Quantitative Trait Data Research Articles

FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes.

Field images are important sources of information for research in the natural sciences. However, images that lack photogrammetric scale bars, including most iNaturalist observations, cannot yield accurate trait measurements. We introduce FieldPrism, a novel system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers. Our photogrammetric background templates (FieldSheets) increase the utility of field images by providing machine-readable scale bars and photogrammetric reference points to automatically correct image distortion and calculate a pixel-to-metric conversion ratio. Users can generate a QR code flipbook derived from a specimen identifier naming hierarchy, enabling machine-readable specimen identification for automatic file renaming. We also developed FieldStation, a Raspberry Pi-based mobile imaging apparatus that records images, GPS location, and metadata redundantly on up to four USB storage devices and can be monitored and controlled from any Wi-Fi connected device. FieldPrism is a flexible software tool designed to standardize and improve the utility of images captured in the field. When paired with the optional FieldStation, researchers can create a self-contained mobile imaging apparatus for quantitative trait data collection.

Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field.

Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.

Novel plasma and brain proteins that are implicated in multiple sclerosis.

Understanding how variations in the plasma and brain proteome contribute to multiple sclerosis susceptibility can provide important insights to guide drug repurposing and therapeutic development for the disease. However, the role of genetically predicted protein abundance in multiple sclerosis remains largely unknown. Integrating plasma proteomics (n = 3301) and brain proteomics (n = 376 discovery; n = 152 replication) into multiple sclerosis genome-wide association studies (n = 14 802 cases and 26 703 controls), we employed summary-based methods to identify candidate proteins involved in multiple sclerosis susceptibility. Next, we evaluated associations of the corresponding genes with multiple sclerosis at tissue-level using large gene expression quantitative trait data from whole-blood (n = 31 684) and brain (n = 1194) tissue. Further, to assess transcriptional profiles for candidate proteins at cell-level, we examined gene expression patterns in immune cell types (Dataset 1: n = 73 cases and 97 controls; Dataset 2: n = 31 cases and 31 controls) for identified plasma proteins, and in brain cell types (Dataset 1: n = 4 cases and 5 controls; Dataset 2: n = 5 cases and 3 controls) for identified brain proteins. In a longitudinal multiple sclerosis cohort (n = 203 cases followed up to 15 years), we also assessed the corresponding gene-level associations with the outcome of disability worsening. We identified 39 novel proteins associated with multiple sclerosis risk. Based on five identified plasma proteins, four available corresponding gene candidates showed consistent associations with multiple sclerosis risk in whole-blood, and we found TAPBPL upregulation in multiple sclerosis B cells, CD8+ T cells and natural killer cells compared with controls. Among the 34 candidate brain proteins, 18 were replicated in a smaller cohort and 14 of 21 available corresponding gene candidates also showed consistent associations with multiple sclerosis risk in brain tissue. In cell-specific analysis, six identified brain candidates showed consistent differential gene expression in neuron and oligodendrocyte cell clusters. Based on the 39 protein-coding genes, we found 23 genes that were associated with disability worsening in multiple sclerosis cases. The findings present a set of candidate protein biomarkers for multiple sclerosis, reinforced by high concordance in downstream transcriptomics findings at tissue-level. This study also highlights the heterogeneity of cell-specific transcriptional profiles for the identified proteins and that numerous candidates were also implicated in disease progression. Together, these findings can serve as an important anchor for future studies of disease mechanisms and therapeutic development.

Genome-Wide Association Study of Intracranial Artery Stenosis Followed by Phenome-Wide Association Study.

