Genome-wide association study and development of molecular markers for yield and quality traits in peanut (Arachis hypogaea L.).

BACKGROUND: This study aims to decipher the genetic basis governing yield components and quality attributes of peanuts, a critical aspect for advancing molecular breeding techniques. Integrating genotype re-sequencing and phenotypic evaluations of seven yield components and two grain quality traits across four distinct environments allowed for the execution of a genome-wide association study (GWAS). RESULTS: The nine phenotypic traits were all continuous and followed a normal distribution. The broad heritability ranged from 88.09 to 98.08%, and the genotype-environment interaction effects were all significant. There was a highly significant negative correlation between protein content (PC) and oil content (OC). The 10× genome re-sequencing of 199 peanut accessions yielded a total of 631,988 high-quality single nucleotide polymorphisms (SNPs), with 374 significant SNP loci identified in association with the nine traits of interest. Notably, 66 of these pertinent SNPs were detected in multiple environments, and 48 of them were linked to multiple traits of interest. Five loci situated on chromosome 16 (Chr16) exhibited pleiotropic effects on yield traits, accounting for 17.64-32.61% of the observed phenotypic variation. Two loci on Chr08 were found to be strongly associated with protein and oil contents, accounting for 12.86% and 14.06% of their respective phenotypic variations, respectively. Linkage disequilibrium (LD) block analysis of these seven loci unraveled five nonsynonymous variants, leading to the identification of one yield-related candidate gene and two quality-related candidate genes. The correlation between phenotypic variation and SNP loci in these candidate genes was validated by Kompetitive allele-specific PCR (KASP) marker analysis. CONCLUSIONS: Overall, molecular markers were developed for genetic loci associated with yield and quality traits through a GWAS investigation of 199 peanut accessions across four distinct environments. These molecular tools can aid in the development of desirable peanut germplasm with an equilibrium of yield and quality through marker-assisted breeding.

Genetic linkage mapping and quantitative trait locus (QTL) analysis of sweet basil (Ocimum basilicum L.) to identify genomic regions associated with cold tolerance and major volatiles.

Chilling sensitivity is one of the greatest challenges affecting the marketability and profitability of sweet basil (Ocimum basilicum L.) in the US and worldwide. Currently, there are no sweet basils commercially available with significant chilling tolerance and traditional aroma profiles. This study was conducted to identify quantitative trait loci (QTLs) responsible for chilling tolerance and aroma compounds in a biparental mapping population, including the Rutgers advanced breeding line that served as a chilling tolerant parent, 'CB15', the chilling sensitive parent, 'Rutgers Obsession DMR' and 200 F2 individuals. Chilling tolerance was assessed by percent necrosis using machine learning and aroma profiling was evaluated using gas chromatography (GC) mass spectrometry (MS). Single nucleotide polymorphism (SNP) markers were generated from genomic sequences derived from double digestion restriction-site associated DNA sequencing (ddRADseq) and converted to genotype data using a reference genome alignment. A genetic linkage map was constructed and five statistically significant QTLs were identified in response to chilling temperatures with possible interactions between QTLs. The QTL on LG24 (qCH24) demonstrated the largest effect for chilling response and was significant in all three replicates. No QTLs were identified for linalool, as the population did not segregate sufficiently to detect this trait. Two significant QTLs were identified for estragole (also known as methyl chavicol) with only qEST1 on LG1 being significant in the multiple-QTL model (MQM). QEUC26 was identified as a significant QTL for eucalyptol (also known as 1,8-cineole) on LG26. These QTLs may represent key mechanisms for chilling tolerance and aroma in basil, providing critical knowledge for future investigation of these phenotypic traits and molecular breeding.

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.).

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

QTL Mapping Using RIL Population.

Genetic maps are an excellent tool for the analysis of important traits, the development of which is the result of the combined expression of several genes, enabling the genomic localization of the factors determining them. Such features, characterized by a normal distribution of values, are referred to as quantitative or polygenic. The analysis of their genetic background using a chromosome map is called the mapping of quantitative traits loci (QTL). QTL analysis is a statistical method of determining the genetic association of phenotypic data (trait measurements) with genotypic data (DNA markers assigned to linkage groups).There are numerous tools developed for QTL mapping. This chapter introduces Windows QTL Cartographer with Composite Interval Mapping (CIM) method, which estimates the QTL position by combining interval mapping with multiple regression. The genotypic and phenotypic data used in the exemplary QTL mapping procedure were obtained for the recombinant inbred line (RIL) population of rye. Plant height, assessed in three seasons, was the exemplary trait under study.

