Pseudotsuga menziesii Mirb. (Franco)) and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200–50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F1 families and on 1,126 Interior spruce trees, representing 25 open-pollinated  families. Both trials are located on 3 sites in British Columbia, Canada.The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with ~ 20 Gbp genomes, Douglas-fir (As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000–15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size.Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits. With genotyping costs at the lowest they have ever been (and still on a decreasing trajectory), genomic selection (GS) becomes an ever-more viable option for forest tree breeders. This should subsequently result in beneficial gains to the industry in terms of improved wood quality, yield per unit time , and stress tolerance (biotic and abiotic) ,2. In a Ne) and marker density [Ne as the number of randomly mating individuals that would cause the observed inbreeding rate of a population. As a result of inbreeding, which is more likely at low Ne, allele frequencies become skewed. In this situation (at low Ne) certain individuals have an increased chance to reproduce, causing genetic drift as their genes are passed more frequently onto the next generation [Ne populations are subject to stronger drift, which in turn is one of the major driving forces influencing linkage disequilibrium (LD) between loci. Lower Ne populations have higher LD, and this LD between markers and trait QTLs is essential for the prediction of trait performance from markers [Ne can be artificially reduced by using half-sib or within-family populations. This is a good technique to use on outbred species, such as forest trees, when applying GS.One of the major determinants of GS success is the relationship between effective population size . Meuwissen [NeL markers should be used to obtain accurate predictions in GS, where L is the total length of the genome in Morgans. As Lin et al. [Malus sp.) trees with an assumed Ne of 1,000 and a genome of approximately 13 Morgans, 130,000 markers are necessary for accurate GS predictions. However Kumar et al. [Malus domestica Borkh with an accuracy of 0.7, using only 2,500 markers. This can be attributed to the bi-parental design used [Ne is no greater than 30 and larger Ne may require up to 20 markers/cM. Yet higher marker densities may be required for situations in which the training and validation populations are not derived genetically from the same base population [Pseudotsuga menziesii Mirb. (Franco)) with an Ne ~21. By the same reasoning we should aim to use up to 40,000 markers for an Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.) population, with an Ne ~93. Indeed Howe et al. [Ne ~30. These numbers, for populations with low Ne, are in line with Meuwissen's [eL markers should be used as a minimum, which gives us 10×21×20 = 4,200 markers for Douglas-fir. Yet using the same calculation from Meuwissen [The determination of the number of markers required for GS is largely based on the occurrence of LD, which in turn is determined by population structure and g et al.  did someeuwissen  proposedn et al.  discuss,r et al.  successfign used . Within-ign used . Grattapign used , using apulation . Using Gpulation  calculatpulation , we shoue et al.  concludeeuwissen  calculatGlycine max L.). They tested three methods of marker selection: random sampling, haplotype block analysis, and evenly sampling markers. They found that for plant height, only marginal differences in prediction accuracy were obtained with the three sampling methods. However, for grain yield, the haplotype block analysis out-performed the other two methods by about 4%. This preselection method offers a comprehensive, yet cost-efficient, option for implementing GS by reducing the number of SNPs required to just one SNP per haplotype block plus those not contained within blocks. However, an in-depth understanding of the structure and LD across the genome is required for this. For this reason, we have concentrated only on the random sampling method for Douglas-fir and Interior spruce where current genome assemblies are highly fragmented and not conducive to analyses of genome wide patterns of LD.Ma et al.  investigNe and subsequently lower LD are thought to impede prediction accuracies in studies based on these half-sib populations. Larger Ne leads to more recombination and therefore more diversity within a population. Drift associated with small population size is not a significant factor under these conditions (open-pollinated and highly outcrossing species) and subsequently do not significantly contribute to the build-up of LD. Nonetheless, as can be seen in vice versa.The two species studied here are representative of full-sib (Douglas-fir) and half-sib (Interior spruce) populations. Their differing pedigree structure should be reflected in the prediction accuracies we obtain through GS. Previously, using full-sib families, GS prediction accuracy has been shown to be moderate to high in general ,20, and It is, however, becoming seemingly more apparent that perhaps LD is not the main driving force in all GS studies ,30. StudFor Douglas-fir, the average prediction accuracy for height genomic estimated breeding values (GEBVs) ranged from 0.63 (standard error: ±0.040) to 0.87 (±0.008), and for Interior spruce 0.31 (±0.028) to 0.63 (±0.019), representative of GS testing SNP sets of 200 and 50,000 SNP number totals, respectively, for each species. Similarly, for Douglas-fir, the average accuracy of wood density GEBVs ranged from 0.62 (±0.023) to 0.83 (±0.011), and for Interior spruce, 0.29 (±0.040) to 0.62 (±0.017), using SNP set totals of 200 and 50,000, respectively. Accordingly, the effect of marker number on the prediction accuracy of multi-site cross-validation data, showed a clear trend of increased predictive accuracy with increasing marker density. However, the magnitude of prediction accuracy gains starts to plateau around a threshold of around 10,000–15,000 markers for both Douglas-fir and Interior spruce . DespiteThe full-sib structure related to the Douglas-fir models outperformed those of the half-sib structure of the Interior spruce models for all SNP set totals . The preNe of the populations need to be considered. Data with a higher Ne (>93) may require more markers to obtain similar prediction accuracies.Generally, prediction accuracies for height and wood density GEBVs of both Douglas-fir and Interior spruce, increased with increasing number of SNPs . The datThe observed GEBV coalesce similar results obtained in previous breeding program studies and generalized simulations ,30,38–42Ne. These characteristics actively preclude the buildup of population-wide LD [The role of short-range marker-QTL LD on GS prediction accuracy was imperceptible compared to the effect of relatedness. This is a somewhat anticipated result given that most conifers are relatively undomesticated even within breeding programs, and are known to be highly outbred species with large -wide LD ,46. Corr-wide LD ,30,40. T-wide LD ,30. As r1 hybrids, Tan et al. [Hordeum vulgare L.) population with high LD, the number of markers used could be reduced to 384, with minor impact on prediction accuracy for disease resistance . Marker selection via a k-means clustering algorithm to sample LD space had only very modest advantages over random sampling. Additionally, in dairy breeding, multiple studies have shown that prediction accuracy can be maintained despite significantly reducing marker density [Ne [Ne, fewer independent genomic segments arise, decreasing the number of markers required to track these segments [Ne than our sampled populations. Lowering Ne in this way would be disadvantageous when predicting across families.Even in species, which do not display the complexity of conifer genomes, similar trends due to marker density reduction have been seen. Using two Eucalyptus species and their Fn et al.  found non et al.  noted th density ,49. In tsity [Ne  which issegments . These pComplex traits are thought to be largely governed by noncoding variants seemingly affecting gene regulation and expression ,51. The Although genome-wide variation in LD is virtually unknown in conifers, data from other plant species suggest that LD is higher but also more variable in intergenic compared to genic regions. There are several reasons for this. First, intergenic regions in many species are replete with repetitive elements, mainly various Long Terminal Repeats (LTR) transposable elements ,53, and Ne values .To further elucidate the role of markers on resolving the pedigree which in turn affected height and wood density predictive accuracies for the two studied species. It should be noted that the Douglas-fir full-sib outperformed the Interior spruce half-sib structure  . These rAlthough advantageous within a population, reducing marker density may not be the most effective or economical method of carrying out GS, especially in conifers. As mentioned previously we have yet to trace LD with the current array of markers available, only genetic relationships, which is not the intended use of GS. Given the genetic diversity of conifer species, it would perhaps be more prudent to create denser marker arrays that can be used across populations and breeding programs . IncreasPseudotsuga menziesii Mirb. (Franco)). This population was originally established by