Common maternal and fetal genetic variants show expected polygenic effects on the probability of being born small- or large-for-gestational-age (SGA or LGA), except in the smallest 3% of babies

Babies born clinically Small- or Large-for-Gestational-Age (SGA or LGA; sex- and gestational age-adjusted birth weight (BW) <10th or >90th percentile, respectively), are at higher risks of complications. SGA and LGA include babies who have experienced growth-restriction or overgrowth, respectively, and babies who are naturally small or large. However, the relative proportions within each group are unclear. We aimed to assess the extent to which the genetics of normal variation in birth weight influence the probability of SGA/LGA. We calculated independent fetal and maternal genetic scores (GS) for BW in 12,125 babies and 5,187 mothers. These scores capture the direct fetal and indirect maternal (via intrauterine environment) genetic contributions to BW, respectively. We also calculated maternal fasting glucose (FG) and systolic blood pressure (SBP) GS. We tested associations between each GS and probability of SGA or LGA. For the BW GS, we used simulations to assess evidence of deviation from an expected polygenic model. Higher BW GS were strongly associated with lower odds of SGA and higher odds of LGA (ORfetal=0.65 (0.60,0.71) and 1.47 (1.36,1.59); ORmaternal=0.80 (0.76,0.87) and 1.23 (1.15,1.31), respectively per 1 decile higher GS). Associations were in accordance with a polygenic model except in the smallest 3% of babies (Pfetal=0.0034, Pmaternal=0.023). Higher maternal GS for FG and SBP were associated with higher odds of LGA and SGA respectively (both P<0.01). While lower maternal FG and SBP are generally considered healthy in pregnancy, we found some evidence of association with higher odds of SGA (P=0.015) and LGA (P=0.14) respectively. We conclude that common genetic variants contribute to risk of SGA and LGA, but that additional factors become more important for risk of SGA in the smallest 3% of babies. Naturally low maternal glucose and blood pressure levels may additionally contribute to risk of SGA and LGA, respectively. Author Summary Babies in the lowest or highest 10% of the population distribution of birth weight (BW) for a given gestational age are referred to as Small- or Large-for-Gestational-Age (SGA or LGA) respectively. These babies have higher risks of complications compared to babies with BW closer to the mean. SGA and LGA babies may have experienced growth restriction or overgrowth, respectively, but may alternatively just be at the tail ends of the normal growth distribution. The relative proportions of normal vs. sub-optimal growth within these groups is unclear. To examine the role of common genetic variation in SGA and LGA, we tested their associations with a fetal genetic score (GS) for BW in 12,125 European-ancestry individuals. We also tested associations with maternal GS (5,187 mothers) for offspring BW, fasting glucose and systolic blood pressure, each of which influences fetal growth via the in utero environment. We found all fetal and maternal GS were associated with SGA and LGA, supporting strong maternal and fetal genetic contributions to birth weight in both tails of the distribution. However, within the smallest 3% of babies, the maternal and fetal GS for BW were higher than expected, suggesting factors additional to common genetic variation are more important in determining birth weight in these very small babies.


Introduction 74
Size at birth is an important factor in new-born and infant survival. Term-born babies are 75 most frequently admitted to the neonatal unit when born at the extremes of the birth weight 76 distribution [1]. Small for Gestational Age (SGA; defined as birth weight adjusted for sex and 77 gestational age that is below the 10 th percentile of the population or customized standard) is 78 often used as a proxy indicator of fetal or intrauterine growth restriction (FGR or IUGR [2]). 79 A fetus is described as growth-restricted when it has failed to reach its growth potential due 80 to impaired placental function [3] or due to fetal or maternal reasons, and SGA fetuses are at 81 higher risk of adverse outcomes such as stillbirth [4]. It is likely that SGA infants who are 82 genetically small are at a lower risk of future morbidity than FGR infants. Risks of adverse 83 outcomes are increased in preterm babies, and the mechanisms associated with SGA in 84

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Prevalence of SGA and LGA is strongly associated with maternal and fetal genetic scores for 134 birth weight 135 The prevalence of SGA and LGA by percentile of fetal or maternal GS in ALSPAC 136 (N=6,621) is shown in Figures S1 and S2, and the mean BW in ALSPAC by percentile of 137 fetal and maternal GS is shown in Figure S3 GS (and vice versa) in mother-child pairs (Table S4b), indicating that the use of the adjusted 145 fetal and maternal weights [15] resulted in fetal and maternal GSs that were already 146 independent of one another (Table S4). 147 148 Evidence that the GS in the lowest 3% of the population was higher than expected 149 Using simulation analyses in ALSPAC and EFSOCH (N=5,187) we identified evidence of 150 deviation from the expected additive polygenic model in the lowest 10% bin of the phenotype 151 distribution, which we then narrowed down to the lowest 3% bin for both the fetal and 152 maternal GS for birth weight (Pfetal=0.0034, Pmaternal=0.023; Figure 3; Table S5). The birth 153 weight GS for both maternal and fetal genotype were higher than expected within this group 154 indicating that there is a proportion of the SGA group whose birth weight is lower than would 155 be expected given their GS. 156 9 There was no evidence of deviation from the expected additive polygenic model for the fetal 158 GS in the top 10% of the phenotype distribution (Pfetal= 0.1082). We saw some evidence of a 159 lower maternal GS than expected in the top 10% group (Pmaternal=0.012) but in the top 3% of 160 the phenotype distribution there was no evidence of deviation (Pmaternal=0.15; Table S5 although confidence intervals were wide and it would be informative to look at this 175 association in studies with larger sample size ( Figure 4, Table S6). 176

Discussion 177
We have shown that common birth weight-associated genetic variation in both the mother 178 and the fetus contribute to the probability that term infants will be born small or large for 179 gestational age. While these results indicate that a large proportion of those infants classified 180 as SGA/LGA represent the tail ends of the normal distribution of birth weight, we also found 181 some evidence of an excess of individuals with a higher GS for birth weight than shown that maternal FG and SBP have causal effects on birth weight in the normal range 224 [15,16]. In women at highest genetic risk for raised FG and SBP, these could potentially 225 contribute to LGA and SGA, respectively. We therefore investigated the associations 12 between maternal genetic scores for FG or SBP and the risk of SGA or LGA. In line with 227 known consequences of maternal gestational diabetes or hypertension, respectively, a higher 228 maternal FG GS is associated with higher odds of LGA, while a higher maternal SBP GS 229 increased odds of SGA. A lower GS for maternal FG was also associated with higher odds of 230 SGA. This is consistent with naturally low maternal glucose levels contributing to SGA risk. 231 A lower GS for maternal SBP showed weak evidence of association with LGA. Replication 232 in larger studies will be necessary to determine the potential contribution of lower maternal 233 blood pressure to LGA risk. 234

235
The current study has a number of strengths and limitations. Strengths include the fact that 236 we have been able to construct independent maternal and fetal GS for birth weight. 237 Separating maternal and fetal genetic contributions to birth weight is important because 238 maternal and fetal genotype are correlated. This means that to avoid confounding, it is 239 necessary to account for this correlation and obtain independent estimates of maternal and 240 fetal genetic effects. Both a limitation and strength of our study is that all of the cohorts used 241 were of European ancestry. There is a need for genetic studies in populations of ancestries 242 other than Europeans, however, the GS were discovered in European ancestry individuals and 243 are not necessarily applicable to other ancestry populations. A further limitation is that the 244 cohorts analysed in this study contributed to the GWAS meta-analysis that identified SNPs 245 contributing to the GS. However, these studies only constitute 3.8% of the fetal and 4.2% of 246 the maternal samples included in the GWASs so any impact of winner's curse on the 247 associations in the current study is likely to be small. The use of only singleton, term babies 248 in our analysis means that our results do not necessarily translate to pre-term babies or 249 multiple births. While we had sufficient sample size to estimate the association of birth 250 weight GS with SGA/LGA, the limited number of mother-child pairs which were available to 251 13 examine the associations of FG and SBP GS with SGA/LGA meant that these estimates had 252 large confidence intervals. Larger numbers of mother-child pairs would allow for more 253 precise estimates, for example of the association of low SBP on LGA which has potentially 254 clinically relevant implications. Although SGA and FGR are often used synonymously, there 255 are differences between the terms. In our study do not have information required to 256 distinguish FGR babies from SGA ones, meaning that we were not able to examine the 257 association between BW GS and FGR specifically. 258 259 Our analysis has shown that common birth weight-associated genetic variation contributes to 260 the risk of babies being born small or large for gestational age, and that the genetics 261 underlying maternal fasting glucose and systolic blood pressure in the normal range also 262 contribute to this risk. We found evidence of deviation from the polygenic model in the 263 smallest 3% of babies, consistent with enrichment for fetal growth restriction in this group. 264 265 14 Methods 266

Cohort descriptions 267
Our analysis included a total of 6,938 individuals with birth weight and fetal genotype data, 268 from 2 studies, plus 5,187 mother-offspring pairs with maternal and fetal genotype data and 269 birth weight from 2 further studies. Studies are described below, and summary data is shown 270 in Since different mechanisms may lead to SGA and LGA in term and preterm infants, and in 308 multiple births, we focused on term, singleton infants. Within each cohort, birth weight was 309 regressed against sex and gestational age in term births (gestational age >= 37 weeks), and 310 residuals from the regression model were calculated. Individuals with the lowest and highest 311 10% of this adjusted birth weight within each cohort respectively were defined as SGA and 312 LGA. Controls for comparison with SGA were taken as birth weight >=10%, and for 313 comparison with LGA as birth weight <=90%.