Genetic Map Construction Using F2 and RIL/DH Mapping Population.

Genetic mapping is the determination of the position and relative genetic distance between genes or molecular markers in the chromosomes of a particular species. The construction of genetic maps uses data from the genotyping of the mapping population. Among the different mapping populations used, two are relatively common: the F2 and recombinant inbred lines (RILs) obtained as a result of the controlled crossing of genetically diverse parental forms (e.g., inbred lines). Also, the dihaploid (DH) population is often used in plants, but obtaining DHs in different crops, including rye, is very difficult or even impossible. Any molecular marker system can be used for genotyping. Polymorphic markers are used for linkage analysis, differentiating parental forms with segregation in the mapping population, consistent with the appropriate single-gene model. A genetic map is a great source of information on a species and can be an exquisite tool for analyzing important quantitative traits (QT).This chapter presents the procedure of genetic map construction with two different algorithms using the JoinMap5.0 program. First, the Materials section briefly informs about the mapping program, showing how to obtain a mapping population and prepare data for mapping. Finally, the Methods section describes the protocol for the mapping procedure itself.

A combination of QTL mapping and genome-wide association study revealed the key gene for husk number in maize.

KEY MESSAGE: Two key genes Zm00001d021232 and Zm00001d048138 were identified by QTL mapping and GWAS. Additionally, they were verified to be significantly associated with maize husk number (HN) using gene-based association study. As a by-product of maize production, maize husk is an important industrial raw material. Husk layer number (HN) is an important trait that affects the yield of maize husk. However, the genetic mechanism underlying HN remains unclear. Herein, a total of 13 quantitative trait loci (QTL) controlling HN were identified in an IBM Syn 10 DH population across different locations. Among these, three QTL were individually repeatedly detected in at least two environments. Meanwhile, 26 unique single nucleotide polymorphisms (SNPs) were detected to be significantly (p < 2.15 × 10-6) associated with HN in an association pool. Of these SNPs, three were simultaneously detected across multiple environments or environments and best linear unbiased prediction (BLUP). We focused on these environment-stable and population-common genetic loci for excavating the candidate genes responsible for maize HN. Finally, 173 initial candidate genes were identified, of which 22 were involved in both multicellular organism development and single-multicellular organism process and thus confirmed as the candidate genes for HN. Gene-based association analyses revealed that the variants in four genes were significantly (p < 0.01/N) correlated with HN, of which Zm00001d021232 and Zm00001d048138 were highly expressed in husks and early developing ears among different maize tissues. Our study contributes to the understanding of genetic and molecular mechanisms of maize husk yield and industrial development in the future.

Expression, Polymorphism, and Potential Functional Sites of the BMPR1A Gene in the Sheep Horn.

Sheep horns are composed of bone and sheaths, and the BMPR1A gene is required for cartilage and osteogenic differentiation. Therefore, the BMPR1A gene may have a function related to the sheep horn, but its relationship with the sheep horn remains unclear. In this study, we first utilized RNA sequencing (RNA-seq) data to investigate the expression of the BMPR1A gene in different tissues and breeds of sheep. Second, whole-genome sequencing (WGS) data were used to explore the functional sites of the BMPR1A gene. Lastly, the allele-specific expression of the BMPR1A gene was explored. Our results indicate that BMPR1A gene expression is significantly higher in the normal horn groups than in the scurred groups. Importantly, this trend is consistent across several sheep breeds. Therefore, this finding suggests that the BMPR1A gene may be related to horn type. A total of 43 Single-Nucleotide Polymorphisms (SNPs) (F-statistics > 0.15) and 10 allele-specific expressions (ASEs) exhibited difference between the large and small horn populations. It is probable that these sites significantly impact the size of sheep horns. Compared to other polled species, we discovered ten amino acid sites that could influence horn presence. By combining RNA-seq and WGS functional loci results, we identified a functional site at position 40574836 on chromosome 25 that is both an SNP and exhibits allele-specific expression. In conclusion, we demonstrated that the BMPR1A gene is associated with horn type and identified some important functional sites which can be used as molecular markers in the breeding of sheep horns.

