Bias, Accuracy, and Impact of Indirect Genetic Effects in Infectious Diseases

Estimating effects of parental and sibling genotypes (indirect genetic effects) can provide insight into how the family environment influences phenotypic variation. There is growing molecular genetic evidence for effects of parental phenotypes on their offspring (e.g. parental educational attainment), but the extent to which siblings affect each other is currently unclear.Here we used data from samples of unrelated individuals, without (singletons) and with biological full-siblings (non-singletons), to investigate and estimate sibling effects. Indirect genetic effects of siblings increase (or decrease) the covariance between genetic variation and a phenotype. It follows that differences in genetic association estimates between singletons and non-singletons could indicate indirect genetic effects of siblings.We used UK Biobank data to estimate polygenic risk score (PRS) associations for height, BMI and educational attainment in singletons (N = 50,143) and non-singletons (N = 328,549). The educational attainment PRS association estimate was 12% larger (95% C.I. 3%, 21%) in the non-singleton sample than in the singleton sample, but the height and BMI PRS associations were consistent. Birth order data suggested that the difference in educational attainment PRS associations was driven by individuals with older siblings rather than firstborns. The relationship between number of siblings and educational attainment PRS associations was non-linear; PRS associations were 24% smaller in individuals with 6 or more siblings compared to the rest of the sample (95% C.I. 11%, 38%). We estimate that a 1 SD increase in sibling educational attainment PRS corresponds to a 0.025 year increase in the index individual’s years in schooling (95% C.I. 0.013, 0.036).Our results suggest that older siblings influence the educational attainment of younger siblings, adding to the growing evidence that effects of the environment on phenotypic variation partially reflect social effects of germline genetic variation in relatives.

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Indirect Genetic Effects for Growth in Pigs Affect Behaviour and Weight Around Weaning

Behavior Genetics ◽

10.1007/s10519-018-9911-5 ◽

2018 ◽

Vol 48 (5) ◽

pp. 413-420 ◽

Cited By ~ 3

Author(s):

Irene Camerlink ◽

Winanda W. Ursinus ◽

Andrea C. Bartels ◽

Piter Bijma ◽

J. Elizabeth Bolhuis

Keyword(s):

Genetic Effects ◽

Indirect Genetic Effects

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Models with indirect genetic effects depending on group sizes: a simulation study assessing the precision of the estimates of the dilution parameter

Genetics Selection Evolution ◽

10.1186/s12711-019-0466-6 ◽

2019 ◽

Vol 51 (1) ◽

Author(s):

Marzieh Heidaritabar ◽

Piter Bijma ◽

Luc Janss ◽

Chiara Bortoluzzi ◽

Hanne M. Nielsen ◽

...

Keyword(s):

Simulation Study ◽

Genetic Effects ◽

Indirect Genetic Effects

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Pervasive indirect genetic effects on behavioral development in polymorphic eastern mosquitofish

Behavioral Ecology ◽

10.1093/beheco/arx180 ◽

2017 ◽

Vol 29 (2) ◽

pp. 289-300 ◽

Cited By ~ 3

Author(s):

Brittany Kraft ◽

Valerie A Lemakos ◽

Joseph Travis ◽

Kimberly A Hughes

Keyword(s):

Behavioral Development ◽

Genetic Effects ◽

Indirect Genetic Effects ◽

Eastern Mosquitofish

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Air-borne genotype by genotype indirect genetic effects are substantial in the filamentous fungus Aspergillus nidulans

Heredity ◽

10.1038/hdy.2017.9 ◽

2017 ◽

Vol 119 (1) ◽

pp. 1-7 ◽

Cited By ~ 6

Author(s):

N O Rode ◽

P Soroye ◽

R Kassen ◽

H D Rundle

Keyword(s):

Aspergillus Nidulans ◽

Filamentous Fungus ◽

Genetic Effects ◽

Indirect Genetic Effects

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

Genetics Selection Evolution ◽

10.1186/s12711-020-00578-y ◽

2020 ◽

Vol 52 (1) ◽

Cited By ~ 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.

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