Genetic Architecture Underlying Nascent Speciation-The Evolution of Eurasian Pigs under Domestication.

Speciation is a process whereby the evolution of reproductive barriers leads to isolated species. Although many studies have addressed large-effect genetic footprints in the advanced stages of speciation, the genetics of reproductive isolation in nascent stage of speciation remains unclear. Here, we show that pig domestication offers an interesting model for studying the early stages of speciation in great details. Pig breeds have not evolved the large X-effect of hybrid incompatibility commonly observed between "good species." Instead, deleterious epistatic interactions among multiple autosomal loci are common. These weak Dobzhansky-Muller incompatibilities confer partial hybrid inviability with sex biases in crosses between European and East Asian domestic pigs. The genomic incompatibility is enriched in pathways for angiogenesis, androgen receptor signaling and immunity, with an observation of many highly differentiated cis-regulatory variants. Our study suggests that partial hybrid inviability caused by pervasive but weak interactions among autosomal loci may be a hallmark of nascent speciation in mammals.

Discovery of a major QTL for root-knot nematode (Meloidogyne incognita) resistance in cultivated sweetpotato (Ipomoea batatas).

KEY MESSAGE: Utilizing a high-density integrated genetic linkage map of hexaploid sweetpotato, we discovered a major dominant QTL for root-knot nematode (RKN) resistance and modeled its effects. This discovery is useful for development of a modern sweetpotato breeding program that utilizes marker-assisted selection and genomic selection approaches for faster genetic gain of RKN resistance. The root-knot nematode [Meloidogyne incognita (Kofoid & White) Chitwood] (RKN) causes significant storage root quality reduction and yields losses in cultivated sweetpotato [Ipomoea batatas (L.) Lam.]. In this study, resistance to RKN was examined in a mapping population consisting of 244 progenies derived from a cross (TB) between 'Tanzania,' a predominant African landrace cultivar with resistance to RKN, and 'Beauregard,' an RKN susceptible major cultivar in the USA. We performed quantitative trait loci (QTL) analysis using a random-effect QTL mapping model on the TB genetic map. An RKN bioassay incorporating potted cuttings of each genotype was conducted in the greenhouse and replicated five times over a period of 10 weeks. For each replication, each genotype was inoculated with ca. 20,000 RKN eggs, and root-knot galls were counted ~62 days after inoculation. Resistance to RKN in the progeny was highly skewed toward the resistant parent, exhibiting medium to high levels of resistance. We identified one major QTL on linkage group 7, dominant in nature, which explained 58.3% of the phenotypic variation in RKN counts. This work represents a significant step forward in our understanding of the genetic architecture of RKN resistance and sets the stage for future utilization of genomics-assisted breeding in sweetpotato breeding programs.

The recombination landscape and multiple QTL mapping in a Solanum tuberosum cv. 'Atlantic'-derived F1 population.

There are many challenges involved with the genetic analyses of autopolyploid species, such as the tetraploid potato, Solanum tuberosum (2n = 4x = 48). The development of new analytical methods has made it valuable to re-analyze an F1 population (n = 156) derived from a cross involving 'Atlantic', a widely grown chipping variety in the USA. A fully integrated genetic map with 4285 single nucleotide polymorphisms, spanning 1630 cM, was constructed with MAPpoly software. We observed that bivalent configurations were the most abundant ones (51.0~72.4% depending on parent and linkage group), though multivalent configurations were also observed (2.2~39.2%). Seven traits were evaluated over four years (2006-8 and 2014) and quantitative trait loci (QTL) mapping was carried out using QTLpoly software. Based on a multiple-QTL model approach, we detected 21 QTL for 15 out of 27 trait-year combination phenotypes. A hotspot on linkage group 5 was identified with co-located QTL for maturity, plant yield, specific gravity, and internal heat necrosis resistance evaluated over different years. Additional QTL for specific gravity and dry matter were detected with maturity-corrected phenotypes. Among the genes around QTL peaks, we found those on chromosome 5 that have been previously implicated in maturity (StCDF1) and tuber formation (POTH1). These analyses have the potential to provide insights into the biology and breeding of tetraploid potato and other autopolyploid species.