Quantitative trait loci detection for three tiller-related traits and the effects on wheat (Triticum aestivum L.) yields.

KEY MESSAGE: A total of 38 putative additive QTLs and 55 pairwise putative epistatic QTLs for tiller-related traits were reported, and the candidate genes underlying qMtn-KJ-5D, a novel major and stable QTL for maximum tiller number, were characterized. Tiller-related traits play an important role in determining the yield potential of wheat. Therefore, it is important to elucidate the genetic basis for tiller number when attempting to use genetic improvement as a tool for enhancing wheat yields. In this study, a quantitative trait locus (QTL) analysis of three tiller-related traits was performed on the recombinant inbred lines (RILs) of a mapping population, referred to as KJ-RILs, that was derived from a cross between the Kenong 9204 (KN9204) and Jing 411 (J411) lines. A total of 38 putative additive QTLs and 55 pairwise putative epistatic QTLs for spike number per plant (SNPP), maximum tiller number (MTN), and ear-bearing tiller rate (EBTR) were detected in eight different environments. Among these QTLs with additive effects, three major and stable QTLs were first documented herein. Almost all but two pairwise epistatic QTLs showed minor interaction effects accounting for no more than 3.0% of the phenotypic variance. The genetic effects of two colocated major and stable QTLs, i.e., qSnpp-KJ-5D.1 and qMtn-KJ-5D, for yield-related traits were characterized. The breeding selection effect of the beneficial allele for the two QTLs was characterized, and its genetic effects on yield-related traits were evaluated. The candidate genes underlying qMtn-KJ-5D were predicted based on multi-omics data, and TraesKN5D01HG00080 was identified as a likely candidate gene. Overall, our results will help elucidate the genetic architecture of tiller-related traits and can be used to develop novel wheat varieties with high yields.

Identification of QTLs for symbiotic nitrogen fixation and related traits in a soybean recombinant inbred line population.

KEY MESSAGE: The genetic architecture of symbiotic N fixation and related traits was investigated in the field. QTLs were identified for percent N derived from the atmosphere, shoot [N] and C to N ratio. Soybean [Glycine max (L.) Merr.] is cultivated worldwide and is the most abundant source of plant-based protein. Symbiotic N2 fixation (SNF) in legumes such as soybean is of great importance; however, yields may still be limited by N in both high yielding and stressful environments. To better understand the genetic architecture of SNF and facilitate the development of high yielding cultivars and sustainable soybean production in stressful environments, a recombinant inbred line population consisting of 190 lines, developed from a cross between PI 442012A and PI 404199, was evaluated for N derived from the atmosphere (Ndfa), N concentration ([N]), and C to N ratio (C/N) in three environments. Significant genotype, environment and genotype × environment effects were observed for all three traits. A linkage map was constructed containing 3309 single nucleotide polymorphism (SNP) markers. QTL analysis was performed for additive effects of QTLs, QTL × environment interactions, and QTL × QTL interactions. Ten unique additive QTLs were identified across all traits and environments. Of these, two QTLs were detected for Ndfa and eight for C/N. Of the eight QTLs for C/N, four were also detected for [N]. Using QTL × environment analysis, six QTLs were detected, of which five were also identified in the additive QTL analysis. The QTL × QTL analysis identified four unique epistatic interactions. The results of this study may be used for genomic selection and introgression of favorable alleles for increased SNF, [N], and C/N via marker-assisted selection.

Fine mapping and candidate gene analysis of qSRC3 controlling the silk color in maize (Zea mays L.).

