Quantitative Trait Loci Mapping Studies Research Articles

GENOME-WIDE ASSOCIATION STUDY OF YIELD-RELATED TRAITS IN A NESTED ASSOCIATION MAPPING POPULATION GROWN IN KAZAKHSTAN

This study evaluated 290 recombinant inbred lines (RILs) from the Nested Association Mapping (NAM) population in the UK, consisting of 24 hybrid families. All genotypes were grown in southeastern Kazakhstan (Kazakh Research Institute of Agriculture and Plant Growing, Almaty region, 2021-2022) and northern Kazakhstan (Alexandr Barayev Scientific-Production Center for Grain Farming, Akmola region, 2020). The studied traits included six yield-related characteristics: spike length (SL, cm), number of productive spikes per plant (NPS, pcs), number of kernels per spike (NKS, pcs), weight of kernels per spike (WKS, g), thousand kernel weight (TKW, g), and yield per square meter (YM2, g/m2). The significant phenotypic variability among genotypes was observed, which was suitable for the genome-wide association study of yield-related traits. Pearson's index showed positive correlations among most yield-related traits, although a negative correlation was found between NKS and TKW in southeastern, and no correlation was recorded for northern regions. Top-performing RILs, surpassing local checks, were identified for NKS, TKW, and YM2, suggesting their potential for breeding programs. The application of GWAS allowed the identification of 72 quantitative trait loci (QTLs), including 36 QTLs in the southeastern region, 16 QTLs in the northern region, and 19 in both locations. Eleven QTLs matched those reported in previous QTL mapping studies and GWAS for studied traits. The results can be used for further studies related to the adaptation and productivity of wheat in breeding projects for higher grain productivity.

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

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.

QTL Mapping and Genome-Wide Association Study Reveal Genetic Loci and Candidate Genes Related to Soluble Solids Content in Melon.

Melon (Cucumis melo L.) is an economically important Cucurbitaceae crop grown around the globe. The sweetness of melon is a significant factor in fruit quality and consumer appeal, and the soluble solids content (SSC) is a key index of melon sweetness. In this study, 146 recombinant inbred lines (RILs) derived from two oriental melon materials with different levels of sweetness containing 1427 bin markers, and 213 melon accessions containing 1,681,775 single nucleotide polymorphism (SNP) markers were used to identify genomic regions influencing SSC. Linkage mapping detected 10 quantitative trait loci (QTLs) distributed on six chromosomes, seven of which were overlapped with the reported QTLs. A total of 211 significant SNPs were identified by genome-wide association study (GWAS), 138 of which overlapped with the reported QTLs. Two new stable, co-localized regions on chromosome 3 were identified by QTL mapping and GWAS across multiple environments, which explained large phenotypic variance. Five candidate genes related to SSC were identified by QTL mapping, GWAS, and qRT-PCR, two of which were involved in hydrolysis of raffinose and sucrose located in the new stable loci. The other three candidate genes were involved in raffinose synthesis, sugar transport, and production of substrate for sugar synthesis. The genomic regions and candidate genes will be helpful for molecular breeding programs and elucidating the mechanisms of sugar accumulation.

The genetic and evolutionary basis of gene expression variation in East Africans

BackgroundMapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases, though most studies have focused on individuals of European descent. While important progress has been made to study a greater diversity of human populations, many groups remain unstudied, particularly among indigenous populations within Africa. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations.ResultsWe find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine-mapping of causal variation. Integrating our QTL scans with signatures of natural selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation.ConclusionExtending QTL mapping studies beyond European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.

Development and characterization of a sorghum multi-parent advanced generation intercross (MAGIC) population for capturing diversity among seed parent gene pool.

Multiparent advanced eneration inter-cross (MAGIC) populations improve the precision of quantitative trait loci (QTL) mapping over biparental populations by incorporating increased diversity and opportunities to reduce linkage disequilibrium among variants. Here, we describe the development of a MAGIC B-Line (MBL) population from an inter-cross among 4 diverse founders of grain sorghum [Sorghum bicolor (L.) Moench] across different races (kafir, guinea, durra, and caudatum). These founders were selected based on genetic uniqueness and several distinct qualitative features including panicle architecture, plant color, seed color, endosperm texture, and awns. A whole set of MBL (708 F6) recombinant inbred lines along with their founders were genotyped using Diversity Arrays Technology (DArTseq) and 5,683 single-nucleotide polymorphisms (SNPs) were generated. A genetic linkage map was constructed using a set of polymorphic, quality-filtered markers (2,728 SNPs) for QTL interval-mapping. For population validation, 3 traits (seed color, plant color, and awns) were used for QTL mapping and genome-wide association study (GWAS). QTL mapping and GWAS identified 4 major genomic regions located across 3 chromosomes (Chr1, Chr3, and Chr6) that correspond to known genetic loci for the targeted traits. Founders of this population consist of the fertility maintainer (A/B line) gene pool and derived MBL lines could serve as female/seed parents in the cytoplasmic male sterility breeding system. The MBL population will serve as a unique genetic and genomic resource to better characterize the genetics of complex traits and potentially identify superior alleles for crop improvement efforts to enrich the seed parent gene pool.

Deciphering key genomic regions controlling flag leaf size in wheat via integration of meta-QTL and in silico transcriptome assessment

BackgroundGrain yield is a complex and polygenic trait influenced by the photosynthetic source-sink relationship in wheat. The top three leaves, especially the flag leaf, are considered the major sources of photo-assimilates accumulated in the grain. Determination of significant genomic regions and candidate genes affecting flag leaf size can be used in breeding for grain yield improvement.ResultsWith the final purpose of understanding key genomic regions for flag leaf size, a meta-analysis of 521 initial quantitative trait loci (QTLs) from 31 independent QTL mapping studies over the past decades was performed, where 333 loci eventually were refined into 64 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs was 5.28 times less than that of the initial QTLs. Thirty-three MQTLs overlapped the marker trait associations (MTAs) previously reported in genome-wide association studies (GWAS) for flag leaf traits in wheat. A total of 2262 candidate genes for flag leaf size, which were involved in the peroxisome, basal transcription factor, and tyrosine metabolism pathways were identified in MQTL regions by the in silico transcriptome assessment. Of these, the expression analysis of the available genes revealed that 134 genes with > 2 transcripts per million (TPM) were highly and specifically expressed in the leaf. These candidate genes could be critical to affect flag leaf size in wheat.ConclusionsThe findings will make further insight into the genetic determinants of flag leaf size and provide some reliable MQTLs and putative candidate genes for the genetic improvement of flag leaf size in wheat.

Combined linkage mapping and association analysis uncovers candidate genes for 25 leaf-related traits across three environments in maize.

Combined linkage and association analysis revealed five co-localized genetic loci across multiple environments. The key gene Zm00001d026491 was further verified to influence leaf length by candidate gene association analysis. Leaf morphology and number determine the canopy structure and thus affect crop yield. Herein, the genetic basis and key genes for 25 leaf-related traits, including leaf lengths (LL), leaf widths (LW), and leaf areas (LA) of eight continuous leaves under the tassel, and the number of leaves above the primary ear (LAE), were dissected by using an association panel and a biparental population. Using an intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, 290 quantitative trait loci (QTL) controlling these traits were detected across different locations, among which 115 QTL were individually repeatedly identified in at least two environments. Using the association panel, 165 unique significant single-nucleotide polymorphisms (SNPs) were associated with target traits (P < 2.15E-06), of which 35 were separately detected across multiple environments. In total, 42 pleiotropic QTL/SNPs (pQTL/SNPs) were responsible for at least two of the LL, LW, LA, and LAE traits across multiple environments. Combining the QTL mapping and association study, five unique SNPs were located within the confidence intervals of seven QTL, and 77 genes were identified based on the linkage disequilibrium regions of co-localized SNP loci. Gene-based association studies verified that the intragenic variants in the candidate gene Zm00001d026491 influenced LL of the third leaf counted from the top node. These findings will provide vital information to understanding the genetic basis of leaf-related traits and help to cultivate maize varieties with ideal plant architecture.

