Mapping Of Complex Traits Research Articles

High-resolution mapping of complex traits with a four-parent advanced intercross yeast population.

A large fraction of human complex trait heritability is due to a high number of variants with small marginal effects and their interactions with genotype and environment. Such alleles are more easily studied in model organisms, where environment, genetic makeup, and allele frequencies can be controlled. Here, we examine the effect of natural genetic variation on heritable traits in a very large pool of baker's yeast from a multiparent 12th generation intercross. We selected four representative founder strains to produce the Saccharomyces Genome Resequencing Project (SGRP)-4X mapping population and sequenced 192 segregants to generate an accurate genetic map. Using these individuals, we mapped 25 loci linked to growth traits under heat stress, arsenite, and paraquat, the majority of which were best explained by a diverging phenotype caused by a single allele in one condition. By sequencing pooled DNA from millions of segregants grown under heat stress, we further identified 34 and 39 regions selected in haploid and diploid pools, respectively, with most of the selection against a single allele. While the most parsimonious model for the majority of loci mapped using either approach was the effect of an allele private to one founder, we could validate examples of pleiotropic effects and complex allelic series at a locus. SGRP-4X is a deeply characterized resource that provides a framework for powerful and high-resolution genetic analysis of yeast phenotypes and serves as a test bed for testing avenues to attack human complex traits.

Dissecting Quantitative Traits in Mice

Progress in complex trait mapping in mice has been accelerated by the development of new populations suited to high-resolution mapping and by statistical methodologies that control for population structure. When combined with newly acquired catalogs of sequence variation in inbred strains, the genetic architecture of these new populations makes it possible to dissect complex traits down to the level of single variants. These analyses have shown not only that complex traits are caused by multiple contributing loci but also that each locus is likely due to the combined effects of multiple causal DNA variants. In combination with new rapid methods for producing transgenic mice that make it efficient to test candidate genes and variants, these advances significantly enhance the mouse genetics toolbox for dissecting quantitative traits.

Theodosius Dobzhansky and the genetic race concept

The use of ‘race’ as a proxy for population structure in the genetic mapping of complex traits has provoked controversy about its legitimacy as a category for biomedical research, given its social and political connotations. The controversy has reignited debates among scientists and philosophers of science about whether there is a legitimate biological concept of race. This paper examines the genetic race concept as it developed historically in the work of Theodosius Dobzhansky from the 1930s to 1950s. Dobzhansky’s definitions of race changed over this time from races as ‘arrays of forms’ or ‘clusters’ in 1933–1939, to races as genetically distinct geographical populations in 1940–1946, to races as genetically distinct ‘Mendelian populations’ in 1947–1955. Dobzhansky responded to nominalist challenges by appealing to the biological reality of race as a process. This response came into tension with the object ontology of race that was implied by Dobzhansky’s increasingly holistic treatment of Mendelian populations, a tension, the paper argues, he failed to appreciate or resolve.

A Novel Bayesian Semiparametric Algorithm for Inferring Population Structure and Adjusting for Case‐Control Association Tests

While the population-based case-control approach is the popular study design for association mapping of complex genetic traits because of ease of data collection and statistical analyses, it suffers from the inherent problem of population stratification. There have been methodological developments for adjusting these studies for population substructure, but efficient estimation of the number of subpopulations (K), which has evolutionary significance, remains a statistical challenge. In this article, we propose a Bayesian semiparametric approach to estimate population substructure under the assumption that K is random. Using extensive simulations, we find that our proposed method is not only computationally much faster than an existing Bayesian approach Structure, but also estimates the number of subpopulations more accurately, and thus, yields more power in detecting association in case-control studies.

Similarity in recombination rate and linkage disequilibrium at CYP2C and CYP2D cytochrome P450 gene regions among Europeans indicates signs of selection and no advantage of using tagSNPs in population isolates