KEY MESSAGE: Fine mapping of the maize QTL qSRC3, responsible for red silk, uncovered the candidate gene ZmMYB20, which encodes an R2R3-MYB transcription factor, has light-sensitive expression, and putatively regulates genes expression associated with anthocyanin biosynthesis. Colorless silk is a key characteristic contributing to the visual quality of fresh corn intended for market distribution. Nonetheless, the identification of Mendelian trait loci and associated genes that control silk color has been scarce. In this study, a F2 population arising from the hybridization of the single-segment substitution line qSRC3MT1 with red silk, carrying an introgressed allele from teosinte (Zea mays ssp. mexicana), and the recurrent maize inbred line Mo17, characterized by light green silk, was utilized for fine mapping. We found that the red silk trait is controlled by a semi-dominant genetic locus known as qSRC3, and its expression is susceptible to light-mediated inhibition. Moreover, qSRC3 explained 68.78% of the phenotypic variance and was delimited to a 133.2 kb region, which includes three genes. Subsequent expression analyses revealed that ZmMYB20 (Zm00001d039700), which encodes an R2R3-MYB transcription factor, was the key candidate gene within qSRC3. Yeast one-hybrid and dual-luciferase reporter assays provided evidence that ZmMYB20 suppresses the expression of two crucial anthocyanin biosynthesis genes, namely ZmF3H and ZmUFGT, by directly binding to their respective promoter regions. Our findings underscore the significance of light-inhibited ZmMYB20 in orchestrating the spatial and temporal regulation of anthocyanin biosynthesis. These results advance the production of colorless silk in fresh corn, responding to the misconception that fresh corn with withered colored silk is not fresh and providing valuable genetic resources for the improvement of sweet and waxy maize.

Potential locus W and candidate gene McPRR2 associated with pericarp pigment accumulation in bitter gourd (Momordica charantia L.) revealed via BSA-seq analysis.

Pericarp color is a prominent agronomic trait that exerts a significant impact on consumer and breeder preferences. Genetic analysis has revealed that the pericarp color of bitter gourd is a quantitative trait. However, the underlying mechanism for this trait in bitter gourd remains largely unknown. In the present study, we employed bulked segregant analysis (BSA) to identify the candidate genes responsible for bitter gourd pericarp color (specifically, dark green versus white) within F2 segregation populations resulting from the crossing of B07 (dark green pericarp) and A06 (white pericarp). Through genomic variation, genetic mapping, and expression analysis, we identified a candidate gene named McPRR2, which was a homolog of Arabidopsis pseudo response regulator 2 (APRR2) encoded by LOC111023472. Sequence alignment of the candidate gene between the two parental lines revealed a 15-bp nucleotide insertion in the coding region of LOC111023472, leading to a premature stop codon and potentially causing a loss-of-function mutation. qRT-PCR analysis demonstrated that the expression of McPRR2 was significantly higher in B07 compared to A06, and it was primarily expressed in the immature fruit pericarp. Moreover, overexpression of McPRR2 in tomato could enhance the green color of immature fruit pericarp by increasing the chlorophyll content. Consequently, McPRR2 emerged as a strong candidate gene regulating the bitter gourd pericarp color by influencing chlorophyll accumulation. Finally, we developed a molecular marker linked to pericarp color, enabling the identification of genotypes in breeding populations. These findings provided valuable insights into the genetic improvement of bitter gourd pericarp color.

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.).

Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean.

A k-mer-based bulked segregant analysis approach to map seed traits in unphased heterozygous potato genomes.

High-throughput sequencing-based methods for bulked segregant analysis (BSA) allow for the rapid identification of genetic markers associated with traits of interest. BSA studies have successfully identified qualitative (binary) and quantitative trait loci (QTLs) using QTL mapping. However, most require population structures that fit the models available and a reference genome. Instead, high-throughput short-read sequencing can be combined with BSA of k-mers (BSA-k-mer) to map traits that appear refractory to standard approaches. This method can be applied to any organism and is particularly useful for species with genomes diverged from the closest sequenced genome. It is also instrumental when dealing with highly heterozygous and potentially polyploid genomes without phased haplotype assemblies and for which a single haplotype can control a trait. Finally, it is flexible in terms of population structure. Here, we apply the BSA-k-mer method for the rapid identification of candidate regions related to seed spot and seed size in diploid potato. Using a mixture of F1 and F2 individuals from a cross between 2 highly heterozygous parents, candidate sequences were identified for each trait using the BSA-k-mer approach. Using parental reads, we were able to determine the parental origin of the loci. Finally, we mapped the identified k-mers to a closely related potato genome to validate the method and determine the genomic loci underlying these sequences. The location identified for the seed spot matches with previously identified loci associated with pigmentation in potato. The loci associated with seed size are novel. Both loci are relevant in future breeding toward true seeds in potato.