A Novel Locus for Bakanae Disease Resistance, qBK4T, Identified in Rice

Bakanae disease caused by Fusarium fujikuroi causes crop failure and yield losses in the majority of rice-growing countries. In this study, we employed a joint strategy quantitative trait locus (QTL) mapping–Genome-Wide Association Study (GWAS) to investigate novel genetic loci associated with Bakanae disease resistance using a population of 143 BC1F8 RILs derived from a cross between Ilpum × Tung Tin Wan Hien1. The phenotypic data from the bioassay and the genotypic data generated using a DNA chip were utilized to perform QTL mapping and GWAS study. Our results identified a novel genetic locus qBK4T associated with Bakanae disease resistance, which was mapped on chromosome 4 and flanked by AX-116847364 (33.12 Mbp) and AX-115752415 (33.44 Mbp) markers covering a region of 324kbp. There were 34 genes in this region including Os04g55920 (encoding a zinc-finger protein, OsJAZ1), Os04g55970 (encoding AP2-like ethylene-responsive transcription factor), etc. This study proposes qBK4T as a novel locus for Bakanae disease resistance. The identification of qBK4T and its flanking marker information could be useful for marker-assisted breeding and functional characterization of resistance genes against bakanae disease.

A genetic linkage map of the Pacific white shrimp (Litopenaeus vannamei): QTL mapping for low-temperature tolerance and growth-related traits and identification of the candidate genes

Low water temperature is a prevailing abiotic stress which adversely impacts global shrimp growth and production. In this study, a high-density genetic linkage map of L. vannamei was constructed using double-digest restriction site-associated DNA sequencing (ddRAD-seq) with an F1 full-sib family mapping panel comprising 148 progenies and 2 parents. A total of 2528 single-nucleotide polymorphisms (SNPs) were assigned to 48 linkage groups (LGs), with a total genetic map length of 3227.383 centimorgans (cM) and an inter-locus density of 0.783 cM/locus. The number of markers per LG ranged from 12 to 190, with a mean of 56. The length of LGs varied from 15.07 to 158.314 cM, with a mean length of 67.237 cM. Five quantitative trait loci (QTLs) for low-temperature tolerance with a logarithm of odds (LOD) threshold≥2.5 were detected, of which only one was identified based on a linkage group-wide significant LOD threshold. Notably, two growth-trait (body weight) related QTLs were located on LG36 with a genome-wide significant LOD threshold. According to the genome and relevant annotations of L. vannamei, a major growth-related candidate gene, tRNA methyltransferase 10 homolog A (TRMT10A), was identified from genomic scaffolds. Further analysis demonstrated that four linked missense mutation SNPs within TRMT10A were closely related to the body weight. Taken together, the constructed genetic linkage map under low-temperature stress provides a valuable reference for QTL mapping and future genomic studies. Its application can facilitate comparative mapping with other populations, as well as marker-assisted selection (MAS) breeding of L. vannamei.

Meta-QTL analysis for mining of candidate genes and constitutive gene network development for fungal disease resistance in maize (Zea mays L.)

The development of resistant maize cultivars is the most effective and sustainable approach to combat fungal diseases. Over the last three decades, many quantitative trait loci (QTL) mapping studies reported numerous QTL for fungal disease resistance (FDR) in maize. However, different genetic backgrounds of germplasm and differing QTL analysis algorithms limit the use of identified QTL for comparative studies. The meta-QTL (MQTL) analysis is the meta-analysis of multiple QTL experiments, which entails broader allelic coverage and helps in the combined analysis of diverse QTL mapping studies revealing common genomic regions for target traits. In the present study, 128 (33.59%) out of 381 reported QTL (from 82 studies) for FDR could be projected on the maize genome through MQTL analysis. It revealed 38 MQTL for FDR (12 diseases) on all chromosomes except chromosome 10. Five MQTL namely 1_4, 2_4, 3_2, 3_4, and 5_4 were linked with multiple FDR. Total of 1910 candidate genes were identified for all the MQTL regions, with protein kinase gene families, TFs, pathogenesis-related, and disease-responsive proteins directly or indirectly associated with FDR. The comparison of physical positions of marker-traits association (MTAs) from genome-wide association studies with genes underlying MQTL interval verified the presence of QTL/candidate genes for particular diseases. The linked markers to MQTL and putative candidate genes underlying identified MQTL can be further validated in the germplasm through marker screening and expression studies. The study also attempted to unravel the underlying mechanism for FDR resistance by analyzing the constitutive gene network, which will be a useful resource to understand the molecular mechanism of defense-response of a particular disease and multiple FDR in maize.

