Intu) and a previously ear-associated gene (Tbx15) causes deviating mouse ear morphology.Human ear morphology, a complex anatomical structure represented by a multidimensional set of correlated and heritable phenotypes, has a poorly understood genetic architecture. In this study, we quantitatively assessed 136 ear morphology traits using deep learning analysis of digital face images in 14,921 individuals from five different cohorts in Europe, Asia, and Latin America. Through GWAS meta-analysis and C-GWASs, a recently introduced method to effectively combine GWASs of many traits, we identified 16 genetic loci involved in various ear phenotypes, eight of which have not been previously associated with human ear features. Our findings suggest that ear morphology shares genetic determinants with other surface ectoderm-derived traits such as facial variation, mono eyebrow, and male pattern baldness. Our results enhance the genetic understanding of human ear morphology and shed light on the shared genetic contributors of different surface ectoderm-derived phenotypes. Additionally, gene editing experiments in mice have demonstrated that knocking out the newly ear-associated gene ( Human ear morphology varies widely, with largely inherited differences. However, the genetic basis for these differences is not well understood. To better understand the genetics of human ear morphology, we analyzed 136 ear traits from digital facial images of 14,921 individuals from three continents using GWAS meta-analysis and the recently developed C-GWAS method. We identified 16 genetic loci associated with ear morphology, eight of which had not been previously associated with ear features. These genetic loci can serve as candidates for future research to understand the function of these genes. Additionally, we have demonstrated the impact of two ear-associated loci on ear morphology in gene-edited mice. This study provides new insights into the complex genetics of the human ear. Human ear morphology is a complex trait that is influenced by a multidimensional set of correlated and heritable phenotypes. Studies have estimated the heritability of ear morphology phenotypes to be between 29% and 61% [EDAR, CART1, SP5, MPRS22, LRBA, LOC153910, and LOC100287225 [Previous genome-wide association studies (GWASs) on ear morphology have focused on a limited number of qualitative ear features and have not utilized quantitative ear traits. A GWAS in 5,062 Latin Americans identified significant associations at seven genetic loci for ear lobe size and attachment, folding of antihelix, helix rolling, ear protrusion and antitragus size, suggesting the involvement of the genes Due to the complex and multidimensional nature of ear morphology, focusing on a limited number of qualitative ear features, as done in previous GWASs, can result in missing important phenotypic information and genetic associations. Additionally, traditional methods such as human perception  and quesHigh correlations between various ear traits may be influenced by genetic variants with multi-trait effects. However, traditional single-trait GWAS, as used in previous ear GWASs ,2, has lN = 4,740), Asia , and Latin America  to investigate the genetic factors that contribute to ear morphology. We quantified 136 quantitative ear phenotypes using deep learning analyses of digital face images, and conducted GWASs and GWAS meta-analyses. These results were then combined using a recently developed C-GWAS method [In this study, we used a sample of 14,921 multi-ethnic individuals from Europe  approach  for compThis study included 14,921 individuals from five different population cohorts from three continents: Europe, Asia, and Latin America . These cP > 0.05 after multiple testing correction, as shown in P = 3.5e-145) and age (min P = 5.9e-99) on ear phenotypes were investigated in detail using individual-level data from RS. These factors together explained up to 16.9% of the variance for certain ear phenotypes (e.g. phenotype L7-L14) (The majority 82–100%) of the 136 ear phenotypes studied were normally distributed, as determined by the Kolmogorov-Smirnov normality test ( L7-L14) . Women h L7-L14) . Aging w L7-L14) . Unsuper L7-L14) . The gen L7-L14) , indicat L7-L14) . These e L7-L14) . The gen00% of thP ≤ 5e-8) can be considered as the study-wide significance threshold in this study. Additional information on the C-GWAS method can be found in other publications [Single-trait GWASs of 136 ear phenotypes were conducted in each of the five cohorts separately (as shown in ications .PC-GWAS = 