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Evaluating indirect genetic effects of siblings using singletons [1]

['Laurence J. Howe', 'Medical Research Council Integrative Epidemiology Unit', 'Population Health Sciences', 'University Of Bristol', 'Bristol', 'United Kingdom', 'Bristol Medical School', 'David M. Evans', 'University Of Queensland Diamantina Institute', 'University Of Queensland']

Date: 2022-09

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 if there is no heterogeneity in other sources of genetic association between singletons and non-singletons. We used UK Biobank data to estimate polygenic score (PGS) associations for height, BMI and educational attainment in self-reported singletons (N = 50,143) and non-singletons (N = 328,549). The educational attainment PGS 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 PGS associations were consistent. Birth order data suggested that the difference in educational attainment PGS associations was driven by individuals with older siblings rather than firstborns. The relationship between number of siblings and educational attainment PGS associations was non-linear; PGS 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 PGS 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 may 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.

Associations between genetic variants and phenotypes in the same individual will capture effects of sibling genetic variants because of the genomic similarity between siblings. We propose that sibling effects can be evaluated by comparing genetic association estimates between singletons (no siblings), who cannot be plausibly influenced by sibling effects, and non-singletons (one or more siblings).

Genetic data from families can be used to evaluate social effects of parents on their offspring. For example, non-transmitted parental genetic variants have been shown to associate with offspring educational attainment indicative of parental effects. Siblings may also influence one another but available data has limited our understanding of sibling effects.

Data Availability: Access to UK Biobank data was granted as part of application 8786 (PI: NMD). Interested researchers can obtain the same datasets as used in this manuscript. Please contact [email protected] for further information or go to https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access . Summary data from the within-sibship GWAS is available from https://gwas.mrcieu.ac.uk/ Code for simulations is available at SiblingIGE/simulations.R at main · LaurenceHowe/SiblingIGE ( github.com ).

The association between an individual’s genotype (G I ) and a phenotype (P I ) will capture the following: 1) direct genetic effects (DGE) of inheriting a variant or a correlated variant. 2) indirect genetic effects (IGE M,P,S ) of parental (maternal/paternal G M,P ) and sibling genotypes (G S ) on P I via the shared environment because of the correlations between G I , G M,P and G S . 3) Confounding factors (C) such as population stratification and assortative mating which confound the associations between genetic variants and phenotypes. If an individual has no siblings (or is raised apart from their siblings) then the G I , P I association cannot, by definition, be affected by indirect genetic effects of siblings. Therefore, indirect genetic effects of siblings are a possible explanation for heterogeneity in genetic associations between singletons and non-singletons. Note that this figure features several simplifications such as maternal and paternal indirect genetic effects being consistent and no paths between confounding factors and parental or sibling genotypes.

In a population sample of unrelated individuals, the association between a genetic variant and a phenotype reflects the effect of the genetic variant (or a correlated variant) on the phenotype in the index individual (direct effect), indirect genetic effects of relatives which strengthen (or weaken) the genotype-phenotype covariance, as well as demography (assortative mating, population stratification) [ 4 , 16 , 17 ]. One approach to evaluate indirect genetic effects is to compare genetic associations between non-adopted and adopted individuals. Adopted individuals are raised apart from their biological families so genetic associations will not capture indirect genetic effects of relatives [ 11 , 18 ]. Extending this intuition, we note that indirect genetic effects of siblings will not impact genetic association estimates from singletons (individuals without siblings). If other sources of genetic association are consistent between singletons and non-singletons, then differences between the singleton and non-singleton genetic association estimates could indicate indirect genetic effects of siblings. Potential group-level differences between singletons and non-singletons are unlikely to confound genetic associations unless the factor can influence genotype (e.g. ancestry) ( Fig 1 ).

Phenotypic data from twins (monozygotic/dizygotic), foster siblings and only children can be used to estimate sibling effects by considering differences in phenotypic variance [ 8 , 9 ]. A complementary approach is to use molecular genetic data from extended families to estimate indirect genetic effects of parents and siblings [ 1 , 4 , 10 , 11 , 12 – 14 ], which can then inform the effects of parental and sibling phenotypes. However, existing studies that have sampled family members have limited power to estimate sibling effects because of the paucity of available family data and the statistical inefficiency of within-family models, even when genotypes of missing first degree relatives are imputed [ 12 , 13 , 15 ]. Here, we propose an alternative molecular genetics approach for evaluating indirect genetic effects of siblings which can use large samples of unrelated individuals and so is likely to have higher statistical power than within-family approaches.

Parents transmit genetic variation to their offspring and shape their early-life environment [ 1 , 2 ]. Parental effects on the family environment partially relate to effects of parental genotypes (indirect genetic effects). For example, parental genotypes which influence their behaviour could impact the offspring’s environment [ 1 , 3 , 4 ] Other relative classes such as siblings may also have indirect effects on their relatives [ 5 ]; older siblings could influence the school achievement of younger siblings [ 6 ] or their smoking behaviour [ 7 ].

As discussed in the Methods , comparisons of genetic association estimates between singletons and non-singletons may be sensitive to group-level differences. We compared the sex, age, height, BMI, measured educational attainment and educational attainment PGS of singletons and non-singletons. Singletons were more likely to be male (+1.0%; 95% C.I. 0.6%, 1.5%), older (+2.6 years; 95% C.I. 2.6, 2.7) and were born further south (5.2 km; 95% C.I. 3.6, 6.8) and east (3.5km; 95% C.I. 2.7, 4.3). After adjusting for age and sex, we found evidence that singletons are taller (+0.15 cm; 95% C.I. 0.09, 0.21), have higher BMI (+0.06 kg/m 2 ; 95% C.I. 0.01, 0.10) and have more years in full-time education (+0.25 years; 95% C.I. 0.23, 0.27). However, we found no strong evidence of differences between singletons and non-singletons for educational attainment PGS (-0.002 SD difference; 95% C.I. -0.011, 0.008) ( Tables C and D in S1 Text ). Similar differences were also observed between singletons and firstborn non-singletons for age and BMI but contrastingly, singletons were shorter (-0.23 cm; -0.31, -0.14) and had fewer years in full-time education (-0.09 years; 95% C.I. -0.12, -0.06) than firstborns ( Table E in S1 Text ). These findings illustrate differences between singletons and non-singletons, which could relate to parental differences (e.g. education) but also birth order effects or factors influencing study participation. However, we note that group-level differences by family size are unlikely to confound our genetic association analyses with the exception of ancestral differences and that some of the observed differences are relatively modest (e.g. 5.2 km difference in birth coordinates).

We also repeated the linear analysis after removing outlying individuals with extreme values, considering individuals with 6 or more siblings as outliers based on an outlier threshold of 5% because this group corresponded to 4.9% of the total sample. In this sample of individuals with 5 or fewer siblings we found evidence for a linear relationship; each additional sibling corresponded to an increase of 0.012 in the PGS association estimate (95% C.I. 0.006, 0.018; P = 4.3x10 -5 ). Under assumptions discussed in the Methods , this estimate can be scaled (multipled by two) to provide an estimate of sibling indirect genetic effects; a 1 SD increase in the educational attainment PGS of a sibling increases an index individual’s years in schooling by 0.025 years (0.013, 0.036). For comparison, this estimate is 11% (95% C.I. 5%, 16%) of the magnitude of the PGS association estimate in the full sample (0.23 years, 95% C.I. 0.23, 0.24).

We evaluated whether there is a non-linear relationship between number of siblings and the PGS associations by applying a quadratic model including the square of the number of siblings and a quadratic interaction term. This model provided evidence of a non-linear relationship with the linear interaction estimate indicating a 0.017 increase in PGS association estimate per each additional sibling (95% C.I. 0.008, 0.025; P = 0.0001) in the opposite direction to the quadratic interaction estimate which indicated a 0.002 decrease in PGS association estimate per each unit increase in the square of the number of siblings (95% C.U. -0.001, -0.003; P = 0.0003).

