scholarly journals General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models

Genetics ◽  
2016 ◽  
Vol 204 (3) ◽  
pp. 1281-1294 ◽  
Author(s):  
Pierre de Villemereuil ◽  
Holger Schielzeth ◽  
Shinichi Nakagawa ◽  
Michael Morrissey
Keyword(s):  
Mixed Models ◽  
Quantitative Genetic ◽  
Genetic Inference

scholarly journals Private Genomes and Public SNPs: Homomorphic Encryption of Genotypes and Phenotypes for Shared Quantitative Genetics

Genetics ◽  
2020 ◽  
Vol 215 (2) ◽  
pp. 359-372
Author(s):  
Richard Mott ◽  
Christian Fischer ◽  
Pjotr Prins ◽  
Robert William Davies
Keyword(s):  
Mixed Models ◽  
Homomorphic Encryption ◽  
Orthogonal Transformation ◽  
High Dimensional ◽  
Brute Force ◽  
Genetic Associations ◽  
Privacy Concerns ◽  
Quantitative Genetic ◽  
Heritability Estimation ◽  
Phenotype Data

Sharing human genotype and phenotype data is essential to discover otherwise inaccessible genetic associations, but is a challenge because of privacy concerns. Here, we present a method of homomorphic encryption that obscures individuals’ genotypes and phenotypes, and is suited to quantitative genetic association analysis. Encrypted ciphertext and unencrypted plaintext are analytically interchangeable. The encryption uses a high-dimensional random linear orthogonal transformation key that leaves the likelihood of quantitative trait data unchanged under a linear model with normally distributed errors. It also preserves linkage disequilibrium between genetic variants and associations between variants and phenotypes. It scrambles relationships between individuals: encrypted genotype dosages closely resemble Gaussian deviates, and can be replaced by quantiles from a Gaussian with negligible effects on accuracy. Likelihood-based inferences are unaffected by orthogonal encryption. These include linear mixed models to control for unequal relatedness between individuals, heritability estimation, and including covariates when testing association. Orthogonal transformations can be applied in a modular fashion for multiparty federated mega-analyses where the parties first agree to share a common set of genotype sites and covariates prior to encryption. Each then privately encrypts and shares their own ciphertext, and analyses all parties’ ciphertexts. In the absence of private variants, or knowledge of the key, we show that it is infeasible to decrypt ciphertext using existing brute-force or noise-reduction attacks. We present the method as a challenge to the community to determine its security.

Towards evolutionary agroecology

2018 ◽  
Vol 6 (14) ◽  
pp. 51 ◽  
Author(s):  
Kristin L. Mercer
Keyword(s):  
Social Dynamics ◽  
Agricultural Systems ◽  
Short Term ◽  
Ecological Processes ◽  
Genetic Analyses ◽  
Holistic Perspective ◽  
Evolutionary Study ◽  
Quantitative Genetic ◽  
Field Evolution

Agroecology derives much of its strength from interactions between disciplines that produce a holistic perspective on agricultural systems and issues.  Although ongoing integration of social dynamics into agroecology has strengthened the field, evolution and genetics have not been embraced to the same degree, despite the fact that they have been are discussed in some common agroecology texts.  I argue that the field of agroecology could extend its reach and depth by embracing the evolutionary study of agroecosystems.  Areas of evolutionary inquiry with relevance to agriculture focus on long or short term processes, encompass a range of scales, incorporate molecular or quantitative genetic analyses, and explore ecological processes to differing degrees.

Maintenance of Quantitative Genetic Variation

2018 ◽  
Author(s):  
Bruce Walsh ◽  
Michael Lynch
Keyword(s):  
Genetic Variation ◽  
Quantitative Genetics ◽  
Stabilizing Selection ◽  
Quantitative Genetic ◽  
Wide Range

One of the major unresolved issues in quantitative genetics is what accounts for the amount of standing genetic variation in traits. A wide range of models, all reviewed in this chapter, have been proposed, but none fit the data, either giving too much variation or too little apparent stabilizing selection.

scholarly journals A Quantitative Genetic Analysis of Nuclear-Cytoplasmic Male Sterility in Structured Populations of Silene vulgaris

Genetics ◽  
2001 ◽  
Vol 158 (2) ◽  
pp. 833-841 ◽  
Author(s):  
Douglas R Taylor ◽  
Matthew S Olson ◽  
David E McCauley
Keyword(s):  
Population Structure ◽  
Sex Ratio ◽  
Genetic Analysis ◽  
Cytoplasmic Male Sterility ◽  
Male Sterility ◽  
Sex Expression ◽  
Silene Vulgaris ◽  
Population Variance ◽  
Quantitative Genetic ◽  
Quantitative Genetic Analysis

Abstract Gynodioecy, the coexistence of functionally female and hermaphroditic morphs within plant populations, often has a complicated genetic basis involving several cytoplasmic male-sterility factors and nuclear restorers. This complexity has made it difficult to study the genetics and evolution of gynodioecy in natural populations. We use a quantitative genetic analysis of crosses within and among populations of Silene vulgaris to partition genetic variance for sex expression into nuclear and cytoplasmic components. We also use mitochondrial markers to determine whether cytoplasmic effects on sex expression can be traced to mitochondrial variance. Cytoplasmic variation and epistatic interactions between nuclear and cytoplasmic loci accounted for a significant portion of the variation in sex expression among the crosses. Source population also accounted for a significant portion of the sex ratio variation. Crosses among populations greatly enhanced the dam (cytoplasmic) effect, indicating that most among-population variance was at cytoplasmic loci. This is supported by the large among-population variance in the frequency of mitochondrial haplotypes, which also accounted for a significant portion of the sex ratio variance in our data. We discuss the similarities between the population structure we observed at loci that influence sex expression and previous work on putatively neutral loci, as well as the implications this has for what mechanisms may create and maintain population structure at loci that are influenced by natural selection.

scholarly journals 222 Partitioning of retained energy between protein and fat gain, and heat of product formation and support metabolism in growing beef cattle

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 158-158
Author(s):  
Phillip A Lancaster
Keyword(s):  
Linear Regression ◽  
Mixed Models ◽  
Multivariate Regression ◽  
Literature Search ◽  
Energy Use ◽  
Random Variable ◽  
Product Formation ◽  
Metabolizable Energy ◽  
Overall Efficiency ◽  
Metabolizable Energy Intake

Abstract Multiple linear regression inaccurately computes the efficiency of energy use for protein and fat gain. The objective was to quantify efficiency of metabolizable energy use for protein and fat gain along with heats of product formation and support metabolism. A literature search was performed to compile data (31 studies, 214 treatment means) on metabolizable energy intake (MEI) and composition of empty body gain in growing steers and heifers. Data analyses were performed using R statistical package for mixed models with study as random variable. Linear regression of MEI on energy gain (EG; P < 0.001; R2 = 0.627) resulted in an estimate of metabolizable energy for maintenance (MEm) of 156 kcal/kg.75 and efficiency of ME use for gain of 0.518. Linear regression of MEI on EG as protein and fat (P < 0.001; R2 = 0.623) resulted in an estimate of MEm of 149 kcal/kg.75, and efficiency of protein (kp) and fat (kf) gain of 0.274 and 0.585, respectively, resulting in an overall efficiency of EG of 0.520. Nonlinear regression model (EG = kg*(MEI-MEm)) resulted in an estimate of MEm of 103 kcal/kg.75 and efficiency of EG of 0.342. The heat of product formation was assumed to be 0.48 (1 – 0.52) and the heat of support metabolism (HiEv) 0.18 (0.52 – 0.34). Multivariate regression was used to fit simultaneous models for EG as protein (EGp = (kp+HiEvp)*k*MEA) and fat (EGf = (kf+(0.18-HiEvp))*(1-k)*MEA). Estimates (P < 0.001) of kp and kf were 0.12 ± 0.01 and 0.63 ± 0.02, and HiEvp and proportion of ME available for protein gain (k) were 0.11 ± 0.01 and 0.75 ± 0.01, respectively. The heat of product formation and support metabolism for protein were 0.77 and 0.11, and fat were 0.30 and 0.07, respectively. In conclusion, efficiency of ME use for protein was lesser than for fat gain, and heat of support metabolism was greater for protein than fat gain.

scholarly journals SAS for Mixed Models: Introduction and Basic Applications

2021 ◽  
Vol 75 (2) ◽  
pp. 231-231
Author(s):  
Christine R. Wells
Keyword(s):  
Mixed Models
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