1.2e-19), where the G-allele mainly led to an increased vertical length of the ear. This SNP was top-associated with L6-L15 (PMinGWAS = 4.7e-10) and showed nominally significant association with L6-L15 in all cohorts, with similar effect sizes (except for TwinsUK due to QC failure). This SNP is located approximately 200kbp upstream of the HAS2 gene, which encodes hyaluronan synthase 2, a protein that plays an important role in embryonic development of branchial arches [HAS2 have been previously associated with face morphology [PL3-L15 = 2.7e-7, P = 0.03) and the A-allele was associated with an increased prevalence.MinGWAS and C-GWAS together identified a total of 1,205 ear-associated SNPs at 16 distinct genetic loci that exceeded the study-wide significance . Eight orphology , body hePC-GWAS = 2.5e-15), where the G-allele mainly led to an increased ear width. This SNP was top-associated with L15-L16 (PMinGWAS = 2.3e-9) and showed nominally significant association with L15-L16 in almost all cohorts, with similar effect sizes . This locus is remarkable as it represents a gene desert and the nearest gene, INTU, is 408kb away from the region’s lead SNP .The second most significant novel finding was rs57788627 at 4q28.1 . The East Asian-specific missense variant of EDAR  had been previously associated with a wide range of endoderm-derived phenotypes, such as chin protrusion [EDAR is an example of the increased power of C-GWAS in detecting multi-trait effects. A multivariable fitting analysis in the RS cohort based on individual-level data showed that the lead SNPs from C-GWAS-significant loci explained on average 1.5 times and up to 3.1 times more phenotypic ear variance than the SNPs from MinGWAS-significant loci did  and MinGWAS (P = 2e-5) p-values highly significantly deviated from the null, while C-GWAS p-values obviously deviated further from the null than MinGWAS p-values did , which were particularly associated with earlobe phenotypes in our study, were previously reported to have association with male pattern baldness in the GWAS catalog [EDAR and 6q21 ATG5, were previously associated with mono eyebrow [Both the ear and face belong to craniofacial phenotypes for which shared genetic effects maybe expected. Therefore, we examined the 238 distinct genetic loci previously associated with human facial shape variation ,21–30 inctively,  showed sctively, . The finSeveral lines of evidence support the functional implications of the 16 discovered ear-associated loci, including 8 novel loci. A gene ontology enrichment analysis highlighted 6 ear development-related biological process terms  have previously been linked to facial morphology [Embryonic cranial neural crest cells (CNCCs) are temporary, migratory stem cells that play a crucial role in the formation of the ear during embryonic development. We compared the expression of 16 genes located near the 16 ear-associated SNPs with a random set of 16 genes selected near randomly chosen and frequency-matched 16 SNPs in CNCCs and 49 other cell types for 10000 replicates. Compared with the randomly selected gene sets, this set of 16 targeted genes near the ear-associated SNPs had significantly higher expression in 23 types of cells , while homozygous loss-of-function of Intu was lethal. Quantitative assessment of mouse ear shape  revealed significant differences (linear regression PPC3 = 1.5e-3)  and D3_6     between  = 1.74)  after FD = 0.06) . Overallenotypes . Furtherb length , which wTbx15 mice i.e., 9 homozygous Tbx15-/-, 18 heterozygous Tbx15+/-, 10 wild-type WT (Tbx15+/+). Previous gene editing experiments have already shown that Tbx15-/- mutant mice demonstrate facial variation, loss of weight, shorter limbs and a distinct “droopy ear” feature [Tbx15 genotype was significantly associated with the ear landmarks PC1 (P = 3.2e-3) and PC4 (P = 1.9e-3) (Tbx15 mutant mice were obviously longer and wider than those of the WT mice (Our breeding experiment generated 37 F2 9-weeks adult  feature . In our  1.9e-3) . Overall WT mice .INTU has a functional impact on ear morphology. Our findings also highlight shared genetic contributors between some surface ectoderm-derived appearance phenotypes, with an emphasis on ear and facial morphology, male pattern baldness, and mono eyebrow.In this study of the genetic architecture of ear morphology, we developed a CNN-based deep learning model to automatically locate 17 anatomical ear landmarks, allowing us to quantify 136 ear traits in 14,921 individuals from three continents. We then used a recently developed C-GWAS method [INTU, 