Linkage disequilibrium (LD) and recombination rate variations are known to vary considerably between human genome regions and populations mostly because of the combined effects of mutation, recombination, and demographic history. Thus, the pattern of LD is a key issue to disentangle variants associated with complex traits. Here, we aim to describe the haplotype structure and LD variation at the pharmacogenetically relevant cytochrome P450 CYP2C and CYP2D gene regions among European populations. To assess the haplotype structure, LD pattern, and recombination rate variations in the clinically significant CYP2C and CYP2D regions, we genotyped 143 single-nucleotide polymorphisms (SNPs) across these two genome regions in a diverse set of 11 European population samples and one sub-Saharan African sample. Our results showed extended patterns of LD and in general a low rate of recombination at these loci, and a low degree of allele differentiation for the two cytochrome P450 regions among Europeans, with the exception of the Sami and the Finns as European outliers. The Sami sample showed reduced haplotype diversity and higher LD for the two cytochrome P450 regions than the other Europeans, a feature that is proposed to enhance the LD mapping of underlying common complex traits. However, recombination hotspots and LD blocks at these two regions showed highly consistent structures across Europeans including Finns and Sami. Moreover, we showed that the CEPH sample has significantly higher tag transferability among Europeans and a more efficient tagging of both the rare CYP2C9 and the common CYP2C19 functional variants than the Sami. Our data set included CYP2C9*3 (rs1057910) and CYP2C19*2 (rs4244285) enzyme activity-altering variants associated in a recent genome-wide study with acenocoumarol-induced and warfarin-induced anticoagulation or to the antiplatelet effect of clopidogrel, respectively. Including these known activity-altering variants, we showed the haplotype variation and high derived allele frequencies of novel recently identified acenocoumarol genome-wide associated SNPs at CYP2C9 (rs4086116) and CYP2C18 (rs12772169, rs1998591, rs2104543, rs1042194) loci in a comprehensive set of 11 European populations. Furthermore, a significant frequency difference of a CYP2C19*2 gene mutation causing variable drug reactions was observed among Europeans. The CEPH sample representing the general European population as such in the HapMap project seems to be the optimal population sample for the LD mapping of common complex traits among Europeans. Nevertheless, it is still argued that the unique pattern of LD in the Sami may offer an advantage for further association mapping, especially if multiple rare variants play a role in disease etiology. However, besides the activity-altering CYP2C9*3 (rs1057910) and CYP2C19*2 (rs4244285) variants, the high derived allele frequencies of novel recently identified acenocoumarol genome-wide associated SNPs at CYP2C9 (rs4086116) and CYP2C18 (rs12772169, rs1998591, rs2104543, rs1042194) loci variants indicated that the CYP2C region may have been influenced by selection. Thus, this fine-scale haplotype map of the CYP2C and CYP2D regions may help to choose markers for further association mapping of complex pharmacogenetic traits at these loci.

Structural mapping: how to study the genetic architecture of a phenotypic trait through its formation mechanism

Traditional approaches for genetic mapping are to simply associate the genotypes of a quantitative trait locus (QTL) with the phenotypic variation of a complex trait. A more mechanistic strategy has emerged to dissect the trait phenotype into its structural components and map specific QTLs that control the mechanistic and structural formation of a complex trait. We describe and assess such a strategy, called structural mapping, by integrating the internal structural basis of trait formation into a QTL mapping framework. Electrical impedance spectroscopy (EIS) has been instrumental for describing the structural components of a phenotypic trait and their interactions. By building robust mathematical models on circuit EIS data and embedding these models within a mixture model-based likelihood for QTL mapping, structural mapping implements the EM algorithm to obtain maximum likelihood estimates of QTL genotype-specific EIS parameters. The uniqueness of structural mapping is to make it possible to test a number of hypotheses about the pattern of the genetic control of structural components. We validated structural mapping by analyzing an EIS data collected for QTL mapping of frost hardiness in a controlled cross of jujube trees. The statistical properties of parameter estimates were examined by simulation studies. Structural mapping can be a powerful alternative for genetic mapping of complex traits by taking account into the biological and physical mechanisms underlying their formation.

Image analysis for QTL mapping of flower colour and leaf characteristics in pot azalea (Rhododendron simsii hybrids)

In azalea breeding, flower colour is the most essential selection criterion, but leaf morphology also influences attractiveness of the plant. Despite extensive study of the inheritance of flower colour, no explanation has yet been found for the pink phenotype. We have used image analysis to quantify flower colour and leaf morphology. Flower colour was quantified in a whole population. Pink flower colour has been confirmed to be the result of a gene-dosage effect; two major QTLs were found for flower colour as well as some minor QTLs that seem to be related to pink coloration. Leaf morphology (colour and shape) was scored in four unrelated populations by means of image analysis. The image analysis generated continuous, highly informative data for QTL mapping. Both classical parameters and symmetrical elliptic Fourier descriptors successfully described leaf morphology. Image analysis resulted in large data sets that had to be combined in principal components. Only a limited number of QTLs were found for leaf colour, but we could discern some major QTLs for leaf shape and size that were consistent over the different mapping populations. These QTLs are the most interesting candidates for future analysis of the selected traits. The multi-population approach certainly proved to be valuable for QTL mapping of complex traits. For leaf morphology, however, more research is needed to identify the most valuable QTLs for future use in MAS.

Fine mapping of complex traits in non-model species: using next generation sequencing and advanced intercross lines in Japanese quail

BackgroundAs for other non-model species, genetic analyses in quail will benefit greatly from a higher marker density, now attainable thanks to the evolution of sequencing and genotyping technologies. Our objective was to obtain the first genome wide panel of Japanese quail SNP (Single Nucleotide Polymorphism) and to use it for the fine mapping of a QTL for a fear-related behaviour, namely tonic immobility, previously localized on Coturnix japonica chromosome 1. To this aim, two reduced representations of the genome were analysed through high-throughput 454 sequencing: AFLP (Amplified Fragment Length Polymorphism) fragments as representatives of genomic DNA, and EST (Expressed Sequence Tag) as representatives of the transcriptome.ResultsThe sequencing runs produced 399,189 and 1,106,762 sequence reads from cDNA and genomic fragments, respectively. They covered over 434 Mb of sequence in total and allowed us to detect 17,433 putative SNP. Among them, 384 were used to genotype two Advanced Intercross Lines (AIL) obtained from three quail lines differing for duration of tonic immobility. Despite the absence of genotyping for founder individuals in the analysis, the previously identified candidate region on chromosome 1 was refined and led to the identification of a candidate gene.ConclusionsThese data confirm the efficiency of transcript and AFLP-sequencing for SNP discovery in a non-model species, and its application to the fine mapping of a complex trait. Our results reveal a significant association of duration of tonic immobility with a genomic region comprising the DMD (dystrophin) gene. Further characterization of this candidate gene is needed to decipher its putative role in tonic immobility in Coturnix.

Genotype-based association analysis via entropy

With the advent of large-scale genotyping technologies, enormous quantities of genotype data that were generated have been well exploited through phased haplotypes, and the haplotype-based association study is used as one of the major statistical methods for gene mapping of human complex traits. However, haplotype-based method depends on the information of haplotype frequencies, which results in infeasible computation when haplotypes are not directly observed. This paper provides a genotype-based statistic with multiple tightly linked markers for association analysis using entropy theory. The statistic here does not require haplotype phasing and only requires genotype data. The distribution and the power of the statistic are investigated by simulative study. The results show that the statistic has very reasonable performance. We demonstrated the powerfulness of the statistic by applying our approach to a specific example on hereditary hemochromatosis.

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.

Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines

Chromosome segment substitution lines have been created in several experimental models, including many plant and animal species, and are useful tools for the genetic analysis and mapping of complex traits. The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect. However, current methods cannot uncover the entire genetic structure of complex traits. For example, current methods cannot distinguish between main effects and epistatic effects. In this paper, a linear epistatic model was constructed to dissect complex traits. First, all the long substituted segments were divided into overlapping small bins, and each small bin was considered a unique independent variable. The genetic model for complex traits was then constructed. When considering all the possible main effects and epistatic effects, the dimensions of the linear model can become extremely high. Therefore, variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study. Furthermore, we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects, examined the fully Bayesian SSVS (stochastic search variable selection) approach, tested the empirical Bayes (E-BAYES) method, and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs. Simulation studies suggested that all of the above methods, excluding the LASSO and PENAL approaches, performed satisfactorily. The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.

Genetic structure and linkage disequilibrium pattern of a rapeseed (Brassica napus L.) association mapping panel revealed by microsatellites

Understanding the population structure and linkage disequilibrium (LD) is a prerequisite for association mapping of complex traits in a target population. In this study, we assessed the genetic diversity, population structure and the extent of LD in a panel of 192 inbred lines of Brassica napus from all over the world using 451 single-locus microsatellite markers. The inbred lines could be divided into P1 and P2 groups by a model-based population structure analysis. Out of the 142 inbred lines in the P1 group, 126 lines were from China and Japan, and the remaining 16 lines were from Europe, Canada and Australia. In the P2 group, 33 out of the 50 lines were from Europe, Canada, and Australia, and the remaining 17 lines were from China. Structure analysis further divided each group into two subgroups. AMOVA, pairwise F (ST) and neutrality analyses confirmed the differentiation between groups and subgroups. More than 80 % of the pairwise kinship estimates between inbred lines were <0.05, indicating that relative kinship is weak in our panel. Only 6 % linked marker pairs showed LD, suggesting the low level of LD in this association panel. The LD decayed within 0.5-1 cM at the genome level, and varied considerably across each group and subgroup, due to the population size, genetic background and genetic drift. The characterization of the population structure and LD patterns would be useful for performing association studies for complex agronomic traits in rapeseed.

Genome wide linkage disequilibrium in Chinese asparagus bean (Vigna. unguiculata ssp. sesquipedialis) germplasm: implications for domestication history and genome wide association studies

Association mapping of important traits of crop plants relies on first understanding the extent and patterns of linkage disequilibrium (LD) in the particular germplasm being investigated. We characterize here the genetic diversity, population structure and genome wide LD patterns in a set of asparagus bean (Vigna. unguiculata ssp. sesquipedialis) germplasm from China. A diverse collection of 99 asparagus bean and normal cowpea accessions were genotyped with 1127 expressed sequence tag-derived single nucleotide polymorphism markers (SNPs). The proportion of polymorphic SNPs across the collection was relatively low (39%), with an average number of SNPs per locus of 1.33. Bayesian population structure analysis indicated two subdivisions within the collection sampled that generally represented the 'standard vegetable' type (subgroup SV) and the 'non-standard vegetable' type (subgroup NSV), respectively. Level of LD (r(2)) was higher and extent of LD persisted longer in subgroup SV than in subgroup NSV, whereas LD decayed rapidly (0-2 cM) in both subgroups. LD decay distance varied among chromosomes, with the longest (≈ 5 cM) five times longer than the shortest (≈ 1 cM). Partitioning of LD variance into within- and between-subgroup components coupled with comparative LD decay analysis suggested that linkage group 5, 7 and 10 may have undergone the most intensive epistatic selection toward traits favorable for vegetable use. This work provides a first population genetic insight into domestication history of asparagus bean and demonstrates the feasibility of mapping complex traits by genome wide association study in asparagus bean using a currently available cowpea SNPs marker platform.

Genetic mapping of complex traits by minimizing integrated square errors

BackgroundGenetic mapping has been used as a tool to study the genetic architecture of complex traits by localizing their underlying quantitative trait loci (QTLs). Statistical methods for genetic mapping rely on a key assumption, that is, traits obey a parametric distribution. However, in practice real data may not perfectly follow the specified distribution.ResultsHere, we derive a robust statistical approach for QTL mapping that accommodates a certain degree of misspecification of the true model by incorporating integrated square errors into the genetic mapping framework. A hypothesis testing is formulated by defining a new test statistics - energy difference.ConclusionsSimulation studies were performed to investigate the statistical properties of this approach and compare these properties with those from traditional maximum likelihood and non-parametric QTL mapping approaches. Lastly, analyses of real examples were conducted to demonstrate the usefulness and utilization of the new approach in a practical genetic setting.

Recombination rates across porcine autosomes inferred from high‐density linkage maps

Studies of the variation in recombination rate across the genome provide a better understanding of evolutionary genomics and are also an important step towards mapping and dissecting complex traits in domestic animals. With the recent completion of the porcine genome sequence and the availability of a high-density porcine single nucleotide polymorphism (SNP) array, it is now possible to construct a high-density porcine linkage map and estimate recombination rate across the genome. A total of 416 animals were genotyped with the Porcine SNP60BeadChip, and high-density chromosome linkage maps were constructed using CRI-MAP, assuming the physical order of the Sscrofa10 assembly. The total linkage map length was 2018.79 cM, using 658 meioses and 14,503 SNPs. The estimated average recombination rate across the porcine autosomes was 0.86 cM/Mb. However, a large variation in recombination rate was observed among chromosomes. The estimated average recombination rates (cM/Mb) per chromosome ranged from 0.48 in SSC1 to 1.48 in SSC10, displaying a significant negative correlation with the chromosome sizes. In addition, the analysis of the variation in the recombination rates taking 1-Mb sliding windows has allowed us to demonstrate the variation in recombination rates within chromosomes. In general, a larger recombination rate was observed in the extremes than in the centre of the chromosome. Finally, the ratio between female and male recombination rates was also inferred, obtaining a value of 1.38, with the heterogametic sex having the least recombination.

Next-Generation Mapping of Complex Traits with Phenotype-Based Selection and Introgression

Finding the genes underlying complex traits is difficult. We show that new sequencing technology combined with traditional genetic techniques can efficiently identify genetic regions underlying a complex and quantitative behavioral trait. As a proof of concept we used phenotype-based introgression to backcross loci that control innate food preference in Drosophila simulans into the genomic background of D. sechellia, which expresses the opposite preference. We successfully mapped D. simulans introgression regions in a small mapping population (30 flies) with whole-genome resequencing using light coverage (∼1×). We found six loci contributing to D. simulans food preference, one of which overlaps a previously discovered allele. This approach is applicable to many systems, does not rely on laborious marker development or genotyping, does not require existing high quality reference genomes, and needs only small mapping populations. Because introgression is used, researchers can scale mapping population size, replication, and number of backcross generations to their needs. Finally, in contrast to more widely used mapping techniques like F(2) bulk-segregant analysis, our method produces near-isogenic lines that can be kept and reused indefinitely.

Integrative deep sequencing of the mouse lung transcriptome reveals differential expression of diverse classes of small RNAs in response to respiratory virus infection.

ABSTRACTWe previously reported widespread differential expression of long non-protein-coding RNAs (ncRNAs) in response to virus infection. Here, we expanded the study through small RNA transcriptome sequencing analysis of the host response to both severe acute respiratory syndrome coronavirus (SARS-CoV) and influenza virus infections across four founder mouse strains of the Collaborative Cross, a recombinant inbred mouse resource for mapping complex traits. We observed differential expression of over 200 small RNAs of diverse classes during infection. A majority of identified microRNAs (miRNAs) showed divergent changes in expression across mouse strains with respect to SARS-CoV and influenza virus infections and responded differently to a highly pathogenic reconstructed 1918 virus compared to a minimally pathogenic seasonal influenza virus isolate. Novel insights into miRNA expression changes, including the association with pathogenic outcomes and large differences between in vivo and in vitro experimental systems, were further elucidated by a survey of selected miRNAs across diverse virus infections. The small RNAs identified also included many non-miRNA small RNAs, such as small nucleolar RNAs (snoRNAs), in addition to nonannotated small RNAs. An integrative sequencing analysis of both small RNAs and long transcripts from the same samples showed that the results revealing differential expression of miRNAs during infection were largely due to transcriptional regulation and that the predicted miRNA-mRNA network could modulate global host responses to virus infection in a combinatorial fashion. These findings represent the first integrated sequencing analysis of the response of host small RNAs to virus infection and show that small RNAs are an integrated component of complex networks involved in regulating the host response to infection.

Gold nanoparticles for high-throughput genotyping of long-range haplotypes

Completion of the Human Genome Project and the HapMap Project has led to increasing demands for mapping complex traits in humans to understand the aetiology of diseases. Identifying variations in the DNA sequence, which affect how we develop disease and respond to pathogens and drugs, is important for this purpose, but it is difficult to identify these variations in large sample sets. Here we show that through a combination of capillary sequencing and polymerase chain reaction assisted by gold nanoparticles, it is possible to identify several DNA variations that are associated with age-related macular degeneration and psoriasis on significant regions of human genomic DNA. Our method is accurate and promising for large-scale and high-throughput genetic analysis of susceptibility towards disease and drug resistance.

Brazilian urban population genetic structure reveals a high degree of admixture

Advances in genotyping technologies have contributed to a better understanding of human population genetic structure and improved the analysis of association studies. To analyze patterns of human genetic variation in Brazil, we used SNP data from 1129 individuals--138 from the urban population of Sao Paulo, Brazil, and 991 from 11 populations of the HapMap Project. Principal components analysis was performed on the SNPs common to these populations, to identify the composition and the number of SNPs needed to capture the genetic variation of them. Both admixture and local ancestry inference were performed in individuals of the Brazilian sample. Individuals from the Brazilian sample fell between Europeans, Mexicans, and Africans. Brazilians are suggested to have the highest internal genetic variation of sampled populations. Our results indicate, as expected, that the Brazilian sample analyzed descend from Amerindians, African, and/or European ancestors, but intermarriage between individuals of different ethnic origin had an important role in generating the broad genetic variation observed in the present-day population. The data support the notion that the Brazilian population, due to its high degree of admixture, can provide a valuable resource for strategies aiming at using admixture as a tool for mapping complex traits in humans.

Progress and promise of genome-wide association studies for human complex trait genetics.

Enormous progress in mapping complex traits in humans has been made in the last 5 yr. There has been early success for prevalent diseases with complex phenotypes. These studies have demonstrated clearly that, while complex traits differ in their underlying genetic architectures, for many common disorders the predominant pattern is that of many loci, individually with small effects on phenotype. For some traits, loci of large effect have been identified. For almost all complex traits studied in humans, the sum of the identified genetic effects comprises only a portion, generally less than half, of the estimated trait heritability. A variety of hypotheses have been proposed to explain why this might be the case, including untested rare variants, and gene-gene and gene-environment interaction. Effort is currently being directed toward implementation of novel analytic approaches and testing rare variants for association with complex traits using imputed variants from the publicly available 1000 Genomes Project resequencing data and from direct resequencing of clinical samples. Through integration with annotations and functional genomic data as well as by in vitro and in vivo experimentation, mapping studies continue to characterize functional variants associated with complex traits and address fundamental issues such as epistasis and pleiotropy. This review focuses primarily on the ways in which genome-wide association studies (GWASs) have revolutionized the field of human quantitative genetics.