scholarly journals Family Analysis with Mendelian Imputations

2020 ◽  
Author(s):  
Augustine Kong ◽  
Stefania Benonisdottir ◽  
Alexander I. Young
Keyword(s):  
Population Stratification ◽  
Predictive Power ◽  
Genetic Effect ◽  
Age At First Birth ◽  
Direct Effects ◽  
Uk Biobank ◽  
Polygenic Score ◽  
Family Analysis ◽  
Comprehensive Framework ◽  
Direct Genetic Effect

AbstractGenotype-phenotype associations can be results of direct effects, genetic nurturing effects and population stratification confounding. Genotypes from parents and siblings of the proband can be used to statistically disentangle these effects. To maximize power, a comprehensive framework for utilizing various combinations of parents’ and siblings’ genotypes is introduced. Central to the approach is mendelian imputation, a method that utilizes identity by descent (IBD) information to non-linearly impute genotypes into untyped relatives using genotypes of typed individuals. Applying the method to UK Biobank probands with at least one parent or sibling genotyped, for an educational attainment (EA) polygenic score that has an R2 of 5.7% with EA, its predictive power based on direct genetic effect alone is demonstrated to be only about 1.4%. For women, the EA polygenic score has a bigger estimated direct effect on age-at-first-birth than EA itself.

scholarly journals Comparing within- and between-family polygenic score prediction

2019 ◽  
Author(s):  
Saskia Selzam ◽  
Stuart J. Ritchie ◽  
Jean-Baptiste Pingault ◽  
Chandra A. Reynolds ◽  
Paul F. O’Reilly ◽  
...  
Keyword(s):  
Assortative Mating ◽  
Complex Traits ◽  
Population Stratification ◽  
Educational Achievement ◽  
Economic Status ◽  
Life Outcomes ◽  
Polygenic Score ◽  
Family Effects ◽  
Polygenic Scores ◽  
Cognitive Traits

AbstractPolygenic scores are a popular tool for prediction of complex traits. However, prediction estimates in samples of unrelated participants can include effects of population stratification, assortative mating and environmentally mediated parental genetic effects, a form of genotype-environment correlation (rGE). Comparing genome-wide polygenic score (GPS) predictions in unrelated individuals with predictions between siblings in a within-family design is a powerful approach to identify these different sources of prediction. Here, we compared within- to between-family GPS predictions of eight life outcomes (anthropometric, cognitive, personality and health) for eight corresponding GPSs. The outcomes were assessed in up to 2,366 dizygotic (DZ) twin pairs from the Twins Early Development Study from age 12 to age 21. To account for family clustering, we used mixed-effects modelling, simultaneously estimating within- and between-family effects for target- and cross-trait GPS prediction of the outcomes. There were three main findings: (1) DZ twin GPS differences predicted DZ differences in height, BMI, intelligence, educational achievement and ADHD symptoms; (2) target and cross-trait analyses indicated that GPS prediction estimates for cognitive traits (intelligence and educational achievement) were on average 60% greater between families than within families, but this was not the case for non-cognitive traits; and (3) this within- and between-family difference for cognitive traits disappeared after controlling for family socio-economic status (SES), suggesting that SES is a source of between-family prediction through rGE mechanisms. These results provide novel insights into the patterns by which rGE contributes to GPS prediction, while ruling out confounding due to population stratification and assortative mating.

A note on heritability estimates for growth traits in male and female Romanov sheep

1993 ◽  
Vol 57 (2) ◽  
pp. 326-328 ◽  
Author(s):  
G. A. María ◽  
K. G. Boldman ◽  
L. D. van Vleck
Keyword(s):  
Animal Model ◽  
Birth Weight ◽  
Maximum Likelihood ◽  
Random Effects ◽  
Fixed Effects ◽  
Growth Traits ◽  
Genetic Effect ◽  
Male And Female ◽  
Males And Females ◽  
Direct Genetic Effect

A total of 1855 records were analysed using restricted maximum likelihood (REML) techniques to estimate heritabilities separately for males and females lambs on birth weight (BW), weaning weight (WW), 90-day weight (W90) and average daily gains birth to weaning (Cl) and weaning to 90 days (C2). An animal model including fixed effects of year × season, parity, litter size and rearing type; and random effects of direct genetic effect (h2D) and residual was applied. Estimates ofh2Dfor BWwere 048 (males) and 0·50 (females); for WW 0·35 (males) and 0·22 (females); for W90 0·21 (males) and 0·31 (females); for Cl 0·20 (males) and 0·25 (females); and for C2 0·18 (males) and 0·29 (females).

scholarly journals Effect of polygenic risk scores on depression in childhood trauma

2014 ◽  
Vol 205 (2) ◽  
pp. 113-119 ◽  
Author(s):  
Wouter J. Peyrot ◽  
Yuri Milaneschi ◽  
Abdel Abdellaoui ◽  
Patrick F. Sullivan ◽  
Jouke J. Hottenga ◽  
...  
Keyword(s):  
Childhood Trauma ◽  
Meta Analysis ◽  
Genetic Effect ◽  
Risk Scores ◽  
Environment Interaction ◽  
Polygenic Risk ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Direct Genetic Effect

BackgroundResearch on gene×environment interaction in major depressive disorder (MDD) has thus far primarily focused on candidate genes, although genetic effects are known to be polygenic.AimsTo test whether the effect of polygenic risk scores on MDD is moderated by childhood trauma.MethodThe study sample consisted of 1645 participants with a DSM-IV diagnosis of MDD and 340 screened controls from The Netherlands. Chronic or remitted episodes (severe MDD) were present in 956 participants. The occurrence of childhood trauma was assessed with the Childhood Trauma Interview and the polygenic risk scores were based on genome-wide meta-analysis results from the Psychiatric Genomics Consortium.ResultsThe polygenic risk scores and childhood trauma independently affected MDD risk, and evidence was found for interaction as departure from both multiplicativity and additivity, indicating that the effect of polygenic risk scores on depression is increased in the presence of childhood trauma. The interaction effects were similar in predicting all MDD risk and severe MDD risk, and explained a proportion of variation in MDD risk comparable to the polygenic risk scores themselves.ConclusionsThe interaction effect found between polygenic risk scores and childhood trauma implies that (1) studies on direct genetic effect on MDD gain power by focusing on individuals exposed to childhood trauma, and that (2) individuals with both high polygenic risk scores and exposure to childhood trauma are particularly at risk for developing MDD.

scholarly journals Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Bjarke G. Poulsen ◽  
Birgitte Ask ◽  
Hanne M. Nielsen ◽  
Tage Ostersen ◽  
Ole F. Christensen
Keyword(s):  
Growth Rate ◽  
Animal Model ◽  
Genetic Effect ◽  
Predictive Performance ◽  
Genetic Effects ◽  
Relationship Matrix ◽  
Genomic Information ◽  
Indirect Genetic Effects ◽  
Group Members ◽  
Direct Genetic Effect

Abstract Background Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix ($${\mathbf{A}}$$ A ) and a combined pedigree and genomic relationship matrix ($${\mathbf{H}}$$ H ); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect). Results The highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when $${\mathbf{H}}$$ H was used rather than $${\mathbf{A}}$$ A . Conclusions This study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with $${\mathbf{H}}$$ H than $${\mathbf{A}}$$ A ; (3) using $${\mathbf{H}}$$ H rather than $${\mathbf{A}}$$ A primarily improves the predictive performance of direct genetic effects.

Genetic parameters for calving difficulty using complex genetic models in five beef breeds in Australia

2016 ◽  
Vol 56 (5) ◽  
pp. 927 ◽  
Author(s):  
M. G. Jeyaruban ◽  
D. J. Johnston ◽  
B. Tier ◽  
H.-U. Graser
Keyword(s):  
Genetic Parameters ◽  
Linear Models ◽  
Random Effect ◽  
Genetic Correlations ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Gestation Length ◽  
Calving Ease ◽  
Direct Genetic Effect ◽  
Calving Difficulty

Data on Angus (ANG), Charolais (CHA), Hereford (HER), Limousin (LIM) and Simmental (SIM) cattle were used to estimate genetic parameters for calving difficulty (CD), birthweight (BWT) and gestation length (GL) using threshold-linear models and to examine the effect of inclusion of random effect of sire × herd interaction (SxH) in the models. For models without SxH, estimated heritabilities for direct genetic effect of CD were 0.24 (±0.02), 0.22 (±0.04), 0.31 (±0.02), 0.22 (±0.04) and 0.17 (±0.01) for ANG, CHA, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.13 to 0.20. Estimated heritabilities for direct genetic effect of BWT were 0.38 (±0.01), 0.37 (±0.03), 0.46 (±0.01), 0.35 (±0.02) and 0.36 (±0.01) for ANG, CHR, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.08 to 0.11. Estimated heritabilities for direct genetic effect of GL were 0.59 (±0.02), 0.42 (±0.04), 0.50 (±0.03), 0.45 (±0.04) and 0.42 (±0.03) for ANG, CHR, HER, LIM and SIM, respectively, whereas maternal heritabilities ranged from 0.03 to 0.09. Genetic correlations between direct genetic effects of CD with BWT were highly positive and with GL were moderately positive for all five breeds. Estimated genetic correlations between direct genetic effects and maternal genetic effects (rdm) ranged across the five breeds from –0.40 (±0.05) to –0.16 (±0.02), –0.41 (±0.03) to –0.27 (±0.08) and –0.47 (±0.10) to –0.06 (±0.12) for BWT, GL and CD, respectively. Fitting SxH interaction as additional random effect significantly increased the log-likelihood for analyses of BWT, GL and CD of all breeds, except for GL of CHA. The estimated heritabilities were less than or equal to the estimates obtained with models omitting SxH. The rdm increased (i.e. became less negative) for BWT, GL and CD of all five breeds. However, the increase for GL was not substantially high in comparison to the increase observed for BWT and CD. Genetic parameters obtained for BWT, GL and CD, by fitting SxH as an additional random effect, are more appropriate to use in the genetic evaluation of calving ease in BREEDPLAN.

Genetic evaluation of lamb performance in Outaouais Arcott and Suffolk sheep, their crosses and Canadian Arcott- or Hampshire-sired three-breed crosses

1996 ◽  
Vol 76 (1) ◽  
pp. 15-22 ◽  
Author(s):  
A. M. Shafto ◽  
G. H. Crow ◽  
R. J. Parker ◽  
W. M. Palmer ◽  
J. N. B. Shrestha ◽  
...  
Keyword(s):  
Animal Model ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Unbalanced Data ◽  
Sheep Flock ◽  
Data Set ◽  
Significant Difference ◽  
Breed Crosses ◽  
Terminal Sire ◽  
Direct Genetic Effect

A crossbreeding study was used to assess the growth performance of the Outaouais Arcott and Suffolk breeds, their two breed crosses and specific three-breed crosses sired by either Canadian Arcott or Hampshire rams. In a sheep flock maintained under a semi-confinement management system, Suffolk lambs, weighing 3.8 kg at birth, were not significantly heavier than Canadian Arcott- or Hampshire-sired crossbred lambs. Lambs of the Outaouais breed were 26% lighter (P < 0.05) than those of the Suffolk breed, and their two breed crosses had birth weights between the two parental purebreds. The relative ranks among the breeds and their crosses had not changed by 42 d of age with lamb weights ranging from 10.6 to 12.6 kg. By 120 d of age the Canadian Arcott- and Hampshire-sired lambs weighed approximately 28–29 kg and were not significantly different in weight from the Outaouais and Suffolk breeds and their two breed crosses. Additive and maternal genetic effects m the Suffolk breed were significantly greater than in the Outaouais breed for birth weight by 12.9 and 12.6% respectively. By 42 d, no difference was detected between breeds for direct genetic effect. However, the maternal effect favoured the Suffolk breed by 15.8% (P < 0.05). By 120 d, the direct genetic effect was significantly greater in the Outaouais breed by 9.8%, but maternal genetic effects continued to favour the Suffolk breed by 12.5% (P < 0.05). Heterosis effects were generally small (< 3.1%) and not significant for lamb weights. The terminal sire breeds showed no significant difference between Canadian- and Hampshire-sired lamb weights. Generally, results obtained for two analyses (least squares model and multi-trait animal model) of the same data set were consistent. However, the animal model would be preferable when estimating parameters and breeding values from an unbalanced data set with unequal subclass frequencies. Key words: Lambs, weight, genetic effects, heterosis, animal model

scholarly journals Population stratification in GWAS meta-analysis should be standardized to the best available reference datasets

2020 ◽  
Author(s):  
Aliya Sarmanova ◽  
Tim Morris ◽  
Daniel John Lawson
Keyword(s):  
Population Stratification ◽  
Association Studies ◽  
Meta Analysis ◽  
Principal Component ◽  
Underlying Structure ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
External Reference ◽  
Major Disadvantage ◽  
The Uk

AbstractPopulation stratification has recently been demonstrated to bias genetic studies even in relatively homogeneous populations such as within the British Isles. A key component to correcting for stratification in genome-wide association studies (GWAS) is accurately identifying and controlling for the underlying structure present in the sample. Meta-analysis across cohorts is increasingly important for achieving very large sample sizes, but comes with the major disadvantage that each individual cohort corrects for different population stratification. Here we demonstrate that correcting for structure against an external reference adds significant value to meta-analysis. We treat the UK Biobank as a collection of smaller studies, each of which is geographically localised. We provide software to standardize an external dataset against a reference, provide the UK Biobank principal component loadings for this purpose, and demonstrate the value of this with an analysis of the geographically sampled ALSPAC cohort.

scholarly journals Heritable spouse effects increase evolutionary potential of human reproductive timing

2018 ◽  
Vol 285 (1876) ◽  
pp. 20172763
Author(s):  
Simon R. Evans ◽  
Dominique Waldvogel ◽  
Nina Vasiljevic ◽  
Erik Postma
Keyword(s):  
Life Histories ◽  
Genetic Effect ◽  
Age At First Birth ◽  
Reproductive Timing ◽  
Evolutionary Potential ◽  
Heritable Variation ◽  
Indirect Genetic Effect ◽  
Component Traits ◽  
Human Reproductive ◽  
Female Specific

Sexual reproduction is inherently interactive, especially in animal species such as humans that exhibit extended pair bonding. Yet we have little knowledge of the role of male characteristics and their evolutionary impact on reproductive behavioural phenotypes, to the extent that biologists typically consider component traits (e.g. reproductive timing) as female-specific. Based on extensive genealogical data detailing the life histories of 6435 human mothers born across four centuries of modern history, we use an animal modelling approach to estimate the indirect genetic effect of men on the reproductive phenotype of their partners. These analyses show that a woman's reproductive timing (age at first birth) is influenced by her partner's genotype. This indirect genetic effect is positively correlated with the direct genetic effect expressed in women, such that total heritable variance in this trait is doubled when heritable partner effects are considered. Our study thus suggests that much of the heritable variation in women's reproductive timing is mediated via partner effects, and that the evolutionary potential of this trait is far greater than previously appreciated.

scholarly journals Modeling honey yield, defensive and swarming behaviors of Italian honey bees (Apis mellifera ligustica) using linear-threshold approaches

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Sreten Andonov ◽  
Cecilia Costa ◽  
Aleksandar Uzunov ◽  
Patrizia Bergomi ◽  
Daniela Lourenco ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Maternal Effect ◽  
Honey Bees ◽  
Defensive Behavior ◽  
Genetic Effect ◽  
Breeding Values ◽  
Linear Threshold ◽  
Swarming Behavior ◽  
Direct Genetic Effect ◽  
Honey Yield

Abstract Background Genetic improvement of honey bees is more difficult compared to other livestock, due to the very different reproductive behavior. Estimation of breeding values requires specific adjustment and the use of sires in the pedigree is only possible when mating of queens and drones is strictly controlled. In the breeding program of the National Registry for Italian Queen Breeders and Bee Producers the paternal contribution is mostly unknown. As stronger modeling may compensate for the lack of pedigree information, we tested two models that differed in the way the direct and maternal effects were considered. The two models were tested using 4003 records for honey yield, defensive and swarming behaviors of Italian honey bee queens produced between 2002 and 2014. The first model accounted for the direct genetic effect of worker bees and the genetic maternal effect of the queen, whereas model 2 considered the direct genetic effect of the queen without maternal effect. The analyses were performed by linear (honey production) and threshold (defensive and swarming behavior) single-trait models; estimated genetic correlations among traits were obtained by a three-trait linear-threshold model. Results For all traits, the highest predictability (correlation between breeding values estimated with and without performance records) was obtained with model 2, where direct genetic effect of queens was considered. With this model, heritability estimates were 0.26 for honey yield, 0.36 for defensive behavior, and 0.34 for swarming behavior. Multi-trait estimation resulted in similar or higher heritability estimates for all traits. A low, positive genetic correlation (0.19) was found between honey yield and defensive behavior, whereas the genetic correlation between honey yield and swarming behavior was moderate (0.41). A strong, positive genetic correlation was found between defensive and swarming behaviors (0.62). Predictability for multi-trait evaluations was higher for honey yield (0.46) and defensive behavior (0.30) but almost identical for swarming behavior (0.45) compared to corresponding single-trait predictability. Conclusions Multi-trait evaluation using a model that accounts for the direct genetic effect of queen was the best approach for breeding value estimation of Italian honey bees. The results suggest a new direction for selection of linear and categorical traits in breeding programs where drone origin is unknown.

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