5p15.1 ANKH, 8q24.13 HAS2/HAS2-AS1, 10q22.2 C10orf11, and 20q11.22 UQCC1/GDF5 are supported by other evidence, whereas for 3 novel ear genes  no such evidence is currently available. The strong evidence for INTU and HAS2 has been detailed above. The gene ANKH, which regulates inorganic pyrophosphate transport, is located near the lead SNP rs10062331 at 5p15.1. ANKH has been linked to diseases such as Craniometaphyseal Dysplasia (OMIM: 123000) and Chondrocalcinosis 2 (OMIM: 118600), both of which are characterized by abnormal development of bones and connective tissue. ANKH’s association with skeletal disorders suggests it may play a role in ear cartilage development. The lead SNP rs10824309 at 10q22.2 is an intronic variant of C10orf11, also known as LRMDA. This gene has been associated with craniofacial traits such as adolescent idiopathic scoliosis [UQCC1, which encodes a transmembrane protein involved in the assembly of the ubiquinol-cytochrome c reductase complex. Polymorphisms in this gene have been linked to human height [UQCC1 in muscle-skeletal tissue, as well as its expression in multiple tissues including fibroblasts, muscle-skeletal tissue, and skin [UQCC1 is expressed in the branchial arches and head surface ectoderm during early embryonic development in mice [UQCC1 in cranial neural crest cells, suggest that rs2378353 may play a role in ear morphogenesis by regulating the expression of UQCC1 during early development. The functional information for the other 3 novel ear-associated loci identified in this study is limited.The novel ear-associations at 5 of the 8 loci we identified here i.e., 4q28.1 coliosis , heel bocoliosis , and chin height  and osteand skin . UQCC1 i in mice . These fTBX15 [EDAR [SP5/MYO3B [PISRT1/MRPS22 [LRBA [PRDM1/ATG5 [LOC153910/HIVEP2 [DLX6/SEM1 [LRBA and 7q21.3 DLX6/SEM1), which were solely identified by C-GWAS in our study, were previously identified in a single GWAS of earlobe attachment involving ~70,000 samples; hence, in a study that was five times larger than our current study. That besides the 5x smaller sample size, our study identified 8 novel loci, half of which were identified only by C-GWAS, confirms the power gain of C-GWAS in detecting small but multi-trait effects, as previously highlighted also for human face shape traits [Eight of the 16 identified loci we identified here with association with quantitative ear traits have been previously reported to be associated with qualitative ear features (mainly earlobe features) in single-trait GWASs, including 1p12 e traits .A significant portion of the 16 ear-related loci discussed here have been shown to have strong ties to human facial structure ,21,22,30HAS2 gene influences the L3-L15 phenotype, which roughly represents the Darwin tubercle, marking the beginning of understanding this intriguing aspect of human evolution.Our study sheds light on the genetic basis of the Darwin tubercle, a special ear phenotype that is equivalent to the ear tip of other mammals and represents a trace of human evolution ,46. PrevINTU) and one previously reported gene (TBX15). Nevertheless, future studies in independent population samples from different continental populations are warranted to further verify our novel findings.This study involved cohorts belonging to three continental populations from Europe, Asia, and America. For detecting cross-population allelic effects, C-GWAS demonstrated noticeably improved power than MinGWAS as evident by boosting the significance of known ear-associated loci while keeping the same study-wide type-I error rate as MinGWAS. However, because our approach required the a priori exclusion of SNPs not polymorphic in one of the studied cohorts, population-specific association signals, could not be detected. Another flip-side caveat of our study, in which we maximized the sample size used for discovery, is that no additional population samples were available for direct replication of our novel findings. However, that half of the loci we identified represent previously known ear loci that we re-discovered with our approach, puts confidence in our approach and thus also in the true-positive status of the newly identified loci. Additional confidence is provided by other evidence we accumulated from the literature for most of the novel loci as well as by direct functional evidence in mice for one novel gene  has been approved by the Medical Ethics Committee of the Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare and Sport . The TwinsUK Study (TwinsUK) has been approved by the St. Thomas’ Hospital