OcBSA: An NGS-based bulk segregant analysis tool for outcross populations.

Constructing inbred lines for self-incompatible species and species with long generation times is challenging, making the use of F1 outcross/segregating populations the main strategy for genetic studies of such species. However, there is a lack of dedicated algorithms/tools for rapid quantitative trait locus (QTL) mapping using the F1 populations. To this end, we have designed and developed an algorithm/tool called OcBSA specifically for QTL mapping of F1 populations. OcBSA transforms the four-haplotype inheritance problem from the two heterozygous diploid parents of the F1 population into the two-haplotype inheritance problem common in current genetic studies by removing the two haplotypes from the heterozygous parent that do not contribute to phenotype segregation in the F1 population. Testing of OcBSA on 1800 simulated F1 populations demonstrated its advantages over other currently available tools in terms of sensitivity and accuracy. In addition, the broad applicability of OcBSA was validated by QTL mapping using seven reported F1 populations of apple, pear, peach, citrus, grape, tea, and rice. We also used OcBSA to map the QTL for flower color in a newly constructed F1 population of potato generated in this study. The OcBSA mapping result was verified by the insertion or deletion markers to be consistent with a previously reported locus harboring the ANTHOCYANIN 2 gene, which regulates potato flower color. Taken together, these results highlight the power and broad utility of OcBSA for QTL mapping using F1 populations and thus a great potential for functional gene mining in outcrossing species. For ease of use, we have developed both Windows and Linux versions of OcBSA, which are freely available at: https://gitee.com/Bioinformaticslab/OcBSA.

The QTL and Candidate Genes Regulating the Early Tillering Vigor Traits of Late-Season Rice in Double-Cropping Systems.

Rice effective panicle is a major trait for grain yield and is affected by both the genetic tiller numbers and the early tillering vigor (ETV) traits to survive environmental adversities. The mechanism behind tiller bud formation has been well described, while the genes and the molecular mechanism underlying rice-regulating ETV traits are unclear. In this study, the candidate genes in regulating ETV traits have been sought by quantitative trait locus (QTL) mapping and bulk-segregation analysis by resequencing method (BSA-seq) conjoint analysis using rice backcross inbred line (BIL) populations, which were cultivated as late-season rice of double-cropping rice systems. By QTL mapping, seven QTLs were detected on chromosomes 1, 3, 4, and 9, with the logarithm of the odds (LOD) values ranging from 3.52 to 7.57 and explained 3.23% to 12.98% of the observed phenotypic variance. By BSA-seq analysis, seven QTLs on chromosomes 1, 2, 4, 5, 7, and 9 were identified using single-nucleotide polymorphism (SNP) and insertions/deletions (InDel) index algorithm and Euclidean distance (ED) algorithm. The overlapping QTL resulting from QTL mapping and BSA-seq analysis was shown in a 1.39 Mb interval on chromosome 4. In the overlap interval, six genes, including the functional unknown genes Os04g0455650, Os04g0470901, Os04g0500600, and ethylene-insensitive 3 (Os04g0456900), sialyltransferase family domain containing protein (Os04g0506800), and ATOZI1 (Os04g0497300), showed the differential expression between ETV rice lines and late tillering vigor (LTV) rice lines and have a missense base mutation in the genomic DNA sequences of the parents. We speculate that the six genes are the candidate genes regulating the ETV trait in rice, which provides a research basis for revealing the molecular mechanism behind the ETV traits in rice.

Application of bulk segregant RNA-Seq (BSR-Seq) and allele-specific primers to study soybean powdery mildew resistance.

BACKGROUND: Powdery mildew (PM) is one of the important soybean diseases, and host resistance could practically contribute to soybean PM management. To date, only the Rmd locus on chromosome (Chr) 16 was identified through traditional QTL mapping and GWAS, and it remains unclear if the bulk segregant RNA-Seq (BSR-Seq) methodology is feasible to explore additional PM resistance that might exist in other varieties. RESULTS: BSR-Seq was applied to contrast genotypes and gene expressions between the resistant bulk (R bulk) and the susceptible bulk (S bulk), as well as the parents. The ∆(SNP-index) and G' value identified several QTL and significant SNPs/Indels on Chr06, Chr15, and Chr16. Differentially expressed genes (DEGs) located within these QTL were identified using HISAT2 and Kallisto, and allele-specific primers (AS-primers) were designed to validate the accuracy of phenotypic prediction. While the AS-primers on Chr06 or Chr15 cannot distinguish the resistant and susceptible phenotypes, AS-primers on Chr16 exhibited 82% accuracy prediction with an additive effect, similar to the SSR marker Satt431. CONCLUSIONS: Evaluation of additional AS-primers in the linkage disequilibrium (LD) block on Chr16 further confirmed the resistant locus, derived from the resistant parental variety 'Kaohsiung 11' ('KS11'), not only overlaps with the Rmd locus with unique up-regulated LRR genes (Glyma.16G213700 and Glyma.16G215100), but also harbors a down-regulated MLO gene (Glyma.16G145600). Accordingly, this study exemplified the feasibility of BSR-Seq in studying biotrophic disease resistance in soybean, and showed the genetic makeup of soybean variety 'KS11' comprising the Rmd locus and one MLO gene.

Accounting for isoform expression increases power to identify genetic regulation of gene expression.

A core problem in genetics is molecular quantitative trait locus (QTL) mapping, in which genetic variants associated with changes in the molecular phenotypes are identified. One of the most-studied molecular QTL mapping problems is expression QTL (eQTL) mapping, in which the molecular phenotype is gene expression. It is common in eQTL mapping to compute gene expression by aggregating the expression levels of individual isoforms from the same gene and then performing linear regression between SNPs and this aggregated gene expression level. However, SNPs may regulate isoforms from the same gene in different directions due to alternative splicing, or only regulate the expression level of one isoform, causing this approach to lose power. Here, we examine a broader question: which genes have at least one isoform whose expression level is regulated by genetic variants? In this study, we propose and evaluate several approaches to answering this question, demonstrating that "isoform-aware" methods-those that account for the expression levels of individual isoforms-have substantially greater power to answer this question than standard "gene-level" eQTL mapping methods. We identify settings in which different approaches yield an inflated number of false discoveries or lose power. In particular, we show that calling an eGene if there is a significant association between a SNP and any isoform fails to control False Discovery Rate, even when applying standard False Discovery Rate correction. We show that similar trends are observed in real data from the GEUVADIS and GTEx studies, suggesting the possibility that similar effects are present in these consortia.

Fine mapping of ClLOX, a QTL for powdery mildew resistance in watermelon (Citrullus lanatus L.).

KEY MESSAGE: ClLOX, is located on chromosome 2 and encodes a lipoxygenase gene, which induced watermelon powdery mildew resistance by inhibiting pathogen spread. Powdery mildew is one of the most severe fungal diseases reducing yield and quality of watermelon (Citrullus lanatus L.) and other cucurbit crops. Genes responsible for powdery mildew resistance in watermelon are highly valuable. In this study, we first identified the QTL pm-lox for powdery mildew resistance in watermelon, located within a 0.93 Mb interval of chromosome 2, via XP-GWAS method using two F2 populations. The F2:3 families from one of the F2 populations were then used for fine-mapping the pm-lox locus into a 9,883 bp physical region between 29,581,906 and 29,591,789, containing only two annotated genes. Of these, only ClG42_02g0161300 showed a significant differential expression between the resistant and susceptible lines after powdery mildew inoculation based on RNA sequencing (RNA-seq) and qRT-PCR analysis, and is designated ClLOX. Derived Cleaved Amplified Polymorphic Sequence (dCAPs) markers were developed and validated. In addition, our tests showed that the resistance was anti-spread rather than anti-infection of the pathogen. This study identified a new resistance gene (ClLOX), provided insights into the mechanism of powdery mildew resistance, and developed a molecular marker for watermelon breeding.

Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer.

INTRODUCTION: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. METHODS: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). RESULTS: Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. CONCLUSION: Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

Integration of expression QTLs with fine mapping via SuSiE.

Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference.