scholarly journals Genome-wide associations for udder and teat conformational risk factors for mastitis in Holstein cows

2019 ◽  
Author(s):  
Asha M. Miles ◽  
Christian Posbergh ◽  
Heather Jay Huson
Keyword(s):  
Cell Proliferation ◽  
Multiple Testing ◽  
Association Studies ◽  
Principal Component ◽  
Special Focus ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Genome Wide ◽  
Length Width ◽  
Mastitis Susceptibility

Abstract BACKGROUND The objective of our study was to conduct high-density genome-wide association studies of dairy cow udder and teat conformation with direct phenotyping. We identified and compared quantitative trait loci ( QTL ) for a novel composite mastitis risk trait and considered environmental impact of milking by comparing primiparous cows only. Cows (N = 471) were genotyped on the Illumina BovineHD 777K beadchip and scored for front and rear teat length, width, end shape, and placement, fore udder attachment, udder cleft, udder depth, rear udder height, and rear udder width. Principal component analysis was performed on fore udder attachment, rear teat end shape, rear teat width, and rear udder height, to create a single new phenotype describing mastitis susceptibility based on these high-risk traits.RESULTS Over all 14 traits of interest, a total of 56 genome-wide associations were performed and 28 significantly associated (Bonferroni multiple testing correction < 0.05) QTL were identified. The linkage disequilibrium ( LD ) block surrounding the associated QTL or a 1 Mb window in the absence of LD was interrogated for candidate genes, resulting in the identification of genes with functions related to both cell proliferation and immune signaling, including ZNF683, DHX9, CUX1, TNNT1 , and SPRY1 . We assessed a primiparous only subset of cows (n = 144) to account for the possibility that the genetic variance component of the phenotype is greater for cows who have had less exposure to the environment, and observed different associated QTL and inheritance patterns for udder depth in primiparous cows compared to the total cohort.CONCLUSION Special focus was given to the aforementioned mastitis risk traits, and candidate gene investigation revealed both immune function and cell proliferation related genes in the areas surrounding significantly associated QTL, suggesting that selecting for mastitis resistant cows based on these traits would be an effective method for increasing mastitis resiliency in a herd.

scholarly journals Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

2018 ◽  
Author(s):  
David M. Howard ◽  
Mark J. Adams ◽  
Toni-Kim Clarke ◽  
Jonathan D. Hafferty ◽  
Jude Gibson ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Drug Repositioning ◽  
Association Studies ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Synaptic Structure ◽  
Genome Wide

AbstractMajor depression is a debilitating psychiatric illness that is typically associated with low mood, anhedonia and a range of comorbidities. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximise sample size, we meta-analysed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 gene-sets associated with depression, including both genes and gene-pathways associated with synaptic structure and neurotransmission. Further evidence of the importance of prefrontal brain regions in depression was provided by an enrichment analysis. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant following multiple testing correction. Based on the putative genes associated with depression this work also highlights several potential drug repositioning opportunities. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding aetiology and developing new treatment approaches.

scholarly journals networkGWAS: A network-based approach for genome-wide association studies in structured populations

2021 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O'Bray ◽  
Laetitia Meng-Papaxanthos ◽  
Juliane Klatt ◽  
Karsten Borgwardt
Keyword(s):  
Genetic Markers ◽  
Complex Traits ◽  
Multiple Testing ◽  
Association Studies ◽  
Search Space ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Genome Wide

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose <tt>networkGWAS</tt>, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. <tt>networkGWAS</tt> successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

scholarly journals Novel bioinformatics approach to investigate quantitative phenotype-genotype associations in neuroimaging studies

2015 ◽  
Author(s):  
Sejal Patel ◽  
Min Tae M Park ◽  
Mallar M Chakravarty ◽  
Jo Knight
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Genetic Research ◽  
Hippocampal Volume ◽  
Imaging Genetics ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Novel Approach ◽  
Genome Wide ◽  
Novel Method

Imaging genetics is an emerging field in which the association between genes and neuroimaging-based quantitative phenotypes are used to explore the functional role of genes in neuroanatomy and neurophysiology in the context of healthy function and neuropsychiatric disorders. The main obstacle for researchers in the field is the high dimensionality of the data in both the imaging phenotypes and the genetic variants commonly typed. In this article, we develop a novel method that utilizes Gene Ontology, an online database, to select and prioritize certain genes, employing a stratified false discovery rate (sFDR) approach to investigate their associations with imaging phenotypes. sFDR has the potential to increase power in genome wide association studies (GWAS), and is quickly gaining traction as a method for multiple testing correction. Our novel approach addresses both the pressing need in genetic research to move beyond candidate gene studies, while not being overburdened with a loss of power due to multiple testing. As an example of our methodology, we perform a GWAS of hippocampal volume using the Alzheimer's Disease Neuroimaging Initiative sample.

scholarly journals An approach to gene-based testing accounting for dependence of tests among nearby genes

2021 ◽  
Author(s):  
Ronald J Yurko ◽  
Kathryn Roeder ◽  
Bernie Devlin ◽  
Max G'Sell
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Autism Spectrum ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Strongly Correlated ◽  
Test Statistics ◽  
Test Statistic ◽  
Genome Wide ◽  
Insight Into

In genome-wide association studies (GWAS), it has become commonplace to test millions of SNPs for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive p-value thresholding (AdaPT), guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

scholarly journals Maximizing the Power of Principal-Component Analysis of Correlated Phenotypes in Genome-wide Association Studies

2014 ◽  
Vol 94 (5) ◽  
pp. 662-676 ◽  
Author(s):  
Hugues Aschard ◽  
Bjarni J. Vilhjálmsson ◽  
Nicolas Greliche ◽  
Pierre-Emmanuel Morange ◽  
David-Alexandre Trégouët ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Association Studies ◽  
Principal Component ◽  
Component Analysis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals The harmonic mean p-value for combining dependent tests

2019 ◽  
Vol 116 (4) ◽  
pp. 1195-1200 ◽  
Author(s):  
Daniel J. Wilson
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Scientific Discovery ◽  
Association Studies ◽  
Harmonic Mean ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Familywise Error Rate ◽  
Significance Threshold ◽  
Genome Wide

Analysis of “big data” frequently involves statistical comparison of millions of competing hypotheses to discover hidden processes underlying observed patterns of data, for example, in the search for genetic determinants of disease in genome-wide association studies (GWAS). Controlling the familywise error rate (FWER) is considered the strongest protection against false positives but makes it difficult to reach the multiple testing-corrected significance threshold. Here, I introduce the harmonic mean p-value (HMP), which controls the FWER while greatly improving statistical power by combining dependent tests using generalized central limit theorem. I show that the HMP effortlessly combines information to detect statistically significant signals among groups of individually nonsignificant hypotheses in examples of a human GWAS for neuroticism and a joint human–pathogen GWAS for hepatitis C viral load. The HMP simultaneously tests all ways to group hypotheses, allowing the smallest groups of hypotheses that retain significance to be sought. The power of the HMP to detect significant hypothesis groups is greater than the power of the Benjamini–Hochberg procedure to detect significant hypotheses, although the latter only controls the weaker false discovery rate (FDR). The HMP has broad implications for the analysis of large datasets, because it enhances the potential for scientific discovery.

scholarly journals Genetic polymorphisms associated with sleep-related phenotypes; relationships with individual nocturnal symptoms of insomnia in the HUNT study

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniela Bragantini ◽  
Børge Sivertsen ◽  
Philip Gehrman ◽  
Stian Lydersen ◽  
Ismail Cüneyt Güzey
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Sleep Onset ◽  
Early Morning ◽  
Genome Wide Association Studies ◽  
False Discovery ◽  
Genome Wide ◽  
Hunt Study ◽  
Logistic Regressions ◽  
Insomnia Symptoms

Abstract Background In recent years, several GWAS (genome wide association studies) of sleep-related traits have identified a number of SNPs (single nucleotides polymorphism) but their relationships with symptoms of insomnia are not known. The aim of this study was to investigate whether SNPs, previously reported in association with sleep-related phenotypes, are associated with individual symptoms of insomnia. Methods We selected participants from the HUNT study (Norway) who reported at least one symptom of insomnia consisting of sleep onset, maintenance or early morning awakening difficulties, (cases, N = 2563) compared to participants who presented no symptoms at all (controls, N = 3665). Cases were further divided in seven subgroups according to different combinations of these three symptoms. We used multinomial logistic regressions to test the association among different patterns of symptoms and 59 SNPs identified in past GWAS studies. Results Although 16 SNPS were significantly associated (p < 0.05) with at least one symptom subgroup, none of the investigated SNPs remained significant after correction for multiple testing using the false discovery rate (FDR) method. Conclusions SNPs associated with sleep-related traits do not replicate on any pattern of insomnia symptoms after multiple tests correction. However, correction in this case may be overly conservative.

scholarly journals Mixture model-based association analysis with case-control data in genome wide association studies

2017 ◽  
Vol 16 (3) ◽  
Author(s):  
Fadhaa Ali ◽  
Jian Zhang
Keyword(s):  
Mixture Model ◽  
Multiple Testing ◽  
Hypothesis Test ◽  
Association Studies ◽  
Real Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Model Based ◽  
Genome Wide ◽  
The Individual

AbstractMultilocus haplotype analysis of candidate variants with genome wide association studies (GWAS) data may provide evidence of association with disease, even when the individual loci themselves do not. Unfortunately, when a large number of candidate variants are investigated, identifying risk haplotypes can be very difficult. To meet the challenge, a number of approaches have been put forward in recent years. However, most of them are not directly linked to the disease-penetrances of haplotypes and thus may not be efficient. To fill this gap, we propose a mixture model-based approach for detecting risk haplotypes. Under the mixture model, haplotypes are clustered directly according to their estimated disease penetrances. A theoretical justification of the above model is provided. Furthermore, we introduce a hypothesis test for haplotype inheritance patterns which underpin this model. The performance of the proposed approach is evaluated by simulations and real data analysis. The results show that the proposed approach outperforms an existing multiple testing method.

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