Local Research Ethics Committee. The Taizhou Longitudinal Study (TZL) has been approved by the Ethics Committee of Human Genetic Resources at the Shanghai institute of life Sciences, Chinese Academy of Sciences (ER-SIBS-261410). The National Survey of Physical Traits Study (NSPT) has been approved by the Ethics Committee of Human Genetic Resources of School of Life Sciences, Fudan University, Shanghai (14117). The Consortium for the Analysis of the Diversity and Evolution of Latin America (CANDELA) Study has been approved by ethical committee at universities in all samples countries: the Universidad Nacional Autonoma de Mexico (Mexico), the Universidad de Antioquia (Colombia), the Universidad Peruana Cayetano Heredia (Peru), the Universidad de Tarapaca (Chile), the Universidade Federal do Rio Grande do Sul (Brazil) as well as at the University College London (UK).N = 3,675; the TwinsUK study, N = 1,065), Asia , and Latin America . Details about these five cohorts are included below.This study used a total of 14,921 samples that passed quality control from five cohorts of multi-ethnic ancestries from Europe  and into the WHO International Clinical Trials Registry Platform  under shared catalogue number NTR6831. All participants provided written informed consent to participate in the study. A total of 5,604 participants not wearing make-up, cream nor jewelry, were photographed using a Premier 3dMD face3-plus UHD camera . Each person’s 3D avatar is rendered from three 2D high resolution photos taken from predefined angles, i.e., from front-top, left-down, and right-down directions. Ears in the left-down and right-down photos were manually segmented and chopped for subsequent phenotyping. Genotyping was carried out using the Infinium II HumanHap 550K Genotyping BeadChip version 3 . All SNPs were imputed using MACH software (www.sph.umich.edu/csg/abecasis/MaCH/) based on the 1000-Genomes Project reference population information [The Rotterdam Study (RS) is a population-based prospective study of individuals aged ≥ 45 years living in a suburb of Rotterdam, the Netherlands. Details regarding the cohort profile have been described elsewhere . The Rotormation . Genotypormation . In currThe TwinsUK Study (TwinsUK) included 1,153 phenotyped participants  within the TwinsUK adult twin registry based at St. Thomas’ Hospital in London. Volunteers gave written informed consent under a protocol reviewed by the St. Thomas’ Hospital Local Research Ethics Committee. Participants were photographed using a Premier 3dMD face3-plus UHD camera , and 3D avatars were rendered from two 2D photos taken from the left and right directions. Ears in the 2D photos were manually segmented and chopped for subsequent phenotyping. Genotyping of the TwinsUK cohort was done with a combination of Illumina HumanHap300 and HumanHap610Q chips. Intensity data for each of the arrays were pooled separately and genotypes were called with the Illuminus32 calling algorithm, thresholding on a maximum posterior probability of 0.95 as previously described . ImputatP < 1e-5) were also excluded. After applying these filters, we obtained a dataset of 2,600 samples with 776,213 SNPs. The chip genotype data were firstly phased using SHAPEIT [The Taizhou Longitudinal Study (TZL) includes Han Chinese sampled in Taizhou, Jiangsu province in 2014 . In tota SHAPEIT . IMPUTE2 SHAPEIT  was thenhttps://www.wegene.com/). Individuals with more than 5% missing data, related individuals, and the ones that failed the X-chromosome sex concordance check or had ethnic information incompatible with their genetic information were excluded. In total, 2,487 individuals were enrolled . The genotype data were phased using SHAPEIT [P < 1×10−6). After all phenotype and genotype quality controls, the current study included a total of 8,018,212 imputed and genotyped SNPs and 2,487 individuals of NSPT.The National Survey of Physical Traits Study (NSPT) is part of the National Science & Technology Basic Research Project, which contained four sub-cohorts collected from three different regions of China in different years: Taizhou, Jiangsu province in 2015 and 2019, Zhengzhou, Henan province in 2017, Nanning, Guangxi province in 2018. The NSPT project was approved by the Ethics Committee of Human Genetic Resources of School of Life Sciences, Fudan University, Shanghai (14117). All participants provided written informed consent. Participants were photographed using a Canon EOS700D camera. Ten digital photographs of the face: left side (−90°)*2, left angle (−45°)*2, frontal (0°)*2, right angle (45°)*2, right side (90°)*2 were taken from ∼1.5 meters. Photos in each direction include one with eyes open and one with eyes closed. Ears were cropped from the left side facial photographs with open eyes. DNA was extracted from blood samples using the MagPure Blood DNA KF Kit. The DNA samples were genotyped on the Illumina Infinium Global Screening Array that investigates 707,180 variants which is a fully custom array designed by WeGene ( SHAPEIT  and impu SHAPEIT . VariantLatin America (CANDELA) Study [The Consortium for the Analysis of the Diversity and Evolution of A) Study  consistshttps://github.com/Fun-Gene/EarPhenotyping. In all cohorts, we used the same pipeline to minimize potential variation caused by different methodology. Specifically, we used the previously established two-stage ear landmark detector consisting of two CNNs deep-learnt from ~15,000 labeled ear images [st CNN detects various orientations and sizes and rectifies all coordinates at a coarse-grained level. The 2nd CNN was trained in a more controlled scenario for accurate landmarking of 55 ear landmarks. The performance of this two-stage landmark detector has been verified in various scenarios of difficulty that represents the state-of-the-art in ear landmark detection. In our application, we double checked all resultant landmarks from all images by eye-balling and removed those low-quality images that failed in CNN-based landmarking. After obtaining the coordinates of 55 landmarks, Generalized Procrustes Analysis (GPA) was used to remove affine variations due to shifting, rotation, and scaling. We then focused on the 17 most anatomical meaningful landmarks covering the entire ear , Pearson’s correlations between the rater and the CNN approach were calculated.2 = 2(r(MZ)-r(DZ)). SNP-based heritability was estimated from all GWAS SNPs using the restricted maximum likelihood estimation in GCTA [Genetic correlations were estimated from all SNPs using GEMMA  based on in GCTA .GWASs were independently carried out in each cohort. GWASs in unrelated individuals  were performed using linear models assuming an additive allele effect adjusted for covariates sex, age, BMI, and top 5–10 genomic PCs using PLINK 1.9 . GWASs ihttps://github.com/Fun-Gene/CGWAS. In the current application, default parameters were used. The summary statistics of the 136 meta-analyses were used as the input of C-GWAS. Details of C-GWAS method has been described previously [In the application of C-GWAS analysis on 136 ear meta-analyses, we focused on SNPs with an observable frequency (MAF > 0.01) in three different continental groups . The C-GWAS is an R library that is freely available at eviously . In brieSNPs and nearby genes were annotated using ANNOVAR . EnrichmIntu knockout mice using the one-step CRISPR/Cas9 method [Tbx15 and Pax1 genes [Intu were fatal. Compared with the intact allele (402kb), the mutant allele had a reduced sequence (251kb) with a removal of exons from 2 to 15 . GPA was used to remove the effects of translation, rotation and scaling. After superimposition of the GPA-adjusted coordinates, only the shape component remained in the aligned specimens. Mice with the outlier in PC1 were excluded then the GPA and PCA were redone. The first 10 PCs were used to represent dominant but different dimensional variations of all landmarks. The ear shape visualization of shape variation based on the maximum and minimum PC compare to mean ear shape. The inter-landmark also distances also were visualized . The coordinates of 9 anatomical landmarks  and 12 pseudo-landmarks were obtained using Geomagic Wrap . The 12 sualized . Z-transS1 Fig(A) The location of selected 17 ear landmarks. (B) Design of the current study.(TIF)Click here for additional data file.S2 Fig(A) correlation between left ear phenotypes by manual-landmarking (expert 1 vs. expert 2). (B) correlation between right ear phenotypes by manual-landmarking and auto-landmarking (expert 1 vs. auto). (C) correlation between phenotypes from left and right ears by manual-landmarking. (D) correlation between phenotypes from left and right ears by auto-landmarking.(TIF)Click here for additional data file.S3 FigPlease note the different figure legends in these two figures.(TIF)Click here for additional data file.S4 Fig(A) Two clusters for 136 phenotypes. (B) Phenotypic (right up) and genetic correlation matrix (left down) within and between the cluster.(TIF)Click here for additional data file.S5 Fig(A) Twin heritability. (B) SNP-based heritability.