Thousands Of Quantitative Trait Loci Research Articles

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

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

Rare variants contribute disproportionately to quantitative trait variation in yeast.

How variants with different frequencies contribute to trait variation is a central question in genetics. We use a unique model system to disentangle the contributions of common and rare variants to quantitative traits. We generated ~14,000 progeny from crosses among 16 diverse yeast strains and identified thousands of quantitative trait loci (QTLs) for 38 traits. We combined our results with sequencing data for 1011 yeast isolates to show that rare variants make a disproportionate contribution to trait variation. Evolutionary analyses revealed that this contribution is driven by rare variants that arose recently, and that negative selection has shaped the relationship between variant frequency and effect size. We leveraged the structure of the crosses to resolve hundreds of QTLs to single genes. These results refine our understanding of trait variation at the population level and suggest that studies of rare variants are a fertile ground for discovery of genetic effects.

Mapping of quantitative trait loci for reproductive traits in pigs

This paper reviews the major approaches used to map quantitative trait loci (QTL) and current knowledge about QTL affecting reproductive trait loci in pigs. Three different approaches, i.e. functional candidate gene analyses, genome-wide linkage studies (GWLS) and genome-wide association studies (GWAS) have been used to map QTL in pigs. The interest and limits of each of the three approaches are discussed. Candidate genes and QTL have been reviewed based on PigQTLdb at http://www.animalgenome. org. A total of 29 candidate genes affecting reproductive traits located on 14 autosomes have been considered. Some of them are well established results, but most results originate from single studies of limited scale and need to be confirmed. Several thousands of QTL affecting 15 male and 15 female reproductive traits have been identified on 17 and 19 different chromosomes, respectively, using GWLS. Yet, the majority of them are only putative QTL and few QTL regions overlap between studies. Epistatic interactions between QTL appear as rather important in the single study investigating the effects of epistasis on pig reproductive traits. A large number of QTL distributed over almost all pig chromosomes have been identified in the single GWAS study published so far for pig reproductive traits. Use of sequence data, of more complex genetic models and of integrative biology approaches should be considered for more thoroughly investigating the genetic architecture of pig reproductive traits in the future.

Mapping regulatory variants in hiPSC models.

A new study illustrates the power of the human induced pluripotent stem cell (hiPSC) platform by studying hiPSC-derived sensory neurons from 107 individuals. In addition to identifying thousands of quantitative trait loci influencing gene expression, chromatin accessibility and RNA splicing, the work highlights several underappreciated challenges in the hiPSC field.

Molecular and functional variation in iPSC-derived sensory neurons.

Induced pluripotent stem cells (iPSCs), and cells derived from them, have become key tools to model biological processes, particularly in cell types that are difficult to access from living donors. We present the first map of regulatory variants in iPSC-derived neurons, based on 123 differentiations of iPSCs to a sensory neuronal fate. Gene expression was more variable across cultures than in primary dorsal root ganglion, particularly in genes related to nervous system development. Using single-cell RNA-sequencing, we found that the fraction of neuronal vs. contaminating cells was influenced by iPSC culture conditions prior to differentiation. Despite high differentiation-induced variability, using an allele-specific method we detected thousands of quantitative trait loci (QTLs) influencing gene expression, chromatin accessibility, and RNA splicing. Based on our QTLs, we estimate that recall-by-genotype studies using iPSC-derived cells will require at least 20-80 individuals to detect the effects of regulatory variants with moderately large effect sizes.

Quantitative trait loci identification and meta-analysis for rice panicle-related traits.

Rice yield is a complex trait controlled by quantitative trait loci (QTLs). In the past three decades, thousands of QTLs for rice yield traits have been detected, but only a very small percentage has been cloned to date, as identifying the QTL genes requires a substantial investment of time and money. Meta-analysis provides a simple, reliable, and economical method for integrating information from multiple QTL studies across various environmental and genetic backgrounds, detecting consistent QTLs powerfully and estimating their genetic positions precisely. In this study, we aimed to locate consistent QTL regions associated with rice panicle traits by applying a genome-wide QTL meta-analysis approach. We first conducted a QTL analysis of 5 rice panicle traits using 172 plants in 2011 and 138 plants in 2012 from an F2 population derived from a cross between Nipponbare and H71D rice cultivators. A total of 54 QTLs were detected, and these were combined with 1085 QTLs collected from 82 previous studies to perform a meta-analysis using BioMercator v4.2. The integration of 82 maps resulted in a consensus map with 6970 markers and a total map length of 1823.1 centimorgan (cM), on which 837 QTLs were projected. These QTLs were then integrated into 87 meta-quantitative trait loci (MQTLs) by meta-analysis, and the 95% confidence intervals (CI) of them were smaller than the mean value of the original QTLs. Also, 30 MQTLs covered 47 of the 54 QTLs detected from the cross between Nipponbare and H71D in this study. Among them, the two major and stable QTLs, spp10.1 and sd10.1, were found to be included in MQTL10.4. The three other major QTLs, pl3.1, sb2.1, and sb10.1, were included in MQTL3.3, MQTL2.2, and MQTL10.3, respectively. A total of 21 of the 87 MQTLs' phenotypic variation were>20%. In total, 24 candidate genes were found in 15 MQTLs that spanned physical intervals<0.2Mb, including genes that have been cloned previously, e.g., EP3, LP, MIP1, HTD1, DSH1, and OsPNH1. However, it would be beneficial to identify a greater number of candidate genes from these MQTLs. Mining new genes that modulate yield and its related traits would assist researchers to better understand the relevant molecular mechanisms. The MQTLs found in this study that have small physical and genetic intervals are useful not only for marker-assisted selection and pyramiding, but they also provide important information of rice yield and related gene mining for future research.

Self-Confirmation and Ascertainment of the Candidate Genomic Regions of Complex Trait Loci - A None-Experimental Solution.

Over the past half century, thousands of quantitative trait loci (QTL) have been identified by using animal models and plant populations. However, the none-reliability and imprecision of the genomic regions of these loci have remained the major hurdle for the identification of the causal genes for the correspondent traits. We used a none-experimental strategy of strain number reduction for testing accuracy and ascertainment of the candidate region for QTL. We tested the strategy in over 400 analyses with data from 47 studies. These studies include: 1) studies with recombinant inbred (RI) strains of mice. We first tested two previously mapped QTL with well-defined genomic regions; We then tested additional four studies with known QTL regions; and finally we examined the reliability of QTL in 38 sets of data which are produced from relatively large numbers of RI strains, derived from C57BL/6J (B6) X DBA/2J (D2), known as BXD RI mouse strains; 2) studies with RI strains of rats and plants; and 3) studies using F2 populations in mice, rats and plants. In these cases, our method identified the reliability of mapped QTL and localized the candidate genes into the defined genomic regions. Our data also suggests that LRS score produced by permutation tests does not necessarily confirm the reliability of the QTL. Number of strains are not the reliable indicators for the accuracy of QTL either. Our strategy determines the reliability and accuracy of the genomic region of a QTL without any additional experimental study such as congenic breeding.

Pooled ChIP-Seq Links Variation in Transcription Factor Binding to Complex Disease Risk

Cis-regulatory elements such as transcription factor (TF) binding sites can be identified genome-wide, but it remains far more challenging to pinpoint genetic variants affecting TF binding. Here, we introduce a pooling-based approach to mapping quantitative trait loci (QTLs) for molecular-level traits. Applying this to five TFs and a histone modification, we mapped thousands of cis-acting QTLs, with over 25-fold lower cost compared to standard QTL mapping. Wefound that single genetic variants frequently affect binding of multiple TFs, and CTCF can recruit all five TFs to its binding sites. These QTLs often affect local chromatin and transcription but can alsoinfluence long-range chromosomal contacts, demonstrating arole for natural genetic variation inchromosomal architecture. Thousands of these QTLs have been implicated in genome-wide association studies, providing candidate molecular mechanisms for many disease risk loci and suggesting that TF binding variation may underlie a large fraction of human phenotypic variation.

Quantitative genetics of CTCF binding reveal local sequence effects and different modes of X-chromosome association.

Associating genetic variation with quantitative measures of gene regulation offers a way to bridge the gap between genotype and complex phenotypes. In order to identify quantitative trait loci (QTLs) that influence the binding of a transcription factor in humans, we measured binding of the multifunctional transcription and chromatin factor CTCF in 51 HapMap cell lines. We identified thousands of QTLs in which genotype differences were associated with differences in CTCF binding strength, hundreds of them confirmed by directly observable allele-specific binding bias. The majority of QTLs were either within 1 kb of the CTCF binding motif, or in linkage disequilibrium with a variant within 1 kb of the motif. On the X chromosome we observed three classes of binding sites: a minority class bound only to the active copy of the X chromosome, the majority class bound to both the active and inactive X, and a small set of female-specific CTCF sites associated with two non-coding RNA genes. In sum, our data reveal extensive genetic effects on CTCF binding, both direct and indirect, and identify a diversity of patterns of CTCF binding on the X chromosome.

A novel strategy for genetic dissection of complex traits: the population of specific chromosome substitution strains from laboratory and wild mice

The mouse is an irreplaceable model for understanding the precise genetic mechanisms of mammalian physiological pathways. Thousands of quantitative trait loci (QTLs) have been mapped onto the mouse genome during the last two decades. However, only a few genes' underlying complex traits have been successfully identified, and rapid fine mapping of QTL genes still remains a challenge for mouse geneticists. Currently, the Collaborative Cross (CC) has proceeded to the goal of establishing more than 1,000 recombinant inbred strains for the sub-centimorgan mapping resolution of QTL loci. In this article, a novel complementary strategy, designated as population of specific chromosome substitution strains or PSCSS, is proposed for rapid fine mapping of QTLs on the substituted chromosome. One specific chromosome (Chr 1) of recipient mouse strain C57BL/6 J has been substituted by homologous counterparts from five different inbred strains (C3H/He, FVB/N, AKR, NOD/LtJ, NZW/LacJ), an outbred line Kunmin mouse in China, and 23 wild mice captured in different localities. The primary genetic studies on the structure of these wild donor chromosomes (Chr 1) show that these donor chromosomes harbor extensive genetic polymorphisms, with a high density of SNP distribution, abundant variations of STR alleles, and a high level of historical recombination accumulation. These specific chromosome substitution strains eventually form a special population that has the identical genetic background of the recipient strain and differs only in the donor chromosomes. With simple association studies, known QTLs on the donor chromosome can be rapidly mapped in high resolution without requirement of further crosses. This approach, taking advantage of the extensive genetic polymorphisms of wild resources and chromosome substitution strategy, brings a new outlook for genetic dissection of complex traits.

Haplotyping a Quantitative Trait with a High-Density Map in Experimental Crosses

BackgroundThe ultimate goal of genetic mapping of quantitative trait loci (QTL) is the positional cloning of genes involved in any agriculturally or medically important phenotype. However, only a small portion (≤ 1%) of the QTL detected have been characterized at the molecular level, despite the report of hundreds of thousands of QTL for different traits and populations.Methods/ResultsWe develop a statistical model for detecting and characterizing the nucleotide structure and organization of haplotypes that underlie QTL responsible for a quantitative trait in an F2 pedigree. The discovery of such haplotypes by the new model will facilitate the molecular cloning of a QTL. Our model is founded on population genetic properties of genes that are segregating in a pedigree, constructed with the mixture-based maximum likelihood context and implemented with the EM algorithm. The closed forms have been derived to estimate the linkage and linkage disequilibria among different molecular markers, such as single nucleotide polymorphisms, and quantitative genetic effects of haplotypes constructed by non-alleles of these markers. Results from the analysis of a real example in mouse have validated the usefulness and utilization of the model proposed.ConclusionThe model is flexible to be extended to model a complex network of genetic regulation that includes the interactions between different haplotypes and between haplotypes and environments.