The genetic background of intracranial artery stenosis (ICAS), a major cause of ischemic stroke, remains elusive. We performed the world's first genome-wide association study (GWAS) of ICAS using DNA samples from Japanese subjects, to identify the genetic factors associated with ICAS and their correlation with clinical features. We also conducted a phenome-wide association study (PheWAS) of the top variant identified via GWAS to determine its association with systemic disease. The GWAS involved 408 patients with ICAS and 349 healthy controls and utilized an Asian Screening Array of venous blood samples. The PheWAS was performed using genotypic and phenotypic data of the Biobank Japan Project, which contained information on 46 diseases and 60 quantitative trait data from > 150,000 Japanese individuals. The GWAS revealed that the East Asian-specific functional variant of RNF213, rs112735431 (c.14429G > A, p.Arg4810Lys), was associated with ICAS (odds ratio, 12.3; 95% CI 5.5 to 27.5; P = 7.8 × 10-10). Stratified analysis within ICAS cases demonstrated that clinical features of those with and without the risk allele were different. PheWAS indicated that high blood pressure and angina were significantly associated with RNF213 rs112735431. The first GWAS of ICAS, which stratifies subpopulations within the ICAS cases with distinct clinical features, revealed that RNF213 rs112735431 was the most significant variant associated with ICAS. Thus, RNF213 rs112735431 shows potential as an important clinical biomarker that characterizes pleiotropic risk in various vascular diseases, such as blood pressure and angina, thereby facilitating personalized medicine for systemic vascular diseases in East Asian populations.

A GWAS top hit for circulating leptin is associated with weight gain but not with leptin protein levels in lithium-augmented patients with major depression

Lithium-treated patients often suffer from weight gain as a common adverse event. In an earlier investigation, we found an impact of two single-nucleotide polymorphisms (rs10487506 and rs2278815) at the leptin gene on weight gain but not on leptin protein levels in serum under lithium augmentation. A recent genome-wide association study identified a polymorphism at the leptin gene locus (rs10487505) associated with circulating leptin protein levels. To characterize potential effects of this variant in acute major depressive disorder, we investigated body mass indices from 180 lithium-augmented patients and serum concentrations of leptin protein from 89 patients using linear mixed model analyses and rs6979832, a proxy SNP of rs10487505. Body mass index was measured before and after 4 weeks of lithium augmentation, in a subsample also after 4 and 7 months. Leptin serum levels were measured before and during lithium augmentation. G-allele homozygotes of rs6979832 had a significantly lower body mass index increase during observation compared to A-allele hetero- and homozygotes. However, we found no influence on leptin serum levels. Joint analyses of rs6979832 with the previously investigated polymorphisms rs10487506 and rs2278815, and expressed quantitative trait data, suggest a complex interplay between SNP alleles at the leptin locus. These results strongly support our earlier findings that common genetic variation at the leptin gene locus may be involved in lithium augmentation-associated weight gain in major depressive disorder.

Genetic Variability and Diversity of the Agro-Morphological Traits of Torch Ginger (Etlingera elatior) Germplasm in Malaysia

Aims: The main objective of this study is to quantify the genetic variability and relationship among the quantitative traits of the torch ginger germplasm collected across Peninsular Malaysia.
 Study design: Initially, the experimental design was randomized complete block design (RCBD) but there were unequal number of replications due to limited planting materials.
 Place and Duration of Study: The study was conducted at the Malaysian Agricultural Research and Development Institute (MARDI) Research Station, Jerangau, Terengganu, Malaysia on latitude 04°57.704’N and longitude 103°11.007’E. Data on agro-morphological characterization were collected for three years from June 2010 until May 2013.
 Methodology: A total of 57 torch ginger accessions were collected from various wild/ cultivated sources from seven states of Peninsular Malaysia namely Terengganu (19), Perak (15), Kedah (7), Johor (6), Pahang (5), Kelantan (3) and Melaka (2). The collection was planted in 2009 and maintained as living collection. At three months old torch ginger plantlets were transplanted into 20 cm × 20 cm × 20 cm holes spaced at 100 cm diameter of culvert. The spacing of culvert between rows and within rows were 2 m respectively. Initially, the experimental design was randomized complete block design (RCBD) but there were unequal number of replications due to limited planting materials. Data on agro-morphological characterization were collected for three years from June 2010 until May 2013. All the 57 accessions were characterized according to descriptors list of genera under Zingiberaceae family with some modifications. A total of 6 qualitative and 16 quantitative descriptors were used. The mean values of each quantitative trait data were computed and subjected to statistical analysis to assess the amount of genetic variation using analysis of variance (ANOVA). The ANOVA was using PROC GLM of SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). Genotypic coefficients of variation and error variance were estimated using PROC VARCOMP method Type I of SAS 9.4 software. Phenotypic coefficients of variation, heritability and genetic advance were calculated. Pearson correlation coefficient was determined using SAS 9.4 for comparing the relationship among the different traits. The morphological traits were analyzed by numerical taxonomic methods via cluster analysis and Principal Component Analysis (PCA) which was done using NTSYS-PC Version 2.1. PCA based on 16 quantitative and six qualitative traits was performed to determine the genetic variation in the torch ginger accessions. The cluster and PCA were performed to reveal clustering and grouping pattern of torch ginger accessions. Euclidean distance coefficients were estimated for all pairs of accessions, which was then used to present cluster analysis.
 Results: Analysis of variance indicated that highly significant variation exists among the 57 accessions for most of the traits. The 57 accessions of torch ginger showed high variability for both qualitative and quantitative traits. Among the quantitative traits, the highest CV was observed from number of spikes with the value of 44.25%. The genetic coefficient of variation for 16 morphological traits ranged from 9.76 to 45.86%. Broad sense heritability estimates varied from low to high. Maximum estimates of broad sense heritability were recorded in bract length (80.16%), bud peduncle length (78.74%), number of leaves (75.66%), number of stems per clump (67.99%), inflorescence peduncle length (67.60%) and bud width (65.11%). The genetic advance (GA) in percent of mean was recorded from 11.82 to 67.97%. Besides that, positive correlation coefficient was obtained between some traits. Generally, the genotypes groups were related with morphological characteristics among the accessions.
 Conclusion: Considering the distribution pattern, vegetative and yield performance of accessions, it is suggested that accessions from cluster VII (KAN022, KAN047 and KAN048) should be selected for future breeding program. These accessions possessed the highest peduncle length that could influence the yield in terms of bud size. Furthermore, the classification and divergence between torch ginger accessions analyzed in this study may assist in conserving plant materials both in-situ and ex-situ.