We next evaluated evidence for a linear relationship between number of siblings and the association of the educational attainment PGS with measured educational attainment. In the full sample (N = 378,445), we found no strong evidence for an interaction (P = 0.24). However, this sample included very large families where data could be more susceptible to misclassification error and the association estimates may be affected by lower parental investment or other confounding factors. Indeed, we found that the PGS association estimate from 18,746 individuals self-reporting having 6 or more siblings was 24% smaller than the estimate from individuals with 5 or fewer siblings (95% C.I. 11%, 38%, P = 5.2x10 -4 ) ( Fig 4 ).

Birth order may influence the magnitude of sibling effects. For example, the schooling decisions of an older sibling are more likely to influence a younger sibling than the converse. We used birth order data (available in a subset of the UK Biobank cohort) to identify firstborns (individuals with only younger siblings) and non-firstborns (individuals with one or more older siblings). We then computed PGS associations, as above, in the firstborn and non-firstborn samples.

As a sensitivity analysis, we repeated the educational attainment analysis using PGS weightings from a recent within-sibship GWAS meta-analysis of educational attainment [ 20 ]. The within-sibship weighted PGS is unlikely to capture effects of population stratification, assortative mating and indirect genetic effects [ 3 , 4 , 20 ]. However, associations between this PGS and educational attainment in our sample (of unrelated individuals) may still be affected by these sources of association. This is because genetic variants in the within-sibship PGS with direct genetic effects on educational attainment may also have non-direct genetic effects which are not controlled for in models that don’t account for parental genotypes. The singleton within-sibship attenuation in the educational attainment PGS association estimate (11%; 95% C.I. -1%, 23%) was highly consistent with the estimate from the primary analysis (12%; 95% C.I. 3%, 21%) ( Table A in S1 Text ).

To evaluate potential indirect genetic effects of siblings, we explored differences in genetic association estimates between singletons and non-singletons for height, BMI and educational attainment genetic variants. We constructed polygenic scores (PGS) for these phenotypes using Genome-wide Association Study (GWAS) summary data independent of UK Biobank [ 19 ]. We estimated associations between the PGS and the relevant phenotype in the singleton and non-singleton samples, adjusting for sex, birth year and the first 10 ancestry informative principal components. We then estimated the difference (% attenuation) from the non-singleton to the singleton PGS association estimate for each phenotype.

Discussion

Here, we proposed that differences in genetic association estimates between singletons and non-singletons can be used to evaluate indirect genetic effects of siblings. Using UK Biobank data, we found that the association between the educational attainment PGS and educational attainment was larger for non-singletons than singletons. This difference was driven by individuals with older siblings rather than firstborns. We found that the relationship between number of siblings and educational attainment PGS associations was non-linear, with PGS associations attenuating substantially in larger families with more than 6 children. After removing these families, we found strong evidence for a linear relationship between number of siblings and the educational attainment PGS associations. These findings are suggestive of indirect genetic effects of siblings; older siblings influence the education of younger siblings.

There are alternative explanations for our findings. First, group-level differences between singletons and non-singletons, such as for parental education [21] or health outcomes [22–24], could have led to differences in sources of genetic association [25]. For example, direct and indirect genetic effects on educational attainment could vary by socio-economic position [26–28] or by other covariates. Indeed, we observed differences between singletons and non-singletons for age, sex, and several phenotypes, consistent with parental differences, birth order effects or selection bias although we did not observe differences for the educational attainment PGS. Second, our results could have been impacted by collider bias [25,29], because stratifying on number of siblings (which is non-random) could distort associations between factors which influence number of siblings. Third, indirect genetic effects of parents may be stronger in singletons because of additional parental investment with fewer offspring. Similarly birth order may influence the magnitude of direct genetic effects and how an individual is affected by their parents [30,31]. However, differences in indirect genetic effects and birth order are unlikely to explain the observed differences in PGS associations for educational attainment. Previous literature [32] suggests that singletons and firstborns are more likely to receive additional parental investment, which would likely result in larger indirect genetic effects of parents on educational attainment and a larger PGS association estimate. Inconsistent with this explanation, we observed larger PGS estimates in non-singletons and non-firstborns. A more plausible explanation for the difference in association of education and the educational attainment PGS between firstborns and non-firstborns is that older siblings influence the educational decisions of younger siblings. Whereas an individual’s decision to go to university is less likely to be strongly influenced by younger siblings.

