scholarly journals Fast and flexible linear mixed models for genome-wide genetics

2018 ◽  
Author(s):  
Daniel E Runcie ◽  
Lorin Crawford
Keyword(s):  
Genetic Variation ◽  
Quantitative Genetics ◽  
Mixed Models ◽  
Genetic Variance ◽  
Linear Mixed Models ◽  
Additive Genetic Variance ◽  
Multiple Sources ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Genome Wide

AbstractLinear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.Author summaryThe goal of quantitative genetics is to characterize the relationship between genetic variation and variation in quantitative traits such as height, productivity, or disease susceptibility. A statistical method known as the linear mixed effect model has been critical to the development of quantitative genetics. First applied to animal breeding, this model now forms the basis of a wide-range of modern genomic analyses including genome-wide associations, polygenic modeling, and genomic prediction. The same model is also widely used in ecology, evolutionary genetics, social sciences, and many other fields. Mixed models are frequently multi-faceted, which is necessary for accurately modeling data that is generated from complex experimental designs. However, most genomic applications use only the simplest form of linear mixed methods because the computational demands for model fitting can be too great. We develop a flexible approach for fitting linear mixed models to genome scale data that greatly reduces their computational burden and provides flexibility for users to choose the best statistical paradigm for their data analysis. We demonstrate improved accuracy for genetic association tests, increased power to discover causal genetic variants, and the ability to provide accurate summaries of model uncertainty using both simulated and real data examples.

scholarly journals Peak Expiratory Flow During Mechanical Insufflation-Exsufflation: Endotracheal Tube Versus Facemask

2021 ◽  
Author(s):  
Sung Eun Hyun ◽  
Sang-Min Lee ◽  
Hyung-Ik Shin
Keyword(s):  
Pressure Gradient ◽  
Endotracheal Tube ◽  
Peak Expiratory Flow ◽  
Mixed Models ◽  
Associated Factors ◽  
Linear Mixed Models ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Expiratory Flow ◽  
Tube Versus

Abstract Background: Mechanical insufflation-exsufflation (MI-E) applied through the endotracheal tube (ET) can effectively eliminate airway secretions in intubated patients. However, the effect of the interface (ET vs. facemask) on expiratory airflow generated by MI-E has not been investigated. This study aimed to investigate the effect of the ET on peak expiratory flow (PEF), along with other associated factors that could influence PEF generated by MI-E. Methods: Intubated participants received two sessions of MI-E via ET therapy per day for two consecutive days. One MI-E session consisted of five sets of either constant (+40/-40 cmH2O) or incremental (+30/-30 to +50/-50 cmH2O) pressure applications. Following extubation, MI-E sessions were repeated using facemask. Expiratory airflow during MI-E therapy was measured, and repetitive PEF measurements during each session were analysed using linear mixed-effect and generalised linear mixed models. Results: A total of 12 participants (9 [75.0%] men; mean [SD] age, 74.0 [10.2] years) completed all MI-E sessions with both ET and facemask interfaces. The PEF generated during MI-E treatment was influenced by the pressure gradient, number of session repetitions, and interface (ET vs. facemask). Adjusted mean PEF values for MI-E via ET and facemask at +40/-40 cmH2O were -2.521 and -3.114 L/s, respectively, and -2.956 and -3.364 L/s at +50/-50 cmH2O, respectively. At a pressure gradient of +40/-40 cmH2O, only 172 of 528 MI-E trials via ET (32.6%) achieved a PEF faster than -2.7 L/s, whereas 304 of 343 MI-E trials via facemask (88.6%) exceeded the PEF cut-off value.Conclusions: MI-E via ET generated slower PEF than via facemask, suggesting that a higher-pressure protocol should be prescribed for intubated patients. An insufflation-exsufflation pressure of at least +50/-50 cmH2O should be considered to produce a PEF faster than 2.7 L/s, and the applications were safe and feasible for patients under invasive mechanically ventilation.

scholarly journals How does parental environment influence the potential for adaptation to global change?

2018 ◽  
Vol 285 (1886) ◽  
pp. 20181374 ◽  
Author(s):  
Evatt Chirgwin ◽  
Dustin J. Marshall ◽  
Carla M. Sgrò ◽  
Keyne Monro
Keyword(s):  
Genetic Variation ◽  
Global Change ◽  
Adaptive Capacity ◽  
Genetic Variance ◽  
Life Stage ◽  
Additive Genetic Variance ◽  
Parental Exposure ◽  
Future Warming ◽  
The Mean ◽  
Negative Impacts

Parental environments are regularly shown to alter the mean fitness of offspring, but their impacts on the genetic variation for fitness, which predicts adaptive capacity and is also measured on offspring, are unclear. Consequently, how parental environments mediate adaptation to environmental stressors, like those accompanying global change, is largely unknown. Here, using an ecologically important marine tubeworm in a quantitative-genetic breeding design, we tested how parental exposure to projected ocean warming alters the mean survival, and genetic variation for survival, of offspring during their most vulnerable life stage under current and projected temperatures. Offspring survival was higher when parent and offspring temperatures matched. Across offspring temperatures, parental exposure to warming altered the distribution of additive genetic variance for survival, making it covary across current and projected temperatures in a way that may aid adaptation to future warming. Parental exposure to warming also amplified nonadditive genetic variance for survival, suggesting that compatibilities between parental genomes may grow increasingly important under future warming. Our study shows that parental environments potentially have broader-ranging effects on adaptive capacity than currently appreciated, not only mitigating the negative impacts of global change but also reshaping the raw fuel for evolutionary responses to it.

scholarly journals A Genome-Wide Association Analysis Reveals Epistatic Cancellation of Additive Genetic Variance for Root Length in Arabidopsis thaliana

2014 ◽  
Author(s):  
Jennifer Lachowiec ◽  
Xia Shen ◽  
Christine Queitsch ◽  
Örjan Carlborg
Keyword(s):  
Arabidopsis Thaliana ◽  
Candidate Genes ◽  
Root Length ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Broad Sense ◽  
Genome Wide Association ◽  
Broad Sense Heritability ◽  
Narrow Sense ◽  
Genome Wide

Efforts to identify loci underlying complex traits generally assume that most genetic variance is additive. Here, we examined the genetics of Arabidopsis thaliana root length and found that the narrow-sense heritability for this trait was statistically zero. This low additive genetic variance likely explains why no associations to root length could be found using standard additive-model-based genome-wide association (GWA) approaches. However, the broad-sense heritability for root length was significantly larger, and we therefore also performed an epistatic GWA analysis to map loci contributing to the epistatic genetic variance. This analysis revealed four interacting pairs involving seven chromosomal loci that passed a standard multiple-testing corrected significance threshold. Explorations of the genotype-phenotype maps for these pairs revealed that the detected epistasis cancelled out the additive genetic variance, explaining why these loci were not detected in the additive GWA analysis. Small population sizes, such as in our experiment, increase the risk of identifying false epistatic interactions due to testing for associations with very large numbers of multi-marker genotypes in few phenotyped individuals. Therefore, we estimated the false-positive risk using a new statistical approach that suggested half of the associated pairs to be true positive associations. Our experimental evaluation of candidate genes within the seven associated loci suggests that this estimate is conservative; we identified functional candidate genes that affected root development in four loci that were part of three of the pairs. In summary, statistical epistatic analyses were found to be indispensable for confirming known, and identifying several new, functional candidate genes for root length using a population of wild-collected A. thaliana accessions. We also illustrated how epistatic cancellation of the additive genetic variance resulted in an insignificant narrow-sense, but significant broad-sense heritability that could be dissected into the contributions of several individual loci using a combination of careful statistical epistatic analyses and functional genetic experiments.

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 Autosomal and X-Linked Additive Genetic Variation for Lifespan and Aging: Comparisons Within and Between the Sexes in Drosophila melanogaster

2016 ◽  
Vol 6 (12) ◽  
pp. 3903-3911 ◽  
Author(s):  
Robert M Griffin ◽  
Holger Schielzeth ◽  
Urban Friberg
Keyword(s):  
Drosophila Melanogaster ◽  
Genetic Variation ◽  
X Chromosome ◽  
Dosage Compensation ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Conclusive Evidence ◽  
Substitution Lines ◽  
Chromosome Substitution Lines ◽  
Sexually Antagonistic Selection

Abstract Theory makes several predictions concerning differences in genetic variation between the X chromosome and the autosomes due to male X hemizygosity. The X chromosome should: (i) typically show relatively less standing genetic variation than the autosomes, (ii) exhibit more variation in males compared to females because of dosage compensation, and (iii) potentially be enriched with sex-specific genetic variation. Here, we address each of these predictions for lifespan and aging in Drosophila melanogaster. To achieve unbiased estimates of X and autosomal additive genetic variance, we use 80 chromosome substitution lines; 40 for the X chromosome and 40 combining the two major autosomes, which we assay for sex-specific and cross-sex genetic (co)variation. We find significant X and autosomal additive genetic variance for both traits in both sexes (with reservation for X-linked variation of aging in females), but no conclusive evidence for depletion of X-linked variation (measured through females). Males display more X-linked variation for lifespan than females, but it is unclear if this is due to dosage compensation since also autosomal variation is larger in males. Finally, our results suggest that the X chromosome is enriched for sex-specific genetic variation in lifespan but results were less conclusive for aging overall. Collectively, these results suggest that the X chromosome has reduced capacity to respond to sexually concordant selection on lifespan from standing genetic variation, while its ability to respond to sexually antagonistic selection may be augmented.

scholarly journals Genetic variation in varying environments

1981 ◽  
Vol 37 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Trudy F. C. Mackay
Keyword(s):  
Body Weight ◽  
Genetic Variation ◽  
Spatial Heterogeneity ◽  
Temporal Variation ◽  
Genetic Variance ◽  
Environmental Variability ◽  
Additive Genetic Variance ◽  
Environmental Variation ◽  
Heterogeneous Environments ◽  
Varying Environments

SUMMARYIn order to assess the relationship between genetic and environmental variability, a large natural population of Drosophila melanogaster was replicated as eight subpopulations, which were subjected to four different patterns of environmental variation. The environmental variable imposed was presence of 15% ethanol in the culture medium. Experimental treatments of the populations were intended to simulate constant environmental conditions, spatial heterogeneity in the environment, and two patterns of temporal environmental variation with different periodicity (long- and short-term temporal variation). Additive genetic and phenotypic variation in sternopleural and abdominal chaeta number, and body weight, were estimated in two successive years, and measurements were taken of the genotype–environment correlation of body weight and sternopleural bristle score with medium type.Additive genetic variance of sternopleural chaeta number and of body weight was significantly greater in the three populations experiencing environmental heterogeneity than in the control population, but additive genetic variance of abdominal bristle score was not clearly affected by exposing populations to varying environments. Temporal environmental variation was equally, if not more, efficient in promoting the maintenance of genetic variation than spatial heterogeneity, but the cycle length of the temporal variation was of no consequence. Specific genotype–environment interactions were not present, therefore adaptation to heterogeneous environments is by selection of heterozygosity per se, rather than by differential survival of genotypes in the alternate niches.

scholarly journals Paper Birch and European White Birch Vary in Growth and Resistance to Bronze Birch Borer

1991 ◽  
Vol 116 (3) ◽  
pp. 580-584 ◽  
Author(s):  
Raymond O. Miller ◽  
Paul D. Bloese ◽  
James W. Hanover ◽  
Robert A. Haack
Keyword(s):  
Genetic Variation ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Species Variation ◽  
Betula Papyrifera ◽  
Narrow Sense ◽  
White Birch ◽  
Northeastern North America ◽  
Bronze Birch Borer ◽  
Paper Birch

A test of Michigan half-sib progeny of paper birch (Betula papyrifera Marsh.) and European white birch (B. pendula Roth.) was conducted in Michigan to examine species variation in growth, bark color, and resistance to bronze birch borer (Agrilus anxius Gory). Paper birch was superior to European white birch in height and borer resistance at age 12 years from seed. Families of paper birch were identified that grew exceptionally well, had developed white bark within 6 years, and exhibited borer resistance. The magnitude of additive genetic variance and narrow-sense family heritability estimates for paper birch indicated that sufficient genetic variation and inheritance exist to support selection and breeding for height. Paper birch may be an acceptable substitute for European white birch as a landscape species in northeastern North America.

scholarly journals THE GENETIC STRUCTURE OF NATURAL POPULATIONS OF DROSOPHILA MELANOGASTER. XVIII. CLINAL AND UNIFORM GENETIC VARIATION OVER POPULATIONS

Genetics ◽  
1984 ◽  
Vol 108 (3) ◽  
pp. 617-632
Author(s):  
Shinichi Kusakabe ◽  
Terumi Mukai
Keyword(s):  
Genetic Variation ◽  
Population Size ◽  
Mutation Rate ◽  
Genetic Variance ◽  
Balancing Selection ◽  
Additive Genetic Variance ◽  
Southern Population ◽  
Effective Population ◽  
Northern Population ◽  
The Difference

ABSTRACT It has been reported in the previous papers of this series that in the eastern United States and Japan there is a north-to-south cline of additive genetic variance of viability and that the amount of the additive genetic variance in the northern population can be explained by mutation-selection balance. To determine whether or not the difference in the genetic variation in northern and southern populations can be explained by the differences in mutation rate and/or effective population size, numerical calculations were made using population genetic parameters. In addition, the average heterozygosities of the northern and southern populations at ten of 19 polymorphic structural loci surveyed were estimated in relation to the cline of additive genetic variance of viability, and the following findings were obtained. (1) The changes in mutation rate and population size cannot simultaneously explain the difference in additive genetic variance and inbreeding decline between the northern and southern populations. Thus, the operation of some kind of balancing selection, most likely diversifying selection, was suggested to explain the observed excess of additive genetic variance. (2) Estimates of the average heterozygosities of the southern population were not significantly different from those of the northern population. Thus, it was strongly suggested that the excess of additive genetic variance in the southern population cannot be caused by structural loci, but by factors outside the structural loci, and that protein polymorphisms are selectively neutral or nearly neutral.

scholarly journals Ludicrous Speed Linear Mixed Models for Genome-Wide Association Studies

2017 ◽  
Author(s):  
Carl Kadie ◽  
David Heckerman
Keyword(s):  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Linear Mixed Models ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Confounding Factors ◽  
Genome Wide ◽  
A Genome

AbstractWe have developed Ludicrous Speed Linear Mixed Models, a version of FaST-LMM optimized for the cloud. The approach can perform a genome-wide association analysis on a dataset of one million SNPs across one million individuals at a cost of about 868 CPU days with an elapsed time on the order of two weeks. A Python implementation is available at https://fastlmm.github.io/.SignificanceIdentifying SNP-phenotype correlations using GWAS is difficult because effect sizes are so small for common, complex diseases. To address this issue, institutions are creating extremely large cohorts with sample sizes on the order of one million. Unfortunately, such cohorts are likely to contain confounding factors such as population structure and family/cryptic relatedness. The linear mixed model (LMM) can often correct for such confounding factors, but is too slow to use even with algebraic speedups known as FaST-LMM. We present a cloud implementation of FaST-LMM, called Ludicrous Speed LMM, that can process one million samples and one million test SNPs in a reasonable amount of time and at a reasonable cost.

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