Solanum pennellii, to examine the genetic basis of intraspecific variation in UI against other species, and to determine whether loci underlying SI are genetically associated with this variation. We found that F2 individuals vary in the rate at which UI rejection occurs. One large effect QTL detected for this trait co-localized with the SI-determining S-locus. Moreover, individuals that expressed S-RNase-the S-locus protein involved in SI pollen rejection-in their styles had much more rapid UI responses compared with those without S-RNase protein. Our analysis shows that intraspecific variation at mate choice loci-in this case at loci that prevent self-fertilization-can contribute to variation in the expression of interspecific isolation, including postmating prezygotic barriers. Understanding the nature of such intraspecific variation can provide insight into the accumulation of these barriers between diverging lineages.

Genetic and transcriptional dissection of resistance to Claviceps purpurea in the durum wheat cultivar Greenshank

Key messageFour QTL for ergot resistance (causal pathogen Claviceps purpurea) have been identified in the durum wheat cultivar Greenshank.Claviceps purpurea is a pathogen of grasses that infects flowers, replacing the seed with an ergot sclerotium. Ergot presents a significant problem to rye, barley and wheat, in particular hybrid seed production systems. In addition, there is evidence that the highly toxic alkaloids that accumulate within sclerotia can cross-contaminate otherwise healthy grain. Host resistance to C. purpurea is rare, few resistance loci having been identified. In this study, four ergot resistance loci are located on chromosomes 1B, 2A, 5A and 5B in the durum wheat cv. Greenshank. Ergot resistance was assessed through analysis of phenotypes associated with C. purpurea infection, namely the number of inoculated flowers that produced sclerotia, or resulted in ovary death but no sclerotia, the levels of honeydew produced, total sclerotia weight and average sclerotia weight and size per spike. Ergot testing was undertaken in Canada and the UK. A major effect QTL, QCp.aafc.DH-2A, was detected in both the Canadian and UK experiments and had a significant effect on honeydew production levels. QCp.aafc.DH-5B had the biggest influence on total sclerotia weight per spike. QCp.aafc.DH-1B was only detected in the Canadian experiments and QCp.aafc.DH-5A in the UK experiment. An RNASeq analysis, undertaken to identify wheat differentially expressed genes associated with different combinations of the four ergot resistance QTL, revealed a disproportionate number of DEGs locating to the QCp.aafc.DH-1B, QCp.aafc.DH-2A and QCp.aafc.DH-5B QTL intervals.

Nested association mapping of important agronomic traits in three interspecific soybean populations

Glycine soja germplasm can be used to successfully introduce new alleles with the potential to add valuable new genetic diversity to the current elite soybean gene pool. Given the demonstrated narrow genetic base of the US soybean production, it is essential to identify beneficial alleles from exotic germplasm, such as wild soybean, to enhance genetic gain for favorable traits. Nested association mapping (NAM) is an approach to population development that permits the comparison of allelic effects of the same QTL in multiple parents. Seed yield, plant maturity, plant height and plant lodging were evaluated in a NAM panel consisting of 392 recombinant inbred lines derived from three biparental interspecific soybean populations in eight environments during 2016 and 2017. Nested association mapping, combined with linkage mapping, identified three major QTL for plant maturity in chromosomes 6, 11 and 12 associated with alleles from wild soybean resulting in significant increases in days to maturity. A significant QTL for plant height was identified on chromosome 13 with the allele increasing plant height derived from wild soybean. A significant grain yield QTL was detected on chromosome 17, and theallele from Glycine soja had a positive effect of 166kgha-1; RIL's with the wild soybean allele yielded on average 6% more than the lines carrying the Glycine max allele. These findings demonstrate the usefulness and potential of alleles from wild soybean germplasm to enhance important agronomic traits in a soybean breeding program.

Trait-based mapping to identify the genetic factors underlying anaerobic germination of rice: Phenotyping, GXE, and QTL mapping