(TIF)Click here for additional data file.S6 FigP < 1e-3) and top-SNP identified in our analysis. Effect plot (right lower) shows effect sizes for the effect allele of top-SNPs from the association with top-associated ear phenotype in all 5 GWASs and meta-analysis.The first 8 (A-H) were novel loci, others 8 were previously reported loci (I-P). Each figure includes three figures, LocusZoom (up) shows regional association plots for the top-associated ear phenotype  with candidate genes aligned below according to the chromosomal positions (GRCh37.p13) followed by the linkage disequilibrium (LD) patterns (r2) of European. Ear map (left lower) shows the association  between all ear phenotypes ((DOCX)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S9 FigTbx15-/- mutant mice , heterozygous Tbx15+/-  and C57BL/6 WT+/+ control mice  were compared for ear and body morphological differences. (A) The schematic diagram of the one-step CRISPR/Cas9 technology used in Tbx15 knockout mice. (B) Example of left profile craniofacial photo of Tbx15-/- mutant mice with removal hair. (C) The principal component analysis for 21 landmarks of Tbx15-/- mutant mice, heterozygous Tbx15+/- and WT+/+ mice. The upper layer shown the detailed contribution proportion of 21 landmarks to the first 10 principal components. The middle layer shown the screenplot of first 10 PCs, the significant association between the genotype and PCs which including PC1 and PC4 . The bottom shown the maximum PC1-, minimum PC1-,maximum PC4-, minimum PC4-, and mean ear shapes. (D) The pattern of genetic association in humans (left) and in mice (right). (E) Effect of Tbx15 knock-out on ear phenotypes in mice (blue for effect of heterozygote mutant and red for wildtype).Homozygous (TIF)Click here for additional data file.S10 Fig(A) The schematic diagram. (B) the sequence of gRNA. (C) Genotype was identified by PCR (Positive 251kb (loss-function), negative 402kb (wild type), heterozygous including 251kb and 402kb).(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.S11 Table(XLSX)Click here for additional data file.S12 Table(XLSX)Click here for additional data file.S13 Table(XLSX)Click here for additional data file.S14 Table(XLSX)Click here for additional data file. 3 Jan 2023Dear Dr Liu,Thank you very much for submitting your Research Article entitled 'Combined multi-ethnic genome-wide association studies of 136 quantitative ear morphology traits identify 16 loci with 8 novel ones' to PLOS Genetics.The manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the current manuscript. Based on the reviews, we will not be able to accept this version of the manuscript, but we would be willing to review a much-revised version. We cannot, of course, promise publication at that time.Should you decide to revise the manuscript for further consideration here, your revisions should address the specific points made by each reviewer. In particular, Reviewer 2 has made a number of suggestions that can lead to substantial improvement of the paper.  We will also require a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript.  plosgenetics@plos.org.If you decide to revise the manuscript for further consideration at PLOS Genetics, please aim to resubmit within the next 60 days, unless it will take extra time to address the concerns of the reviewers, in which case we would appreciate an expected resubmission date by email to Submission Checklist.If present, accompanying reviewer attachments are included with this email; please notify the journal office if any appear to be missing. They will also be available for download from the link below. 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All points should be backed up by data provided with the submission.Please be aware that our Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool.  PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.While revising your submission, please upload your figure files to the Similarity Check, powered by iThenticate, into its journal-wide submission system in order to screen submitted content for originality before publication. Each PLOS journal undertakes screening on a proportion of submitted articles. You will be contacted if needed following the screening process.PLOS has incorporated To resubmit, use the link below and 'Revise Submission' in the 'Submissions Needing Revision' folder.We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Hua TangSection EditorPLOS GeneticsScott WilliamsSection EditorPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This is an impressive paper representing an enormous amount of work by many parties. The methods