Quantitative Trait Locus Mapping for Common Scab Resistance in a Tetraploid Potato Full-Sib Population.

Despite the negative impact of common scab (Streptomyces spp.) on the potato industry, little is known about the genetic architecture of resistance to this bacterial disease in the crop. We evaluated a mapping population (∼150 full sibs) derived from a cross between two tetraploid potatoes ('Atlantic' × B1829-5) in three environments (MN11, PA11, ME12) under natural common scab pressure. Three measures to common scab reaction, namely percentage of scabby tubers and disease area and lesion indices, were found to be highly correlated (>0.76). Because of the large environmental effect, heritability values were zero for all three traits in MN11, but moderate to high in PA11 and ME12 (∼0.44 to 0.79). We identified a single quantitative trait locus (QTL) for lesion index in PA11, ME12, and joint analyses on linkage group 3, explaining ∼22 to 30% of the total variation. The identification of QTL haplotypes and candidate genes contributing to disease resistance can support genomics-assisted breeding approaches in the crop.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Assembly of whole-chromosome pseudomolecules for polyploid plant genomes using outbred mapping populations.

Despite advances in sequencing technologies, assembly of complex plant genomes remains elusive due to polyploidy and high repeat content. Here we report PolyGembler for grouping and ordering contigs into pseudomolecules by genetic linkage analysis. Our approach also provides an accurate method with which to detect and fix assembly errors. Using simulated data, we demonstrate that our approach is of high accuracy and outperforms three existing state-of-the-art genetic mapping tools. Particularly, our approach is more robust to the presence of missing genotype data and genotyping errors. We used our method to construct pseudomolecules for allotetraploid lawn grass utilizing PacBio long reads in combination with restriction site-associated DNA sequencing, and for diploid Ipomoea trifida and autotetraploid potato utilizing contigs assembled from Illumina reads in combination with genotype data generated by single-nucleotide polymorphism arrays and genotyping by sequencing, respectively. We resolved 13 assembly errors for a published I. trifida genome assembly and anchored eight unplaced scaffolds in the published potato genome.

Sequencing depth and genotype quality: accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops.

KEY MESSAGE: Polypoid crop breeders can balance resources between density and sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture. The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene-action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental F1 population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: (i) do polyploid crop breeders need to invest more for additional sequencing depth? (ii) how many markers are required to make selection decisions? (iii) does considering non-additive genetic effects improve predictive ability (PA)? (iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction in the type of populations we analyzed. We also show that considering dosage information and models considering only additive effects had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations.

Multiple QTL Mapping in Autopolyploids: A Random-Effect Model Approach with Application in a Hexaploid Sweetpotato Full-Sib Population.

In developing countries, the sweetpotato, Ipomoea batatas (L.) Lam. [Formula: see text], is an important autopolyploid species, both socially and economically. However, quantitative trait loci (QTL) mapping has remained limited due to its genetic complexity. Current fixed-effect models can fit only a single QTL and are generally hard to interpret. Here, we report the use of a random-effect model approach to map multiple QTL based on score statistics in a sweetpotato biparental population ('Beauregard' × 'Tanzania') with 315 full-sibs. Phenotypic data were collected for eight yield component traits in six environments in Peru, and jointly adjusted means were obtained using mixed-effect models. An integrated linkage map consisting of 30,684 markers distributed along 15 linkage groups (LGs) was used to obtain the genotype conditional probabilities of putative QTL at every centiMorgan position. Multiple interval mapping was performed using our R package QTLpoly and detected a total of 13 QTL, ranging from none to four QTL per trait, which explained up to 55% of the total variance. Some regions, such as those on LGs 3 and 15, were consistently detected among root number and yield traits, and provided a basis for candidate gene search. In addition, some QTL were found to affect commercial and noncommercial root traits distinctly. Further best linear unbiased predictions were decomposed into additive allele effects and were used to compute multiple QTL-based breeding values for selection. Together with quantitative genotyping and its appropriate usage in linkage analyses, this QTL mapping methodology will facilitate the use of genomic tools in sweetpotato breeding as well as in other autopolyploids.

Quantitative trait loci and differential gene expression analyses reveal the genetic basis for negatively associated ß-carotene and starch content in hexaploid sweetpotato [Ipomoea batatas (L.) Lam.].

KEY MESSAGE: ß-Carotene content in sweetpotato is associated with the Orange and phytoene synthase genes; due to physical linkage of phytoene synthase with sucrose synthase, ß-carotene and starch content are negatively correlated. In populations depending on sweetpotato for food security, starch is an important source of calories, while ß-carotene is an important source of provitamin A. The negative association between the two traits contributes to the low nutritional quality of sweetpotato consumed, especially in sub-Saharan Africa. Using a biparental mapping population of 315 F1 progeny generated from a cross between an orange-fleshed and a non-orange-fleshed sweetpotato variety, we identified two major quantitative trait loci (QTL) on linkage group (LG) three (LG3) and twelve (LG12) affecting starch, ß-carotene, and their correlated traits, dry matter and flesh color. Analysis of parental haplotypes indicated that these two regions acted pleiotropically to reduce starch content and increase ß-carotene in genotypes carrying the orange-fleshed parental haplotype at the LG3 locus. Phytoene synthase and sucrose synthase, the rate-limiting and linked genes located within the QTL on LG3 involved in the carotenoid and starch biosynthesis, respectively, were differentially expressed in Beauregard versus Tanzania storage roots. The Orange gene, the molecular switch for chromoplast biogenesis, located within the QTL on LG12 while not differentially expressed was expressed in developing roots of the parental genotypes. We conclude that these two QTL regions act together in a cis and trans manner to inhibit starch biosynthesis in amyloplasts and enhance chromoplast biogenesis, carotenoid biosynthesis, and accumulation in orange-fleshed sweetpotato. Understanding the genetic basis of this negative association between starch and ß-carotene will inform future sweetpotato breeding strategies targeting sweetpotato for food and nutritional security.

Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping.

The hexaploid sweetpotato (Ipomoea batatas (L.) Lam., 2n = 6x = 90) is an important staple food crop worldwide and plays a vital role in alleviating famine in developing countries. Due to its high ploidy level, genetic studies in sweetpotato lag behind major diploid crops significantly. We built an ultra-dense multilocus integrated genetic map and characterized the inheritance system in a sweetpotato full-sib family using our newly developed software, MAPpoly. The resulting genetic map revealed 96.5% collinearity between I. batatas and its diploid relative I. trifida We computed the genotypic probabilities across the whole genome for all individuals in the mapping population and inferred their complete hexaploid haplotypes. We provide evidence that most of the meiotic configurations (73.3%) were resolved in bivalents, although a small portion of multivalent signatures (15.7%), among other inconclusive configurations (11.0%), were also observed. Except for low levels of preferential pairing in linkage group 2, we observed a hexasomic inheritance mechanism in all linkage groups. We propose that the hexasomic-bivalent inheritance promotes stability to the allelic transmission in sweetpotato.

Genomic Selection with Allele Dosage in Panicum maximum Jacq.

Genomic selection is an efficient approach to get shorter breeding cycles in recurrent selection programs and greater genetic gains with selection of superior individuals. Despite advances in genotyping techniques, genetic studies for polyploid species have been limited to a rough approximation of studies in diploid species. The major challenge is to distinguish the different types of heterozygotes present in polyploid populations. In this work, we evaluated different genomic prediction models applied to a recurrent selection population of 530 genotypes of Panicum maximum, an autotetraploid forage grass. We also investigated the effect of the allele dosage in the prediction, i.e., considering tetraploid (GS-TD) or diploid (GS-DD) allele dosage. A longitudinal linear mixed model was fitted for each one of the six phenotypic traits, considering different covariance matrices for genetic and residual effects. A total of 41,424 genotyping-by-sequencing markers were obtained using 96-plex and Pst1 restriction enzyme, and quantitative genotype calling was performed. Six predictive models were generalized to tetraploid species and predictive ability was estimated by a replicated fivefold cross-validation process. GS-TD and GS-DD models were performed considering 1,223 informative markers. Overall, GS-TD data yielded higher predictive abilities than with GS-DD data. However, different predictive models had similar predictive ability performance. In this work, we provide bioinformatic and modeling guidelines to consider tetraploid dosage and observed that genomic selection may lead to additional gains in recurrent selection program of P. maximum.