Identification of QTLs and a putative candidate gene involved in rhizome enlargement of Asian lotus (Nelumbo nucifera).

QTL mapping studies identified threereliable QTLs of rhizome enlargement in lotus. NnBEL6 located within the confidence interval of the major QTL cqREI-LG2 is a key candidate gene enhancing rhizome enlargement. Lotus (Nelumbo) is perennial aquatic plant with nutritional, pharmacological, and ornamental significance. Rhizome is an underground lotus stem that acts as a storage organ and as a reproductive tissue for asexual production. The enlargement of lotus rhizome is an important adaptive strategy for surviving the cold winter. The aims of this study were to identify quantitative trait loci (QTLs) for rhizome enlargement traits including rhizome enlargement index (REI) and number of enlarged rhizome (NER), and to uncover their associated candidate genes. A high-density genetic linkage map was constructed, consisting of 2935 markers binned from 236,840 SNPs. A total of 14 significant QTLs were detected for REI and NER, which explained 6.7-22.3% of trait variance. Three QTL regions were repeatedly identified in at least 2years, and a major QTL, designated cqREI-LG2, with a rhizome-enlargement effect and about 20% of the phenotypic contribution was identified across the 3 climatic years. A candidate NnBEL6 gene located within the confidence interval of cqREI-LG2 was considered to be putatively involved in lotus rhizome enlargement. The expression of NnBEL6 was exclusively induced by rhizome swelling. Sequence comparison of NnBEL6 among lotus cultivars revealed a functional Indel site in its promoter that likely initiates the rhizome enlargement process. Transgenic potato assay was used to confirm the role of NnBEL6 in inducing tuberization. The successful identification QTLs and functional validation of NnBEL6 gene reported in this study will enrich our knowledge on the genetic basis of rhizome enlargement in lotus.

WGNAM: whole-genome nested association mapping

Key messageA powerful QTL analysis method for nested association mapping populations is presented. Based on a one-stage multi-locus model, it provides accurate predictions of founder specific QTL effects.Nested association mapping (NAM) populations have been created to enable the identification of quantitative trait loci (QTL) in different genetic backgrounds. A whole-genome nested association mapping (WGNAM) method is presented to perform QTL analysis in NAM populations. The WGNAM method is an adaptation of the multi-parent whole genome average interval mapping approach where the crossing design is incorporated through the probability of inheriting founder alleles for every marker across the genome. Based on a linear mixed model, this method provides a one-stage analysis of raw phenotypic data, molecular markers, and crossing design. It simultaneously scans the whole-genome through an iterative process leading to a model with all the identified QTL while keeping the false positive rate low. The WGNAM approach was assessed through a simulation study, confirming to be a powerful and accurate method for QTL analysis for a NAM population. This novel method can also accommodate a multi-reference NAM (MR-NAM) population where donor parents are crossed with multiple reference parents to increase genetic diversity. Therefore, a demonstration is presented using a MR-NAM population for wheat (Triticum aestivum L.) to perform a QTL analysis for plant height. The strength and size of the putative QTL were summarized enhancing the understanding of the QTL effects depending on the parental origin. Compared to other methods, the proposed methodology based on a one-stage analysis provides greater power to detect QTL and increased accuracy in the estimation of their effects. The WGNAM method establishes the basis for accurate QTL mapping studies for NAM and MR-NAM populations.

Estimating genetic variance contributed by a quantitative trait locus: A random model approach.