BackgroundAnaerobic germination is one of the most important traits for rice under direct-seeded conditions. The trait reduces risk of crop failure due to waterlogged conditions after seeding and allows water to be used as a means of weed control. The identification of QTLs and causal genes for anaerobic germination will facilitate breeding for improved direct-seeded rice varieties. In this study, we explored a BC1F2:3 population developed from a cross between BJ1, an indica landrace, and NSIC Rc222, a high-yielding recurrent parent. The population was phenotyped under different screening methods (anaerobic screenhouse, anaerobic tray, and aerobic screenhouse) to establish the relationship among the methods and to identify the most suitable screening method, followed by bulk segregant analysis (BSA) to identify large-effect QTLs.ResultsThe study showed high heritability for survival (SUR) under all three phenotyping conditions. Although high correlation was observed within screening environments between survival at 14 and 21 days after seeding, the correlation across environments was low. Germination under aerobic and anaerobic conditions showed very low correlation, indicating the independence of their genetic control. The results were further confirmed through AMMI analysis. Four significant markers with an effect on anaerobic germination were identified through BSA. CIM analysis revealed qAG1–2, qAG6–2, qAG7–4, and qAG10–1 having significant effects on the trait. qAG6–2 and qAG10–1 were consistent across screening conditions and seedling age while qAG1–2 and qAG7–4 were specific to screening methods. All QTLs showed an effect when survival across all screening methods was analyzed. Together, the QTLs explained 39 to 55% of the phenotypic variation for survival under anaerobic conditions. No QTL effects were observed under aerobic conditions.ConclusionsThe study helped us understand the effect of phenotyping method on anaerobic germination, which will lead to better phenotyping for this trait in future studies. The QTLs identified through this study will allow the improvement of breeding lines for the trait through marker-assisted selection or through forward breeding approaches such as genomic selection. The high frequency of the BJ1 allele of these QTLs will enhance the robustness of germination under anaerobic conditions in inbred and hybrid rice varieties.

Genomic Prediction of Columnaris Disease Resistance in Catfish.

Catfish is an important aquaculture species in the USA. Columnaris disease is distributed worldwide, affecting a wide variety of fish species including catfish . It leads to huge economic losses each year to the US catfish industry. Channel catfish in general is highly resistant to the disease, while blue catfish is highly susceptible. Genomic selection is an effective and accurate way to predict the breeding values and thus was expected to improve the prediction veracity of columnaris disease resistance in catfish effectively. In this study, two different methods, elastic net genomic best linear unbiased prediction (ENGBLUP) and genomic best linear unbiased prediction (GBLUP), were used to predict the columnaris disease resistance evaluated by binary survival status. Cross-validation showed that the prediction accuracy of ENGBLUP and GBLUP was 0.7347 and 0.4868, respectively, showing that ENGBLUP had a high prediction accuracy. It was shown that fitting QTL and polygenic effect with different distribution will improve genomic prediction accuracy for binary traits. In this study, an accurate and effective genomic selection method was proposed to predict the columnaris resistance in catfish, and its application should be beneficial to catfish breeding.

QTL Mapping of Kernel Traits and Validation of a Major QTL for Kernel Length-Width Ratio Using SNP and Bulked Segregant Analysis in Wheat

One RIL population derived from the cross between Dalibao and BYL8 was used to examine the phenotypes of kernel-related traits in four different environments. Six important kernel traits, kernel length (KL), kernel width (KW), kernel perimeter (KP), kernel area (KA), kernel length/width ratio (KLW), and thousand-kernel weight (TKW) were evaluated in Yangling, Shaanxi Province, China (2016 and 2017), Nanyang, Henan Province, China (2017) and Suqian, Jiangsu Province, China (2017). A genetic linkage map was constructed using 205 SSR markers, and a total of 21 significant QTLs for KL, KW, KP, KA, KLW and TKW were located on 10 of the 21 wheat chromosomes, including 1A, 1B, 2A, 2B, 2D, 3D, 4D, 5A, 5B, and 7D, with a single QTL in different environments explaining 3.495–30.130% of the phenotypic variation. There were four loci for KLW, five for KA, five for KL, three for KP, two for KW, and two for TKW among the detected QTLs. We used BSA + 660 K gene chip technology to reveal the positions of major novel QTLs for KLW. A total of 670 out of 5285 polymorphic SNPs were detected on chromosome 2A. The SNPs in 2A are most likely related to the major QTL, and there may be minor QTLs on 5B, 7A, 3A and 4B. SSR markers were developed to verify the chromosome region associated with KLW. A linkage map was constructed with 7 SSR markers, and a major effect QTL was identified within a 21.55 cM interval, corresponding to a physical interval of 10.8 Mb in the Chinese Spring RefSeq v1.0 sequence. This study can provide useful information for subsequent construction of fine mapping and marker-assisted selection breeding.

