CYP2C9 and VKORC1 and genotyping by DMET® Plus Assay of cardiovascular patients were performed. We also aimed at characterizing the genomic structure and admixture pattern of this study cohort. Our study used the Extreme Discordant Phenotype approach to perform a case-control association analysis. The CYP2C9 variant rs2860905, which was found in all the major haplotypes occurring in the Puerto Rican population, showed stronger association with warfarin sensitivity (<4 mg/day) than common variants CYP2C9*2 and CYP2C9*3. Although, CYP2C9*2 and CYP2C9*3 are separately contained within two of the haplotypes, 10 subjects with the sensitive phenotype were carriers of only the CYP2C9 rs2860905 variant. Other polymorphisms in CES2 and ABCB1 were found to be associated with warfarin resistance. Incorporation of rs2860905 in a regression model  that also includes additional genetics  and non-genetic factors  showed better prediction of warfarin dose requirements than CYP2C9*2 and CYP2C9*3 combined . The genetic background of Puerto Ricans in the study cohort showed a tri-hybrid admixture pattern, with a slightly higher than expected contribution of Native American ancestry (25%). The genomic diversity of Puerto Ricans is highlighted by the presence of four different major haplotype blocks in the CYP2C9 locus. Although, our findings need further replication, this study contributes to the field by identifying novel genetic variants that increase predictability of stable warfarin dosing among Caribbean Hispanics.Existing algorithms account for ~50% of observed variance in warfarin dose requirements after including common polymorphisms. However, they do not perform as well in populations other than Caucasians, in part because some ethno-specific genetic variants are overlooked. The objective of the present study was to identify genetic polymorphisms that can explain variability in warfarin dose requirements among Caribbean Hispanics of Puerto Rico. Next-Generation Sequencing of candidate genes  Warfarin  is an oral anticoagulant commonly prescribed to prevent thromboembolisms in patients with atrial valve replacement, pulmonary embolism, deep vein thrombosis, myocardial infarction and atrial fibrillation, among other indicationsVKORC1 encodes for a vitamin K-dependent epoxide reductase complex, subunit 1, that allows the recycling of vitamin K cofactor for further post-translational activation of clotting factors. CYP2C9 and VKORC1 are the two most important pharmacogenes predicting warfarin response , CYP2C9*3 , and VKORC1-1639G>A (rs9923231) are important predictors of anticoagulation therapy response among Caucasians and Asians. Although, other genetic variants in the CYP2C9  have been shown to have an impact on warfarin's requirements among African descendants    of which only 6%  were Hispanics . Participants were treated with warfarin and recruited from the Veteran's Affairs Caribbean Healthcare System (VACHS)-affiliated anticoagulation clinic in San Juan, Puerto Rico, which serves a predominantly Caribbean Hispanic population. Participants self-reported as Caribbean Hispanic Puerto Ricans, were ≥21 years of age and on a stable maintenance dose of warfarin. For the purpose of this study, stable warfarin dose was defined as the average daily amount of drug required to maintain stable anticoagulation levels . A full description of the study population cohort as well as detailed information on the patient's recruitment process can be found elsewhere  and to select the cohort (n = 115) for the current study  or resistant  to warfarin therapy. Stable warfarin doses under 4 mg/day or over 6 mg/day deviate from the standard of care (5 mg/day) by at least 20%, which is considered to be clinically significant . Study design and experimental procedures are summarized in the flowchart of Figure A histogram was performed to observe the distribution of warfarin dose requirements among the recruited subjects (hort n = 15 for thCYP2C9 and VKORC1 loci as well as their flanking regions (5 kb up and downstream). The designed panel targeted 54,998 bp of the selected regions (corresponding to 75% of CYP2C9 and 65% of VKORC1 according to the hg19 version of the human genome)  using the QIAamp DNA Blood Maxi kit (spin protocol) from Qiagen  and quantified with TaqMan® RNase P real-time PCR method from Life Technologies  according to the manufacturer's protocol. The Ion AmpliSeq™ Designer software was used to generate a custom panel of 339 PCR primer pairs directed toward ) Figure . DNA libQ < 30) and indels. For case-control association tests, 207 variants were evaluated after filtering for minor allele frequency and Hardy-Weinberg equilibrium.  from Life Technologies , which generates single-end reads with lengths of 150–300 bp. A total of 108 barcoded samples were sequenced in the Ion 316 chip (300–600 Mb of throughput) in order to achieve an average sequencing depth of 300X, which is sufficient to identify germline-derived single nucleotide polymorphisms (SNPs). The sequencing results were analyzed with the Torrent Suite Software (v4.6.), which automatically performed the base-calling and quality filtering processes of the raw data , trimmed the adapter sequences and low-quality 3′ ends and aligned the resulting data to the hg19 genome build version of the human genome  by means of the Torrent Mapping Alignment Program (TMAP). The aligned sequences were then uploaded to the Ion Reporter software cloud for variant calling and annotation with the Torrent Variant Caller algorithm (TVC v4.4-8). Briefly, the TVC tool identified single nucleotide variants (SNVs), multiple nucleotide variants (MNPs) and insertions and deletions (indels), evaluated and filtered the SNVs on the basis of the number of reads representing the candidate variant (coverage) and the signal quality of the called bases. Finally, variant call files (VCF) were transformed into PLINK v.1.07 input files (ped and map files) for the case control association analysis , a total of 825 variants remained for the case control association analysis. A set of 115 DNA samples were analyzed from which 96 were processed using both methods (NGS and DMET Plus array), 12 were sequenced only and 7 were genotyped only .The DMET™ Plus  microarray interrogates 1931 sequence variations including single nucleotide variants (SNVs), indels and copy number variations across 225 genes of pharmacogenetic relevance  approach that has been proposed as an alternative to recruiting a large cohort mostly representing “normal” responders. The EDP approach relies on the comparison of excellent vs. poor responders based on the probability of having an enrichment of the genomic signal causing the observed phenotype Nebert, . For thiP-value below alpha = 0.01. The permutation method was used to correct for multiple testing since family-wise error rate  assume independence between markers  which is not true. False discovery rate is another approach based on an arbitrary expected proportion of false positives, but this proportion would be affected by dependence (linkage disequilibrium) among markers  were considered for the analyses. Locus Zoom and Haploview were used to display findings from case-control associations  against non-cases  with potential impact on warfarin response. Identified variants were evaluated separately with the Combined Annotation Dependent Depletion tool to predict potential deleteriousness  was used as a comparison with our study cohort of cardiovascular patients on warfarin. Data was prepared as PLINK v.1.07  were selected from the DMET Plus panel for admixture analysis as previously identified  was calculated by averaging the absolute values of the difference between predicted and actual doses . The precision of the model was defined as the mean percentage of difference .A stepwise multivariate regression model was performed incorporating biological , clinical , and genetic information with variants that showed nominal significance in the case-control association analysis using SPSS software. Variables with P-value = 0.5707; Welch's T-test). Notably, both are below the threshold value of 0.15  as per Wright's criteria , CYP2C9*3 (rs1057910) and VKORC1-1639 G>A (rs9923231), as well as other variants associated with warfarin sensitivity are presented in Table P-value = 1.00 × 10−6; OR = 7.07 (CI 95%: 3.33–15.03). The rs2860905 is an intronic variant that maps to CYP2C9  and is within a cluster that includes other nine variants  for warfarin sensitivity. This is not surprising since this variant is known to be in perfect LD with VKORC1-1639 G>A, the most important genetic predictor of warfarin dose requirements in worldwide populations and included in the CPIC guidelines  that includes the variants rs9934438 (R2 = 1), rs8050894 (R2 = 0.84), and rs2359612 (R2 = 0.88).A total of 207 variants were considered for case-control association tests. Information about nomenclature of SNVs is available in Table ts Table . AlthougP-value = 3.65 × 10−3; OR = 2.53; CI 95%: 1.36–4.64). Other variants at CYP2C9 (rs1934965 and rs2096069) were also found in association with warfarin resistance with OR > 1  Figure . These s 1 Table .VKORC1 Haplotype A (VKORC1-1639 A) had strong association with warfarin sensitivity when genotyped with DMET Plus array  was associated with warfarin dose requirements >6 mg/day among Puerto Ricans, which is in line with previous studies conducted in Caucasians  and CES2 rs4783745 encoding carboxylesterase 2, showed nominal significance . In addition, rs10276036 located in the ABCB1 gene also had one of the lowest P-values for the resistance association .ype B -169 G was aVKORC1-1639 A , CYP2C9*2 , CYP2C9*3 , and CYP2C9 rs2860905  with low warfarin dose requirements. On the other hand, CYP2C9 rs1856908 , FMO2 rs2020870 , and CES2 rs4783745  were associated with high warfarin dose requirements using linear regression analysis (Table CYP4F2 (CYP4F2*3 and rs3093106) were found strongly associated with high warfarin dose requirements only when the linear regression test was used but not using the case-control association approach. A complete list of genetic variants associated with warfarin response using univariate regression analysis is available at Table Linear regression analysis confirmed association of VKORC1-1639 G>A, CYP2C9*2 and CYP2C9*3) showed that association of CYP4F2 variants is sustained, however the same does not occurred with most of the tested variants. FMO2 rs2020870 and CYP2C9 rs1856908 were not significant after conditioning for VKORC1-1639 and CYP2C9*2 only  among Europeans but low correlation (R2 < 0.2) was observed among Africans and Hispanics. Therefore, we aimed to determine how rs2860905 explained association with warfarin dose requirements within a haplotype-based framework by performing haplotype phasing.b Figure  as obserCYP2C9*2 or CYP2C9*3 respectively  given by PHASE v2.1.1 . CYP2C9*8 was found in Puerto Ricans using warfarin with a frequency of 1.8% . Diabetes contributed to increased warfarin requirements while hypertension and age were factors associated with decreased required dose. Other factors such as weight, use of statins and PC1 were not statistically significant . SNVs tested in the multivariate regression model with P-value > 0.05 and in genes not previously reported to alter warfarin response were excluded for the final model . Table CYP2C9*2 and CYP2C9*3 combined in the multiple regression model   from ancestry. The use of aspirin, azoles and smoking was not included due to a low number of patients reporting using any of those. The genetic variants tested were: y) Table . Our modCYP2C9 locus compared to the VKORC1 region . The sequenced locus at chromosome 16 harbored 33 variants of which 24 corresponded to the VKORC1 gene. Four variants were found upstream of the gene, as well as 2 synonymous, 6 downstream, 1 at the 3′-UTR and 11 intronic variants. Other 9 variants belonged to PRSS53 (protease serine 53). One novel SNP was found at intron 4 of CYP2C9 . This variant is in strong linkage disequilibrium (LD) with rs1057910 . Variants present in <5 individuals were considered of low-frequency and were analyzed separately to estimate their potential deleteriousness , all present in the study cohort, were not found in European descendants from the 1000 Genomes Project but were reported at very low frequencies among European Americans  at eMERGE , CYP2C9*11 (Arg335Trp), CYP2C9*5 (Asp360Glu) and CYP2C9*12 (Pro489Ser) are deleterious. The CYP2C9*8 (Arg150His) substitution was predicted as benign and tolerated according to PolyPhen and SIFT scores respectively. Notably, carriers for most of the rare variants (except CYP2C9*5) are within the sensitive group. Most of the observed rare variants, with the exception of CYP2C9*12, are present in Africans with higher frequency compared to other parental populations  was identified in a single patient that according to conservation scores  is not within an evolutionary conserved region.According to predicted deleteriousness estimated by Combined Annotation Depletion (CADD) from the set of identified variants with low-frequency and C-scores preferably higher than 15, nine were in coding regions  and one in a regulatory region Table . The varCYP2C9  and other pharmacogenes  that were found for the first-time to be significantly associated with the warfarin dose requirements in a cohort of Caribbean Hispanics. Also, the genomic structure and LD patterns of CYP2C9 were described for the first time in Puerto Ricans. Given the lack of relevant clinical and genomic data in Caribbean Hispanics , our findings contribute substantially to fill a current gap of knowledge in the field and, hence, to reduce existing disparities in the implementation of the pharmacogenetic paradigm for warfarin therapy across multiple ethnic groups.This study contributes to the field of Pharmacogenetics by identifying novel genetic variants that increase predictability of stable warfarin dosing among Caribbean Hispanics. We described some variants in CYP2C9 and VKORC1 in combination with the DMET array provides a detailed insight into the genetic variability of the Puerto Rican population. As Kohei et al. described, approximately 80% of genetic variants found within the CYP family are population specific, i.e., biomarkers in CYP genes have a characteristic distribution across subpopulations and/or ethnic groups  that account in part for the rich genetic diversity of Puerto Ricans. CYP2C9*2 and CYP2C9*3 are components of two of these haplotypes  that represent the European contribution in Puerto Ricans. Although, CYP2C9*2 and CYP2C9*3 were also important genetic predictors of warfarin dose requirements among Puerto Ricans, testing only these two genetic variants will be a bias toward the portion of the population that is more genetically similar to Europeans, reducing thus predictability among individuals with high African contribution. Conversely, rs2860905 is a strong genetic predictor of warfarin dose requirements that is relevant for all Puerto Ricans, independently of their ancestral contributions. These haplotypes contain other variants identified by ClinVar as drug-response alleles (CYP2C9*8 and rs4917639) based on sustained evidence that showed their association with warfarin response . Nonetheless, D' values for LD of rs2860905 with CYP2C9*2 and CYP2C9*3 were 0.93 and 1.0, respectively. This suggests that even though CYP2C9*2 and CYP2C9*3 do not have the same frequencies as CYP2C9 rs2860905 (MAF = 0.21) in Puerto Ricans, every time the CYP2C9*2 (MAF = 0.10) or CYP2C9*3 (MAF = 0.06) are present in haplotype 1 and 2, respectively, they are in company of rs2860905. However, the opposite is not true.Notably, the corresponding CYP2C9 associated with warfarin dose requirements that are in LD with rs2860905. The top genome-wide signals were CYP2C9 rs9332238 and rs4917639 in the warfarin-treated Brazilians and Europeans cohorts respectively  of the Puerto Rican population cohort. The H3 was identified in Asians and Africans from the 1000 Genomes Project  or CYP2C9*3  among Puerto Ricans.Interestingly, rs2860905 (MAF = 26.6% in YRI and 22.0–24.5% in African Americans) has previously been reported to be associated with warfarin dosing in individuals of mostly African ancestry (African Americans)  to H3 and H4 (CYP2C9*2 or *3 absent). Furthermore, the predictive power of rs2860905 accounted for the observed variability in warfarin dose requirements among patients with different genomic structures which was higher than the composite CYP2C9*2 and CYP2C9*3 effect (adjusted R2 = 0.60 vs. 0.58). Interestingly, the effect of rs2860905 on warfarin stable dose variability in our cohort was larger than the sum of the effects of CYP2C9*2 and CYP2C9*3 combined in the multiple regression model . Even when our multivariate regression model explains ~60% of variability in warfarin dose requirements among Puerto Ricans, a previous study published by our group found that an admixture-adjusted PGx-based dosing algorithm that includes clinical factors explained up to 70% of variability  that varies across the island, with a continuous number of gradations or clines of ancestral proportions from one extreme to the other , CYP2C9*2, and CYP2C9*3 with warfarin dose requirements among Puerto Ricans. Other genes not previously linked with the warfarin pathway were found in association with dose requirements among Puerto Ricans. CES2 had not been found to be associated with warfarin metabolism in previous reports nor shown to be involved in a biological pathway that is directly related to warfarin response or coagulation .It is not clear whether S2 Table . SeveralS2 Table . A studyS2 Table . AccordiS2 Table . CES2 isABCB1, NQO1 and CYP4F2  method was preferred to maximize our statistical power to detect significant effects given the relatively small size of the discovery cohort. However, it was not able to detect variants with small effect size. This effect might be explained by the inability of case-control association analysis to control for factors that may cover weaker associations. Additionally, common variants generally have low penetrance so that SNPs that alter risk for a particular trait cause small changes in gene expression. Consequently, to detect strong associations using the conventional case-control approach requires a greater effect size , CYP2C9 (rs2860905 and 1856908), CES2 (rs4783745), and ABCB1 (rs10276036), explained 60% of the variability in warfarin dose requirements among Puerto Ricans. Noteworthy, this algorithm will be useful to predict optimal warfarin dosing in patients commencing the anticoagulation therapy, which is precisely the stage  of higher risk for adverse events  that are more frequent among African descendants including Caribbean Hispanics. The COAG randomized clinical trial for example, used a model that lacked ethno-specific variants that are more frequent in individuals of African heritage resulting in a dose prediction error of 2.0–2.2 mg/day . AG performed part of the data analysis (haplotype phasing). JR-T and DP worked with the corresponding author in the realization of the experiments. GRM was responsible of the study coordination and recruitment of patients at VACHS. SS contributed in the elaboration of the manuscript. GR is the consultant of the study. CC participated in the design of the study, provided the infrastructure and contributed to the elaboration of the manuscript. JD-S is the principal investigator.KC, JD-S, and AR held a without compensation (WOC) employment status with the Pharmacy Service, VA Caribbean Healthcare Systems (VACHS) in San Juan, Puerto Rico at the time of conducting the study. The other 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


Triticum turgidum; 2n = 4x = 28, AABB subgenomes) with the progenitor species Aegilops tauschii . The population was phenotyped for 10 fungal/nematode resistance traits as well as two abiotic stresses. We showed that the wild D subgenome dominated the additive effect and this dominance affected the A more than the B subgenome. We provide evidence that this dominance was not inflated by population structure, relatedness among individuals or by longer linkage disequilibrium blocks observed in the D subgenome within the population used for this study. The cumulative size of the three homoeologs of the seven chromosomal groups showed a weak but significant positive correlation with their cumulative explained additive variance. Furthermore, an average of 69% for each chromosomal group's cumulative additive variance came from one homoeolog that had the highest explained variance within the group across all 12 traits. We hypothesize that structural and functional changes during diploidization may explain chromosomal group relations as allopolyploids keep balanced dosage for many genes. Our results contribute to a better understanding of trait evolution mechanisms in polyploidy, which will facilitate the effective utilization of wheat wild relatives in breeding.Whole genome duplication (WGD) is an evolutionary phenomenon, which causes significant changes to genomic structure and trait architecture. In recent years, a number of studies decomposed the additive genetic variance explained by different sets of variants. However, they investigated diploid populations only and none of the studies examined any polyploid organism. In this research, we extended the application of this approach to polyploids, to differentiate the additive variance explained by the three subgenomes and seven sets of homoeologous chromosomes in synthetic allohexaploid wheat (SHW) to gain a better understanding of trait evolution after WGD. Our SHW population was generated by crossing improved durum parents ( Polyploidization, whole genome duplication (WGD), is a natural process in which a single genome can be duplicated to form autopolyploids with more than two homologs for each chromosome, or multiple genomes are duplicated following hybridization between two or more species to form allopolyploids with multiple pairs of homologs derived from different ancestral genomes, termed homoeologs. Following WGD, multiple copies of duplicated genes may be lost, diverge in function, or silenced through a phenomenon called “diploidization” in which balanced dosages for many genes can be retrieved Ohno, . Rapid gTriticum aestivum, 2n = 6x = 42, AABBDD) which suffered a severe genetic bottleneck during its origin  with Aegilops tauschii , mimicking the natural evolutionary origin of bread wheat. SHW germplasm is a proven source of genetic diversity to improve yield  for major genes associated with these different stresses  and septoria nodorum glume blotch (SNG), root lesion nematodes [Pratylenchus neglectus (Pn) and Pratylenchus thornei (Pt)] and cereal cyst nematode (CCN) resistance. Experimental details were previously described in  for Lr; 98–1,2,3,5,6 (accession number 781219) for Sr; and 134 E16A (021510) for Yr. Four different isolates  were used in four replicates under greenhouse conditions for SNG and SNL. YLS was also screened in a controlled environment against isolates 03–0148, 03–0152, and 03–0053. For CCN, plants were considered resistant if they had less than five cysts per plant root while plants were considered susceptible if they had more than 30 cysts. Plants with 5–30 cysts were considered moderately resistant to moderately susceptible. The severity of Pn and the number of Pt nematodes per plant were used to infer the score of resistance by comparing the plant response to resistant and susceptible checks. Br tolerance was phenotyped by measuring root growth at the seedling stage on a filter paper soaked with boron while Al tolerance was measured using the hematoxylin staining of root apices method  and boron (Br) tolerance, stem (Sr), yellow (Yr) and leaf (Lr) rusts, crown rot (Cr), yellow leaf spot (YLS), We estimated 21 genetic relatedness matrices (GRMs) from SNPs located on each one of the SHW chromosomes following the method described in , β is a vector of fixed effects, X is an incidence matrix that relates individuals to fixed effects and ε is a vector of random errors. gi is a vector of random additive genetic effect attribute to chromosome i. The variance structure of phenotype is equal to:Where Ai is the GRM for chromosome i, i, I is an identity matrix and Where http://www.cog-genomics.org/plink/1.9/). To further investigate the effect of the correlation between different chromosomes due to shared structure among chromosomes  as fixed effects. Including a number of PCs in the model can control for population structure in the germplasm; thus, the effect of population structure will be minimal if the model that fits PCs revealed similar results to the model that does not include PCs  was used to determine significant differences among the three subgenomes considering (1) the subgenome size as a covariate or (2) the chromosome size as a covariate. The fitted model for the first ANCOVA analysis was: Additive Effect ~ subgenome + subgenome size. For the second analysis, we fitted the model twice, with and without including the interaction between chromosome size and subgenome. Thus, the models were: Additive Effect ~ subgenome + chromosome size; and Additive Effect ~ subgenome For each trait, a Chi-square test was performed to test whether the actual additive variance explained by the three subgenomes lies within the expected range for their values. The genome size for A, B and D subgenomes is 5727, 6274, and 4945 Mb, respectively. Thus, the expected contribution for each subgenome to the additive variance was calculated as the proportion of the subgenome size to the whole genome size, which was 33.8, 37, and 29.2% for A, B, and D subgenomes, respectively.N . This analysis allows us to compare our findings to the null hypothesis given our data. True differences among subgenomes/chromosomal groups should be detected when using our empirical phenotypes and not simulated ones.To further confirm that the differences among subgenomes are true and have not been inflated because of relatedness among individuals, we ran 100 replicates of the GREML analysis using randomly sampled phenotypes from the normal distribution attenuated correlation” for all our correlation analyses following Charles . Pearson correlation coefficients between additive variances of both models (full and reduced) for all chromosomes across all traits were computed. The reliability was estimated as the square of the average Pearson correlation coefficient over the 100 replicates. The reliability was used to calculate the “ Charles  implemen Charles . Calcula2). The considerably low reliability is a result of small population size and relatedness among individuals.The SHW dataset included 6,176 GBS based SNPs with missing data <20% and minor allele frequency >5%. The total heritability values ranged from 44.8 to 60.5% for resistance to Sr and SNG, respectively, Table . The nump-value = 0.04 and r = 0.45  which was the only significant component in both models.The cumulative fractional additive variance significantly varied between the three subgenomes. The median values for the percentage of additive variance contributed by A, B, and D subgenomes were 23.7, 33, and 38.7%; respectively Figure . These vFor individual traits, Chi-square tests showed significant differences between the actual and the expected subgenome contribution to all traits except for Br, Lr, and SNG. For Al, CCN, Cr, Pt, and Yr, only the contribution of the D subgenome was higher than expected, while the contributions of the B and D subgenomes were higher than expected for Pn, SNL, and YLS Table . Br, Lr,Population structure, linkage disequilibrium, and relatedness among individuals did not have an effect on our results. The inclusion of the first 10 principal components as covariates in the model did not have a large effect on heritability estimates (data not shown) which means that population structure has minimal effect on the heritability estimations. Similarly, further analysis with a randomly chosen subset of SNPs did not affect the results either Table , indicatEstimating the conditional effect for each chromosome based on the other 20 chromosomes showed considerable correlation among chromosomes Table . On averDecomposing additive genetic variance based on different set of SNPs has become a commonly used method in quantitative genetics in recent years  can cause a huge bias for decomposing additive variance analysis as the variance estimation depends on the LD between the causal variant and the closest genotyped SNPs (Speed et al., Almost all studies that have partitioned additive variance have shown a significant correlation exists between chromosome size and variance (e.g., Yang et al., In contrast to what we found for all individual chromosomes, a significant but weak correlation was found between the cumulative sizes and cumulative additive variances for each chromosomal group Figure . In polyAe. tauschii to hexaploid wheat is partially or fully suppressed by unknown mechanisms while Kerber and Green (Pont et al.  showed tnd Green  reportednd Green . Thus, eAJ: suggested and planned the study, analyzed the data and drafted the manuscript; RJ: assisted with R scripting and drafted the manuscript; SS: provided the GBS data; FO: planned the study, provided the phenotypic data, drafted the manuscript and gave the final acceptance for the manuscript to be submitted; All authors read and approved the final copy of the 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.


Fasting glucose and fasting insulin are glycemic traits closely related to diabetes, and understanding the role of genetic factors in these traits can help reveal the etiology of type 2 diabetes. Although single nucleotide polymorphisms (SNPs) in several candidate genes have been found to be associated with fasting glucose and fasting insulin, copy number variations (CNVs), which have been reported to be associated with several complex traits, have not been reported for association with these two traits. We aimed to identify CNVs associated with fasting glucose and fasting insulin.SFMBT1) gene with FPG . No significant association was observed between global rare CNVs and FPG or FPI. The deletions in 20 individuals with DNA samples available were successfully validated using PCR-based amplification. The association of the deletions in SFMBT1 with FPG was further evaluated using an independent population-based replication sample obtained from the Taiwan Biobank. An association p-value of 0.065, which was close to the significance level of 0.05, for FPG was obtained by testing 9 individuals with CNVs in the SFMBT1 gene region and 11,692 individuals with normal copies in the replication cohort.We conducted a genome-wide CNV association analysis for fasting plasma glucose (FPG) and fasting plasma insulin (FPI) using a family-based genome-wide association study sample from a Han Chinese population in Taiwan. A family-based CNV association test was developed in this study to identify common CNVs , and a generalized estimating equation approach was used to test the associations between the traits and counts of global rare CNVs . We found a significant genome-wide association for common deletions with a frequency of 5.2% in the Scm-like with four mbt domains 1  contains supplementary material, which is available to authorized users. Fasting glucose and fasting insulin are glycemic traits closely related to diabetes. Understanding the genetic factors associated with these traits can help identify pathways causing pathological glucose levels and type 2 diabetes , 2. HeriCommon and rare copy number variations (CNVs) have been shown to be associated with many complex traits –12, inclp-values were validated using PCR-based amplification. Moreover, we performed a replication analysis for the significant CNVs using another independent population-based cohort obtained from the Taiwan Biobank (https://www.twbiobank.org.tw).To investigate the role of CNVs in fasting glucose and fasting insulin, in this study, we performed a genome-wide CNV association study for fasting plasma glucose (FPG) and fasting plasma insulin (FPI) based on a GWAS dataset from the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance (SAPPHIRe) family study . A familThe samples were collected from the SAPPHIRe family study. Individuals were recruited from five sites in Taiwan, Hawaii, and San Francisco. The sample consisted of both concordant sib pairs (both with hypertension) and discordant sib pairs (one with and one without hypertension) from the Chinese and Japanese populations. Subjects were recruited as probands if their age at onset for hypertension was between 35 and 60 years or if their age was >60 years but they had records of hypertension before 60 years. Subjects with pre-existing malignancies or major chronic diseases  were excluded from the study. More details of the ascertainment criteria can be found in Wu et al. .The samples were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0, which contains more than 1,878,000 probes. The samples were assigned randomly to batches of 96 samples for genotyping following the Affymetrix protocol. Genotypes were called using Affymetrix Power Tools (APT), which implements the Birdseed algorithm  for genoStudies have found that different CNV calling algorithms have advantages and disadvantages for different types of analyses , 24. Thep-values <10−4 were excluded. The PLINK PI_HAT statistic, which is the proportion of loci that are identity-by-descent between a pair of individuals, was used to examine the relatedness among samples based on the SNP genotypes that passed QC. Samples that were reported as sib pairs but with PI_HAT <0.05 were removed. We also removed an individual if the median of PI_HAT of the individual with others was greater than 0.05. In stage 2, we followed the suggestions in the PennCNV manual to perform QC based on the CNV calls generated by Birdseye and PennCNV. Adjacent CNVs that were classified into the same state were merged into the same CNV if the length of the gaps (measured based on the number of probes) between them was less than 20% of the length of either one of the adjacent CNVs. CNVs containing less than 10 SNPs or that were smaller than 10 kb were removed. Spurious CNV calls in regions such as immunoglobulin, centromeric and telomeric regions were also removed. Samples with a standard deviation for the log ratio of observed probe intensity to expected intensity larger than 0.35 were removed, as suggested in the PennCNV manual. Samples with more than 100 CNV calls generated by PennCNV were removed. Because Birdsuite generated many more CNV calls than PennCNV, samples with more than 200 CNV calls generated by Birdseye were removed. After the QC steps were applied to the CNV calls generated by PennCNV and Birdseye separately, consensus calls were generated from the two sets of calls. A consensus call was defined as the intersection of CNV calls with the same state from the two algorithms.We applied a two-stage QC procedure. In stage 1, PLINK  was usedThe clinical measurements of the participants were taken at 8 am after an 8–10 h overnight fast. The glucose oxidase method on a Beckman Glucose Analyzer II  was used to determine plasma glucose concentrations, and plasma insulin was measured using a commercial immunoradiometric kit . The intra-assay and inter-assay coefficients of variation for glucose were 0.6% and 1.5%, respectively. The intra-assay and inter-assay coefficients of variation for insulin were 2.2 and 6.5%, respectively. Subjects diagnosed with diabetes were excluded from the study. Moreover, subjects with FPG levels >126 mg/dl were defined as having diabetes and were excluded.Phenotypes were first adjusted for covariates such as age, sex, body mass index (BMI), ethnicity, and site. As samples were recruited based on the hypertension status, phenotypes were also adjusted for hypertension status as an additional covariate. Moreover, as a large cohort study suggested that genetic variants associated with BMI may also have associations with metabolic traits such as fasting glucose , adjustip-value for the test was the proportion of the permuted statistics that were equal to or more extreme than the original statistic. A two-sided test was performed. The null hypothesis was that the CNV state is not associated with the phenotype. Because subjects can have CNVs with different lengths in the same region, we performed the test based on the locations of SNPs. The CNV state of a SNP for an individual was defined as the CNV state for the region where the SNP was located. To account for multiple testing, the permuted statistics were also used to calculate the permutation adjusted p-values and false discovery rate (FDR) [p-values and FDR because the correlation structures were maintained in the permuted statistics. Based on our power calculations shown in the Results section, the test maintained reasonable power for CNVs with frequencies ≥ 5% given the sample size of the study dataset. Therefore, we focused on testing CNVs with frequencies ≥ 5%.We developed a family-based association test to evaluate the associations between CNV calls and the phenotypes. The test statistic was the difference in the mean phenotypic value between an abnormal CNV state  and the normal state calculated based on the phenotypic values for siblings in all families. To evaluate the significance of the test statistic, we randomly permuted the phenotypic values for siblings within each family, and the permuted statistics were calculated over a large number of permutations . The te (FDR)  based onte (FDR) . Note thAs some studies have suggested that genome-wide rare CNVs are associated with complex traits, we performed a global burden analysis for CNVs with a frequency < 5%. PLINK was used to extract the CNVs with a frequency < 5% and calculate the number of CNVs across the genome for each individual. A regression analysis based on the GEEs was used to test the association between the trait and the CNV count, while family correlation was considered using the “exchangeable” within-cluster correlation structure in the GEE.p-value were calculated.We performed a replication analysis using a population-based cohort from the Taiwan Biobank (TWB) for the CNVs passing the multiple testing threshold. The TWB has recruited more than 80,000 population-based samples with survey data such as basic demographic variables, lifestyle, and family history of common diseases, body measurements such as weight, height, and blood pressure, and blood and urine measurements such as fasting glucose and urinary microalbumin . A portiFigure A total of 513 samples from the SAPPHIRe study were successfully genotyped. After the two-stage QC, there were 444 samples in 192 families. Table p-values <0.01 for the CNVs with the two traits using the model adjusted for age, sex, BMI, ethnicity, and site. As shown in Table p-value of 2 × 10−4, with a permutation adjusted p-value of 0.0478, which passed the multiple testing threshold of 0.05. The FDR for the p-value was 0.0669. Similar p-values of 1 × 10−4 and 3 × 10−4 were observed for the two SNPs using the model that included hypertension status as an additional covariate or the model not adjusted for BMI, respectively. The two SNPs are both in the Scm-like with four mbt domains 1 (SFMBT1) gene and are on the same CNV segment. A total of 23 individuals from 18 families in the sample carried the deletions with similar lengths, as shown in Fig. p-value of 3.8 × 10−3, with a permutation adjusted p-value of 0.0776, which is close to the multiple testing threshold of 0.05. The FDR for the p-value was 0.1593. However, the p-value for the two SNPs became 0.202 using the model that was not adjusted for BMI. We did not observe significant association of the count of genome-wide rare CNVs with the two traits. The burden test p-values for FPG and FPI were 0.781 and 0.289, respectively, for deletion, while the burden test p-values for FPG and FPI were 0.844 and 0.616, respectively, for duplication.A total of 272 SNPs were tested for duplication and 188 SNPs were tested for deletion in the common CNVs. Table SFMBT1 identified in the SAPPHIRe sample. We selected 20 individuals with DNA samples available in our lab from the 23 individuals shown in Fig. We used PCR-based amplification to validate the deletions in SFMBT1 with FPG. To calculate the type I error rate, we randomly generated deletions in the family samples with a frequency of 5.2%, which was the same as the deletion frequency observed in the gene, while the trait values for the family samples remained the same. A total of 5000 replicates of the simulated family samples were generated to calculate the type I error rate. The estimated type I error rate was 0.052 with a 95% CI of  at the significance level of 0.05, whereas the type I error rate was 0.008 with a 95% CI of  at the significance level of 0.01. These results suggest that the type I error rates were properly maintained by the test with a CNV frequency of 5.2%. We then used a bootstrap procedure [p-values less than the specified significance level in the 1000 tests. The power was estimated as 88.3% and 79.8% at the 0.05 and 0.01 significance levels, respectively. Therefore, given the trait values and sample size, this study had sufficient power to detect a CNV with frequency of 5.2% associated with the trait.We evaluated the type I error rate and power of the family-based CNV test for detecting the association of the deletions in rocedure  to calcuSFMBT1 deletion region  and FPG was evaluated in the TWB replication sample. After QC, there were 11,701 unrelated samples. Table p-value for the deletions with FPG was 0.065, which was close to the 0.05 nominal level, where the association p-value for the duplication with FPG was 0.389. More interestingly, the adjusted mean for FPG in the individuals with deletions was 87.52 with a 95% CI of , while the adjusted mean FPG in the individuals with normal copies was 91.98 with a 95% CI of . Therefore, the results were consistent with those in the SAPPHIRe sample that the mean FPG was lower in individuals with deletions in SFMBT1 than that in individuals with normal copies.The association between the candidate SFMBT1  significantly lowered FPG level in the SAPPHIRe sample, with a genome-wide significant p-value of 2 × 10−4. Interestingly, the same trend was also observed in the replication cohort  that samples with deletions had lower mean FPG level than the mean FPG level in samples with normal copies. Due to the restriction of the genotyping platform in TWB, only 9 individuals with larger CNVs covering the candidate SFMBT1 region were identified. However, the association p-value of 0.065 was close to the 0.05 significance level, supporting that the deletions in SFMBT1 have effects on FPG.Our analysis identified a candidate region of deletions in SFMBT1 gene encodes a protein containing four malignant brain tumor repeat domains. Interestingly, SNPs in SFMBT1 have been reported to be associated with mean systolic and diastolic blood pressure, and significantly differential expression was observed for the gene between hypertensive cases and normal controls in another Han Chinese study in Taiwan [SFMBT1 may have functional implications in some metabolic related traits. As shown in Fig. SFMBT1 were also found in Database of Genomic Variants (DGV) [The n Taiwan . A largen Taiwan . Anothern Taiwan , and somn Taiwan . Hence, ts (DGV)  and the ts (DGV) , suggestSFMBT1 region were observed in the SAPPHIRe sample, and only one duplication was observed in the TWB sample. A total of 30 CNVs in the gene were found in the DGV based on results from various studies, where Caucasian samples were mainly analyzed. Only two of the 30 CNVs were duplications, while the others were deletions. Therefore, duplications in SFMBT1 could also be rare in the Han Chinese population. Further studies to evaluate whether duplications in SFMBT1 elevate fasting glucose levels in the Han Chinese population will be important. However, a large sample size with dense probes will be required to achieve the goal.No duplications in the candidate Although rare CNVs have been found to be associated with several complex traits, our burden analysis did not identify any significant associations between global rare CNVs and the two traits. This may be due to the limited size of our sample, where many rare CNVs were not observed. Again, a large sample size will be required to further evaluate the role of global rare CNVs in FPG and FPI.SFMBT1 that were significantly associated with FPG in the SAPPHIRe sample, and the deletions also showed marginal significance in the TWB sample. The deletions in the SAPPHIRe sample were validated using PCR-based amplification. Based on previous findings and our results, SFMBT1 may have functional implications in FPG and other metabolic traits. Our power study suggest that the proposed family-based CNV test had sufficient power to identify the deletions associated with FPG given the sample size. Further studies should be conducted to evaluate the role of duplications in the SFMBT1 gene and FPG.We identified deletions in


Mendelian randomization (MR), the use of genetic variants as instrumental variables (IVs) to test causal effects, is increasingly used in aetiological epidemiology. Few of the methodological developments in MR have considered the specific situation of using genetic IVs to test the causal effect of exposures in pregnant women on postnatal offspring outcomes. In this paper, we describe specific ways in which the IV assumptions might be violated when MR is used to test such intrauterine effects. We highlight the importance of considering the extent to which there is overlap between genetic variants in offspring that influence their outcome with genetic variants used as IVs in their mothers. Where there is overlap, and particularly if it generates a strong association of maternal genetic IVs with offspring outcome via the offspring genotype, the exclusion restriction assumption of IV analyses will be violated. We recommend a set of analyses that ought to be considered when MR is used to address research questions concerned with intrauterine effects on post-natal offspring outcomes, and provide details of how these can be undertaken and interpreted. These additional analyses include the use of genetic data from offspring and fathers, examining associations using maternal non-transmitted alleles, and using simulated data in sensitivity analyses (for which we provide code). We explore the extent to which new methods that have been developed for exploring violation of the exclusion restriction assumption in the two-sample setting (MR-Egger and median based methods) might be used when exploring intrauterine effects in one-sample MR. We provide a list of recommendations that researchers should use when applying MR to test the effects of intrauterine exposures on postnatal offspring outcomes and use an illustrative example with real data to demonstrate how our recommendations can be applied and subsequent results appropriately interpreted. However, evidence that the proxy measures used by IADPSG developers to indicate offspring obesity  are accurate predictors of future risk of childhood obesity, or that treating women with the IADPSG criteria will effectively reduce childhood obesity, is lacking. In other areas women’s lifestyle in pregnancy is potentially being blamed for all future health risks in her offspring. It is essential, therefore, that methods are developed that can provide valid causal answers to questions about the long-term effects of intrauterine exposures. However, conventional methods are unlikely to be suitable; conventional approaches applied to cohort study data are likely to be influenced by residual confounding, and it is infeasible or extremely difficult to undertake randomised controlled trials (RCTs) of the effects of maternal pregnancy exposures on long-term offspring outcomes2.The possibility that a wide-range of maternal pregnancy exposures, such as her gestational adiposity, smoking, diet and mental health, have long-term effects on an equally wide-range of post-natal offspring outcomes has gained such traction that it is influencing antenatal care. For example, the Independent Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria for diagnosing gestational diabetes, which have been adopted by the World Health Organisation and many other national and international policy and guideline groups, aim to identify women whose children are at risk of future obesity, in order to ultimately prevent childhood obesity though antenatal care5, is increasingly used in aetiological epidemiology, including to test the effects of intrauterine exposures on long-term offspring outcomes10. Increased confidence in the use of MR to improve causal understanding has been achieved through proof-of-concept studies, such as those confirming the causal effects of greater body mass index (BMI)11, systolic blood pressure12, and low density lipoprotein cholesterol (LDLc) on coronary heart disease (CHD)14, and of smoking intensity and duration on lung cancer17, as well as concerted efforts by researchers using MR to acknowledge its underlying assumptions and how these might be violated, together with the development of methods to test assumption violations and/or be able to relax these24.Mendelian randomization (MR), the use of genetic variants as instrumental variables (IVs) to test causal effects2. The first paper to describe the MR method, highlighted the value of having genetic (MTHFR) data on trios (both parents and offspring) when using MR to test the intrauterine effects of folic acid on offspring neural tube defects3. However, the majority of methodological developments in MR have not considered the case, wherematernal genetic variants are used as IVs to test the effect of a pregnancy (intrauterine) exposure onoffspring outcomes. The aim of this paper is to describe specific ways in which the MR assumptions might be violated in studies concerning the effect of maternal pregnancy exposures on offspring outcomes, and to provide recommendations for how to test and potentially reduce the biases that might result from these assumption violations.Methods to improve causal understanding of intrauterine exposures on offspring outcomes are important, given the likelihood for residual confounding in conventional multivariable analyses, and the infeasibility, or marked difficulty, of using RCTs to test effects of maternal pregnancy exposures on long-term offspring outcomes10. Some MR studies of maternal pregnancy effects, for example those related to alcohol consumption, have used candidate genes that are functionally related to the exposure of interest, but these have also been identified in non-pregnant women and men9. Thus, a key question for the use of MR in these studies is whether genetic variants identified in non-pregnant women and men are valid IVs for pregnancy exposures.As with most MR studies, those that have been undertaken to test the effect of maternal pregnancy exposures on offspring outcomes have used variants that have been shown to be robustly related to the exposure in large genome-wide association studies (GWAS) conducted in non-pregnant women and men25. It has been shown that, for several genetic variants, associations with exposures measured in pregnancy are similar to those seen in GWAS of non-pregnant women and men. This appears to be so for BMI, fasting glucose, fasting lipids, 25(OH)D, adiponectin, thyroid levels and thyroid stimulating hormone10. Genetic variants that have been shown to have genome-wide significant associations with type 2 diabetes have been shown to relate similarly to gestational diabetes28, those related to smoking intensity and duration in GWAS, are related to difficulty quitting smoking in pregnancy29, and variants known to influence alcohol metabolism relate similarly to alcohol consumption in pregnant women as they do in non-pregnant women and men30. Whilst these findings are reassuring, it is important to demonstrate the relationship of genetic instrumental variables with the exposures of interest in pregnant women when using MR to test causal intrauterine effects on offspring outcomes  with the exposure assessed in pregnancy.Explore the association of the maternal genetic IV with observed maternal and offspring potential confounders of the exposure-outcome association. Explore associations of the maternal genetic IV with potential confounders, with and without adjustment for the same offspring genetic variants that are going to be controlled for in the main MR analyses.33, to determine whether they are importantly related to the (offspring) outcome of interest.Consider how likely genetic variants in the offspring, which are (strongly) associated with the offspring outcome of interest, overlap with genetic variants used in the maternal IV. Where the maternal exposure is the same as the offspring outcome, it is clear that there will be substantial overlap. In situations where the extent of any overlap is unclear, maternal genetic IVs should be looked-up in relevant publicly available datasets, such as those curated in MR-BaseWhere there is no overlap between offspring genetic variants that influence their outcome and maternal genetic IV(s) there is no need to adjust for offspring genetic variants or undertake analyses with non-transmitted maternal genetic alleles.If one is unsure about the extent to which there is overlap between offspring genetic variants that affect their outcome and maternal genetic IV(s), including after trying to explore this in publicly available datasets, it would be sensible to undertake MR analyses adjusted for offspring genetic variants and MR using non-transmitted maternal alleles as sensitivity analyses.Where there is overlap between offspring genetic variants that affect their outcome and maternal genetic IV(s), the main MR analyses should use maternal genetic variant IVs adjusted for the same genetic variants in offspring  and/or MR using maternal non-transmitted alleles as IVs.Undertake as many of the following additional sensitivity analyses as possible with the available data:Informed simulation analyses to test for acknowledged biases .Weighted  median method to explore violation of the exclusion restriction criteria, other than via offspring genotype. If using this in situations where there is concern about violation via offspring genotype, this sensitivity analysis is best done on IVs that are maternal genetic variants adjusted for the same offspring  genetic variants.MR-Egger  method to explore violation of the exclusion restriction criteria, other than via offspring genotype. If using this in situations where there is concern about violation via offspring genotype, this sensitivity analysis is best done on IVs that are maternal genetic variants adjusted for the same offspring  genetic variants.Triangulate with other approaches that can be used to test causal inference that make different assumptions to MR and have different sources of potential bias.31. However, we recommend that associations of maternal genetic IVs are checked with observed variables which are potential confounders of the maternal exposure and offspring outcome association. For some research questions, it will be optimal to adjust the maternal genetic IV for equivalent offspring  genetic variants . Therefore, we recommend testing associations between maternal variants and observed potential confounders with and without adjustment for offspring  genetic variants when such adjustments are used in the main MR analyses. Furthermore, as confounding paths between maternal pregnancy exposures and offspring outcome are likely to involve both maternal and offspring characteristics  genetic variants. The issue then is whether it is better to adjust for offspring genetic variants  or not (and risk introducing bias because of violating the exclusion restriction criteria via offspring genotype).Where overlap between maternal genetic IVs and variants related to offspring outcomes are likely, the path from maternal genetic IVs to offspring outcomes via the offspring genotype needs to be blocked for the MR results to be valid. Consequently, many studies, which have used an MR approach to explore such relationships, have adjusted for offspring  genetic variantsariants . Intuitiariants 34–36. 40. Studies using simulation and real data suggest that in most such cases, the more proximal source of bias is likely to be the most important40. Thus, here we would anticipate that adjusting for offspring genetic variants, which are more proximal to maternal genetic variants, and offspring outcome, is more important than not, and that the potential bias from the more distal spurious association of paternal genetic variants with maternal variants is likely to be less problematic. In practice, whether the more proximal or distal bias is likely to be greatest will depend on the particular research question and the magnitudes of real and spurious associations.This is similar to what has been referred to as ‘M-bias’, where colliders exist and questions arise concerning whether or not to adjust for certain variable(s)For other questions, where maternal exposure is sufficiently different from the offspring outcome that it is unlikely that the same genetic variants that robustly influence maternal exposure would also influence (offspring) outcome, it might be reasonable to assume that there is no need to adjust for offspring genotype. For example, if we were interested in the causal effect of maternal pregnancy blood pressure on subsequent offspring depression, violation of the exclusion restriction assumption via offspring genotype is less likely than in the situation shown inIn addition to the spurious maternal-paternal genetic association induced by adjusting for offspring genetic variants, there may be a real association between maternal and paternal genetic variants generated by assortative mating. Assortment leads to an association between a couple’s genotypes and can be generated for a number of different reasons, including spouses being attracted to each other on the basis of heritable traits, including intelligence and physical appearance. This association  could also result in bias, due to violation of the exclusion restriction criteria if there is assortment on the exposure of interest, and paternal genotype  and its associated phenotypes influence offspring outcome. Assortative mating would generally produce a positive bias. As adjusting for offspring genotype induces an inverse association between maternal and paternal genotype (and hence negative bias), it is possible that in the presence of assortative mating adjustment for offspring genotype produces a minimally biased result as the two cancel each other.Maternal genetic variants influence exposure pre- and post-pregnancy. Since genetic variants are allocated at conception, they conceivably influence traits across the entire life course. As a result, MR studies are often assessing the causal effect of a life-time cumulative exposure on outcomes41. This is potentially problematic when we are using MR  to address research questions concerned with exposure during a specific time period (such as the intrauterine period)41.pre-pregnancy levels of the maternal exposure through an impact on primordial oocytes , which relates to subsequent offspring outcomes , then this would provide a potential violation of the exclusion restriction criteria when exploring a ‘purely’ intrauterine effect via a pre-conceptual path , and then via modelled offspring exposure to their outcome  exposure of interest is a behaviour that is ‘visible’ to their offspring, such as alcohol consumption, there is also potential for violation of the exclusion restriction criteria by aoutcome . For matTrue pleiotropic effects of the maternal genetic instrumental variable. The mechanisms described above in the two previous sections are specific to the situation where maternal genetic variants are used as instruments to test the effect of a maternal pregnancy (intrauterine) exposure on an offspring outcome. However, it is important to acknowledge that these studies are also  vulnerable to violation of the exclusion restriction criteria, due to horizontal pleiotropy  outcome of interest.For many research questions concerned with intrauterine effects, violation of the exclusion restriction assumption via offspring genotype is likely to be a key concern. Therefore, researchers should start by considering the extent to which the maternal genetic IV variants overlap with offspring variants related to the outcome of interest. Where the maternal exposure is the same as the offspring outcome , it is clear that the overlap is substantial. In situations where the extent of any overlap is unclear, maternal variants used as IVs should be looked-up in relevant publicly available datasets, such as those curated in MR-Baseother than via offspring/paternal genotypeMR-Egger and median based methods to explore violations) method might work when addressing questions about intrauterine effects on offspring outcomes, we realised that it might be theoretically possible in some extremely specific cases that the problem of violation of the exclusion restriction assumption via offspring  genotype might be solved by a very simple correction of the unadjusted MR result. However, we believe this would only work in a very specific situation where, amongst other criteria, the maternal exposure and offspring outcomes areexactly the same , and where the model is additive and linear. If all criteria for this approach are present, a simple correction of theunadjusted MR result, achieved by subtracting 0.5 from this unadjusted result, this should yield an asymptotically unbiased estimate of the direct effect of maternal exposure on child’s outcome . The criteria required for this simple adjustment and our reasoning behind it are discussed in detail inIn considering how the MR-Egger 45. If either or both mother or child are homozygotes then allele transmission can be unambiguously defined. To determine which allele has been transmitted from the mother in the situation where both mother and offspring are heterozygotes  requires additional genetic information. For example, the most likely pattern of transmission can often be determined by constructing local haplotypes of adjacent SNPs in mother and offspring around each SNP instrument 45.A second main analysis that we recommend , in situations where we are concerned that there will be violation of the exclusion restriction criteria via offspring genotype, is to use a maternal genetic IV that is based only on her non-transmitted allelesIn addition to the methods described above – and in particular where it might not be possible to adjust for offspring  genotype or construct haplotypes to use maternal non-transmitted alleles as IVs – we would suggest using simulations to explore the likely consequences of violation of the exclusion restriction criteria via offspring/paternal genotype. Here we provide methods, code and example results for doing such analyses.Methods for simulation analyses. We performed a number of simulations to quantify the bias using different approaches for estimating the causal relationship between maternal exposure and offspring outcome: (1) unadjusted MR analysis involving maternal genotype, maternal exposure and offspring outcome; (2) MR analysis involving maternal genotype, maternal exposure and offspring outcome adjusting for offspring genotype; (3) MR analysis involving maternal genotype, maternal exposure and offspring outcome adjusting for both paternal and offspring genotype; and (4) MR analysis utilizing the non-transmitted maternal alleles , maternal exposure and offspring outcome.47. We also allowed the IV strength, impact of confounders on offspring outcome, and the true causal effect of maternal exposure on offspring outcome to vary in these simulations. For each scenario, we generated 1000 replicates consisting of 10000 parent offspring trios. Full details of these simulations, including R code, are provided inFor each simulation strategy, we tested the impact of different levels of effect of offspring and paternal genotype (via a direct path or via their phenotype) on offspring outcome . This range is to provide examples that might be seen across different plausible questions concerned with intrauterine effects on offspring outcomesResults of simulation studies.Except in the situation where offspring genotype was not associated with offspring outcome, failure to adjust for offspring genotype resulted in a large positive bias , which increased with stronger associations between offspring genotype and outcome. There was no impact on the causal effect estimate according to the magnitude of association between father’s genotype and offspring outcome, when no adjustment for offspring genotype is made. Adjustment for offspring genotype resulted in a modest negative bias , unless the association between father’s genotype and offspring outcome was null. The magnitude of this negative bias increased with the strength of the association between father’s genotype and offspring outcome.The results from all 1800 different simulation conditions that we undertook can be found in21. Whilst these enable one to relax the exclusion restriction assumption, they have additional assumptions that are likely to be violated in situations where there is overlap between offspring genetic variants related to their outcome and the maternal genetic IVs. Therefore, we recommend these methods are used only as sensitivity analyses for exploring violations of the exclusion restriction assumption through paths other than offspring genotype , and are applied after adjustment for offspring  genotype. Methods for using MR-Egger or weighted medians with maternal non-transmitted alleles have not been developed, though as we discuss below it is theoretically possible that with large numbers of trios this approach could be combined with two-sample MR (seeOne- and two-sample Mendelian randomization).Two relatively novel methods for exploring the extent to which the exclusion restriction assumption might be violated are the MR-Egger and median based methodsMR-Egger. The MR-Egger method was developed to test for violation of the exclusion restriction assumption when using aggregate data from multiple genetic IVs24, as done in two-sample MR48. It is related to the inverse-variance weighted (IVW) method, in which a weighted linear regression of each of the genetic IV–outcome coefficients on the genetic IV–exposure coefficients, with the latter orientated to be positive, is used. In IVW, the regression line is constrained to pass through zero (i.e. the exclusion restriction criteria is assumed and as such it is assumed if the genetic IV-exposure association is zero the genetic IV-outcome association must be zero)21. Unlike the standard IVW approach that assumes no violation of the exclusion restriction criteria, the intercept in MR-Egger regression is not constrained to zero, and therefore provides an estimate of the average pleiotropic effect across all of the genetic variants21. The MR-Egger method (through the MR-Egger slope) provides consistent estimates of the true casual effect, even if all genetic variants are invalid due to violation of the exclusion restriction assumption, but requires an additional assumption known as InSIDE (instrument strength independent of direct effect). The InSIDE assumption states that there should be no correlation between instrument strength (i.e. the SNP-exposure association) and the “direct” effect of the variant on the outcome (i.e. the effect on the outcome that is not mediated by the exposure of interest). The InSIDE assumption is likely to be violated in MR studies testing the effect of a maternal pregnancy exposure on an offspring outcome when the maternal exposure and offspring outcome are very similar21. This is most easily illustrated if we think of the example where maternal exposure and offspring outcome are the same characteristic – e.g. BMI as in50. Thus, if offspring BMI is measured in infancy or early childhood, the magnitude of a genetic BMI allele score with BMI may differ between pregnant adult women and their infant/childhood offspring. Nonetheless we would still anticipate some positive correlation between the two.52. Unlike in the two sample context where simple methods exist to detect and adjust for weak instruments53, no simple solution has yet been developed for a one-sample analysis . The direction of bias in this case is towards the  confounded multivariable regression estimate rather than the null. This issue will be exacerbated if the genetic variants are weighted (by the magnitude of their effect on exposure) using internal weights .MR-Egger was developed for use within a two-sample MR framework using aggregate data, but studies of maternal pregnancy exposures on offspring outcomes will generally be done in the same sample(s) using individual participant data. The use of MR-Egger (and IVW) in one-sample MR is more problematic, however, because its performance deteriorates rapidly in the presence of weak instrument biasMedian based methods. Median based methods have also been developed to explore violation of the exclusion restriction criteria using two-sample MR with aggregate data. A simple (without weights) median method is obtained as the median of the ordered set of ratio estimates obtained from using each genetic variant as a single IV24. The weighted median estimator is an extension of this method, which is more statistically efficient. It is the median of the inverse variance weighted empirical distribution function of IV estimates24. This method can consistently estimate the true causal effect when at least 50% of the weight in the analysis stems from variants that are valid instruments. As a corollary, if a single genetic variant contributes more than 50% of the weight, then that variant must be valid. In MR studies where the maternal exposure and offspring outcome are the same  confounded multivariable regression estimate when using weak instruments in the one sample setting, the deterioration in performance is not expected to be as dramatic as in MR-Egger. This is because it is still a function of two-stage least squares (TSLS) estimates, rather than an (ordinary least squares) regression model.55. We have recently shown, using MR, that greater maternal pregnancy BMI is likely to be causally (via intrauterine mechanisms) related to offspring birthweight and ponderal index10. We have also explored the likelihood that there may be lasting effects of maternal pregnancy BMI on offspring BMI and fat mass index (FMI) in offspring between ages 7 to 18 years56, using data from the Avon Longitudinal Study of Parents and Children 58. In this illustrative example, we examine the same research question and use ALSPAC data, but here we focus specifically on the analytical methods and sensitivity analyses recommended inThere is increasing concern that greater maternal adiposity in pregnancy results in greater offspring adiposity in later life via intrauterine mechanisms, and that this could perpetuate the obesity epidemic because female offspring born to women who are more adipose would enter their pregnancies more adipose and the effect would cycle through generations46, along with adjustment for the same weighted allele score in offspring. Second, we examined the causal effect using the non-transmitted (to offspring) haplotype approach, as described by Zhang and colleagues45. We also undertook three sensitivity analyses with real data - MR-Egger and weighted median methods applied to the maternal weighted allele score with adjustment for offspring allele and the simple subtraction of 0.5 from the unadjusted MR estimate and each value of its 95% confidence interval. In addition, we undertook simulation sensitivity analyses using the methods described in ‘Methods for simulation analyses’ and also inDetails of the study population and analyses that we used in this illustrative example are provided inThese statistical analyses were undertaken in StataIC version 14.46. The first stage F-statistic was > 45. The maternal genetic IV  was positively associated with household social class and to some extent with parity, but not notably with other potential confounders  women that were used to identify the 97 genetic instruments for this illustrative study, and to identify external weights, were 0.022SD and 2.7%ounders .The magnitudes of associations and patterns of differences between each approach were broadly the same for offspring BMI and offspring FMI. We found a positive effect of maternal early pregnancy BMI on offspring BMI and FMI at age 18 in the unadjusted MR weighted allele score using TSLS, which was considerably stronger than the multivariable regression result. However, with adjustment for offspring allele score  this attenuated considerably and was consistent with the null . SimilarThe MR-Egger intercept was consistent with the null and the slope was similar to the two main analysis results, suggesting no strong evidence for a causal intrauterine effect of maternal pregnancy BMI on offspring BMI or FMI at age 18-years . Both thThe simulation studies suggest that whether we assume a null, or positive, association the MR method with adjustment for offspring and paternal genetic variants and that using maternal non-transmitted alleles were unbiased. The simulated results with adjustment for offspring genetic variants only  were modestly inversely biased, whereas this approach with real ALSPAC data (one of our main MR approaches) had point estimates very close to the null for both BMI and FMI. The difference between this simulated result and our real data may be explained by our assumption in the simulated data that paternal genotype will explain 1% of offspring BMI ; in the ALSPAC cohort paternal genotype might have less impact on offspring BMI once offspring genotype is adjusted for.We acknowledge that we have limited statistical power from this one study. However, the aim was to illustrate the series of analyses we recommend inThe main output of this paper is a list of recommendations for undertaking MR in situations where one is interested in the causal effect of an intrauterine exposure that is measured and instrumented for in pregnant women on an outcome that occurs (and hence is measured) in their offspring. We have tried to provide a clear rationale for these recommendations but we do not claim that they are necessarily complete. As MR is further used to address such questions we envisage additional issues may be identified and also that additional methods (to those discussed here) will be developed. Below we discuss two further related issues to addressing questions of causal effects of maternal pregnancy exposures on offspring outcomes.48. There are advantages to two-sample MR, in particular with increasing availability of complete summary results data from a large number of GWAS, it is possible to apply two-sample MR to these summary data and undertake MR in very large numbers of participants59. However, because it is essential to link exposure in mothers to outcome in their offspring it is not possible to use such summary data from two-samples to assess most questions related to intrauterine effects on offspring postnatal outcomes, though two-sample MR has been used to explore the causal effect of birthweight (as a proxy for some aspects of intrauterine environment) on later outcomes within the same individuals62. In theory two-sample MR could be used to explore the effect of maternal pregnancy exposures on offspring outcomes, but in general it would be essential to have individual participant data on maternal genetic variants that were used as IVs linked to offspring outcomes. For example, the association of maternal genetic instruments with offspring outcomes [ideally adjusted for offspring  genotype] could be divided by results of the maternal genetic instruments with maternal exposure, with the latter obtained from a different sample to the former. Ideally, one would want the latter to assess maternal exposure during pregnancy, but if one were able to show (in a reasonably sized sample of pregnant women) that the genetic instruments related to maternal pregnancy exposure with the same magnitude and direction as that seen in the general (aggregate) GWAS data of (non-pregnant) women and men, it might be valid to use aggregate GWAS data for the genetic IV-‘maternal’ exposure sample (ratio estimate denominator). In practice, overlapping sample MR48 might be more common and useful. For example, for expensive or unusual pregnancy exposures there may only be measurements in a sub-sample of the cohorts being used, in which case the denominator of the ratio IV estimate  might be done only in that smaller sample, but the numerator  done in the large sample.In one-sample MR, both the genetic IV-exposure and genetic IV-outcome associations are computed in the same study sample; in two-sample MR the two are computed in independent samplesThe one exception to this general need to have maternal genetic IVs directly linked to offspring outcomes, would be in the case where it was possible to have ‘fully’ estimated maternal non-transmitted alleles. For example, if it were possible to have large numbers of trios with genetic data together with pregnancy exposure data from the mothers in those trios. Theoretically one could correctly identify the non-transmitted haplotype scores in the mothers and use those to determine the associations of these haplotype scores with the maternal pregnancy exposure in the sample from which the trios came. Then, look up the haplotype scores, which are the same as the maternal non-transmitted scores, in a general population sample to estimate their associations on ‘offspring’ outcomes. Currently, there are too few birth/pregnancy cohorts with genetic data on trios and maternal  measurements in pregnancy. However, as more pregnancy/birth cohorts collect genetic data on trois and work together collaboratively, there is future potential to explore this theoretical approach further.54, we feel that using methods such as MR is important in this field, and in41. The idea of triangulation is that if the results from two or more different approaches, each of which has different and unrelated sources of bias, point in the same direction this supports those results pointing to the correct causal answer. In the case of the illustrative example that we present here, the fact that negative control studies 63, and within sibship analyses64, each of which have different key sources of bias to the MR approach used here41, all point towards there being no strong causal effect of maternal greater pregnancy BMI on her offspring BMI, strengthens the conclusion that there is no causal intrauterine effect in this specific example.This paper highlights specific sources of bias when MR is used to explore the causal effect of maternal pregnancy exposures on offspring outcomes. Given the difficulty of establishing causality using conventional multivariable approaches or RCTs to address research questions concerned with the effect of maternal pregnancy exposures on long-term offspring outcomes, together with increasing pressure to direct antenatal care and public health policy towards preventing future ill-health via antenatal interventionshttp://www.bristol.ac.uk/alspac/researchers/access/. The ALSPAC data management plan  describes in detail the policy regarding data sharing. A sampler 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 this website, which also provides full details and distributions of the ALSPAC study variables: The article addresses the important methodological issues surrounding application of genetic instrumental variable (IV) analysis, or Mendelian randomization, to maternal-child exposure-outcome associations. The authors describe scenarios in which the so-called exclusion restriction might be violated and then describe analytic approaches to address the potential bias in IV estimates that could arise. The article is clearly written with apt examples and is very thorough, especially with the abundant supplemental materials including code. This manuscript is likely to be valuable to the community of researchers interested in IV applications to maternal-child associations. There are several issues that it would be helpful if the authors could address to make the manuscript a more comprehensive resource.1The authors describe the usefulness of examining associations between the maternal genetic IV and measured confounders under several different conditions (page 4). However, it is not clear how to optimally address associations between the genetic IV and measured confounders (see comment 2a below) as the bias introduced by such IV-confounder associations on the IV-outcome association estimate are not always clear.It would help if the authors could specifically address adaptation of their simulation-based sensitivity analyses to examine pleiotropy. Two examples that came to mind when reading the manuscript:In the example evaluating a genetic IV to examine the association between maternal pregnancy BMI and childhood BMI, the authors describe associations between the IV and measured confounders . Depending on how the DAG is conceived, one could see this as an example of pleiotropy leading to a violation of the IV assumption that all directed paths from the IV to the outcome pass through maternal pregnancy BMI . Furthermore, there might be plausible paths connecting the BMI IV, maternal pregnancy BMI, parity, and the outcome that would suggest that bias could be introduced by including parity as a covariate in either the multivariable regression or the IV analysis. Could the authors address their choice to include social class but not parity in their multivariable regression model and discuss adaptations of their simulations to address possible contributions of pleiotropy to IV estimates?The authors’ discussion of assortative mating is clear. However, they do not address the scenario in which the assortment occurs not on the exposure of interest but on a correlated trait . In this scenario, accounting for paternal and offspring diabetes genetic variants is unlikely to block all paths between the IV and outcome that do not go through the exposure of interest, and adding covariates related to assortment  might induce bias in the IV-outcome association estimate. While this is essentially a special case of pleiotropy, it may be worth making the distinction between assortment based on the exposure of interest (for which the IV is selected) and assortment based on traits correlated with the exposure of interest. The impact of the latter case on the IV-outcome estimate can likely be addressed through simulation as well.  The authors do not include offspring genetic variants for BMI in their multivariable regression model in the example derived from ALSPAC data. This would seem to inflate the difference in estimates of exposure-outcome association compared to the IV-outcome association . While this does not affect the causal effect estimate from the IV analysis, it does impact any statistical test for difference in effect estimates comparing the multivariable regression model and the IV model. Can the authors address this discrepancy?2 that the authors do not describe in their manuscript: 1. The assumption of no effect modification by the instrument on the exposure-outcome association; 2. The assumption of monotonicity between instrument and exposure. In the context of genetic instruments for which we infrequently understand the causal variants or biological pathways involved, the assumption of no effect modification is difficult to verify. Similarly, when a genetic instrument is used for a dichotomous exposure (e.g. diabetes or obesity), the monotonicity assumption is not always satisfied . It might be helpful to include a brief discussion of sources of bias common to all genetic IV analyses, irrespective of whether an intrauterine exposure is of interest, that warrant consideration.Two additional sources of bias in IV estimates that are not unique to maternal-offspring IV analyses arise from additional assumptions Minor comments:through antenatal care.”Typo- Para 1: sentence beginning “For example, IADSPG….Intro, last para, sentence that starts “However, the majority ….”, there is an incorrect comma.True pleiotropic effects of the maternal genetic instrumental variable.”Extraneous comma in the last sentence of para starting “  I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is a clear and balanced article on the use of MR to analyse the effects of maternal pregnancy exposures on offspring outcomes. Our comments are reported below.1) Introduction a. ”However, evidence that the proxy measures used by IADPSG developers to indicate offspring obesity  are accurate predictors of future risk of childhood obesity, or that treating women with the IADPSG criteria will effectively reduce childhood obesity, is lacking”. It would be important to clearly distinguish between prediction and the possibility to prevent obesity through interventions, as the former concept does not require causation. b. “In other areas women’s lifestyle in pregnancy is potentially being blamed for all future health risks in her offspring”. This statement is maybe too strong; in addition strong evidence of causation (or lack of causation) would not solve the blaming component.  c. “However, conventional methods are unlikely to be suitable [to provide valid causal statements]”. Also this statement is maybe too strong. We agree that residual confounding is a major issue for causal inference from observational data, but there are several examples of valid causal inference based on observational studies analysed using conventional approaches.  2) Section “Maternal genetic instrumental variables are not associated with potential confounders of the pregnancy exposure- offspring outcome association” a. “Therefore, we recommend testing associations between maternal variants and observed potential confounders with and without adjustment for offspring  genetic variants when such adjustments are used in the main MR analyses”.  We have some difficulties in understanding the practical implications of this recommendation. It could be useful to add the offspring genetic variants in the DAG reported in Figure 1 to understand what would be the impact of this adjustment on the IV – potential confounders associations. Furthermore, it would be good to clarify the concept of “testing” in this context: is it a statistical test, a judgment on the magnitude of the associations, is it at an univariable or multivariable level?3) Section " Maternal genetic instrumental variables are only related to the offspring outcome through their relationship to the maternal pregnancy exposure (the exclusion restriction assumption)" a. We wonder if the offspring genetic variants could also affect the maternal exposures during pregnancy. It is probably unlikely, but not impossible. This would have implications for the scenario described in Supplementary File 1. b. Figure 2 and the last two sentences on page 5  “Thus, it is rarely possible to be able to deal with this potential new bias by adjusting for paternal (as well as offspring) genetic variants…”.  Cohorts could have information on paternal characteristics e.g. paternal BMI, paternal physical activity, smoking and other phenotypes. Adjustment for paternal phenotypes could block the path B1-->B2àB3 . c. In our opinion, maternal postnatal exposures are potent potential sources of bias, whenever these exposures act on the offspring outcome through various pathways. For example maternal obesity might act on the offspring through different pathways involving diet, physical activity, sedentary behaviours, etc.et al. 2013] A similar comment regards Figure 3 and page 7 – second paragraph – maternal exposure of interest as behaviour “visible” to their offspring and last sentence of the same paragraph (“less visible” exposures). We would not define these postnatal exposures as “visible” or “less visible”. In Figure 3 maternal alcohol consumption might affect offspring cognitive function or other outcomes by mechanisms other than “imitating”. In this specific case mothers with such exposures might have different child-care practice or create diverse environment where child grows that could influence later cognitive function. Maybe “manifest” and “not manifest” would work better [for example we used this terminology in Richiardi L4) Section "Testing effects using maternal non-transmitted genetic variants." a. We should admit that we have not fully understood the inherent assumptions of the analysis restricted to the non-transmitted allele. For example, wouldn’t this imply an inherent conditioning on the offspring genetic variants?5) Supplementary file 1 a. “If offspring BMI is assessed in adulthood then this association is likely to be the case, as the BMI variants will relate to BMI with the same magnitude in adult mothers and their offspring when BMI is assessed in adulthood”. It should be considered however that the maternal exposure is assessed in pregnancy, while the offspring is not. Thus the strength of the IV might be different even if the assessment is made at the same age .6) Illustrative example using real data a. If the standardization of the BMI is not necessary for the application of the method, we would suggest using the actual BMI in the analysis instead of BMI SDs . b. Maybe it is out of the scope of this manuscript, but it would be interesting to discuss the implications of a possible baseline selection related with pre-pregnancy BMI in this cohort (e.g. obese mothers were more -or less- likely to participate in the study). Wouldn’t this be a source of violation of the exclusion restriction assumption (and a possible explanation for the association between the genetic score and SES and parity?) c. “For both BMI and FMI there was statistical evidence that the effects that both of our main MR methods were statistically inconsistent with the association from multivariable regression analyses ”. It seems that something is missing in this sentence. d. Page 11: “In the simulation analyses we examined results assuming a true null result and also a true positive effect of 0.2SD change in the offspring BMI/GFMI per 1SD change in maternal pregnancy BMI” Is this effect supported by the literature? Note that the note in Table 2 reports an assumed true result of 0.1 SD , wile the text reports 0.2 SD.We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is a well-written article clearly addressing the specific, topic which also gives a nice explanation of some the newer Mendelian randomization (MR) concepts and techniques. The paper considers 3 major issues that could arise when using MR to estimate the effect of maternal attributes on offspring outcomes, shown in Figures 1, 2 and 3. It would be very helpful if at the beginning of the section "Mendelian randomization assumptions and assessing causal intrauterine effects" these three different issues were introduced, before discussing Figure 1. In Figure 1 it seems a bit redundant to write the IV assumptions because they can be read off the directed acyclic graph. However it may help some readers. In everyday life it is generally understood that causes, when relevant, act consistently. It seems unnecessary to test that genetic variants act the same in pregnant women as at other times. Instead, it might only be necessary to test that genetic variants act consistently if there was some reason to think otherwise.  The explanation for the result that in some cases the unadjusted MR result can be corrected by subtracting 0.5 (pages 7/8) needs to be included in the text, at least as an intuitive explanation, not just given in a Supplementary File.  From the regularity of the patterns in Table 1 it looks like there is an analytic solution for the simulation. If such an analytic solution could be found and added to the paper it would be very helpful and make the paper more generally applicable. Does weak instrument bias also likely inflate the estimate? A weak instrument might mean the denominator of a ratio is likely to be smaller than it should be.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. It seems that the real data example used here was very recently published by the authors in PLOS Medicine. Assuming this is true, I suggest acknowledging this somewhere with a comment on how the data analysis presented here is different or expands on the published work, or if it is essentially the same.   The authors state that assortative mating could result in bias. Is this an issue specific to maternal exposures? Or a more general problem for any MR study?Figure 3 describes violations of the ER due to pre- and post-natal pathways. Based on this example , it seems reasonable to suspect that if one variant violates the exclusion restriction then all variants use as IV(s) would potentially violate the exclusion restriction through the same pathway. So in this case, would MR-egger and median methods might not be useful (as suggested they would be on page 9)? If not, perhaps an alternative example would better illustrate the point you’re trying to make.The authors state that maternal exposures will typically be studied in the one-sample setting (page 10), which can be problematic for MR-egger. This was counter-intuitive to me because information on the IV-maternal exposure association can be obtained from external data sets and use in a two-sample MR analysis. My confusion was later cleared up in the discussion, where the authors mention that this 2-sample approach is possible. In my opinion, it is not necessary to wait until the discussion to make this point. Furthermore, you may want to reconsider the claim that maternal exposures will generally be studies in the one-sample MR setting.   This is a well-written and important paper, as it considers a special case of MR when maternal genetic variation is use as IV to test to effect of maternal/intrauterine exposures on offspring outcomes. There are specific challenges that arise in such studies, as the IV used for the maternal exposure is likely correlated with genetic factors in the offspring which could affect offspring outcomes (violating the exclusion restriction). Also, adjusting for offspring genotype could introduce collider bias. This paper describes these (and other) potential biases and recommends approaches for avoiding these biases. No major changes suggested, but I have a few comments that may help improve the paper:Commas placement not correct: “The simulation studies suggest that whether we assume a null, or positive, association the MR method …”“Pre/post-pregnancy” is used in text, while “pre/post-natal” is used in Figure 3. Better to be consistent. Title and abstract are reasonable, but consider a more concise title. One suggestion: “Recommendations for avoiding bias in Mendelian randomization studies of maternal exposures and offspring outcomes”. Minor comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.