scholarly journals Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility

2021 ◽  
Author(s):  
Jennifer Zou ◽  
Shyam Gopalakrishnan ◽  
Clarissa C. Parker ◽  
Jerome Nicod ◽  
Richard Mott ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Winner's Curse ◽  
Mineral Density ◽  
Winner’S Curse ◽  
Discovery Sample ◽  
Genome Wide ◽  
Two Factors ◽  
Causal Variants ◽  
Mapping Complex Traits

ABSTRACTCombining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3,076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6 and GM11545 with bone mineral density, and Psmb9 with weight. However replication at a nominal threshold of 0.05 between the two component studies was surprisingly low, with less than a third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner’s Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Available methods to control Winner’s Curse were contingent on the power of the discovery sample, and depending on the method used, both overestimated and underestimated the true effect. Leveraging these observations we integrated information about replication rates, confounding, and Winner’s Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility, and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.

scholarly journals Analysis of independent cohorts of outbred CFW mice reveals novel loci for behavioral and physiological traits and identifies factors determining reproducibility

2021 ◽  
Author(s):  
Jennifer Zou ◽  
Shyam Gopalakrishnan ◽  
Clarissa C Parker ◽  
Jerome Nicod ◽  
Richard Mott ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Winner's Curse ◽  
Mineral Density ◽  
Winner’S Curse ◽  
Discovery Sample ◽  
Genome Wide ◽  
Two Factors ◽  
Causal Variants ◽  
Mapping Complex Traits

Abstract Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3,076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6 and GM11545 with bone mineral density, and Psmb9 with weight. However replication at a nominal threshold of 0.05 between the two component studies was low, with less than a third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner’s Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Leveraging these observations we integrated information about replication rates, study-specific heterogeneity, and Winner’s Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility, and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.

scholarly journals A Pilot Genome-Wide Association Study in Postmenopausal Mexican-Mestizo Women Implicates the RMND1/CCDC170 Locus Is Associated with Bone Mineral Density

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Marisela Villalobos-Comparán ◽  
Rogelio F. Jiménez-Ortega ◽  
Karol Estrada ◽  
Alma Y. Parra-Torres ◽  
Anahí González-Mercado ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Postmenopausal Women ◽  
Bone Mineral ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Mineral Density ◽  
Mexican Mestizo ◽  
Discovery Sample ◽  
Genome Wide ◽  
Study Population

To identify genetic variants influencing bone mineral density (BMD) in the Mexican-Mestizo population, we performed a GWAS for femoral neck (FN) and lumbar spine (LS) in Mexican-Mestizo postmenopausal women. In the discovery sample, 300,000 SNPs were genotyped in a cohort of 411 postmenopausal women and seven SNPs were analyzed in the replication cohort (n=420). The combined results of a meta-analysis from the discovery and replication samples identified two loci, RMND1 (rs6904364, P=2.77×10−4) and CCDC170 (rs17081341, P=1.62×10−5), associated with FN BMD. We also compared our results with those of the Genetic Factors for Osteoporosis (GEFOS) Consortium meta-analysis. The comparison revealed two loci previously reported in the GEFOS meta-analysis: SOX6 (rs7128738) and PKDCC (rs11887431) associated with FN and LS BMD, respectively, in our study population. Interestingly, rs17081341 rare in Caucasians (minor allele frequency < 0.03) was found in high frequency in our population, which suggests that this association could be specific to non-Caucasian populations. In conclusion, the first pilot Mexican GWA study of BMD confirmed previously identified loci and also demonstrated the importance of studying variability in diverse populations and/or specific populations.

scholarly journals A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS

Biostatistics ◽  
2017 ◽  
Vol 18 (3) ◽  
pp. 477-494 ◽  
Author(s):  
Jakub Pecanka ◽  
Marianne A. Jonker ◽  
Zoltan Bochdanovits ◽  
Aad W. Van Der Vaart ◽  
Keyword(s):  
Complex Traits ◽  
Multiple Testing ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Score Test ◽  
Interaction Model ◽  
Type I ◽  
Two Stage ◽  
Genome Wide ◽  
Strong Control

Summary For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the “missing heritability” of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson’s disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.

scholarly journals Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alvaro N. Barbeira ◽  
◽  
Rodrigo Bonazzola ◽  
Eric R. Gamazon ◽  
Yanyu Liang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Data Driven ◽  
Functional Interpretation ◽  
Transcriptome Regulation ◽  
Genome Wide ◽  
Causal Genes ◽  
Dose Dependent ◽  
Single Approach

AbstractThe resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Dinesh K. Saini ◽  
Yuvraj Chopra ◽  
Jagmohan Singh ◽  
Karansher S. Sandhu ◽  
Anand Kumar ◽  
...  
Keyword(s):  
Complex Traits ◽  
Comprehensive Evaluation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Mapping Complex Traits

scholarly journals Genome-wide association study identifies 48 common genetic variants associated with handedness

2019 ◽  
Author(s):  
Gabriel Cuellar Partida ◽  
Joyce Y Tung ◽  
Nicholas Eriksson ◽  
Eva Albrecht ◽  
Fazil Aliev ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Variants ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Left Handedness ◽  
Left Handed ◽  
Genome Wide ◽  
Common Genetic Variants

AbstractHandedness, a consistent asymmetry in skill or use of the hands, has been studied extensively because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and 32 studies from the International Handedness Consortium, we conducted the world’s largest genome-wide association study of handedness (1,534,836 right-handed, 194,198 (11.0%) left-handed and 37,637 (2.1%) ambidextrous individuals). We found 41 genetic loci associated with left-handedness and seven associated with ambidexterity at genome-wide levels of significance (P < 5×10−8). Tissue enrichment analysis implicated the central nervous system and brain tissues including the hippocampus and cerebrum in the etiology of left-handedness. Pathways including regulation of microtubules, neurogenesis, axonogenesis and hippocampus morphology were also highlighted. We found suggestive positive genetic correlations between being left-handed and some neuropsychiatric traits including schizophrenia and bipolar disorder. SNP heritability analyses indicated that additive genetic effects of genotyped variants explained 5.9% (95% CI = 5.8% – 6.0%) of the underlying liability of being left-handed, while the narrow sense heritability was estimated at 12% (95% CI = 7.2% – 17.7%). Further, we show that genetic correlation between left-handedness and ambidexterity is low (rg = 0.26; 95% CI = 0.08 – 0.43) implying that these traits are largely influenced by different genetic mechanisms. In conclusion, our findings suggest that handedness, like many other complex traits is highly polygenic, and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders that has been observed in multiple observational studies.

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