Linkage map construction and QTL analysis for internal heat necrosis in autotetraploid potato.

KEY MESSAGE: A tetraploid potato population was mapped for internal heat necrosis (IHN) using the Infinium ® 8303 potato SNP array, and QTL for IHN were identified on chromosomes 1, 5, 9 and 12 that explained 28.21% of the variation for incidence and 25.3% of the variation for severity. This research represents a significant step forward in our understanding of IHN, and sets the stage for future research focused on testing the utility of these markers in additional breeding populations. Internal heat necrosis (IHN) is a significant non-pathogenic disorder of potato tubers and previous studies have identified AFLP markers linked to IHN susceptibility in the tetraploid, B2721 potato mapping population. B2721 consists of an IHN susceptible×resistant cross: Atlantic×B1829-5. We developed a next-generation SNP-based linkage map of this cross using the Infinium® 8303 SNP array and conducted additional QTL analyses of IHN susceptibility in the B2721 population. Using SNP dosage sensitive markers, linkage maps for both parents were simultaneously analyzed. The linkage map contained 3427 SNPs and totaled 1397.68 cM. QTL were detected for IHN on chromosomes 1, 5, 9, and 12 using LOD permutation thresholds and colocation of high LOD scores across multiple years. Genetic effects were modeled for each putative QTL. Markers associated with a QTL were regressed in models of effects for IHN incidence and severity for all years. In the full model, the SNP markers were shown to have significant effects for IHN (p < 0.0001), and explained 28.21% of the variation for incidence and 25.3% of the variation for severity. We were able to utilize SNP dosage information to identify and model the effects of putative QTL, and identify SNP loci associated with IHN resistance that need to be confirmed. This research represents a significant step forward in our understanding of IHN, and sets the stage for future research focused on testing the utility of these markers in additional breeding populations.

Genetic Architecture of Natural Variation Underlying Adult Foraging Behavior That Is Essential for Survival of Drosophila melanogaster.

Foraging behavior is critical for the fitness of individuals. However, the genetic basis of variation in foraging behavior and the evolutionary forces underlying such natural variation have rarely been investigated. We developed a systematic approach to assay the variation in survival rate in a foraging environment for adult flies derived from a wild Drosophila melanogaster population. Despite being such an essential trait, there is substantial variation of foraging behavior among D. melanogaster strains. Importantly, we provided the first evaluation of the potential caveats of using inbred Drosophila strains to perform genome-wide association studies on life-history traits, and concluded that inbreeding depression is unlikely a major contributor for the observed large variation in adult foraging behavior. We found that adult foraging behavior has a strong genetic component and, unlike larval foraging behavior, depends on multiple loci. Identified candidate genes are enriched in those with high expression in adult heads and, demonstrated by expression knock down assay, are involved in maintaining normal functions of the nervous system. Our study not only identified candidate genes for foraging behavior that is relevant to individual fitness, but also shed light on the initial stage underlying the evolution of the behavior.

Quantitative trait loci influencing forking defects in an outbred pedigree of loblolly pine.