Scientific floras can be reliable sources for some trait data in a system with poor coverage in global trait databases

AbstractAimTrait‐based approaches are increasingly important in ecology and biogeography, but progress is often hampered by the availability of high‐quality quantitative trait data collected in the field. Alternative sources of trait information include scientific floras and taxonomic monographs. Here we test the reliability and usefulness of trait data acquired from scientific floras against trait values measured in the field, and those in TRY, the most comprehensive global plant trait database.LocationTenerife and La Palma, Canary Islands, Spain.MethodsWe measured leaf area and specific leaf area (SLA) in the field for 451 native vascular plant species and compared them with equivalent trait data digitised from the most recent and comprehensive guide of the Canarian flora, and data sourced from TRY. We regressed the field‐measured traits against their equivalents estimated from the literature and used the regression models from one island to predict the trait values on the other island.ResultsFor leaf area, linear models showed good agreement between values from the scientific flora and those measured in the field (r2 = 0.86). These models were spatially transferable across islands. In contrast, for SLA we found a weak relationship between field‐measured values and the best estimates from the scientific flora (r2 = 0.11). Insufficient data were available in the TRY database for our study area to calculate trait correlations with other data sources.ConclusionsScientific floras can act as useful data sources for quantitative plant trait data for some traits but not others, whilst the TRY database contains many traits, but is incomplete in species coverage for our study region, and oceanic islands in general.

A quantitative trait rare variant nonparametric linkage method with application to age-at-onset of Alzheimer's disease.

To analyze pedigrees with quantitative trait (QT) and sequence data, we developed a rare variant (RV) quantitative nonparametric linkage (QNPL) method, which evaluates sharing of minor alleles. RV-QNPL has greater power than the traditional QNPL that tests for excess sharing of minor and major alleles. RV-QNPL is robust to population substructure and admixture, locus heterogeneity, and inclusion of nonpathogenic variants and can be readily applied outside of coding regions. When QNPL was used to analyze common variants, it often led to loci mapping to large intervals, e.g., >40 Mb. In contrast, when RVs are analyzed, regions are well defined, e.g., a gene. Using simulation studies, we demonstrate that RV-QNPL is substantially more powerful than applying traditional QNPL methods to analyze RVs. RV-QNPL was also applied to analyze age-at-onset (AAO) data for 107 late-onset Alzheimer's disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with whole-genome sequence data. When AAO of AD was analyzed regardless of APOE ε4 status, suggestive linkage (LOD = 2.4) was observed with RVs in KNDC1 and nominally significant linkage (p < 0.05) was observed with RVs in LOAD genes ABCA7 and IQCK. When AAO of AD was analyzed for APOE ε4 positive family members, nominally significant linkage was observed with RVs in APOE, while when AAO of AD was analyzed for APOE ε4 negative family members, nominal significance was observed for IQCK and ADAMTS1. RV-QNPL provides a powerful resource to analyze QTs in families to elucidate their genetic etiology.

Private Genomes and Public SNPs: Homomorphic Encryption of Genotypes and Phenotypes for Shared Quantitative Genetics.

Sharing human genotype and phenotype data is essential to discover otherwise inaccessible genetic associations, but is a challenge because of privacy concerns. Here, we present a method of homomorphic encryption that obscures individuals' genotypes and phenotypes, and is suited to quantitative genetic association analysis. Encrypted ciphertext and unencrypted plaintext are analytically interchangeable. The encryption uses a high-dimensional random linear orthogonal transformation key that leaves the likelihood of quantitative trait data unchanged under a linear model with normally distributed errors. It also preserves linkage disequilibrium between genetic variants and associations between variants and phenotypes. It scrambles relationships between individuals: encrypted genotype dosages closely resemble Gaussian deviates, and can be replaced by quantiles from a Gaussian with negligible effects on accuracy. Likelihood-based inferences are unaffected by orthogonal encryption. These include linear mixed models to control for unequal relatedness between individuals, heritability estimation, and including covariates when testing association. Orthogonal transformations can be applied in a modular fashion for multiparty federated mega-analyses where the parties first agree to share a common set of genotype sites and covariates prior to encryption. Each then privately encrypts and shares their own ciphertext, and analyses all parties' ciphertexts. In the absence of private variants, or knowledge of the key, we show that it is infeasible to decrypt ciphertext using existing brute-force or noise-reduction attacks. We present the method as a challenge to the community to determine its security.

A taxonomic conundrum: Characterizing a cryptic radiation of Asian gracile skinks (Squamata: Scincidae: Riopa) in Myanmar

Recognizing species-level diversity is important for studying evolutionary patterns across biological disciplines and is critical for conservation efforts. However, challenges remain in delimiting species-level diversity, especially in cryptic radiations where species are genetically divergent but show little morphological differentiation. Using multilocus molecular data, phylogenetic analyses, species delimitation analyses, and morphological data, we examine lineage diversification in a cryptic radiation of Riopa skinks in Myanmar. Four species of Riopa skinks are currently recognized from Myanmar based on morphological traits, but the boundaries between three of these species, R. anguina, R. lineolata, and R. popae, are not well-defined. We find high levels of genetic diversity within these three species. Our analyses suggest that they may comprise as many as 12 independently evolving lineages, highlighting the extent to which species diversity in the region is underestimated. However, quantitative trait data suggest that these lineages have not differentiated morphologically, possibly indicating that this cryptic radiation represents non-adaptive evolution, although additional data is needed to corroborate this.

Some statistical consideration in transcriptome-wide association studies.

The methodology of transcriptome-wide association studies (TWAS) has become popular in integrating a reference expression quantitative trait (eQTL) data set with an independent main GWAS data set to identify (putatively) causal genes, shedding mechanistic insights to biological pathways from genetic variants to a GWAS trait mediated by gene expression. Statistically TWAS is a (two-sample) 2-stage least squares (2SLS) method in the framework of instrumental variables analysis for causal inference: in Stage 1 it uses the reference eQTL data to impute a genes expression for the main GWAS data, then in Stage 2 it tests for association between the imputed gene expression and the GWAS trait; if an association is detected in Stage 2, a (putatively) causal relationship between the gene and the GWAS trait is claimed. If a nonlinear model or a generalized linear model (GLM) is fitted in Stage 2 (e.g., for a binary GWAS trait), it is known that using only imputed gene expression, as in standard TWAS, in general does not lead to a consistent (i.e., asymptotically unbiased) estimate for the causal effect; accordingly, a variation of 2SLS, called two-stage residual inclusion (2SRI), has been proposed to yield better estimates (e.g., being consistent under suitable conditions). Our main goal is to investigate whether it is necessary or even better to apply 2SRI, instead of the standard 2SLS. In addition, due to the use of imputed gene expression (i.e., with measurement errors), it is known that in general some correction to the standard error estimate of the causal effect estimate has to be applied, while in the standard TWAS no correction is applied. Is this an issue? We also compare one-sample 2SLS with two-sample 2SLS (i.e., the standard TWAS). We used the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and simulated data mimicking the ADNI data to address the above questions. At the end, we conclude that, in practice with the large sample sizes and small effect sizes of genetic variants, the standard TWAS performs well and is recommended.