Genomic Prediction With Different Heritability, QTL, and SNP Panel Scenarios Using Artificial Neural Network

A simulation study was carried out to determine the genomic prediction performance of Artificial Neural Network model with 1 to 10 neurons (ANN-1-10) using 3361 SNP markers from BovineSNP50 (Infinium BeadChip, Illumina, San Diego, CA) on the first chromosome of Brangus beef cattle as a pilot study for two traits with heritabilities of 25% (h 2 T1 = 0.25) and 50% (h 2 T2 = 0.5) determined either by 50, 100, 250 or 500 QTL selected randomly from SNP markers. QTL effects were sampled from a multivariate normal distribution. Genomic predictions were carried out by Feed Forward Multi-Layer Perceptron ANN-1-10 with the back-propagation of errors algorithm employing the Levenberg-Marquardt algorithm to locate the optimal solution. Three sets of SNP panels were used for genomic prediction: only QTL (Panel-1), all SNP markers, including the QTL (Panel-2), and all SNP, excluding the QTL (Panel-3). Correlations between true genetic merits (breeding values) and predicted phenotypes from 10-fold disjoint cross-validation were used to assess predictive ability of ANN-1-10. Results indicated that an increase in heritability resulted in an increased predictive performance of ANN-1-10 for all scenarios. SNP Panels had a greater chance of including markers in LD with QTL, allowed the possibility of predicting the effect of each QTL from the collective action of several markers and performed better than the Panel including only QTL. In the other Panels, predictive performance of ANN-1-10 increased inconsistently with the number of neurons, which indicated that a few numbers of neurons were not be enough to learn specification of data and could cause the under fitting problem. Therefore, high number of neurons could be needed to learn relevant details of the data in the applications of ANN.

Genetic dissection of maternal influence on in vivo haploid induction in maize

In vivo haploid induction based on maternal haploid inducers is the first step in deriving completely homozygous maize doubled haploid (DH) lines. Haploid induction rate (HIR) is influenced by both pollen parent inducing haploidy and the maternal source germplasm used in induction crosses. This study was aimed at analyzing the influence of source germplasm on HIR using 671 tropical inbred lines organized in two association mapping panels. These two association mapping panels (AMP1 and AMP2) were crossed to two different Tropically Adapted Inducer Lines (TAILs). For HIR assessment, seeds from induction crosses were planted in the field and ploidy status of each surviving plant was assessed using a gold standard classification based on visual differences between the haploid and diploid plants. The analysis revealed significant variation for HIR and led to identification of several tropical inbred lines that respond very positively to haploid induction. Use of HIR data in a genome wide association study (GWAS) led to identification of twenty-seven and two SNPs that were significantly associated with HIR in AMP1 and AMP2, respectively. Meta-analysis of AMP1 and AMP2 GWAS led to identification of 52 SNPs with significant effect on HIR across both studies. Genome-wide prediction revealed moderate to high prediction accuracy within AMPs using random SNPs. Inclusion of the SNPs detected in GWAS into the prediction model led to improvement in prediction accuracy. Overall, the study revealed that the maternal influence on HIR is controlled by a few moderate and many small effect QTL.

PSVIII-20 Univariate Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios using Artificial Neural Network

Abstract A simulation study was carried out to determine the likely accuracy of genomic prediction from univariate artificial neural network model with 1 to 10 neurons (ANN-1–10) using the SNPs on the first chromosome of Brangus beef cattle for 50% genetically correlated two traits with heritabilities of 25% and 50% (T1h2=0.25 and T2h2=0.5) determined either by 50, 100, 250 or 500 QTL. After QTL were created by randomly selecting 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals, their effects were sampled from a bivariate normal distribution. The breeding value of animal i in each QTL scenario was generated as Σgijβ j where gij is the genotype of animal i at QTL j and the vector of β j has the effects of QTL j from a bivariate normal distribution for T1h2=0.25 and T2h2=0.5. Phenotypic values (Σgijβ j+ei) of animal i for traits were generated by adding residuals (ei) from a bivariate normal distribution to the Σgijβ j of animal i. Genomic predictions for T1h2=0.25 and T2h2=0.5 were carried out by univariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations between phenotypes and predicted phenotypes from 10-fold cross validation for T1h2=0.25 and T2h2=0.5 were used to assess predictive ability of univariate ANN-1–10 neurons based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that genomic predictions from the trait with high heritability can achieve higher correlation than those from the trait with low heritability. Panels including SNP markers perform better prediction than Panel1 and have a greater chance of including markers in LD with QTL and allow the possibility of predicting each QTL from collective action of several markers.