scholarly journals Genetic association studies in female reproduction: from candidate-gene approaches to genome-wide mapping

2013 ◽  
Vol 19 (10) ◽  
pp. 644-654 ◽  
Author(s):  
Han Zhao ◽  
Zi-Jiang Chen
Keyword(s):  
Candidate Gene ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Female Reproduction ◽  
Genome Wide

scholarly journals Evaluation of genome-wide power of genetic association studies based on empirical data from the HapMap project

2007 ◽  
Vol 16 (20) ◽  
pp. 2494-2505 ◽  
Author(s):  
Yasuhito Nannya ◽  
Kenjiro Taura ◽  
Mineo Kurokawa ◽  
Shigeru Chiba ◽  
Seishi Ogawa
Keyword(s):  
Genetic Association ◽  
Empirical Data ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Hapmap Project ◽  
Genome Wide

Efficient estimation of disease odds ratios for follow-up genetic association studies

2017 ◽  
Vol 28 (7) ◽  
pp. 1927-1941
Author(s):  
Jiyuan Hu ◽  
Wei Zhang ◽  
Xinmin Li ◽  
Dongdong Pan ◽  
Qizhai Li
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Efficient Estimation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Odds Ratios ◽  
Genome Wide ◽  
Follow Up Studies

In the past decade, genome-wide association studies have identified thousands of susceptible variants associated with complex human diseases and traits. Conducting follow-up genetic association studies has become a standard approach to validate the findings of genome-wide association studies. One problem of high interest in genetic association studies is to accurately estimate the strength of the association, which is often quantified by odds ratios in case-control studies. However, estimating the association directly by follow-up studies is inefficient since this approach ignores information from the genome-wide association studies. In this article, an estimator called GFcom, which integrates information from genome-wide association studies and follow-up studies, is proposed. The estimator includes both the point estimate and corresponding confidence interval. GFcom is more efficient than competing estimators regarding MSE and the length of confidence intervals. The superiority of GFcom is particularly evident when the genome-wide association study suffers from severe selection bias. Comprehensive simulation studies and applications to three real follow-up studies demonstrate the performance of the proposed estimator. An R package, “GFcom”, implementing our method is publicly available at https://github.com/JiyuanHu/GFcom .

scholarly journals Genome-Wide Control of Population Structure and Relatedness in Genetic Association Studies via Linear Mixed Models with Orthogonally Partitioned Structure

2018 ◽  
Author(s):  
Matthew P. Conomos ◽  
Alex P. Reiner ◽  
Mary Sara McPeek ◽  
Timothy A. Thornton
Keyword(s):  
Population Structure ◽  
Genetic Association ◽  
Mixed Models ◽  
Association Studies ◽  
Linear Mixed Models ◽  
Genetic Association Studies ◽  
European Ancestry ◽  
Type I ◽  
Genome Wide ◽  
Wbc Count

AbstractLinear mixed models (LMMs) have become the standard approach for genetic association testing in the presence of sample structure. However, the performance of LMMs has primarily been evaluated in relatively homogeneous populations of European ancestry, despite many of the recent genetic association studies including samples from worldwide populations with diverse ancestries. In this paper, we demonstrate that existing LMM methods can have systematic miscalibration of association test statistics genome-wide in samples with heterogenous ancestry, resulting in both increased type-I error rates and a loss of power. Furthermore, we show that this miscalibration arises due to varying allele frequency differences across the genome among populations. To overcome this problem, we developed LMM-OPS, an LMM approach which orthogonally partitions diverse genetic structure into two components: distant population structure and recent genetic relatedness. In simulation studies with real and simulated genotype data, we demonstrate that LMM-OPS is appropriately calibrated in the presence of ancestry heterogeneity and outperforms existing LMM approaches, including EMMAX, GCTA, and GEMMA. We conduct a GWAS of white blood cell (WBC) count in an admixed sample of 3,551 Hispanic/Latino American women from the Women’s Health Initiative SNP Health Association Resource where LMM-OPS detects genome-wide significant associations with corresponding p-values that are one or more orders of magnitude smaller than those from competing LMM methods. We also identify a genome-wide significant association with regulatory variant rs2814778 in the DARC gene on chromosome 1, which generalizes to Hispanic/Latino Americans a previous association with reduced WBC count identified in African Americans.

scholarly journals Mortality Selection in a Genetic Sample and Implications for Association Studies

2016 ◽  
Author(s):  
Benjamin W. Domingue ◽  
Daniel W. Belsky ◽  
Amal Harrati ◽  
Dalton Conley ◽  
David Weir ◽  
...  
Keyword(s):  
Data Collection ◽  
Birth Cohort ◽  
Genetic Association ◽  
Selection Process ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genomic Data ◽  
Mortality Selection ◽  
Snp Data ◽  
Genome Wide

AbstractMortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection—the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection—has been largely ignored. Here we test the hypothesis that such mortality selection may significantly alter estimates in polygenetic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide SNP data collection at older ages using the U.S.-based Health and Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the genetic sample of the HRS. Mortality selection leads to modest drift in estimating time-varying genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection. There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how genetic association studies using HRS data can adjust for mortality selection from study entry to time of genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in genetically-informed samples from other cohort studies.

scholarly journals A genome-wide analysis of population structure in the Finnish Saami with implications for genetic association studies

2010 ◽  
Vol 19 (3) ◽  
pp. 347-352 ◽  
Author(s):  
Jeroen R Huyghe ◽  
Erik Fransen ◽  
Samuli Hannula ◽  
Lut Van Laer ◽  
Els Van Eyken ◽  
...  
Keyword(s):  
Population Structure ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Analysis ◽  
Genome Wide ◽  
A Genome

The emergent phenomenon of aspirin resistance: insights from genetic association studies

2020 ◽  
Vol 21 (2) ◽  
pp. 125-140
Author(s):  
Márcia Ferreira ◽  
Margarida Freitas-Silva ◽  
Joana Assis ◽  
Ricardo Pinto ◽  
José P Nunes ◽  
...  
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Aspirin Resistance ◽  
Interindividual Variation ◽  
Antiplatelet Drug ◽  
Genome Wide ◽  
Complex Process ◽  
Clinical Benefits ◽  
Candidate Gene Studies

Despite the clinical benefits of aspirin, the interindividual variation in response to this antiplatelet drug is considerable. The manifestation of aspirin resistance (AR) is frequently observed, although this complex process remains poorly understood. While AR etiology is likely to be multifactorial, genetic factors appear to be preponderant. According to several genetic association studies, both genome-wide and candidate gene studies, numerous SNPs in cyclooxygenase, thromboxane and platelet receptors-related genes have been identified as capable of negatively affecting aspirin action. Thus, it is essential to understand the clinical relevance of AR-related SNPs as potential predictive and prognostic biomarkers as they may be essential to defining the AR phenotype.

scholarly journals A robust example of collider bias in a genetic association study

2015 ◽  
Author(s):  
Felix Day ◽  
Robert Scott ◽  
Ken Ong ◽  
John Perry
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Positive Association ◽  
Uk Biobank ◽  
False Positive Association ◽  
Genome Wide ◽  
The Uk ◽  
Study Inclusion ◽  
Collider Bias

Recent studies have described the potential for ″collider bias″ to modify the magnitude of genotype-phenotype associations, however the extent to which this effect can induce a completely false-positive association remains unclear. In a sample of 142,630 individuals from the UK Biobank study, inclusion of height (a ″collider″) as a covariate induces biologically spurious, but genome-wide significant, associations between autosomal genetic variants and sex. These associations are non-significant in models unadjusted for height. Our study underpins the importance of causal inference modeling in the design and interpretation of genetic (and non-genetic) association studies.

scholarly journals Evolutionary Triangulation to Refine Genetic Association Studies of Spontaneous Preterm Birth

2017 ◽  
Vol 34 (11) ◽  
pp. 1041-1047
Author(s):  
Minjun Huang ◽  
Louis Muglia ◽  
Scott Williams ◽  
Tracy Manuck
Keyword(s):  
Preterm Birth ◽  
Genetic Association ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Gene List ◽  
Genetic Association Studies ◽  
Spontaneous Preterm Birth ◽  
Genetic Population ◽  
Single Nucleotide ◽  
Genome Wide

Objective The objective of this study was to apply evolutionary triangulation, a novel technique exploiting evolutionary differentiation among three populations with variable disease prevalence, to spontaneous preterm birth (PTB) genetic association studies. Study Design Single nucleotide polymorphism (SNP) allele frequency data were obtained from HapMap for CEU, GIH/MEX, and YRI/ASW populations. Evolutionary triangulation SNPs, then genes, were selected according to the overlaps of genetic population differences (CEU = outlier). Evolutionary triangulation genes were then compared with three PTB gene lists: (1) top maternal and fetal genes from a large genome-wide association study of PTB, (2) 640 genes from the database for PTB, and (3) 118 genes from a recent systematic review. Empirical p-values were calculated to determine whether evolutionary triangulation enriched for putative PTB associating genes compared with randomly selected sample genes. Results Evolutionary triangulation identified 5/17 maternal genes and 8/16 fetal genes from PTB gene list 1. From list 2, 79/640 were identified by CEU_GIH_YRI evolutionary triangulation, and 57/640 were identified by CEU_ASW_MEX evolutionary triangulation. Finally, 20/118 genes were identified by evolutionary triangulation from gene list 3. For all analyses, p < 0.001 except CEU_ASW_MEX analysis of list 3 where p = 0.002. Conclusion Genes identified in prior PTB association studies confirmed by evolutionary triangulation should be prioritized for further genetic prematurity research.

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