the Ministry of Forests, Lands and Natural Resource Operations of British Columbia, Canada in 1975 and it is made up of 165 full-sib families (54 parents), from which 37 families were selected for sampling from three test sites , Fleet River , and Lost Creek ) giving a total of 1,372 trees (N≈500 per site).Predictive models for GS were trained on two progeny testing populations. The first population consist of 38-year-old coastal Douglas-fir (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.) (N≈375 per site). This progeny test trial was established in 1972/73 by the Ministry of Forests, Lands and Natural Resource Operations of British Columbia Canada and is made up of 181 open-pollinated families, of which the best performing 25 families were selected based on their growth attributes. The trial is located on three sites , Prince George Tree Improvement Station (PGTIS) , and Quesnel ).The second population consisted of 1,126 38-year-old Interior spruce trees . Recordings from the Resistograph® were scaled by DBH measurements to obtain wood density indices following El-Kassaby et al. [3 using X-ray densitometry (WDX-ray), which uses increment cores extracted from the trees.Mid-rotation height measurements of the sampled trees were recorded: at age 35 for the Douglas-fir population, and at age 38 Interior spruce (HT: in meters). Estimated breeding values (EBVs) for HT were obtained in ASReml 4.0  and usedy et al. . Wood de©, Florida, US), probes were designed based on the available Douglas-fir transcriptome assembly [Genotyping of the Douglas-fir samples, using whole exome capture, was performed in a commercial facility  following Elshire et al.  and ChenNe) for the Douglas-fir and spruce were estimated using an Excel program developed by Dr. M. Lstiburek  that was based on Lindgren et al. [The effective population size , the average marker densities tested ranged from 0.05–25 markers/cM.The effect of the number of markers on predictive accuracy was ascertained by carrying out a random sampling method for choosing markers from the total ‘pool’ containing 56,454 SNPs for Douglas-fir and 62,190 for Interior spruce. Sets with SNP totals in the range of 200–50,000 were tested and replicated 10 times, randomly sampling SNPs for each repetition. The cross-validation processes of the RR-BLUP model was then performed using these randomly sampled SNP sets. This analysis was carried out on the height and wood density phenotypes. Assuming a genome length of ~2,000cM for both Douglas-fir  and InteEBVs were calculated in ASReml 4.0  via liney is the phenotypic trait measurement, β is a vector of fixed effects (including mean and site effects), a is a vector of individual random additive genetic effects ~ N, sa is a site x additive genetic interaction ~ N, s(rep) is a vector of the block effect within site ~ N2), sf is a random effect site x family interaction ~ N, f is the effect of family ~ N, and e is the random residual effect ~ N, and X, Z1-5 are incidence matrices assigning fixed and random effects to each observation. I is the identity matrix and A the average numerator relationship matrix.Where 2 = σa2 / (σa2 + σsa2 +σsf2 + σf2 + σe2), where σa2, σsa2, σsf2, σf2 and σe2 are the variances of additive genetic, site x additive genetic, site x family, family, and residual effects, respectively.Narrow-sense heritability was calculated as hy is the phenotypic measurement of the analyzed trait, β is a vector of fixed effect , a is a vector of random additive effects ~ N, sa is a site x additive genetic interaction ~ N, s(rep) is a vector of the block effect within site ~ N2), e is a the random residual effect ~ N, and X, Z1-3 are incidence matrices assigning fixed and random effects to each observation. I is the identity matrix and A the average numerator relationship matrix.For the Interior Spruce population, a similar mixed model was used, without family effect:h2 = σa2/ (σa2 + σsa2 + σe2), where σa2, σsa2, and σe2 are the variances of additive genetic, site x additive genetic, and residual effects, respectively.Narrow-sense heritability was calculated as ABLUP cross-validation for both species was performed in ASReml R v4.0 . Ten folEBVy is the vector of n tree EBV records for height, 1 is a vector of 1, μ is the intercept, g is the vector of random marker effects, Z is the design matrix for the random marker effects, and e is the residual random effects vector. In the RR-BLUP procedure, the residuals and marker effects are presumed to follow normal distributions with constant variance, i.e. e ~ g ~ I is an identity matrix. Marker effect solutions are calculated according to the following equation:ʎ = The GS method used in the analysis was ridge regression (RR-BLUP) , and wasPredictive accuracy was used to estimate the performance of this GS method. Predictive accuracy was determined as the mean of the replications of the Pearson product-moment correlation between estimated breeding values (EBVs) of the validation set and their genomic estimated breeding values (GEBVs) or r. Validation was applied as a replicated randomly assigned 10-fold cross validation repeated 10 times in which 9/10 folds were used to train the model, the other fold constituting the validation population. Information from the 3 sites were pooled.


FBN1 mutations. We found strong correlations between features within the same system  suggesting common underlying determinants, while features belonging to different systems were largely uncorrelated. We adapted a classical quantitative genetics model to estimate the heritability of each clinical feature from phenotypic correlations between relatives. Most clinical features showed strong familial aggregation and high heritability. We found a significant contribution by the major locus on the phenotypic variance only for ectopia lentis using a new strategy. Finally, we found evidence for the “Carter effect” in the MFS cardiovascular phenotype, which supports a polygenic model for MFS cardiovascular variability and indicates additional risk for children of MFS mothers with an aortic event. Our results demonstrate that an important part of the phenotypic variability in MFS is under the control of inherited modifiers, widely shared between features within the same system, but not among different systems. Further research must be performed to identify genetic modifiers of MFS severity.Marfan syndrome (MFS) is an autosomal dominant connective tissue disorder with considerable inter- and intra-familial clinical variability. The contribution of inherited modifiers to variability has not been quantified. We analyzed the distribution of 23 clinical features in 1306 well-phenotyped MFS patients carrying  Marfan syndrome MFS MIM#154700)) is a connective tissue disorder with an autosomal dominant inheritance and a prevalence of 1 in 5000 individuals worldwide. Clinical features of MFS involve the skeletal, cardiovascular, and ocular systems, as well as the skin, the lung, and the dura [54700) is4700) is Recently, we showed that the level of residual FBN1 expression in MFS patients with premature termination codons (PTC) was significantly associated with a few clinical features, mainly ectopia lentis . Other gThe purpose of this study was first to describe cross-correlations between MFS clinical features and identify groups of features that could have common genetic or environmental determinants. We then propose a model to estimate each clinical feature’s heritability and the influence of the major locus from phenotypic correlations between related individuals. Finally, based on the significantly higher prevalence of aortic events (dissection or surgery) in males, we looked at the influence of the sex of the transmitting parent on the severity of the cardiovascular features of the disease.This study was based on a cohort of 1306 MFS patients, collected since 1995 by the Centre de référence National sur les syndromes de Marfan et apparentés , in the Bichat university hospital . This ensures a high level of homogeneity and quality of data collection. The diagnosis was made according to the revised Ghent nosology [This research was performed in agreement with French bioethics law, and our institution  was allowed to use all patient data for research. For this research, informed consent was obtained for all patients.Twenty-one typical MFS clinical features were considered: 3 are cardiovascular (thoracic aortic aneurysm (TAA), aortic dissection and aortic surgery), 2 are ophthalmological (ectopia lentis and ectopia lentis surgery), and 16 are found in various systems, notably the skeleton and are named “systemic features” . Aortic dilatation was expressed as a standard deviation of aortic root dilatation with respect to age, sex, and body surface area, calculated according to Campens et al. . For patPhenotypic correlation coefficients were computed on S.A.G.E  release 6.3 , which pTo investigate the relationship between MFS clinical features, we computed cross-correlations between 19 MFS clinical features , after correction for covariates .To confirm individual correlations between clinical features in terms of “clusters”, we applied agglomerative hierarchical clustering. Complete linkage clustering was chosen as linkage criteria, and the metric chosen was the Pearson correlation coefficient provided by S.A.G.E. and corrected for covariates. A 5% threshold was used to define the clusters.Due to the small sample of monozygotic and dizygotic twins in our cohort, we used a classical quantitative genetics model, adapted to the case of autosomal dominant inheritance. The phenotypic value P of a clinical feature was assumed to be given by:G is the genotypic value and E the environmental effect. A and D represent the additive effect of genes and the dominance component, unlinked to the major locus FBN1, respectively. As MFS is considered to be mainly a monogenic disease with autosomal dominant inheritance, we added two other variables that account for the influence of the major locus FBN1. mM refers to the mean phenotypic difference between the MFS population and the general population. For a given phenotypic trait, its value will be the same for any individual diagnosed with MFS, whatever the mutation . However, different mutations might have different phenotypic consequences. This specific effect of a given mutation is taken into account by speM, which has the same value for all affected members within a family (carrying the same mutation) but takes different values for different families with different FBN1 mutations.i and j be two related individuals, r their familial relationship, and d its kinship degree. The phenotypic correlation between i and j for the considered clinical feature is then given by:Var, Cov, and 1 are, respectively, the variance, the covariance, and the indicator function. Heritability (H2) is defined as the fraction of the phenotypic variance that can be explained by variations in genotypes:Let In the case of a monogenic disorder such as MFS, it can be further divided into heritability from the main locus (H2FBN1), and heritability due to genetic modifiers unlinked to FBN1(H2non-FBN1). The latter will be referred to as “non-FBN1 heritability”.Only weak genotype–phenotype correlations are reported in the literature, as far as the FBN1 locus is concerned. Moreover, one of the few correlations that has been well established to date is the association between neonatal MFS in association with mutations within exons 24 to 32. However, these patients are likely never to reach the age of 18, and thus, are not recruited in our study. Therefore, global heritability can be assumed to be mainly driven by non-FBN1 heritability (H2 non-FBN1), and H2FBN1 will first be neglected. Double the phenotypic correlation between parents and offspring or between siblings has often been proposed as an estimator of heritability .H2≈2 CoHowever, siblings’ correlation is likely to overestimate heritability because of common environmental factors. On the other hand, parent–offspring correlation might slightly underestimate it as it does not account for the dominance component of heritability . Therefore, and to increase statistical power, we propose to use the mean of these two estimators.p-value < 0.05 were considered to be significant. To assess correlations between clinical features, as multiple tests were performed simultaneously (171 tests), the significance threshold was adapted according to the Bonferroni correction (p < 3.0 × 10−4).Statistical tests with a Out of the cohort of 1306 MFS patients with an identified FBN1 mutation, 999 patients over 18 years old were included in this study. They belonged to 674 independent families, with 1 to 13 affected individuals in each family. Patients were between 18 and 89 years old at the time of the last follow-up, with a median age of 38, and slightly more females than males . Frequency of clinical features are reported in We also applied hierarchical clustering on clinical features to confirm these individual strong intra-systemic and weak inter-systemic phenotype correlations in terms of “clusters”. Complete linkage clustering was chosen as linkage criteria, and the metric chosen was Pearson’s correlation coefficient corrected for covariates  with two methods.2 obtained in the subgroup of PTC FBN1 families only (estimating H2 non-FBN1) from H2 obtained in all FBN1 families (AF), for siblings ), and also for parent–offspring ). Changing the study population modifies not only the numerator (the genetic variance) but also the denominator  of heritability. Forgetting this would lead to misconceptions in heritability studies [We first focused on families with a PTC in the FBN1 locus, as it provides a genetically homogeneous group, leading in most of the cases to a null allele  = 0). We took advantage of this property to estimate H2FBN1 by subtracting H studies . Formallp < 0.05), (2) that the mean value of H2FBN1 among features was 4.4%, reflecting a low influence of variations at the main locus on the phenotype.We used the mean of these two values. Results are available in d is the kinship degree between relatives i and j. We calculated avuncular and cousins phenotypic correlations for one ocular (ectopia lentis surgery), one skeletal (arachnodactyly), and one cardiovascular (TAA) MFS feature  leads to similar conclusions. Both methods demonstrated a strong intra-systemic phenotype correlation contrasting with a very low inter-systemic correlation. If FBN1 was the main driver of MFS clinical presentation, all symptoms would be correlated. These results, therefore, suggest, first, that the FBN1 locus by itself is not the main driver of phenotypic presentation of individual symptoms despite the FBN1 gene mutation being a necessary condition for MFS to occur. They also suggest that features within the same system share common underlying determinants and, on the other hand, that the non-FBN1 genetic or environmental determinants of cardiovascular, ocular, and systemic features of MFS are likely to be different. It also highlights the importance of considering separately and attributing an independent value to the three main clinical blocks  in the clinical diagnosis of MFS (that is effectively done in the Ghent nosology) and that, finally, has an important impact on future studies aiming to identify genetic modifiers.Altogether, heritability appears quite high, ranging from 40% to 60% for most of the signs despite the fact that most clinical features were quoted as a binary variable. Binary features are often assumed to reflect a latent continuous variable  such that patients with a liability above a given threshold will be diseased. The calculated heritability on the binary scale is then lower than heritability on the continuous liability scale, even more so for features whose prevalence is very high or very low . TherefoCardiovascular features , although well correlated in the phenotypic study, showed an important difference in heritability. While TAA was assessed by a continuous variable, both aortic surgery and dissection are binary features with relatively low prevalence , which may have affected both correlation coefficient and statistical power . Further2 FBN1, reflecting genotype/phenotype correlations) and or the influence of yet unidentified loci independent of FBN1(H2 non-FBN1). This can be estimated through two approaches: 1) comparing the phenotypic correlations for families carrying a null allele mutation  with the phenotypic correlations for all MFS families . Due to low statistical power, we were not able to estimate robustly H2FBN1 for each phenotype. Nevertheless, 16 estimates out of 22 were positive, which probability under the null hypothesis  is only 4.7% suggesting a significant influence of the major locus on phenotypic variability (p < 0.05). The mean average value was H2FBN1 4.4%, indicating a significant but low influence of the major locus on the phenotype. This result was strengthened by the comparison of heritability between close and distant relatives. The influence of the FBN1 locus remains stable, whereas the influence of non FBN1 factors decreases proportionally to 2-d the amount of non FBN1 genetic factors shared. A very high correlation was found between cousins for ectopia lentis surgery . Supposing that the variability at the FBN1 locus has no influence on the ocular phenotype  = 0), a 21% correlation between cousins  would then lead to an absurd 168% H2 heritability (Corr(cousins) = Var(A)/8Var(P) = H2/8 as cousins share on average 1/8 of their genome) [The genetic variability of clinical expression in this dominant disease may reflect both the influence of the FBN1 locus (H genome) . This de genome)  and are, genome) .Finally, it has been reported that males with MFS had a more severe cardiovascular phenotype than females  .In summary, our results demonstrate that an important part of the phenotypic variability in MFS is under the control of inherited modifiers. These modifiers appear to be widely shared between features within the same system , but not among different systems. Therefore, studies limited to one system should be considered carefully. In particular, no extrapolation can be made from one system to another as far as inherited modifiers are concerned. The ocular system showed the strongest heritability (>60%) and is the only one for which a significant impact of variations at the major locus on the phenotype was found. Most skeletal or cardiovascular features displayed 40% to 60% heritability. The observation of a “Carter effect” in the cardiovascular phenotype provides further evidence of the existence of inherited modifiers and supports the high heritability of cardiovascular clinical features. The overall high heritability that was found is a robust result as it was replicated by two different methods (phenotypic correlation between relatives and Carter effect), and as heritability estimates might have been underestimated for two reasons. First, most clinical features were binary, which artificially reduces heritability, even more so for features with low prevalence . Second,


P = 1.97 × 10−13) was then validated using an obstetric measure of glycosuria measured in the same cohort (227/6639). We performed a secondary GWAS in the 1986 Northern Finland Birth Cohort  using midwife-reported glycosuria and offspring genotype as a proxy for maternal genotype. The combined results revealed evidence for a consistent effect on glycosuria at the chromosome 16 locus. In follow-up analyses, we saw little evidence of shared genetic underpinnings with the exception of urinary albumin-to-creatinine ratio , a biomarker of kidney disease. In conclusion, we identified a genetic association with self-reported glycosuria during pregnancy, with the lead SNP located 15kB upstream of SLC5A2, a target of antidiabetic drugs. The lack of strong genetic correlation with seemingly related traits such as type 2 diabetes suggests different genetic risk factors exist for glycosuria during pregnancy.Glycosuria is a condition where glucose is detected in urine at higher concentrations than normal (i.e. not detectable). Glycosuria at some point during pregnancy has an estimated prevalence of 50% and is associated with adverse outcomes in both mothers and offspring. Little is currently known about the genetic contribution to this trait or the extent to which it overlaps with other seemingly related traits, e.g. diabetes. We performed a genome-wide association study (GWAS) for self-reported glycosuria in pregnant mothers from the Avon Longitudinal Study of Parents and Children (cases/controls = 1249/5140). We identified two loci, one of which (lead SNP = rs13337037; chromosome 16; odds ratio of glycosuria per effect allele: 1.42; 95% CI: 1.30, 1.56;  At normal levels, glucose is not detectable in urine. Glycosuria, the presence of glucose in urine above normal levels, may be caused by either an increase in blood glucose such that the renal tubules are overwhelmed and complete reabsorption of presented glucose is not possible, a lowering of the renal threshold, or inhibition of renal tubule reabsorption . AlthougThere is evidence that glycosuria is associated with adverse cardio-metabolic outcomes, including non-alcoholic fatty liver disease, in offspring  and withBoth glucose regulation and renal function are heritable, with reliable evidence for several genetic variants being associated with a range of relevant traits, including fasting glucose ,9, insulA genome-wide association study (GWAS) of self-reported glycosuria during pregnancy performed in ALSPAC represents our primary analysis. Additional work exploring associations further was conducted in the same cohort. Due to lack of available data, a replication GWAS was not possible. We instead sought supporting evidence from a secondary GWAS analysis performed in NFBC1986 and combined GWAS results from ALSPAC and NFBC1986 to identify persistent associations. An overview of the study and its components is presented in P-value = 5 × 10−8) at 55 SNPs  = 28%; P-value = 4.02 × 10−8; MAF = 10%; Supplementary Material, R2 > 0.8; ARMC5, TGFB1I1, SLC5A2 and C16orf58. The lead SNP on chromosome 9 is part of a block of 25 SNPs in high LD  = 0.08), corresponding to an estimated narrow-sense heritability of 10% . The corresponding OR for rs10991823 was 1.10 .The correlation between self-reported glycosuria and glycosuria determined by reagent strip was 0.31  at 49 SNPs on chromosome 6  . The re-scaling of this effect size to approximate the allelic effect in mothers  in the primary analysis showed evidence of association with fasting insulin , with weaker correlations found for type 2 diabetes  and HbA1c  . Our main focus was on trait categories from LD Hub that were a priori related to glycosuria , and results for these are shown in Supplementary Material, Rg = 0.7; SE = 0.24; P-value = 0.004) and urinary albumin-to-creatinine ratio .Of the 832 traits available in LD Hub (date accessed: 19/02/2019), genetic correlations were not returned for 81 traits, with a further two traits showing correlations above 1 and below −1 (LDSR mean ChiARMC5 (a member of the ARM (armadillo/beta-catenin-like repeat) superfamily implicated in mediation of protein–protein interactions  through the Ensembl Variant Effect Predictor. All SNPs were existing variants with the ‘MODIFIER’ impact annotation, suggesting they are non-coding variants or variants affecting non-coding genes  and upsreceptor ) and SLCARMC5, TGFB1I1, SLC5A2 and ZNF843 across a number of different tissues  for a number of nearby genes, including ARMC5, TGFB1I1, SLC5A2 and C16orf58) on chromosome 16 and self-reported phenotype. When examining the relationship between rs13337037 and glycosuria determined by reagent strip, we found consistent evidence for association despite differences in sample size and the frequency of glycosuria status. Each A allele of rs13337037 was associated with a 47% increased risk of self-reported glycosuria and a 64% increased risk of glycosuria determined by reagent strip. While we lacked fully replicable datasets for our GWAS of self-reported glycosuria, we instead made use of offspring genotype as a predictor for maternal genotype in the NFBC1986 collection. The only signal that persisted in efforts to filter out chance associations in ALSPAC by combining GWAS results from the two cohorts was that at rs13337037 on chromosome 16.In a GWAS analysis of glycosuria during pregnancy conducted in ALSPAC , we identified an association between a block of common SNPs with high LD surrounding multiple genes /glucose cotransporter  (ARMC5 is implicated in mediation of protein–protein interactions (https://www.ncbi.nlm.nih.gov/gene/79798); TGFB1I1 encodes a protein that acts as a coactivator of the androgen receptor (https://www.ncbi.nlm.nih.gov/gene/7041); C16orf58 is not well characterized but is thought to be involved in protein–protein interaction (date accessed: 02/22/2019—https://www.ncbi.nlm.nih.gov/gene/64755).rs13337037 lies 15 kb upstream of fic SNPs , evidencycosuria ,22. SLC5orter 2) . For theractions  and may ractions  (date acreceptor  and whicreceptor  (date acSLC5A2-encoded SGLT2 responsible for 90% of this glucose reabsorption and SGLT1 (high affinity and low capacity) responsible for the remaining ~ 10%  is unclear. In our exploration of the shared genetic contribution to glycosuria during pregnancy and other metabolic traits, we found that while there was no overlap in genome-wide significant hits across the traits, estimates of shared heritable contribution (i.e. considering genetic effects across the entire genome) identified urinary albumin-to-creatinine ratio as the most strongly correlated trait. While the LDSR approach used for these analyses provides only an approximation of genetic correlation and results should ideally be followed up by more detailed analyses , these pWe used a glycosuria measurement that was retrospectively reported by mothers in pregnancy for our main ALSPAC analyses. This is likely to be reported with error and will likely include women with very different levels of glycosuria . This is supported by the higher prevalence of glycosuria defined by retrospective report compared with the definition used for glycosuria determined by reagent strip of at least ‘++’ on at least two occasions from all glycosuria measures in antenatal records (20% versus 4%). However, these two measures were correlated. Any misclassification of this maternal retrospective report is likely to be non-differential by genotype (as the women will not know their genotype) and so would be expected to attenuate any genome-wide results towards the null. This is consistent with our finding that the lead SNP was more strongly associated with the reagent strip measure of glycosuria than with the retrospective report.In the absence of other studies with both glycosuria assessed during pregnancy and corresponding associated genome-wide genetic data, we were limited in our ability to perform an independent replication of the GWAS performed in ALSPAC. However, we were able to look for persistent signal using results from a GWAS performed in NFBC1986 using offspring genotype. It has been suggested that allelic effect estimates derived in this way can be re-scaled (multiplied by 2) to give an approximation of the allele effect in the mother. However, given that this re-scaling is based on expectation, one can expect the actual multiplication factor, in any given instance, to vary around this expectation. Furthermore, we did not carry out a comprehensive validation of this approach within the context of this study, where additional issues such as the potential for a direct effect of offspring genotype on maternal glycosuria phenotype could be relevant. Therefore, while the combined results indicate persistence of the primary signal across the two datasets and we see reasonable agreement with respect to the magnitude of effect in the two studies, our results should be treated with caution until replicated, preferably in independent large cohorts where both maternal genotype and phenotype are available.SLC5A2, which has been implicated in familial renal glycosuria and the gene product of which is the target of some type 2 diabetes drugs . Furthermore, hypothesis-free genetic correlation analysis suggested genetic loci underpinning glycosuria susceptibility may be linked with kidney disease, reflecting the joint role of circulating glucose and renal function on glycosuria and the possible effect of hyperglycaemia on renal function. This shared heritability suggests distinguishing gestational diabetes from other presentations of diabetes or kidney disease may not be possible. Further replication of these findings is important.Our GWAS of reported glycosuria during the third trimester of pregnancy has identified associations of genetic variants on chromosome 16 near multiple genes, including st April 1991 and the 31st December 1992  contains details of all the data that is available through a fully searchable data dictionary and variable search tool . Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees . Informed consent for the use of data collected via questionnaire and clinics was obtained from participants following recommendations of the ALSPAC Ethics and Law Committee at the time. Full details of the ALSPAC consent procedures are available on the study website .Participants were mothers from ALSPAC, a large prospective cohort study that recruited 14 541 pregnancies in the former Avon Health Authority area in South West England, with expected delivery dates between the 1ber 1992 ,29 , existing diabetes before the pregnancy (n = 47), gestational diabetes  and glycosuria  . Information on glycosuria  was abstracted from the records of each antenatal clinic visit made by the woman ). Glycosuria was defined as a record of at least ++  on at least two occasions at any time during the pregnancy. In total, 12 281 women had information on reagent strip defined glycosuria.Of the 12 281 women with information on reagent-strip-determined glycosuria, we excluded those without information on pre-existing diabetes and gestational diabetes and glycosuria, and those with pre-existing diabetes or gestational diabetes. Of the remaining 12 177 women, 404 (3%) fulfilled our criteria for glycosuria and 7652 (63%) had available genetic data. Of the 7652 women, 261 (3%) fulfilled our criteria for glycosuria. After excluding women withdrawing consent for the phenotype and genetic data, as well as related individuals, 6866 (91%) women had data on glycosuria determined by reagent strip and genotype data, of which 227 (3%) fulfilled our criteria for glycosuria.P < 1 × 10−06) were removed. ALSPAC children were genotyped using the Illumina HumanHap550 quad chip genotyping platforms by 23andMe subcontracting the Wellcome Trust Sanger Institute, Cambridge, UK and the Laboratory Corporation of America, Burlington, NC, USA. For ALSPAC children, SNPs with MAF of <1%, a call rate of <95% or evidence for violations of Hardy–Weinberg equilibrium (P < 5 × 10−7), were removed. In total, we removed 1946 individuals based on relatedness and withdrawal of consent for both genotype and the phenotype data, leaving 15 896 individuals for further analysis, of which 7921 were mothers. See Supplementary Information for full details on ALSPAC genetic data and imputation.Imputation of ALSPAC mother’s genetic data was performed on a combined mother and child data set using Impute2 against the 1000 Genomes Phase 1 reference panel . ALSPAC st July 1985 and 30th June 1986. A total of 9362 mothers were included in the study, resulting in 99% of all eligible births in the region being recruited (n = 9432 live born children) (The Northern Finland Birth Cohort 1986 (NFBC1986) is a prospective birth cohort that recruited women in the two northernmost provinces of Finland (Oulu and Lapland) with an expected delivery date between 1hildren) (Supplemehildren) . Informehttps://www.oulu.fi/nfbc/node/18143), with information on glycosuria reported in ‘Pregnancy Questionnaire II (yellow form)’, question 7.Data were gathered prospectively at the first antenatal clinic visit and onwards . Mothers provided information on maternal background via self-report questionnaires. Data on antenatal visits, hospital admissions and birth outcomes were obtained from maternity health centres and hospital medical records. Midwives were given questionnaires to complete during the mother’s last visit to the antenatal care unit or during the first home visit after the delivery, which included the following question: ‘urinary glucose’ with possible answers ‘+’  and ‘−’  . PregnanOf the 9362 women included in the study, 9336 had data available on midwife-reported glycosuria. Of these 9336, 1789 (19%) were reported with ‘+’. Of the 9336 women with available midwife-reported glycosuria, 3738 (40%) had quality-controlled offspring genetic data available and, of these mothers, 747 (20%) were reported as having glycosuria.For this GWAS, maternal genotype data were not available and so we tested for an association of offspring genotype with midwife-reported glycosuria as our main outcome.P < 0.0001) or with low call rate (<0.99), imputation was performed using Impute2 against the 1000 Genomes Phase 3 reference panel   using logistic regression and adjusting for the top 10 principal components of genetic ancestry to control for potential confounding by population stratification in SNPTEST (v2.5.2). We present all GWAS results and focus on those that satisfy a conventional genome-wide significance threshold of v1.26.0) . We carrWe examined the relationship between self-reported glycosuria and glycosuria determined by reagent strip performing a Pearson’s correlation test in R   with the mother’s glycosuria in pregnancy as the outcome and offspring’s imputed genotype as the predictor of interest, assuming an additive model and including the top four multidimensional scaling (MDS) coordinates as covariates to adjust for population stratification . We filtBased on quantitative genetics theory (i.e. the rules of inheritance) and assuming an additive genetic effect, we expected that any allelic effect estimated in the NFBC1986 analysis (i.e. using offspring genotype in place of mother’s own) would be smaller compared to that observed using own genotype. More specifically, it has previously been suggested that the average (expected) allelic effect of one allele in the offspring  is half the effect that would be observed using the parent’s own genotype ,16. FurtP-value observed in each study are converted into a signed Z-score indicating the strength and direction of effect , we performed a sample size-based approach in which the direction of effect and f effect . Z-scoreP-value ≤5 × 10−8: HBA1c  extracted from GWAS for the aforementioned traits and effect estimates for the same SNPs in our GWAS of glycosuria. Data were visualized in scatter plots and concordance assessed via Pearson’s product-moment correlation. The numbers of SNPs included in this analysis were as follows: type 2 diabetes = 1792, estimated glomerular filtration rate = 4196, BMI = 1860, fasting glucose = 290 and HBA1c = 821.To investigate evidence of overlap of association between glycosuria and glucose regulation and renal function traits, we examined whether SNPs associated with glycosuria in pregnancy in ALSPAC are also associated with the following traits at a genome-wide significance threshold of 8: HBA1c , fasting8: HBA1c , estimat8: HBA1c  (see Suphttp://ldsc.broadinstitute.org/ldhub/) using linkage disequilibrium (LD) score regression (LDSR)  .R2 ≥ 0.8) on genes and regulatory regions via the Ensembl Variant Effect Predictor  and visit the cohort website (www.oulu.fi/nfbc) for more information.Data are available from the Northern Finland Birth Cohort (NFBC) for researchers who meet the criteria for accessing confidential data. Please contact NFBC project centre  describes in detail the policy regarding data sharing. A sample set of similar data containing relevant ALSPAC variables is available from the European Genome-phenome Archive (accession number: EGAS00001000090): https://www.ebi.ac.uk/ega/studies/EGAS00001000090.ALSPAC data used for this submission will be made available on request to the ALSPAC Executive via the website, which also provides full details and distributions of the ALSPAC study variables: Matthew A. Lee, George McMahon and Ville Karhunen are responsible for data curation, formal analysis, investigation, visualization, writing—original draft preparation. Dr Kaitlin H. Wade, Dr Laura J Corbin and Dr David A Hughes supervised and were responsible for writing—review and editing. Professor George Davey Smith and Professor Debbie A Lawlor were responsible for funding acquisition, writing—review and editing. Professor Marjo-Riitta Jarvelin for conceptualization, funding acquisition, project administration, supervision, writing—review and editing. Professor Nicholas J. Timpson for conceptualization, funding acquisition, methodology, project administration, supervision, writing—review and editing.Supplementary_Figure_1_ddaa054Click here for additional data file.Supplementary_Figure_2_ddaa054Click here for additional data file.Supplementary_Figure_3_ddaa054Click here for additional data file.Supplementary_Figure_4_ddaa054Click here for additional data file.Supplementary_Figure_5_ddaa054Click here for additional data file.Supplementary_Figure_6_ddaa054Click here for additional data file.Supplementary_Figure_7_ddaa054Click here for additional data file.Supplementary_Figure_8_ddaa054Click here for additional data file.Supplementary_Figure_9_ddaa054Click here for additional data file.Supplementary_tables_ddaa054Click here for additional data file.Supplementary_legends_ddaa054Click here for additional data file.HMG-2019-TF-00473_R1_Supplement_LEE_ddaa054Click here for additional data file.


In the last decade, a large number of common variants underlying complex diseases have been identified through genome-wide association studies (GWASs). Summary data of the GWASs are freely and publicly available. The summary data is usually obtained through single marker analysis. Gene-based analysis offers a useful alternative and complement to single marker analysis. Results from gene level association tests can be more readily integrated with downstream functional and pathogenic investigations. Most existing gene-based methods fall into two categories: burden tests and quadratic tests. Burden tests are usually powerful when the directions of effects of causal variants are the same. However, they may suffer loss of statistical power when different directions of effects exist at the causal variants. The power of quadratic tests is not affected by the directions of effects but could be less powerful due to issues such as the large number of degree of freedoms. These drawbacks of existing gene based methods motivated us to develop a new powerful method to identify disease associated genes using existing GWAS summary data.In this paper, we propose a new truncated statistic method (TS) by utilizing a truncated method to find the genes that have a true contribution to the genetic association. Extensive simulation studies demonstrate that our proposed test outperforms other comparable tests. We applied TS and other comparable methods to the schizophrenia GWAS data and type 2 diabetes (T2D) GWAS meta-analysis summary data. TS identified more disease associated genes than comparable methods. Many of the significant genes identified by TS may have important mechanisms relevant to the associated traits. TS is implemented in C program TS, which is freely and publicly available online.The proposed truncated statistic outperforms existing methods. It can be employed to detect novel traits associated genes using GWAS summary data. Even though genome-wide association studies (GWASs) have been remarkably successful in identifying a large number of genetic variants associated with complex traits and diseases, these identified variants can only explain a small to modest fraction of heritability . Larger Most popular gene based association tests (grouping SNPs together into a SNP set (e.g. a gene or surrounding of a gene) to test the joint effects of SNPs in the SNP set ) can oftMost of the existing gene-based methods can be viewed as either burden test methods or quadratic test methods. Burden test methods are usually powerful when the direction of effects of genetic variants in the considered region are the same. Quadratic test methods usually have reasonable power given a wide range of alternative hypotheses. Specifically, burden tests collapse the variants in a genomic region into a single burden variable by using weighted combination of variants, and then test the association between the trait and the single burden variable. Quadratic tests refer to tests with statistics of quadratic forms of the score vector. However, burden tests can suffer loss of statistical power when different directions of effects of causal variants exist, while quadratic tests could be less powerful due to other issues such as the large number of degree of freedoms. Thus, we aim to develop a new powerful method for further analyzing GWAS summary data. In the “To evaluate the performance of the proposed method, we conducted extensive simulation studies and real data analysis. We compared our method TS with five existing gene-based methods: a gene-based association test that uses an extended Simes procedure (GATES) , an adapWe conducted extensive simulation studies to evaluate the performance of TS. We compared the type I error rates and power of TS with the five existing methods by following the simulation setting in . Kwak etZ from a multivariate normal distribution N, where R denotes the corresponding LD matrix of the gene EPB41 (erythrocyte membrane protein band 4.1), which is used in our simulation studies. Authors in P(Qρ), where Qρ=(1−ρ)Q+ρB2 and P(Qρ) denotes the corresponding p-value.Adaptive test (AT), γ∈ΓPSPUs(γ) where Adaptive sum of powered score tests (aSPU), aSPUs= minme is the effective number of independent p-values among the M SNPs, pj) is the effective number of independent p-values among the top j SNPs.Gene-based association test that uses extended Simes procedure (GATES), Please find the brief introduction about these methods and their notations as follows:Z∼MVN, B=M denotes a column vector of length M where every element is equal to one. Therefore, χ1 distribution. Under the null hypothesis, the statistic of the squared sum test Q=χ1 with weights being the eigenvalues of R. We compute the p-value of the T statistic of the adaptive test using an one-dimensional numerical integration, where we will search over ρ∈ following [Because ollowing . We can ollowing  using thZ= represents a vector of Z statistics following a multivariate normal distribution with mean 0 and covariance matrix R, where R is the LD matrix between SNPs. When we try to adapt the truncated statistic method for combining statistical evidence, we want to know whether the test statistic in equation T and have a corresponding covariance matrix Σ1 for n<M. We can then write the test statistic as Zn+1, to the test statistic, when τ gets smaller. The corresponding test Z statistic can be written as:If Zn+1 statistic with the first n Z statistics. From the theory of block matrix inversion, we have:where B represents the correlation between the Because Then, the truncated statistic  is equivn=1, we always have n+1 Z statistics are all independent, it means B=0. Thus, the truncated statistic degenerates to a summation of the square of these n+1 Z statistics, and our method becomes equivalent to the score test.If we want to know whether the test statistic will increase or not when we add a new Z statistic, we only need to check whether the condition Additional file 1 The TS and the other comparison methods are applied to the SCZ data.Additional file 2 The TS and the other comparison methods are applied to the UKB data.


Glycine max (L.) Merr.] have characteristics of larger seeds, less beany flavor, tender texture, and green-colored pods and seeds. Rich in nutrients, vegetable soybeans are conducive to preventing neurological disease. Due to the change of dietary habits and increasing health awareness, the demand for vegetable soybeans has increased. To conserve vegetable soybean germplasms in Taiwan, we built a core collection of vegetable soybeans, with minimum accessions, minimum redundancy, and maximum representation. Initially, a total of 213 vegetable soybean germplasms and 29 morphological traits were used to construct the core collection. After redundant accessions were removed, 200 accessions were retained as the entire collection, which was grouped into nine clusters. Here, we developed a modified Roger’s distance for mixed quantitative–qualitative phenotypes to select 30 accessions (denoted as the core collection) that had a maximum pairwise genetic distance. No significant differences were observed in all phenotypic traits  between the entire and the core collections, except plant height. Compared to the entire collection, we found that most traits retained diversities, but seven traits were slightly lost (ranged from 2 to 9%) in the core collection. The core collection demonstrated a small percentage of significant mean difference (3.45%) and a large coincidence rate (97.70%), indicating representativeness of the entire collection. Furthermore, large values in variable rate (149.80%) and coverage (92.5%) were in line with high diversity retained in the core collection. The results suggested that phenotype-based core collection can retain diversity and genetic variability of vegetable soybeans, providing a basis for further research and breeding programs.Vegetable soybeans [ Glycine max (L.) Merr.] is a legume crop that is rich in protein, oil, and other nutrients such as lecithin and isoflavones. It is one of the most important economic crops worldwide , smooth texture, less beany flavor, and higher contents of vitamin A, vitamin C, sucrose, and starch . The demSoybeans are rich in isoflavones and anthocyanins, which prevent or inhibit the progression of cancer and other chronic diseases . EvidencApart from the amount of nutrient composition in vegetable soybeans, the quality of vegetable soybeans depends on the external appearance . To markThe concept of a CC, defined as a limited set of accessions, was first introduced by Euclidean distance and Manhattan distance, which are well-defined  in Taiwan. The countries of origin of the accessions were grouped into Taiwan, Japan, United States, China, Hong Kong, and Philippines. Phenotypic traits were investigated and recorded in the field at Kaohsiung District Agricultural Research and Extension Station, COA, and followed the guidelines of distinctness, uniformity, and stability (DUS) test for vegetable soybean in four consecutive seasons (1995–1998 autumn). Each accession was characterized for 47 phenotypic traits in relation to morphology (38 traits), growth (5 traits), phenology (2 traits), and production (2 traits). For detailed agronomic descriptors, please refer to t-test only when ANOVA p-value reached significant difference. The results suggested that no significantly environmental effects were observed on the phenotypes investigated in the field at Kaohsiung District Agricultural Research and Extension Station , humidity (%), duration of sunshine (hours), precipitation (mm), and days with precipitation (days), were collected daily during 1995–1998 from the Kaohsiung District Agricultural Research and Extension Station and the Kaohsiung Weather Station of the Central Weather Bureau in Taiwan. One-way ANOVA was conducted to test for differences among four autumn seasons (October–November) and among 4 years, followed by paired samples  Station .Multiple imputation is a mathematically computational method to deal with missing values in phenotypes . There wputation .k-means approach by kamila package in R  is calculated asTo examine the diversity of vegetable soybean germplasms, weighted age in R  was usedail is an n×k assignment matrix, which subjects towhere ail = 1⇔xi belongs to the lth clusterwj is the weight parameter, which is defined byw is an adjustable parameter, and we selected weight as 0.5 by default. d represents the distance between xij and clj. Let xij be the value of the jth phenotype in the ith accession, and cljbe the centroid of the lth cluster in the jth phenotype. The distance of the quantitative trait is measured by Euclidean distance dwhere Y = {Y1,Y2,…,Yn} with p different trait levels is expanded to p indicator variables, denotedasIA(y)The qualitative trait is converted to dummy variable (0 or 1). For instance, a qualitative phenotype P during the procedure of iteration. To select the optimal number of clusters, we set criteria as follows: Shannon diversity index is above 90%, Nei’s diversity index is above 85%, and the proportion of variance explained is at least 70%.Thus, Euclidean distance can be applied to measure the distance. The aim is to minimize the objective function j (∈ C)can be expressed asWe proposed modified Roger’s distance to construct a CC, which is a small collection with minimal redundancy and maximal representative of the ES. The modified Roger’s distance is a suitable algorithm for mixed quantitative–qualitative phenotypes. The distance  is defined as,where Q is the qualitative phenotype, and fij and fi^*j represent the frequency of ith and i∗th accession in jth trait, respectively. Thus, the modified Roger’s distance can be of the form Where H′) and Nei’s diversity index (H′) is defined asPhenotypic diversity is calculated using the Shannon–Weaver diversity index  MD% is small and the percentage of significant mean difference (α = 0.05) between the CC and the EC in all 29 phenotypic traits is no more than 20%, and (2) the CR% is greater than 80%  the mean difference percentage .Levene’s test was conducted, with 1,000 bootstrap iterations, to check homogeneity of variance among groups . After ts t-test . For quas t-test  .p-values > 0.05) among observed and imputed phenotypic traits, suggesting reliable and accurate imputed values.Our data suffered, to a certain extent, from missing rates, ranging from 0.5 to 26.5% . These menotypes . Tables k-means clustering method, using completed phenotypes , to search for the population structure of the whole picture on the EC. k-means clustering algorithm for 29 mixed-type phenotypic traits. We calculated modified Roger’s distance for mixed quantitative–qualitative phenotypic traits to capture pairwise genetic distance among accessions in the EC. The larger genetic distance, the much dissimilar. As a result, 30 accessions  was observed between the CC and the EC in all traits, except plant height , an intermediate value of VD% (52.90%), a large value of CR% (97.70%), a very high value of VR% (149.80%), and a very high percentage of coverage  . These pTo the best of our knowledge, our vegetable soybean germplasms consisted of 213 accessions, which are the largest collection worldwide. Vegetable soybean becomes popular just for more than one decade and mostly in East Asia, e.g., Japan, Taiwan, China, and South Korea; therefore, other countries take merely a small amount of production; the majority of vegetable soybeans are imported from Asia . This isIn this study, a total of 29 traits  were selected from 47 phenotypic traits, based on at most 30% threshold of missing rate. Among them, 5 traits had no missing rates, 10 traits had low missing rates (0.5–11.5%), and the remaining traits had moderate missing rates (25.5–26.5%) . GeneralPhenotype completeness is the key to access in developing a core set of germplasm for efficient exploration and effective utilization in breeding programs. In fact, the impact of missing data on phenotypic traits research can be serious, and the utilization of phenotypes is often limited by incomplete phenotypes of interest . In partthan 1.1 , suggestthan 1.1 . Multiplthan 1.1 . TherefoThe selection of sampling strategies does affect the results of the selection of the CC, as well as its diversity. To have a better illustration, we compared the proposed modified Roger’s distance algorithm with other sampling schemes, including stratified proportional sampling (SPS), simple random sampling (SRS), and advanced M strategy (using PowerCore software). As the difference test, there were only two traits, seed width by advanced M strategy and number of branches by SRS, showing significant mean difference between the EC and the CC . Most ofThe CC collected from distinct sampling strategies was evaluated. We evaluated the percentage of the trait differences between the CC and the EC, using PowerCore, SPS, and SRS methods, compared to our developed modified Roger’s distance  affect our trait values measured during 1995–1998 autumn seasons  of the entire collection. Moreover, the high diversity of the CC indicated that the germplasms were simplified without losing information. The result showed that the modified Roger’s distance algorithm was a good approach to construct the CC. This study provides a detailed list of the CC, which can be a basis for future research of vegetable soybeans and breeding practices.To the best of our knowledge, this study reported results of the first CC for vegetable soybean (or edamame) worldwide. This study consisted of the largest vegetable soybean germplasms worldwide at present, which provided valuable and/or special information on Taiwanese vegetable soybean germplasms. We proposed a modified Roger’s distance algorithm, which is a mixed-type sampling strategy, to construct a CC composed of a total of 30 accessions based on morphological traits. It was evident that the properties of the CC constructed by the modified Roger’s distance algorithm were reliable, stable, and feasible. With the representativeness of the whole germplasms, the high-quality CC possessed small mean difference, high coincidence rate, variable rate, and high coverage. The CC preserved the high phenotypic diversity in germplasms of vegetable soybean. The removal of redundancy enables us to make full use of the vegetable soybean germplasms for overall phenotype clustering. With the support of the material, the CC can be helpful for further breeding of commercial varieties.The original contributions generated for this study are included in the article/C-FK: study conception and design and acquisition and analysis of data. C-FK, S-SH, C-SW, D-GL, and SC: interpretation of data. C-FK and S-SH: drafting the manuscript. C-FK and Z-YL: manuscript revision. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Next, we quantified these distinct facial features as univariate traits in another dataset of 8,246 unaffected European individuals and performed a genome-wide association study. We identified 29 independent genetic loci that were associated (p < 5 × 10–8) with at least one of the tested endophenotypic traits, and nine genetic loci also passed the study-wide threshold (p < 8.47 × 10–10). Of the 29 loci, 22 were in proximity of loci previously associated with normal facial variation, 18 were near genes that show strong evidence in orofacial clefting (OFC), and another 10 showed some evidence in OFC. Additionally, polygenic risk scores for NSCL/P showed associations with the endophenotypic traits. This study thus supports the hypothesis of a shared genetic architecture of normal facial development and OFC.Unaffected relatives of individuals with non-syndromic cleft lip with or without cleft palate (NSCL/P) show distinctive facial features. The presence of this facial endophenotype is potentially an expression of underlying genetic susceptibility to NSCL/P in the larger unselected population. To explore this hypothesis, we first partitioned the face into 63 partially overlapping regions representing global-to-local facial morphology and then defined endophenotypic traits by contrasting the 3D facial images from 264 unaffected parents of individuals with NSCL/P versus 3,171 controls. We observed distinct facial features between parents and controls across 59 global-to-local facial segments at nominal significance ( Orofacial clefting (OFC) is one of the most frequent congenital malformations with a substantial functional, financial, and mental health burden to the persons and families affected . Non-synA comprehensive approach to studying the etiology of NSCL/P requires investigations from different angles. One approach is to study subclinical phenotypes that exist within the range of normal development and are associated with OFC. Such traits are referred to as endophenotypes, which can be present in the biological relatives of individuals with NSCL/P and are potentially an incomplete expression of susceptibility loci for OFC . A numbeExtensive characterization of the NSCL/P facial endophenotype allows us to investigate whether the presence of facial endophenotypes indicates heightened genetic susceptibility for NSCL/P. The possibility that NSCL/P risk variants influence normal-range facial variation raises interesting questions about the intersection of the genetic architecture between normal and dysmorphic facial features. Some NSCL/P candidate genes have already been shown to impact normal facial morphology. In this study, we investigate the intersection of the genetic architectures of normal facial variation and NSCL/P, starting from explicitly defined NSCL/P facial endophenotypes. First, we quantify the NSCL/P facial endophenotype at multiple levels of scale, modeling both global and local facial shape variations in a sample of unaffected parents of individuals with NSCL/P . Next, wThis study involved two datasets, each consisting of two cohorts. The EURO dataset contained 3D images and genome-wide genotype data collected from the United States and United Kingdom. These are individuals of European ancestry recruited for studies of normal-range facial variation. The PARENT dataset contained 3D images of unaffected parents of individuals with NSCL/P and a group of unselected individuals of normal-range facial variation (unaffected but no further exclusion based on a family history in OFC). An overview of the sample sizes is given in The United States samples in the EURO dataset originated from the 3D Facial Norms cohort (3DFN) and studies at the Pennsylvania State University (PSU) and Indiana University-Purdue University Indianapolis (IUPUI). The United Kingdom dataset included samples from the Avon Longitudinal Study of Parents and their Children (ALSPAC). Institutional review board (IRB) approval was obtained at each recruitment site, and all participants gave their written informed consent prior to participation. For children, written consent was obtained from a parent or legal guardian. The individuals of the EURO dataset have been tested for associations with normal-range facial morphology in previous work . In all For the 3DFN sample, 3D images and genotype data were obtained from the 3D Facial Norms repository . The repThe PSU sample included 3D images and genotypes of participants recruited through several studies at the Pennsylvania State University and sampled at the following locations: Urbana-Champaign, IL (PSU IRB 13103); New York, NY (PSU IRB 45727); Cincinnati, OH (UC IRB 2015-3073); Twinsburg, OH (PSU IRB 2503); State College, PA (PSU IRB 44929 and 4320); Austin, TX (PSU IRB 44929); and San Antonio, TX (PSU IRB 1278). Participants self-reported information on age, ethnicity, ancestry, and body characteristics, and data were gathered on height and weight. Individuals were excluded from the analysis if they were below 18 years of age and if they reported a personal history of significant trauma or facial surgery, or any medical condition that might alter the structure of the face. No restriction on ancestry or ethnicity was imposed during recruitment, but only individuals of European descent were used in this study. The intersection of unrelated European participants with quality-controlled images, covariates, and genotype data resulted in 1,990 individuals for analysis.n = 784).The IUPUI sample includes 3D images and genotypic data from individuals recruited in Indianapolis, IN and Twinsburg, OH (IUPUI IRB 1409306349). Participants self-reported information on age, height, weight, and ancestry at the time of the collection. Individuals who were below 18 years of age were included if they had a parent or legal guardian’s signature. No restrictions were placed on the recruitment of participants, but only individuals of European descent and those meeting all quality control criteria were used in this study  project, an international multi-center effort focused on understanding the etiology of non-syndromic OFC . The POFThe group of unselected individuals (to serve as controls) in the PARENT dataset was recruited either as part of normal-range facial variation projects (EURO US Cohort described above) or as controls for the POFC project. There was therefore significant overlap between our unselected sample and the EURO US cohort. In all cases, individuals in this group were excluded if they reported a personal or family history of any birth defect or syndrome affecting the head or face, a personal history of any significant facial trauma or facial surgery, or any medical condition that might alter the structure of the face. Starting from 4,762 individuals, we selected for 3D images acquired with the 3dMDface camera system to avoid camera bias , and selDue to the several genotyping platforms used for the EURO US cohort (details in The raw genotype data from the EURO UK cohort (ALSPAC) was not available and restrictions are in place against merging the ALSPAC genotypes with any others. For this reason, ALSPAC genotypes, phased using SHAPEIT2  and impuFor both datasets, SNPs on the X chromosome were coded 0/2 for hemizygous males, to match with the 0/1/2 coding for females .The 3D facial images were obtained with digital facial stereophotogrammetry systems or laser scanning systems. For the 3DFN sample and the PARENT dataset, 3D images were acquired using the 3dMDface  camera system. PSU sample images were obtained with either the 3dMDface or Vectra H1  systems. The IUPUI sample was also imaged using the Vectra H1 system. The ALSPAC sample was imaged using a Konica Minolta Vivid 900 laser scanner . All participants were asked to have their mouths closed and to maintain a neutral facial expression during image capture.x coordinate. This process resulted in homologous configurations of spatially dense quasi-landmarks for all images. Subsequently the original and reflected images were aligned using Generalized Procrustes Analysis (GPA) to eliminate differences in position, orientation and scale. Because we only wish to study variation in symmetric facial shape in this work, the original and reflected quasi-landmark configurations were averaged and the resulting symmetric configurations were used in further analyses.3D surface images of both the EURO and PARENT dataset were imported into Matlab 2017b to perform the spatially dense registration process in the MeshMonk registration framework . Five poAfter the registration, image quality control was performed to identify poorly remapped faces using two approaches. First, as described in To investigate facial shape on a global as well as a local scale, a data-driven facial segmentation was performed in the EURO dataset . Before R2) was used as a test-statistic using 10,000 permutations. In each segment, the PLSR returned one direction of effect or trait, which is a vector in multidimensional shape space. Traits defined in segments in which a nominally significant difference between parents and unselected individuals was observed were selected (p < 0.05) for further analysis and will be referred to as endophenotypic traits. These endophenotypic traits are not independent due to the overlapping construction of the facial segments and the dependency between neighboring quasi-landmarks of the facial template used in the registration. A Bonferroni significance threshold (p < 1.2 × 10–3) was calculated as 0.05/41, taking into account the multiple testing of the effective number of independent traits. The number of independent traits, 41, was calculated from the eigenvalues of a correlation matrix containing the pairwise multivariate correlations of all 63 traits  were annotated when at least genome-wide significance (p < 5 × 10–8) was reached. Adjacent SNPs were clumped within a 1 Mb window (±500 kb) as the same signal of the lead SNP. Although annotations were performed for loci based on a genome-wide threshold, the study-wide Bonferroni threshold based on 59 tested traits was calculated as 5 × 10–8/59 (p = 8.47 × 10–10). A lead SNP was defined to show strong evidence for a role in OFC genetics if either a gene in the vicinity (±500 kb) showed functional evidence or if evidence from multiple research domains were found: genetic association studies, animal studies, and/or clinical studies. Weaker evidence was annotated to lead SNPs in the following cases: suggestive associations with OFC, a single clinical case study, a role in craniofacial development but not specifically in OFC or associations in the same chromosomal band but more than 500 kb removed from the lead SNP.A GWAS meta-analysis was performed on the endophenotypic scores in the United States and United Kingdom cohorts. For all analyses, the genotypes were coded additively based on the presence of the major allele. In total, 7,417,619 SNPs overlapped in both the United States and the United Kingdom cohort and were tested for association. In both cohorts, the endophenotypic scores were tested for genotype–phenotype associations in a standard linear regression with the SNP genotype as the independent variable and the univariate endophenotypic scores as the dependent variable (function regstats in Matlab 2017b). This function employs a eighting . Lead SNN = 1,411). Mismatched SNPs were removed by PRSice, and SNPs were clumped using an LD r2 threshold of 0.1 in a window of ±250 kb. Polygenic risk scores were calculated with inclusion p-value thresholds ranging from 5 × 10–8 to 1 with a step size of 5 × 10–5, resulting in 104 sets of polygenic risk scores. Finally, for each inclusion threshold, an association between the endophenotypic scores (independent variable) and the polygenic risk scores (dependent variable) was tested using linear regression (function regstats in Matlab2019b). This was done for the United States and United Kingdom cohort separately.To investigate whether the endophenotypic scores capture a heightened genetic susceptibility for NSCL/P in an approach complementary to the GWAS, we calculated polygenic risk scores for NSCL/P in the EURO dataset. To this end, the PRSice software  was usedp < 0.05), as shown in p < 1.3 × 10–3). Each shape regression returned a direction of effect, represented by a vector in multidimensional shape space, which we refer to as the endophenotypic trait. Endophenotypic traits that were identified in segments 1, 2, 6, and 7 are shown in Facial shape in 59 out of 63 segments was found to be different between unaffected parents of individuals with NSCL/P and the unselected individuals at nominal significance (p < 5 × 10–8) (p < 8.47 × 10–10). SNP information and meta-analysis statistics of the 29 best associations  are given in We identified 29 independent genetic loci with a lead SNP associated with at least one of the 59 endophenotypic traits below a genome-wide significance threshold ( × 10–8) . In tota × 10–8) , 12 in q × 10–8) . Nine oup-value inclusion thresholds are visualized in p-value inclusion threshold around 0.001. The traits responsible for these associations are localized in the philtrum area  of the lead SNPs from a previous GWAS of normal facial variation . The oveWLS [MIM: 611514], of which expressed mRNA and protein have been found in the embryonic development of the lip and palate in mice  with NSCL/P in a GWAS with an Asian cohort  in a POFC multi-ethnic GWAS for NSCL/P  in a previous NSCL/P GWAS  at accession #phs000929.v1.p1. The raw source data for the phenotypes—the 3D facial surface models in.obj format—are available through the FaceBase Consortium (https://www.facebase.org) at accession #FB00000491.01. Access to these 3D facial surface models requires proper institutional ethics approval and approval from the FaceBase data access committee. Additional details can be requested from SMW. The participants making up the PSU and IUPUI cohorts were not collected with broad data sharing consent. Given the highly identifiable nature of both facial and genomic information and unresolved issues regarding risk to participants, we opted for a more conservative approach to participant recruitment. Broad data sharing of the raw data from these collections would thus be in legal and ethical violation of the informed consent obtained from the participants. This restriction is not because of any personal or commercial interests. Additional details can be requested from MS and SW for the PSU and IUPUI datasets, respectively. The ALSPAC (United Kingdom) data will be made available to bona fide researchers on application to the ALSPAC Executive Committee . Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. The ALSPAC study website contains details of all the data that are available through a fully searchable data dictionary and variable search tool: http://www.bris.ac.uk/alspac/researchers/our-data/.Publicly available datasets were analyzed in this study. The data can be found here: All of the genotypic markers for the 3DFN dataset are available to the research community through the dbGaP controlled-access repository , and minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.KI performed all analyses and wrote the first draft of the manuscript under the supervision of PC. PC, JS, SMW, and MM conceptualized the design of the study. JS, SMW, MM, RE, SW, JW, and MS organized and imputed the EURO data. ML, GW, LM, JH, RL, KC, FD, JS, MM, and SMW collected and curated the PARENT data. SR coordinated the collection of the ALSPAC images. SN, CL, ML, HH, and JW provided input throughout the analyses and the writing process. All authors contributed to manuscript revision, and read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AR-M declared a past co-authorship with one of the authors, GH, to the handling editor