BACKGROUND: The use of wood as an industrial raw material has led to development of plantation forestry, in which trees are planted, managed, and harvested as crops. The productivity of such plantations often exceeds that of less-intensively-managed forests, and land managers have the option of choosing specific planting stock to produce specific types of wood for industrial use. Stem forking, or division of the stem into two or more stems of roughly equal size, is a character trait important in determining the quality of the stem for production of solid wood products. This trait typically has very low individual-tree heritability, but can be more accurately assessed in clonally-replicated plantings where each genotype is represented by several individual trees. We report results from a quantitative trait mapping experiment in a clonally-replicated full-sibling family of loblolly pine (Pinus taeda L.). RESULTS: Quantitative trait loci influencing forking defects were identified in an outbred full-sibling family of loblolly pine, using single-nucleotide polymorphism markers. Genetic markers in this family segregated either in 1:2:1 (F2 intercross-like segregation) or 1:1 ratio (backcross-like segregation). An integrated linkage map combining markers with different segregation ratios was assembled for this full-sib family, and a total of 409 SNP markers were mapped on 12 linkage groups, covering 1622 cM. Two and three trait loci were identified for forking and ramicorn branch traits, respectively, using the interval mapping method. Three trait loci were detected for both traits using multiple-trait analysis. CONCLUSIONS: The detection of three loci for forking and ramicorn branching in a multiple-trait analysis could mean that there are genes with pleiotropic effects on both traits, or that separate genes affecting different traits are clustered together. The detection of genetic loci associated with variation in stem quality traits in this study supports the hypothesis that marker-assisted selection can be used to decrease the rate of stem defects in breeding populations of loblolly pine.

Mapping epistatic quantitative trait loci.

BACKGROUND: How to map quantitative trait loci (QTL) with epistasis efficiently and reliably has been a persistent problem for QTL mapping analysis. There are a number of difficulties for studying epistatic QTL. Linkage can impose a significant challenge for finding epistatic QTL reliably. If multiple QTL are in linkage and have interactions, searching for QTL can become a very delicate issue. A commonly used strategy that performs a two-dimensional genome scan to search for a pair of QTL with epistasis can suffer from low statistical power and also may lead to false identification due to complex linkage disequilibrium and interaction patterns. RESULTS: To tackle the problem of complex interaction of multiple QTL with linkage, we developed a three-stage search strategy. In the first stage, main effect QTL are searched and mapped. In the second stage, epistatic QTL that interact significantly with other identified QTL are searched. In the third stage, new epistatic QTL are searched in pairs. This strategy is based on the consideration that most genetic variance is due to the main effects of QTL. Thus by first mapping those main-effect QTL, the statistical power for the second and third stages of analysis for mapping epistatic QTL can be maximized. The search for main effect QTL is robust and does not bias the search for epistatic QTL due to a genetic property associated with the orthogonal genetic model that the additive and additive by additive variances are independent despite of linkage. The model search criterion is empirically and dynamically evaluated by using a score-statistic based resampling procedure. We demonstrate through simulations that the method has good power and low false positive in the identification of QTL and epistasis. CONCLUSION: This method provides an effective and powerful solution to map multiple QTL with complex epistatic pattern. The method has been implemented in the user-friendly computer software Windows QTL Cartographer. This will greatly facilitate the application of the method for QTL mapping data analysis.

Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification.

BACKGROUND: Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. The multi-class SVM algorithm of Crammer and Singer provides a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. In this paper, we propose to improve the procedure by imposing shrinkage penalties in learning to enforce solution sparsity. RESULTS: The original multi-class SVM of Crammer and Singer is effective for multi-class classification but does not conduct variable selection. We improved the method by introducing soft-thresholding type penalties to incorporate variable selection into multi-class classification for high dimensional data. The new methods were applied to simulated data and two cancer gene expression data sets. The results demonstrate that the new methods can select a small number of genes for building accurate multi-class classification rules. Furthermore, the important genes selected by the methods overlap significantly, suggesting general agreement among different variable selection schemes. CONCLUSIONS: High accuracy and sparsity make the new methods attractive for cancer diagnostics with gene expression data and defining targets of therapeutic intervention. AVAILABILITY: The source MATLAB code are available from http://math.arizona.edu/~hzhang/software.html.

Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile.

Targeted metabolomics provides an approach to quantify metabolites involved in specific molecular pathways. We applied an electrochemistry-based, targeted metabolomics platform to define changes in tryptophan, tyrosine, purine and related pathways in the depressed and remitted phases of major depressive disorder (MDD). Biochemical profiles in the cerebrospinal fluid of unmedicated depressed (n = 14; dMDD) or remitted MDD subjects (n = 14; rMDD) were compared against those in healthy controls (n = 18; HC). The rMDD group showed differences in tryptophan and tyrosine metabolism relative to the other groups. The rMDD group also had higher methionine levels and larger methionine-to-glutathione ratios than the other groups, implicating methylation and oxidative stress pathways. The dMDD sample showed nonsignificant differences in the same direction in several of the metabolic branches assessed. The reductions in metabolites associated with tryptophan and tyrosine pathways in rMDD may relate to the vulnerability this population shows for developing depressive symptoms under tryptophan or catecholamine depletion.

Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses.

BACKGROUND: Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software. RESULTS: Throughout the paper, we provide compelling empirical evidences that: (1) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate within an acceptable level; (2) the MTMIM method can deliver better parameter estimates and power than single trait multiple interval mapping method; (3) an analysis of Drosophila dataset illustrates how the MTMIM method can better extract information from datasets with measurements in multiple traits. CONCLUSIONS: The MTMIM method represents a convenient statistical framework to test hypotheses of pleiotropic QTL versus closely linked nonpleiotropic QTL, QTL by environment interaction, and to estimate the total genotypic variance-covariance matrix between traits and to decompose it in terms of QTL-specific variance-covariance matrices, therefore, providing more details on the genetic architecture of complex traits.

Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment.

UNLABELLED: Statins are widely prescribed for reducing LDL-cholesterol (C) and risk for cardiovascular disease (CVD), but there is considerable variation in therapeutic response. We used a gas chromatography-time-of-flight mass-spectrometry-based metabolomics platform to evaluate global effects of simvastatin on intermediary metabolism. Analyses were conducted in 148 participants in the Cholesterol and Pharmacogenetics study who were profiled pre and six weeks post treatment with 40 mg/day simvastatin: 100 randomly selected from the full range of the LDL-C response distribution and 24 each from the top and bottom 10% of this distribution ("good" and "poor" responders, respectively). The metabolic signature of drug exposure in the full range of responders included essential amino acids, lauric acid (p<0.0055, q<0.055), and alpha-tocopherol (p<0.0003, q<0.017). Using the HumanCyc database and pathway enrichment analysis, we observed that the metabolites of drug exposure were enriched for the pathway class amino acid degradation (p<0.0032). Metabolites whose change correlated with LDL-C lowering response to simvastatin in the full range responders included cystine, urea cycle intermediates, and the dibasic amino acids ornithine, citrulline and lysine. These dibasic amino acids share plasma membrane transporters with arginine, the rate-limiting substrate for nitric oxide synthase (NOS), a critical mediator of cardiovascular health. Baseline metabolic profiles of the good and poor responders were analyzed by orthogonal partial least square discriminant analysis so as to determine the metabolites that best separated the two response groups and could be predictive of LDL-C response. Among these were xanthine, 2-hydroxyvaleric acid, succinic acid, stearic acid, and fructose. Together, the findings from this study indicate that clusters of metabolites involved in multiple pathways not directly connected with cholesterol metabolism may play a role in modulating the response to simvastatin treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT00451828.

Composite interval mapping and multiple interval mapping: procedures and guidelines for using Windows QTL Cartographer.

Tremendous progress has been made in recent years on developing statistical methods for mapping quantitative trait loci (QTL) from crosses of inbred lines. In this chapter, we provide an introduction of composite interval mapping and multiple interval mapping methods for mapping QTL from inbred line crosses and also detailed instructions to perform the analyses in Windows QTL Cartographer. For each method, we discuss the meaning of each option in the analysis procedures and how to understand and interpret the mapping results through a work-out example.

Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation: selective serotonin reuptake inhibitor response pharmacogenomics.

OBJECTIVE: We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a 'scaffolding' of genome-wide association (GWAS) SNP data to broaden and accelerate 'pharmacometabolomics-informed pharmacogenomic' studies by eliminating the need for initial genotyping and by making broader SNP association testing possible. METHODS: We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics 'signal' associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data. RESULTS: Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for '1000 Genomes' (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies. CONCLUSION: These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other 'omics' approaches makes it possible to rapidly and cost efficiently identify SNP markers to 'broaden' and accelerate pharmacogenomic studies.