Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

"Forward genetics" as a method to maximize power and cost-efficiency in studies of human complex traits.

ABSTRACT: There is increasing interest in methods to disentangle the relationship between genotype and (endo)phenotypes in human complex traits. We present a population-based method of increasing the power and cost-efficiency of studies by selecting random individuals with a particular genotype and then assessing the accompanying quantitative phenotypes. Using statistical derivations, power- and cost graphs we show that such a "forward genetics" approach can lead to a marked reduction in sample size and costs. This approach is particularly apt for implementing in epidemiological studies for which DNA is already available but the phenotyping costs are high.

SUBMITTER: Boks MP

PROVIDER: S-EPMC2886904 | biostudies-other | 2010 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

"Forward genetics" as a method to maximize power and cost-efficiency in studies of human complex traits.

Boks M P M MP Derks E M EM Dolan C V CV Kahn R S RS Ophoff R A RA

Behavior genetics 20100316 4

There is increasing interest in methods to disentangle the relationship between genotype and (endo)phenotypes in human complex traits. We present a population-based method of increasing the power and cost-efficiency of studies by selecting random individuals with a particular genotype and then assessing the accompanying quantitative phenotypes. Using statistical derivations, power- and cost graphs we show that such a "forward genetics" approach can lead to a marked reduction in sample size and c ...[more]

PMID: 20232132

Similar Datasets

A modified forward multiple regression in high-density genome-wide association studies for complex traits.

Project description:Genome-wide association studies (GWAS) have been widely used to identify genetic effects on complex diseases or traits. Most currently used methods are based on separate single-nucleotide polymorphism (SNP) analyses. Because this approach requires correction for multiple testing to avoid excessive false-positive results, it suffers from reduced power to detect weak genetic effects under limited sample size. To increase the power to detect multiple weak genetic factors and reduce false-positive results caused by multiple tests and dependence among test statistics, a modified forward multiple regression (MFMR) approach is proposed. Simulation studies show that MFMR has higher power than the Bonferroni and false discovery rate procedures for detecting moderate and weak genetic effects, and MFMR retains an acceptable-false positive rate even if causal SNPs are correlated with many SNPs due to population stratification or other unknown reasons.

| S-EPMC2732748 | biostudies-literature

Genetics of complex traits in psychiatry.

Project description:Virtually all psychiatric traits are genetically complex. This article discusses the genetics of complex traits in psychiatry. The complexity is accounted for by numerous factors, including multiple risk alleles, epistasis, and epigenetic effects such as methylation. Risk alleles can individually be common or rare, and can include, for example, single nucleotide polymorphisms and copy number variants that are transmitted or are new mutations, and other kinds of variation. Many different kinds of variation can be important for trait risk, either together in various proportions or as different factors in different subjects. Until more recently, approaches to complex traits were limited, and consequently only a few variants, usually of individually minor effect, were identified. At the present time, a much richer armamentarium exists that includes the routine application of genome-wide association studies and next-generation high-throughput sequencing and the combination of this information with other biologically relevant information, such as expression data. We have also seen the emergence of large meta-analysis and mega-analysis consortia. These developments are extremely important for psychiatric genetics, have advanced the field substantially, and promise formidable gains in the years to come as they are applied more widely.

| S-EPMC4282183 | biostudies-literature

Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies.

Project description:Evolve and resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and nonselected populations using next-generation sequencing (NGS), the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations has been examined in previous works. However, the influence of the selection regime, that is the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a genome-wide association study, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.

| S-EPMC6878953 | biostudies-literature

Systems genetics of complex traits in Drosophila melanogaster.

Project description:Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically intercorrelated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression and transcription factor binding sites. The high degree of transcriptional connectivity allows us to infer genetic networks and the function of predicted genes from annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provide insight into the molecular basis of pleiotropy between complex traits.

| S-EPMC2752214 | biostudies-literature

Reverse-genetics studies of lncRNAs-what we have learnt and paths forward.

Project description:Long non-coding RNAs (lncRNAs) represent a major fraction of the transcriptome in multicellular organisms. Although a handful of well-studied lncRNAs are broadly recognized as biologically meaningful, the fraction of such transcripts out of the entire collection of lncRNAs remains a subject of vigorous debate. Here we review the evidence for and against biological functionalities of lncRNAs and attempt to arrive at potential modes of lncRNA functionality that would reconcile the contradictory conclusions. Finally, we discuss different strategies of phenotypic analyses that could be used to investigate such modes of lncRNA functionality.

| S-EPMC7155256 | biostudies-literature

A simple, cost-effective, and robust method for rRNA depletion in RNA-sequencing studies

Project description:These experiments were designed to quantify depletion of rRNA sequencing reads from bacterial RNA-seq libraries and verify that mRNA sequencing reads were not altered. Specifically, we tested an rRNA depletion method using custom-designed biotinylated oligonucleotides and compared these results to undepleted (total RNA) libraries and libraries made with the previously-available Ribo-Zero kit (Illumina).

2019-12-28 | GSE142656 | GEO

The genetics of feed conversion efficiency traits in a commercial broiler line.

Project description:Individual feed conversion efficiency (FCE) is a major trait that influences the usage of energy resources and the ecological footprint of livestock production. The underlying biological processes of FCE are complex and are influenced by factors as diverse as climate, feed properties, gut microbiota, and individual genetic predisposition. To gain an insight to the genetic relationships with FCE traits and to contribute to the improvement of FCE in commercial chicken lines, a genome-wide association study was conducted using a commercial broiler population (n = 859) tested for FCE and weight traits during the finisher period from 39 to 46 days of age. Both single-marker (generalized linear model) and multi-marker (Bayesian approach) analyses were applied to the dataset to detect genes associated with the variability in FCE. The separate analyses revealed 22 quantitative trait loci (QTL) regions on 13 different chromosomes; the integration of both approaches resulted in 7 overlapping QTL regions. The analyses pointed to acylglycerol kinase (AGK) and general transcription factor 2-I (GTF2I) as positional and functional candidate genes. Non-synonymous polymorphisms of both candidate genes revealed evidence for a functional importance of these genes by influencing different biological aspects of FCE.

| S-EPMC4639841 | biostudies-literature

Multi-ethnic studies in complex traits.

Project description:The successes of genome-wide association (GWA) studies have mainly come from studies performed in populations of European descent. Since complex traits are characterized by marked genetic heterogeneity, the findings so far may provide an incomplete picture of the genetic architecture of complex traits. However, the recent GWA studies performed on East Asian populations now allow us to globally assess the heterogeneity of association signals between populations of European ancestry and East Asians, and the possible obstacles for multi-ethnic GWA studies. We focused on four different traits that represent a broad range of complex phenotypes, which have been studied in both Europeans and East Asians: type 2 diabetes, systemic lupus erythematosus, ulcerative colitis and height. For each trait, we observed that most of the risk loci identified in East Asians were shared with Europeans. However, we also observed that a significant part of the association signals at these shared loci seems to be independent between populations. This suggests that disease aetiology is common between populations, but that risk variants are often population specific. These variants could be truly population specific and result from natural selection, genetic drift and recent mutations, or they could be spurious, caused by the limitations of the method of analysis employed in the GWA studies. We therefore propose a three-stage framework for multi-ethnic GWA analyses, starting with the commonly used single-nucleotide polymorphism-based analysis, and followed by a gene-based approach and a pathway-based analysis, which will take into account the heterogeneity of association between populations at different levels.

| S-EPMC3179384 | biostudies-literature

Soybean fast neutron genotyping: 700k Forward genetics

Project description:CGH was used to compare induced structural variation among soybean fast neutron lines, developed in background M92-220.

2011-08-02 | GSE31038 | GEO

Contribution of large region joint associations to complex traits genetics.

Project description:A polygenic model of inheritance, whereby hundreds or thousands of weakly associated variants contribute to a trait's heritability, has been proposed to underlie the genetic architecture of complex traits. However, relatively few genetic variants have been positively identified so far and they collectively explain only a small fraction of the predicted heritability. We hypothesized that joint association of multiple weakly associated variants over large chromosomal regions contributes to complex traits variance. Confirmation of such regional associations can help identify new loci and lead to a better understanding of known ones. To test this hypothesis, we first characterized the ability of commonly used genetic association models to identify large region joint associations. Through theoretical derivation and simulation, we showed that multivariate linear models where multiple SNPs are included as independent predictors have the most favorable association profile. Based on these results, we tested for large region association with height in 3,740 European participants from the Health and Retirement Study (HRS) study. Adjusting for SNPs with known association with height, we demonstrated clustering of weak associations (p = 2x10-4) in regions extending up to 433.0 Kb from known height loci. The contribution of regional associations to phenotypic variance was estimated at 0.172 (95% CI 0.063-0.279; p < 0.001), which compared favorably to 0.129 explained by known height variants. Conversely, we showed that suggestively associated regions are enriched for known height loci. To extend our findings to other traits, we also tested BMI, HDLc and CRP for large region associations, with consistent results for CRP. Our results demonstrate the presence of large region joint associations and suggest these can be used to pinpoint weakly associated SNPs.

| S-EPMC4391841 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data