Detecting quantitative trait loci (QTL) and estimating QTL variances (represented by the squared QTL effects) are two main goals of QTL mapping and genome-wide association studies (GWAS). However, there are issues associated with estimated QTL variances and such issues have not attracted much attention from the QTL mapping community. Estimated QTL variances are usually biased upwards due to estimation being associated with significance tests. The phenomenon is called the Beavis effect. However, estimated variances of QTL without significance tests can also be biased upwards, which cannot be explained by the Beavis effect; rather, this bias is due to the fact that QTL variances are often estimated as the squares of the estimated QTL effects. The parameters are the QTL effects and the estimated QTL variances are obtained by squaring the estimated QTL effects. This square transformation failed to incorporate the errors of estimated QTL effects into the transformation. The consequence is biases in estimated QTL variances. To correct the biases, we can either reformulate the QTL model by treating the QTL effect as random and directly estimate the QTL variance (as a variance component) or adjust the bias by taking into account the error of the estimated QTL effect. A moment method of estimation has been proposed to correct the bias. The method has been validated via Monte Carlo simulation studies. The method has been applied to QTL mapping for the 10-week-body-weight trait from an F2 mouse population.

Characterization and Mapping of retr04, retr05 and retr06 Broad-Spectrum Resistances to Turnip Mosaic Virus in Brassica juncea, and the Development of Robust Methods for Utilizing Recalcitrant Genotyping Data.

Turnip mosaic virus (TuMV) induces disease in susceptible hosts, notably impacting cultivation of important crop species of the Brassica genus. Few effective plant viral disease management strategies exist with the majority of current approaches aiming to mitigate the virus indirectly through control of aphid vector species. Multiple sources of genetic resistance to TuMV have been identified previously, although the majority are strain-specific and have not been exploited commercially. Here, two Brassica juncea lines (TWBJ14 and TWBJ20) with resistance against important TuMV isolates (UK 1, vVIR24, CDN 1, and GBR 6) representing the most prevalent pathotypes of TuMV (1, 3, 4, and 4, respectively) and known to overcome other sources of resistance, have been identified and characterized. Genetic inheritance of both resistances was determined to be based on a recessive two-gene model. Using both single nucleotide polymorphism (SNP) array and genotyping by sequencing (GBS) methods, quantitative trait loci (QTL) analyses were performed using first backcross (BC1) genetic mapping populations segregating for TuMV resistance. Pairs of statistically significant TuMV resistance-associated QTLs with additive interactive effects were identified on chromosomes A03 and A06 for both TWBJ14 and TWBJ20 material. Complementation testing between these B. juncea lines indicated that one resistance-linked locus was shared. Following established resistance gene nomenclature for recessive TuMV resistance genes, these new resistance-associated loci have been termed retr04 (chromosome A06, TWBJ14, and TWBJ20), retr05 (A03, TWBJ14), and retr06 (A03, TWBJ20). Genotyping by sequencing data investigated in parallel to robust SNP array data was highly suboptimal, with informative data not established for key BC1 parental samples. This necessitated careful consideration and the development of new methods for processing compromised data. Using reductive screening of potential markers according to allelic variation and the recombination observed across BC1 samples genotyped, compromised GBS data was rendered functional with near-equivalent QTL outputs to the SNP array data. The reductive screening strategy employed here offers an alternative to methods relying upon imputation or artificial correction of genotypic data and may prove effective for similar biparental QTL mapping studies.

Unravelling consensus genomic regions conferring leaf rust resistance in wheat via meta‐QTL analysis