Previous research has shown that PGS-phenotype associations can differ across ancestry groups as well as by other phenotypes such as socio-economic position, age and sex [27,33]. Indeed, it has been previously demonstrated that educational attainment PGS more strongly predict educational attainment in individuals with one sibling compared to individuals with no siblings, although these findings were not interpreted with respect to sibling IGEs or birth order [27]. As a sensitivity analysis, we performed analyses using an educational attainment PGS weighted by within-sibship GWAS estimates [20] which are robust against population stratification, assortative mating and indirect genetic effects of parents but not indirect genetic effects of siblings. Here we found consistent evidence that PGS association estimates are larger in non-singletons. This suggests that our results are unlikely to be explained by effects of population stratification, assortative mating and indirect genetic effects on the PGS weightings. However, these mechanisms could still affect the association between the PGS and educational attainment differently between singletons and non-singletons because analyses in unrelated individuals do not account for variance in parental genotypes.

Molecular genetic analyses of indirect genetic effects of relatives [1,4,11,12,14] have generally found evidence of imitation rather than contrast effects [8,9,34], i.e. effects of parental genotypes on the shared environment result in children being more similar to their parents. Our results are consistent with imitation effects of siblings for educational attainment as they suggest stronger rather than weaker gene-environment correlations for individuals with older siblings. For example, this could occur if an older sibling going to university increases the probability that their younger sibling will also go to university. Contrastingly, there could be more subtle effects of an individual’s behaviour on the shared family environment. For example, an individual with a higher education PGS may help younger siblings more with their homework.

A previous meta-analysis estimated that parental indirect genetic effects on educational attainment are around half of the magnitude of direct genetic effects of inherited variants [14]. We were unable to estimate direct genetic effects in this study but estimated that the indirect genetic effects of one sibling on education are around a tenth of the total genetic association estimate suggesting that sibling indirect genetic effects on education are likely to be substantially smaller than parental effects. However, there are complexities specific to interpreting estimates of indirect genetic effects of siblings. First, our estimate was derived using all non-singletons, but birth order is likely to affect the magnitude of indirect genetic effects of siblings. For example, supported by our findings, sibling indirect genetic effects could be much larger for non-firstborns. Second, we assumed a linear additive model with each additional sibling increasing the combined magnitude of the sibling indirect genetic effects. However, we observed an attenuated genetic association estimate in larger families suggesting that the relationship between number of siblings and genetic associations may be non-linear. The genetic association attenuation in larger families could also relate to differences in indirect genetic effects of parents or confounding.

In contrast to our findings, previous studies using family-based approaches have reported limited evidence of sibling IGEs [12,13,18]. This could have been because of differences in statistical power or because of genuine heterogeneity. We compared our sibling IGE estimates to the estimates from one of these manuscripts (Kong et al [13]) and found limited evidence of heterogeneity. Our sibling IGE estimate, which was more precisely estimated, was consistent with the estimate and 95% confidence interval from Kong et al [13] (S1 Text). This suggests that the differences in conclusions are likely to relate to differences in statistical power.

Our findings add to the growing evidence for social effects of germline genetic variation. The main limitation of our approach is that it is sensitive to systematic differences between singletons and non-singletons. For example, interactions between the PGS and covariates could have biased our estimates of sibling indirect genetic effects. An additional limitation is that (beyond including birth year as a covariate) we did not account for possible generational effects (i.e. effects of the PGS changing over time) which could induce bias in combination with changes in family size over time. Further caveats are that our analyses may have been affected by selection bias relating to non-random participation in UK Biobank [29,35], that we did not account for possible indirect genetic effects of half siblings in our analyses as this data was not available and that we assumed random mating with assortative mating a likely source of bias. We also did not evaluate whether sibling indirect genetic effects vary by the sex of the index individual and their siblings (e.g. same sex sibling pairs may be more likely to influence one-another). Larger datasets of first-degree relatives will enable more precise estimation of sibling indirect genetic effects and allow the evaluation of sibling effects on a wider range of phenotypes such as smoking behaviour and alcohol consumption.

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[1] Url: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010247

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