Combining Quantitative Data on Growth, Wood Density and Other Traits with SSR Markers to Evaluate Genetic Diversity and Structure in a Planted Population of Eucalyptus camaldulensis Dehn.

Eucalyptus camaldulensis Dehn. is one of the most morphologically and genetically variable Eucalyptus species. Growth, Leptocybe invasa Fisher &amp; La Salle susceptibility, pilodyn penetration and other traits up to age 36 months were assessed in a seed source/family trial in China comprising 112 seedlots representing five natural stand and six exotic seed sources. Genetic diversity and population structure of this trial population were also analyzed using 48 simple sequence repeat (SSR) markers. The key objective was to examine whether the genomic data could provide value over information obtained from just quantitative trait data. Significant genetic variation was found among seed sources and among families within seed sources for most quantitative traits. The ratio of variance among seed sources to variance among families within seed sources, based on variances estimated from quantitative trait data, varied from 0.1% (height at 9 months) up to 75.2% (bark thickness). Equivalent ratios estimated from the AMOVA on SSR loci data were similar for height (ages 24 and 36 months) and also pilodyn penetration at 36 months, but not for 9-month height or 36-month bark thickness. From 48 SSR loci examined, the genetic differentiation coefficient (among seed sources) was 0.086, indicating low genetic differentiation among seed sources. While overall genetic diversity in the trial population examined was high, the levels within the different seed sources varied markedly. Prior to this study, genetic distances among families from the three exotic seed sources (from domesticated Indian populations) in the trial, along with their genetic distances from, and relatedness to, families from five natural stand seed sources (Australian) in the trial were unknown. The SSR loci data removed uncertainties and revealed that the exotic sources increased the breadth of genetic origins represented in the trial population—information that could not have been obtained from just the quantitative trait data.

Bayesian placement of fossils on phylogenies using quantitative morphometric data.

Jointly developing a comprehensive tree of life from living and fossil taxa has long been a fundamental goal in evolutionary biology. One major challenge has stemmed from difficulties in merging evidence from extant and extinct organisms. While these efforts have resulted in varying stages of synthesis, they have been hindered by their dependence on qualitative descriptions of morphology. Though rarely applied to phylogenetic inference, traditional and geometric morphometric data can improve these issues by generating more rigorous ways to quantify variation in morphological structures. They may also facilitate the rapid and objective aggregation of large morphological datasets. I describe a new Bayesian method that leverages quantitative trait data to reconstruct the positions of fossil taxa on fixed reference trees composed of extant taxa. Unlike most formulations of phylogenetic Brownian motion models, this method expresses branch lengths in units of morphological disparity, suggesting a new framework through which to construct Bayesian node calibration priors for molecular dating and explore comparative patterns in morphological disparity. I am hopeful that the approach described here will help to facilitate a deeper integration of neo- and paleontological data to move morphological phylogenetics further into the genomic era.

An Efficient Workflow for Collecting, Entering, and Proofing Field Data: Harnessing Voice Recording and Dictation Software

An Efficient Workflow for Collecting, Entering, and Proofing Field Data: Harnessing Voice Recording and Dictation Software

IDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHR.

A wide range of patient health data is recorded in Electronic Health Records (EHR). This data includes diagnosis, surgical procedures, clinical laboratory measurements, and medication information. Together this information reflects the patient's medical history. Many studies have efficiently used this data from the EHR to find associations that are clinically relevant, either by utilizing International Classification of Diseases, version 9 (ICD-9) codes or laboratory measurements, or by designing phenotype algorithms to extract case and control status with accuracy from the EHR. Here we developed a strategy to utilize longitudinal quantitative trait data from the EHR at Geisinger Health System focusing on outpatient metabolic and complete blood panel data as a starting point. Comprehensive Metabolic Panel (CMP) as well as Complete Blood Counts (CBC) are parts of routine care and provide a comprehensive picture from high level screening of patients' overall health and disease. We randomly split our data into two datasets to allow for discovery and replication. We first conducted a genome-wide association study (GWAS) with median values of 25 different clinical laboratory measurements to identify variants from Human Omni Express Exome beadchip data that are associated with these measurements. We identified 687 variants that associated and replicated with the tested clinical measurements at p<5×10-08. Since longitudinal data from the EHR provides a record of a patient's medical history, we utilized this information to further investigate the ICD-9 codes that might be associated with differences in variability of the measurements in the longitudinal dataset. We identified low and high variance patients by looking at changes within their individual longitudinal EHR laboratory results for each of the 25 clinical lab values (thus creating 50 groups - a high variance and a low variance for each lab variable). We then performed a PheWAS analysis with ICD-9 diagnosis codes, separately in the high variance group and the low variance group for each lab variable. We found 717 PheWAS associations that replicated at a p-value less than 0.001. Next, we evaluated the results of this study by comparing the association results between the high and low variance groups. For example, we found 39 SNPs (in multiple genes) associated with ICD-9 250.01 (Type-I diabetes) in patients with high variance of plasma glucose levels, but not in patients with low variance in plasma glucose levels. Another example is the association of 4 SNPs in UMOD with chronic kidney disease in patients with high variance for aspartate aminotransferase (discovery p-value: 8.71×10-09 and replication p-value: 2.03×10-06). In general, we see a pattern of many more statistically significant associations from patients with high variance in the quantitative lab variables, in comparison with the low variance group across all of the 25 laboratory measurements. This study is one of the first of its kind to utilize quantitative trait variance from longitudinal laboratory data to find associations among genetic variants and clinical phenotypes obtained from an EHR, integrating laboratory values and diagnosis codes to understand the genetic complexities of common diseases.

Characterization of chickpea germplasm conserved in the Indian National Genebank and development of a core set using qualitative and quantitative trait data

Chickpea is the third most important pulse crop as a source of dietary protein. Ever-increasing demand in Asian countries calls for breeding superior desi-type varieties, in turn necessitating the availability of characterized germplasm to breeders. The Indian National Genebank, located at the National Bureau of Plant Genetic Resources, New Delhi, conserves 14,651 accessions of chickpea. The entire set was characterized in a single large-scale experiment. High variation was observed for eight quantitative and 12 qualitative agro-morphological traits. Allelic richness procedure was employed to assemble a core set comprising 1103 accessions, 70.0% of which were of Indian origin. Comparable values of total variation explained by the first three principal components in the entire collection (51.1%) and the core (52.4%) together with conservation of nine pairwise r values among quantitative traits in the core collection and a coincidence rate around 99.7% indicated that the chickpea core was indeed an excellent representation of the entire chickpea collection in the National Genebank. The chickpea core exhibited greater diversity than the entire collection in agro-morphological traits, as assessed by higher variance and Shannon–Weaver diversity indices, indicating that the chickpea core maximized the phenotypic diversity available in the Indian chickpea germplasm. The chickpea core, comprising mainly indigenous desi genotypes, is expected to be an excellent resource for chickpea breeders. Information on the chickpea core can be accessed at http://www.nbpgr.ernet.in/pgrportal.

Common Variants Confer Susceptibility to Barrett's Esophagus: Insights from the First Genome-Wide Association Studies.

Eight loci have been identified by the two genome-wide association studies of Barrett's esophagus that have been conducted to date. Esophageal adenocarcinoma cases were included in the second study following evidence that predisposing genetic variants for this cancer overlap with those for Barrett's esophagus. Genes with roles in embryonic development of the foregut are adjacent to 6 of the loci identified (FOXF1, BARX1, FOXP1, GDF7, TBX5, and ALDH1A2). An additional locus maps to a gene with known oncogenic potential (CREB-regulated transcription coactivator 1), but expression quantitative trait data implicates yet another gene involved in esophageal development (PBX4). These results strongly support a model whereby dysregulation of genes involved in esophageal and thoracic development increases susceptibility to Barrett's esophagus and esophageal adenocarcinoma, probably by reducing anatomical antireflux mechanisms. An additional signal at 6p21 in the major histocompatibility complex also reinforces evidence that immune and inflammatory response to reflux is involved in the development of both diseases. All of the variants identified are intronic or intergenic rather than coding and are presumed to be or to mark regulatory variants. As with genome-wide association studies of other diseases, the functional variants at each locus are yet to be identified and the genes affected need confirming. In this chapter as well as discussing the biology behind each genome-wide association signal, we review the requirements for successfully conducting genome-wide association studies and discuss how progress in understanding the genetic variants that contribute to Barrett's esophagus/esophageal adenocarcinoma susceptibility compares to other cancers.

Evaluating the Calibration and Power of Three Gene-Based Association Tests of Rare Variants for the X Chromosome.

Although genome-wide association studies (GWAS) have identified thousands of trait-associated genetic variants, there are relatively few findings on the X chromosome. For analysis of low-frequency variants (minor allele frequency <5%), investigators can use region- or gene-based tests where multiple variants are analyzed jointly to increase power. To date, there are no gene-based tests designed for association testing of low-frequency variants on the X chromosome. Here we propose three gene-based tests for the X chromosome: burden, sequence kernel association test (SKAT), and optimal unified SKAT (SKAT-O). Using simulated case-control and quantitative trait (QT) data, we evaluate the calibration and power of these tests as a function of (1) male:female sample size ratio; and (2) coding of haploid male genotypes for variants under X-inactivation. For case-control studies, all three tests are reasonably well-calibrated for all scenarios we evaluated. As expected, power for gene-based tests depends on the underlying genetic architecture of the genomic region analyzed. Studies with more (haploid) males are generally less powerful due to decreased number of chromosomes. Power generally is slightly greater when the coding scheme for male genotypes matches the true underlying model, but the power loss for misspecifying the (generally unknown) model is small. For QT studies, type I error and power results largely mirror those for binary traits. We demonstrate the use of these three gene-based tests for X-chromosome association analysis in simulated data and sequencing data from the Genetics of Type 2 Diabetes (GoT2D) study.

Evolutionary rates across gradients

Summary A key question in the fields of macroecology and evolution is how rates of evolution vary across gradients, be they ecological (e.g. temperature, rainfall, net primary productivity), geographic (e.g. latitude, elevation), morphological (e.g. body mass), etc. Evolutionary rates across gradients (evorag 2.0) is a new software package provided as open source in the r language (and available from CRAN) that tests whether rates of trait evolution vary continuously across such continuous gradients. The approach uses quantitative trait data for a series of sister‐pair contrasts (i.e. sister species or other types of sister taxa) and applies Brownian Motion and Ornstein Uhlenbeck models in which parameter (evolutionary rate and constraint) values vary as a function of discrete variables (e.g. male vs. female) and/or continuous variables (e.g. latitude, temperature, body size). We used simulation to test performance of the models in EvoRAG. Our gradient models accurately estimate parameter values, have very low levels of bias, and low rates of model misspecification. The modelling framework developed here provides great flexibility in designing models that test how rates of evolution vary across gradients. We provide an example where both discrete (songbird vs. suboscine) and continuous (latitude) effects on evolutionary rate in avian song were simultaneously estimated.

PhenoMan: phenotypic data exploration, selection, management and quality control for association studies of rare and common variants

Next-generation sequencing and other high-throughput technology advances have promoted great interest in detecting associations between complex traits and genetic variants. Phenotype selection, quality control (QC) and control of confounders are crucial and can have a great impact on the ability to detect associations. Although there are programs to perform association analyses, e.g. PLINK and GenABEL, they cannot be used for comprehensive management and QC of phenotype data. To address this need PhenoMan was developed: to select individuals based on multiple phenotype criteria or population membership; control for missing covariate data; remove related individuals, duplicate samples and individuals with incorrect sex specification; recode primary traits and covariates; transform data; remove or winsorize outliers; select covariates for analysis; and create residuals. To ensure consistency and harmonization between analyses, a report is generated for every dataset. Summary statistics are also provided in graphical or text format. PhenoMan can be used for selection and manipulation of quantitative, disease and control data. Phenoman is freeware that provides approaches for efficient exploration and management of phenotype data. Proper QC of phenotypes before proceeding to the association analysis is critical to ensure control of type I and II errors, reliable effect estimates and consistent results between studies. PhenoMan is highly beneficial for the preparation of qualitative and quantitative trait data for association studies using new datasets as well as those obtained from public repositories. code.google.com/p/phenoman