Leaf rust, caused by the fungus Puccinia triticina Erikss (Pt), is a destructive disease affecting wheat (Triticum aestivum L.) and a threat to food security. Developing resistant cultivars represents a useful method of disease control, and thus, understanding the genetic basis for leaf rust resistance is required. To this end, a comprehensive bibliographic search for leaf rust resistance quantitative trait loci (QTL) was performed, and 393 QTL were collected from 50 QTL mapping studies. Afterward, a consensus map with a total length of 4,567 cM consisting of different types of markers (simple sequence repeat [SSR], diversity arrays technology [DArT], chip-based single-nucleotide polymorphism [SNP] markers, and SNP markers from genotyping-by-sequencing) was used for QTL projection, and meta-QTL (MQTL) analysis was performed on 320 QTL. A total of 75 MQTL were discovered and refined to 15 high-confidence MQTL (hcmQTL). The candidate genes discovered within the hcmQTL interval were then checked for differential expression using data from three transcriptome studies, resulting in 92 differentially expressed genes (DEGs). The expression of these genes in various leaf tissues during wheat development was explored. This study provides insight into leaf rust resistance in wheat and thereby provides an avenue for developing resistant cultivars by incorporating the most important hcmQTL.

Testing quantitative trait locus effects in genetic backcross studies with double recombination occurring

Testing the existence of quantitative trait locus (QTL) effects is an important task in QTL mapping studies. In this paper, we assume the phenotype distributions from a location-scale distribution family, and consider to test the QTL effects in both location and scale in the backcross studies with double recombination occurring. Without equal scale assumption, the log-likelihood function is unbounded, which leads to the traditional likelihood ratio test being invalid. To deal with this problem, we propose a penalized likelihood ratio test (PLRT) for testing the QTL effects. The null limiting distribution of the PLRT is shown to be a supremum of a chi-square process. As a complement, we also investigate the null limiting distribution of the likelihood ratio test for the case with equal scale assumption. The limiting distributions of the two tests under local alternatives are also studied. Simulation studies are performed to evaluate the asymptotic results and a real-data example is given for illustration.

Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed.

Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.

The molecular architecture of Drosophila melanogaster defense against Beauveria bassiana explored through evolve and resequence and quantitative trait locus mapping

Little is known about the genetic architecture of antifungal immunity in natural populations. Using two population genetic approaches, quantitative trait locus (QTL) mapping and evolve and resequence (E&R), we explored D. melanogaster immune defense against infection with the fungus Beauveria bassiana. The immune defense was highly variable both in the recombinant inbred lines from the Drosophila Synthetic Population Resource used for our QTL mapping and in the synthetic outbred populations used in our E&R study. Survivorship of infection improved dramatically over just 10 generations in the E&R study, and continued to increase for an additional nine generations, revealing a trade-off with uninfected longevity. Populations selected for increased defense against B. bassiana evolved cross resistance to a second, distinct B. bassiana strain but not to bacterial pathogens. The QTL mapping study revealed that sexual dimorphism in defense depends on host genotype, and the E&R study indicated that sexual dimorphism also depends on the specific pathogen to which the host is exposed. Both the QTL mapping and E&R experiments generated lists of potentially causal candidate genes, although these lists were nonoverlapping.

Estimation of genetic variance contributed by a quantitative trait locus: correcting the bias associated with significance tests.

The Beavis effect in quantitative trait locus (QTL) mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners' curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners' curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated noncentral Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated noncentral Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300 k single nucleotide polymorphism markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction.

A Multi-Locus Association Model Framework for Nested Association Mapping With Discriminating QTL Effects in Various Subpopulations.

Nested association mapping (NAM) has been an invaluable approach for plant genetics community and can dissect the genetic architecture of complex traits. As the most popular NAM analysis strategy, joint multifamily mapping can combine all information from diverse genetic backgrounds and increase population size. However, it is influenced by the genetic heterogeneity of quantitative trait locus (QTL) across various subpopulations. Multi-locus association mapping has been proven to be powerful in many cases of QTL mapping and genome-wide association studies. Therefore, we developed a multi-locus association model of multiple families in the NAM population, which could discriminate the effects of QTLs in all subpopulations. A series of simulations with a real maize NAM genomic data were implemented. The results demonstrated that the new method improves the statistical power in QTL detection and the accuracy in QTL effect estimation. The new approach, along with single-family linkage mapping, was used to identify QTLs for three flowering time traits in the maize NAM population. As a result, most QTLs detected in single family linkage mapping were identified by the new method. In addition, the new method also mapped some new QTLs with small effects, although their functions need to be identified in the future.