Dataset Information

To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

ABSTRACT: Based on case studies, we discuss the extent to which genome-wide association studies (GWAS) are affected by outlier plants, i.e. those deviating from the expected distribution on a multi-criteria basis. Using a raw dataset consisting of daily measurements of leaf area, biomass, and plant height for thousands of plants, we tested three different cleaning methods for their effects on genetic analyses. No-cleaning resulted in the highest number of dubious quantitative trait loci, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false-positives (with no-cleaning and/or a low threshold for minor allele frequency) and the risk of missing interesting rare alleles. Cleaning can lower the risk of the latter by making it possible to choose a higher threshold in GWAS.

SUBMITTER: Alvarez Prado S

PROVIDER: S-EPMC6685653 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

Alvarez Prado Santiago S Sanchez Isabelle I Cabrera-Bosquet Llorenç L Grau Antonin A Welcker Claude C Tardieu François F Hilgert Nadine N

Journal of experimental botany 20190801 15

Based on case studies, we discuss the extent to which genome-wide association studies (GWAS) are affected by outlier plants, i.e. those deviating from the expected distribution on a multi-criteria basis. Using a raw dataset consisting of daily measurements of leaf area, biomass, and plant height for thousands of plants, we tested three different cleaning methods for their effects on genetic analyses. No-cleaning resulted in the highest number of dubious quantitative trait loci, especially at loc ...[more]

PMID: 31020325

Similar Datasets

Project description:BackgroundMeta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes.MethodologyA new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target.Conclusions/significanceTaken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).

Project description:The rapid evolution of non-native species can facilitate invasion success, but recent reviews indicate that such microevolution rarely yields expansion of the climatic niche in the introduced habitats. However, because some invasions originate from a geographically restricted portion of the native species range and its climatic niche, it is possible that the frequency, direction, and magnitude of phenotypic evolution during invasion have been underestimated. We explored the utility of niche shift analyses in the red seaweed Gracilaria vermiculophylla, which expanded its range from the northeastern coastline of Japan to North America, Europe, and northwestern Africa within the last 100 years. A genetically informed climatic niche shift analysis indicates that native source populations occur in colder and highly seasonal habitats, while most non-native populations typically occur in warmer, less seasonal habitats. This climatic niche expansion predicts that non-native populations evolved greater tolerance for elevated heat conditions relative to native source populations. We assayed 935 field-collected and 325 common-garden thalli from 40 locations, and as predicted, non-native populations had greater tolerance for ecologically relevant extreme heat (40°C) than did Japanese source populations. Non-native populations also had greater tolerance for cold and low-salinity stresses relative to source populations. The importance of local adaptation to warm temperatures during invasion was reinforced by evolution of parallel clines: Populations from warmer, lower-latitude estuaries had greater heat tolerance than did populations from colder, higher-latitude estuaries in both Japan and eastern North America. We conclude that rapid evolution plays an important role in facilitating the invasion success of this and perhaps other non-native marine species. Genetically informed ecological niche analyses readily generate clear predictions of phenotypic shifts during invasions and may help to resolve debate over the frequency of niche conservatism versus rapid adaptation during invasion.

Dataset Information

To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

Publications

To clean or not to clean phenotypic datasets for outlier plants in genetic analyses?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets