scholarly journals The HapMap Resource is Providing New Insights into Ourselves and its Application to Pharmacogenomics

2008 ◽  
Vol 2 ◽  
pp. BBI.S455 ◽  
Author(s):  
Wei Zhang ◽  
Mark J. Ratain ◽  
M. Eileen Dolan
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Drug Response ◽  
Human Populations ◽  
International Hapmap Project ◽  
Hapmap Project ◽  
Phenotypic Data ◽  
Human Lymphoblastoid Cell ◽  
Human Lymphoblastoid Cell Lines ◽  
Japanese Ancestry

The exploration of quantitative variation in complex traits such as gene expression and drug response in human populations has become one of the major priorities for medical genetics. The International HapMap Project provides a key resource of genotypic data on human lymphoblastoid cell lines derived from four major world populations of European, African, Chinese and Japanese ancestry for researchers to associate with various phenotypic data to find genes affecting health, disease and response to drugs. Recent progress in dissecting genetic contribution to natural variation in gene expression within and among human populations and variation in drug response are two examples in which researchers have utilized the HapMap resource. The HapMap Project provides new insights into the human genome and has applicability to pharmacogenomics studies leading to personalized medicine.

scholarly journals Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: evidence for “polygenic epistasis”

2019 ◽  
Author(s):  
Christoph D. Rau ◽  
Natalia M. Gonzales ◽  
Joshua S. Bloom ◽  
Danny Park ◽  
Julien Ayroles ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Statistical Tests ◽  
Inbred Mice ◽  
Statistical Test ◽  
Genetic Ancestry ◽  
Human Populations ◽  
Epistatic Interactions ◽  
Variable Effect ◽  
The Impact

AbstractBackgroundThe majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.ResultsWe applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.ConclusionsUnlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.Author SummaryMany statistical tests which link genetic markers in the genome to differences in traits rely on the assumption that the same polymorphism will have identical effects in different individuals. However, there is substantial evidence indicating that this is not the case. Epistasis is the phenomenon in which multiple polymorphisms interact with one another to amplify or negate each other’s effects on a trait. We hypothesized that individual SNP effects could be changed in a polygenic manner, such that the proportion of as genetic ancestry, rather than specific markers, might be used to capture epistatic interactions. Motivated by this possibility, we develop a new statistical test that allowed us to examine the genome to identify polymorphisms which have different effects depending on the ancestral makeup of each individual. We use our test in two different populations of inbred mice and a yeast panel and demonstrate that these sorts of variable effect polymorphisms exist in 14 different physical traits in mice and 38 phenotypes in yeast as well as in murine gene expression. We use the term “polygenic epistasis” to distinguish these interactions from the more conventional two- or multi-locus interactions.

scholarly journals Replicated effects of sex and genotype on gene expression in human lymphoblastoid cell lines

2006 ◽  
Vol 16 (4) ◽  
pp. 364-373 ◽  
Author(s):  
Allan F. McRae ◽  
Nicholas A. Matigian ◽  
Lata Vadlamudi ◽  
John C. Mulley ◽  
Bryan Mowry ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Lymphoblastoid Cell Lines ◽  
Human Lymphoblastoid Cell ◽  
Human Lymphoblastoid Cell Lines

scholarly journals Genome-wide transcriptomic variations of human lymphoblastoid cell lines: insights from pairwise gene-expression correlations

2012 ◽  
Vol 13 (16) ◽  
pp. 1893-1904 ◽  
Author(s):  
Marc Vincent ◽  
Keren Oved ◽  
Ayelet Morag ◽  
Metsada Pasmanik-Chor ◽  
Varda Oron-Karni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Lymphoblastoid Cell Lines ◽  
Human Lymphoblastoid Cell ◽  
Genome Wide ◽  
Human Lymphoblastoid Cell Lines

scholarly journals iTOP: Inferring the Topology of Omics Data

2018 ◽  
Author(s):  
Nanne Aben ◽  
Johan A. Westerhuis ◽  
Yipeng Song ◽  
Henk A.L. Kiers ◽  
Magali Michaut ◽  
...  
Keyword(s):  
Gene Expression ◽  
Binary Data ◽  
Drug Response ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Omics Data ◽  
Reconstruction Algorithms ◽  
Phenotypic Data ◽  
Rv Coefficient

AbstractMotivationIn biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets.ResultsWe present iTOP, a methodology to infera topology of relationships between datasets. We base this methodology on the RV coefficient, a measure of matrix correlation, which can be used to determine how much information is shared between two datasets. We extended the RV coefficient for partial matrix correlations, which allows the use of graph reconstruction algorithms, such as the PC algorithm, to infer the topologies. In addition, since multi-omics data often contain binary data (e.g. mutations), we also extended the RV coefficient for binary data. Applying iTOP to pharmacogenomics data, we found that gene expression acts as a mediator between most other datasets and drug response: only proteomics clearly shares information with drug response that is not present in gene expression. Based on this result, we used TANDEM, a method for drug response prediction, to identify which variables predictive of drug response were distinct to either gene expression or proteomics.AvailabilityAn implementation of our methodology is available in the R package iTOP on CRAN. Additionally, an R Markdown document with code to reproduce all figures is provided as Supplementary [email protected] and [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Are Genetic Interactions Influencing Gene Expression Evidence for Biological Epistasis or Statistical Artifacts?

2015 ◽  
Author(s):  
Alexandra E. Fish ◽  
John A. Capra ◽  
William S. Bush
Keyword(s):  
Gene Expression ◽  
Complex Disease ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Confounding Factors ◽  
Human Lymphoblastoid Cell ◽  
Statistical Interactions ◽  
Expression Evidence ◽  
Genetically Determined ◽  
Human Lymphoblastoid Cell Lines

AbstractThe importance of epistasis – or statistical interactions between genetic variants – to the development of complex disease in humans has long been controversial. Genome-wide association studies of statistical interactions influencing human traits have recently become computationally feasible and have identified many putative interactions. However, several factors that are difficult to address confound the statistical models used to detect interactions and make it unclear whether statistical interactions are evidence for true molecular epistasis. In this study, we investigate whether there is evidence for epistasis regulating gene expression after accounting for technical, statistical, and biological confounding factors that affect interaction studies. We identified 1,119 (FDR=5%) interactions within cis-regulatory regions that regulate gene expression in human lymphoblastoid cell lines, a tightly controlled, largely genetically determined phenotype. Approximately half of these interactions replicated in an independent dataset (363 of 803 tested). We then performed an exhaustive analysis of both known and novel confounders, including ceiling/floor effects, missing genotype combinations, haplotype effects, single variants tagged through linkage disequilibrium, and population stratification. Every replicated interaction could be explained by at least one of these confounders, and replication in independent datasets did not protect against this issue. Assuming the confounding factors provide a more parsimonious explanation for each interaction, we find it unlikely that cis-regulatory interactions contribute strongly to human gene expression. As this calls into question the relevance of interactions for other human phenotypes, the analytic framework used here will be useful for protecting future studies of epistasis against confounding.

scholarly journals Transcriptional similarity in couples reveals the impact of shared environment and lifestyle on gene regulation through modified cytosines

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2123 ◽  
Author(s):  
Ke Tang ◽  
Wei Zhang
Keyword(s):  
Gene Expression ◽  
Environmental Factors ◽  
Complex Traits ◽  
African Ancestry ◽  
Gene Expression Pattern ◽  
Regulatory Elements ◽  
Living Environment ◽  
Shared Environment ◽  
International Hapmap Project ◽  
The Impact

Gene expression is a complex and quantitative trait that is influenced by both genetic and non-genetic regulators including environmental factors. Evaluating the contribution of environment to gene expression regulation and identifying which genes are more likely to be influenced by environmental factors are important for understanding human complex traits. We hypothesize that by living together as couples, there can be commonly co-regulated genes that may reflect the shared living environment (e.g., diet, indoor air pollutants, behavioral lifestyle). The lymphoblastoid cell lines (LCLs) derived from unrelated couples of African ancestry (YRI, Yoruba people from Ibadan, Nigeria) from the International HapMap Project provided a unique model for us to characterize gene expression pattern in couples by comparing gene expression levels between husbands and wives. Strikingly, 778 genes were found to show much smaller variances in couples than random pairs of individuals at a false discovery rate (FDR) of 5%. Since genetic variation between unrelated family members in a general population is expected to be the same assuming a random-mating society, non-genetic factors (e.g., epigenetic systems) are more likely to be the mediators for the observed transcriptional similarity in couples. We thus evaluated the contribution of modified cytosines to those genes showing transcriptional similarity in couples as well as the relationships these CpG sites with other gene regulatory elements, such as transcription factor binding sites (TFBS). Our findings suggested that transcriptional similarity in couples likely reflected shared common environment partially mediated through cytosine modifications.

scholarly journals Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels

2021 ◽  
Author(s):  
Amanda J Lea ◽  
Julie Peng ◽  
Julien J Ayroles
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
Context Dependency ◽  
Human Populations ◽  
Evolutionary Mechanisms ◽  
Expression Levels ◽  
Gxe Interactions ◽  
Context Dependent ◽  
Gene Expression Levels

There is increasing appreciation that human complex traits are determined by poorly understood interactions between our genomes and daily environments. These "genotype x environment" (GxE) interactions remain difficult to map at the organismal level, but can be uncovered using molecular phenotypes. To do so at large-scale, we profiled transcriptomes across 12 cellular environments using 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression across environments and revealed a context-dependent genetic architecture: the average heritability of gene expression levels increased in treatment relative to control conditions and, on average, each treatment revealed expression quantitative trait loci (eQTL) at 11% of genes. In total, 22% of all eQTL were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses revealed that positive selection has shaped GxE loci involved in responding to immune challenges and hormones, but not man-made chemicals, suggesting there is reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting GxE interactions, and provides new insight into the evolutionary mechanisms that maintain GxE loci in human populations.

scholarly journals Genetic variants modulate gene expression statin response in human lymphoblastoid cell lines

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Elizabeth Theusch ◽  
Yii-Der I. Chen ◽  
Jerome I. Rotter ◽  
Ronald M. Krauss ◽  
Marisa W. Medina
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Genetic Variants ◽  
Lymphoblastoid Cell Lines ◽  
Human Lymphoblastoid Cell ◽  
Human Lymphoblastoid Cell Lines ◽  
Statin Response
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