Association mapping of morphological traits in wild and captive zebra finches: reliable within,but not between populations

Identifying causal genetic variants underlying heritable phenotypic variation is a long‐standing goal in evolutionary genetics. We previously identified several quantitative trait loci (QTL) for five morphological traits in a captive population of zebra finches (Taeniopygia guttata) by whole‐genome linkage mapping. We here follow up on these studies with the aim to narrow down on the quantitative trait variants (QTN) in one wild and three captive populations. First, we performed an association study using 672 single nucleotide polymorphisms (SNPs) within candidate genes located in the previously identified QTL regions in a sample of 939 wild‐caught zebra finches. Then, we validated the most promising SNP–phenotype associations (n = 25 SNPs) in 5228 birds from four populations. Genotype–phenotype associations were generally weak in the wild population, where linkage disequilibrium (LD) spans only short genomic distances. In contrast, in captive populations, where LD blocks are large, apparent SNP effects on morphological traits (i.e. associations) were highly repeatable with independent data from the same population. Most of those SNPs also showed significant associations with the same trait in other captive populations, but the direction and magnitude of these effects varied among populations. This suggests that the tested SNPs are not the causal QTN but rather physically linked to them, and that LD between SNPs and causal variants differs between populations due to founder effects. While the identification of QTN remains challenging in nonmodel organisms, we illustrate that it is indeed possible to confirm the location and magnitude of QTL in a population with stable linkage between markers and causal variants.     

Gene–Environment Interactions at Nucleotide Resolution

Interactions among genes and the environment are a common source of phenotypic variation. To characterize the interplay between genetics and the environment at single nucleotide resolution, we quantified the genetic and environmental interactions of four quantitative trait nucleotides (QTN) that govern yeast sporulation efficiency. We first constructed a panel of strains that together carry all 32 possible combinations of the 4 QTN genotypes in 2 distinct genetic backgrounds. We then measured the sporulation efficiencies of these 32 strains across 8 controlled environments. This dataset shows that variation in sporulation efficiency is shaped largely by genetic and environmental interactions. We find clear examples of QTN:environment, QTN: background, and environment:background interactions. However, we find no QTN:QTN interactions that occur consistently across the entire dataset. Instead, interactions between QTN only occur under specific combinations of environment and genetic background. Thus, what might appear to be a QTN:QTN interaction in one background and environment becomes a more complex QTN:QTN:environment:background interaction when we consider the entire dataset as a whole. As a result, the phenotypic impact of a set of QTN alleles cannot be predicted from genotype alone. Our results instead demonstrate that the effects of QTN and their interactions are inextricably linked both to genetic background and to environmental variation.     

A catalog of nonsynonymous polymorphism on mouse Chromosome 16

Numerous phenotypic traits differ among inbred mice, and the genetic diversity of inbred strains has been exploited in studies of quantitative trait loci (QTL). Sequencing the mouse genome has resulted in improved tools for the study of QTL, but a comprehensive catalog of sequence variants between strains would be of great value in identifying and testing potentially causative alleles. A/J DNA was included in the Celera shotgun sequence of the mouse genome and C57BL/6 DNA was sequenced by an international consortium. We have resequenced A/J and B6 DNA to cover nearly all of the protein-coding portions of mouse Chromosome 16, revealing that there are 106 nonsynonymous substitutions in 74 of the 779 genes on the chromosome. The pattern of substitution is more similar to the spectrum of benign polymorphism in the human population than it is to human disease-causing mutations. In mouse, polymorphic variants tend to be associated with one another on large haplotypes; this pattern also holds true for nonsynonymous polymorphism. However, sufficient fragmentation of haplotypes is present to suggest that only a very-high-resolution haplotype map will enable effective inference of alleles in additional strains. SNP data have been submitted to dbSNP with ssid No. 46531525-46532013.     

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.     

Naturally occurring indel variation in the Brassica nigra COL1 gene is associated with variation in flowering time

Previous QTL mapping identified a Brassica nigra homolog to Arabidopsis thaliana CO as a candidate gene affecting flowering time in B. nigra. Transformation of an A. thaliana co mutant with two different alleles of the B. nigra CO (Bni COa) homolog, one from an early-flowering B. nigra plant and one from a late one, did not show any differential effect of the two alleles on flowering time. The DNA sequence of the coding region of the two alleles was also identical, showing that nucleotide variation influencing flowering time must reside outside the coding region of Bni COa. In contrast, the nucleotide sequence of the B. nigra COL1 (Bni COL1) gene located 3.5 kb upstream of Bni COa was highly diverged between the alleles from early and late plants. One indel polymorphism in the Bni COL1 coding region, present in several natural populations of B. nigra, displayed a significant association with flowering time within a majority of these populations. These data indicate that a quantitative trait nucleotide (QTN) affecting flowering time is located within or close to the Bni COL1 gene. The intergenic sequence between Bni COL1 and Bni COa displayed a prominent peak of divergence 1 kb downstream of the Bni COL1 coding region. This region could contain regulatory elements for the downstream Bni COa gene. Our data suggest that a naturally occurring QTN for flowering time affects the function or expression of either Bni COL1 or Bni COa.     

Integration of genetic and genomic methods for identification of genes and gene variants encoding QTLs in the nonhuman primate

We have developed an integrated approach, using genetic and genomic methods, in conjunction with resources from the Southwest National Primate Research Center (SNPRC) baboon colony, for the identification of genes and their functional variants that encode quantitative trait loci (QTL). In addition, we use comparative genomic methods to overcome the paucity of baboon specific reagents and to augment translation of our findings in a nonhuman primate (NHP) to the human population. We are using the baboon as a model to study the genetics of cardiovascular disease (CVD). A key step for understanding gene–environment interactions in cardiovascular disease is the identification of genes and gene variants that influence CVD phenotypes. We have developed a sequential methodology that takes advantage of the SNPRC pedigreed baboon colony, the annotated human genome, and current genomic and bioinformatic tools. The process of functional polymorphism identification for genes encoding QTLs involves comparison of expression profiles for genes and predicted genes in the genomic region of the QTL for individuals discordant for the phenotypic trait mapping to the QTL. After comparison, genes of interest are prioritized, and functional polymorphisms are identified in candidate genes by genotyping and quantitative trait nucleotide analysis. This approach reduces the time and labor necessary to prioritize and identify genes and their polymorphisms influencing variation in a quantitative trait compared with traditional positional cloning methods.     

From QTL to QTN identification in livestock--winning by points rather than knock-out: a review

Many quantitative trait loci (QTL) affecting economic traits in livestock have now been identified. However, the confidence interval (CI) of individual QTL as determined by linkage analysis often spans tens of map units, containing hundreds of genes. Linkage disequilibrium (LD) mapping can reduce the CI to individual map units, but this reduced interval will still contain tens of genes. Methods suitable for model animals to find and validate specific quantitative trait nucleotides (QTN) underlying the QTL cannot be easily applied to livestock species because of their long generation intervals, the cost of maintaining each animal and the difficulty of producing transgenics or 'knock-outs'. Considering these limitations, we review successful approaches for identifying QTN in livestock and outline a schematic strategy for QTN determination and verification. In addition to linkage and LD mapping, the methods include positional cloning, selection of candidate genes, DNA sequencing and statistical analyses. Concordance determination and functional assays are the critical tests for validation of a QTN; we provide a generalized formula for the probability of concordance by chance. Three genes that meet the burden of proof for QTN identification--DGAT1 in cattle, IGF2 in swine and GDF8 in sheep--are discussed in detail. The genetic and economic ramifications of identified QTN and the horizon for selection and introgression are also considered.     

Targeted resequencing of a genomic region influencing tameness and aggression reveals multiple signals of positive selection

The identification of the causative genetic variants in quantitative trait loci (QTL) influencing phenotypic traits is challenging, especially in crosses between outbred strains. We have previously identified several QTL influencing tameness and aggression in a cross between two lines of wild-derived, outbred rats (Rattus norvegicus) selected for their behavior towards humans. Here, we use targeted sequence capture and massively parallel sequencing of all genes in the strongest QTL in the founder animals of the cross. We identify many novel sequence variants, several of which are potentially functionally relevant. The QTL contains several regions where either the tame or the aggressive founders contain no sequence variation, and two regions where alternative haplotypes are fixed between the founders. A re-analysis of the QTL signal showed that the causative site is likely to be fixed among the tame founder animals, but that several causative alleles may segregate among the aggressive founder animals. Using a formal test for the detection of positive selection, we find 10 putative positively selected regions, some of which are close to genes known to influence behavior. Together, these results show that the QTL is probably not caused by a single selected site, but may instead represent the joint effects of several sites that were targets of polygenic selection.     

从QTL到QTG的路还有多远?

植物大多数重要的经济性状都是数量性状, 人们对许多植物进行了数量性状基因座(QTL)的研究, 并取得了长足的发展。文章详尽地分析了数量性状表型与基因型的复杂关系, 介绍了当前QTL研究领域里的几种精细作图策略。讨论了当前挖掘控制目标性状QTL基因的研究过程中存在的困难和问题, 提出几个有待发展的研究方向, 并展望了该领域的发展前景。因目前的QTL仍然是一个相当大的染色体区段, 往往含有多个候选基因。文章就怎样从QTL粗放位点研究进一步发展到数量性状基因(quantitative trait gene, QTG)水平上的变异, 再从QTG到相应于基因内多态性的数量性状核苷酸(quantitative trait nucleotides, QTN), 提出了一些见解。来迎接后基因组时代数量遗传领域的挑战。     

Mapping Quantitative Trait Loci onto a Phylogenetic Tree

Despite advances in genetic mapping of quantitative traits and in phylogenetic comparative approaches, these two perspectives are rarely combined. The joint consideration of multiple crosses among related taxa (whether species or strains) not only allows more precise mapping of the genetic loci (called quantitative trait loci, QTL) that contribute to important quantitative traits, but also offers the opportunity to identify the origin of a QTL allele on the phylogenetic tree that relates the taxa. We describe a formal method for combining multiple crosses to infer the location of a QTL on a tree. We further discuss experimental design issues for such endeavors, such as how many crosses are required and which sets of crosses are best. Finally, we explore the method's performance in computer simulations, and we illustrate its use through application to a set of four mouse intercrosses among five inbred strains, with data on HDL cholesterol.     

Genetic regulation of bone mineral density in mice

Peak bone mass is a major determinant of risk of osteoporotic fracture. Family and twin studies have found a strong genetic component to the determination of bone mineral density (BMD). However, BMD is a complex trait whose expression is confounded by environmental influences and polygenic inheritance. The number, locations and effects of the individual genes contributing to natural variation in this trait are all unknown. The extreme difficulty of dissecting out environmental factors from genetic ones in humans has motivated the investigation of animal models. Genetically distinct animal strains raised under strict environmental control are critical tools for defining genetic regulation. The availability of inbred strains, combined with its relative fecundity, has established the mouse as the best model system for the study of mammalian genetics and physiology. Importantly, genes identified in murine analyses can usually be readily mapped to particular human chromosomal regions because of the high degree of synteny that exists between the mouse and human genomes. We employed quantitative trait locus (QTL) analysis to examine peak BMD in 24 recombinant inbred (RI) mouse strains, derived from a cross between C57BL/6 (B6) and DBA/2 (D2) progenitors (BXD RI). The distribution of BMD values among these strains clearly indicated the presence of strong genetic influences, with an estimated narrow sense heritability of 35%. The differences in peak whole body BMD in the BXD strains were integrated with a large database of genetic markers previously defined in the RI BXD strains to generate chromosome map sites for QTL locations. This QTL analysis provisionally identified a number of chromosomal sites linked to BMD. In the second phase of our BMD QTL mapping efforts, we used three independent mouse populations (all derived from B6 and D2 progenitor strains) to confirm and narrow the genetic locations of 4 QTLs (on chromosomes 1, 2, 4, and 11) that strongly influence the acquisition of peak BMD in mice. Using a novel, fine-mapping approach (recombinant inbred segregation testing), we have succeeded in narrowing two of the BMD-related chromosomal regions and in the process eliminated a number of candidate genes. The homologous regions in the human genome for each of these murine QTLs have been identified in recent human genetic studies. In light of this, we believe that findings in mice should aid in the identification of specific candidate genes for study in humans.     

An integrative approach for the identification of quantitative trait loci

The genetic dissection of complex traits is one of the most difficult and most important challenges facing science today. We discuss here an integrative approach to quantitative trait loci (QTL) mapping in mice. This approach makes use of the wealth of genetic tools available in mice, as well as the recent advances in genome sequence data already available for a number of inbred mouse strains. We have developed mapping strategies that allow a stepwise narrowing of a QTL mapping interval, prioritizing candidate genes for further analysis with the potential of identifying the most probable candidate gene for the given trait. This approach integrates traditional mapping tools, fine mapping tools, sequence-based analysis, bioinformatics and gene expression.     

Use of a dense single nucleotide polymorphism map for in silico mapping in the mouse

Rapid expansion of available data, both phenotypic and genotypic, for multiple strains of mice has enabled the development of new methods to interrogate the mouse genome for functional genetic perturbations. In silico mapping provides an expedient way to associate the natural diversity of phenotypic traits with ancestrally inherited polymorphisms for the purpose of dissecting genetic traits. In mouse, the current single nucleotide polymorphism (SNP) data have lacked the density across the genome and coverage of enough strains to properly achieve this goal. To remedy this, 470,407 allele calls were produced for 10,990 evenly spaced SNP loci across 48 inbred mouse strains. Use of the SNP set with statistical models that considered unique patterns within blocks of three SNPs as an inferred haplotype could successfully map known single gene traits and a cloned quantitative trait gene. Application of this method to high-density lipoprotein and gallstone phenotypes reproduced previously characterized quantitative trait loci (QTL). The inferred haplotype data also facilitates the refinement of QTL regions such that candidate genes can be more easily identified and characterized as shown for adenylate cyclase 7.     

Four linked genes participate in controlling sporulation efficiency in budding yeast

Quantitative traits are conditioned by several genetic determinants. Since such genes influence many important complex traits in various organisms, the identification of quantitative trait loci (QTLs) is of major interest, but still encounters serious difficulties. We detected four linked genes within one QTL, which participate in controlling sporulation efficiency in Saccharomyces cerevisiae. Following the identification of single nucleotide polymorphisms by comparing the sequences of 145 genes between the parental strains SK1 and S288c, we analyzed the segregating progeny of the cross between them. Through reciprocal hemizygosity analysis, four genes, RAS2, PMS1, SWS2, and FKH2, located in a region of 60 kilobases on Chromosome 14, were found to be associated with sporulation efficiency. Three of the four “high” sporulation alleles are derived from the “low” sporulating strain. Two of these sporulation-related genes were verified through allele replacements. For RAS2, the causative variation was suggested to be a single nucleotide difference in the upstream region of the gene. This quantitative trait nucleotide accounts for sporulation variability among a set of ten closely related winery yeast strains. Our results provide a detailed view of genetic complexity in one “QTL region” that controls a quantitative trait and reports a single nucleotide polymorphism-trait association in wild strains. Moreover, these findings have implications on QTL identification in higher eukaryotes.     

High‐precision genetic mapping of behavioral traits in the diversity outbred mouse population

Historically our ability to identify genetic variants underlying complex behavioral traits in mice has been limited by low mapping resolution of conventional mouse crosses. The newly developed Diversity Outbred (DO) population promises to deliver improved resolution that will circumvent costly fine‐mapping studies. The DO is derived from the same founder strains as the Collaborative Cross (CC), including three wild‐derived strains. Thus the DO provides more allelic diversity and greater potential for discovery compared to crosses involving standard mouse strains. We have characterized 283 male and female DO mice using open‐field, light–dark box, tail‐suspension and visual‐cliff avoidance tests to generate 38 behavioral measures. We identified several quantitative trait loci (QTL) for these traits with support intervals ranging from 1 to 3 Mb in size. These intervals contain relatively few genes (ranging from 5 to 96). For a majority of QTL, using the founder allelic effects together with whole genome sequence data, we could further narrow the positional candidates. Several QTL replicate previously published loci. Novel loci were also identified for anxiety‐ and activity‐related traits. Half of the QTLs are associated with wild‐derived alleles, confirming the value to behavioral genetics of added genetic diversity in the DO. In the presence of wild‐alleles we sometimes observe behaviors that are qualitatively different from the expected response. Our results demonstrate that high‐precision mapping of behavioral traits can be achieved with moderate numbers of DO animals, representing a significant advance in our ability to leverage the mouse as a tool for behavioral genetics .