and results are interesting and convincing. Ideally there would be more detail but the paper is already long. My one request is that they should include a photo or a very good drawing to show what Darwin's tubercle is. My only other request is that the paper should be given a final read by a native English speaker since at the moment there are many spelling errors and infelicities of expression which detract from the overall quality.Reviewer #2: In this manuscript, Li et al use quantitative phenotyping approaches as well as a new method for combining GWAS summary statistics from multiple traits to discover known and novel loci influencing variation in ear morphology in individuals of multiple ancestries. They provide evidence that candidate genes near these loci are preferentially expressed in cranial neural crest cells and having functions related to skeletal, craniofacial, and ear development. They show that one of the candidate genes, Intu, affects ear morphology upon heterozygous loss in mice.Overall, this is a reasonably well-conducted study, with no fundamental claims that are unsupported by data. The deep learning phenotyping pipeline and C-GWAS methodological development could be potentially useful for others in the field. The measurement of ear morphology in knockout mice is another strength. One weakness is that there is a lack of independent replication, but the authors at least note this in their discussion. This is important information for readers to know when interpreting this manuscript. Another general point which confuses me is the authors’ use of the term “ectoderm-derived” when referring to traits such as ear morphology, face shape, etc. While it is true that many  of the contribution to these structures comes from ectoderm (i.e. CNCCs and surface ectoderm), there are numerous other tissues/traits that are also ectoderm-derived  that presumably do not have the same genetics. If the authors are intending to make a more specific point about, for eg, surface ectoderm being a major contributor, then that should be stated, but in my opinion that is not well-supported here. It would be simpler to refer to these related traits as “craniofacial traits” or something along those lines.There are several claims made in the manuscript which in my opinion should be adjusted or supported with additional analyses:Line 85, it is somewhat of an exaggeration to say that this study “corrects the long-term view that ear phenotypes are genetically simple traits.” Indeed previous ear morphology GWAS had already found 8 loci, and combined with the fact that related traits such as face and cranial shape have hundreds of associated loci and are clearly genetically complex, there is very little evidence for such a view of ear morphology being simple.The authors claim (lines 148-149) that their deep learning phenotyping pipeline “convincingly” outperforms human perception in ear landmarking. This is not well supported by the data. The left-right ear correlation (which the authors consider a gold standard of accuracy) for deep learning is 0.52 with sd 0.1, while the correlation for human raters is 0.44 with sd 0.2. Thus human raters are well within one sd with respect to this measure of accuracy. All that can be said from this is that the performance of the deep learning pipeline is roughly similar to that of human raters. This is not a problem (there is no need to demonstrate superior accuracy) but this claim should be rephrased accordingly.The expression analysis of genes near the 16 loci is not convincing. An appropriate background set would be genes near to (or closest to) a random set of control, frequency-matched SNPs that are the same size as the true ear morphology-associated SNPs. Such sets can easily be obtained using a tool such as SNPsnap. Expression within CNCCs, as well as comparing CNCCs to other tissues, should be performed with such a background set. The authors should also perform heritability enrichment analysis (i.e. stratified LD score regression) for the univariate ear morphology traits (only TwinsUK perhaps) with respect to active regulatory regions from CNCCs and other cell types, all of which are publicly available. This would be much more convincing when claiming the importance of certain cell types for ear morphology and would avoid the issue of analyzing small sets of genes/SNPs for statistical significance.Some of the analyses of ear morphology, in particular comparing PC loadings between genotypes, is not entirely statistically sound. Comparing PC loadings between samples upon which PC was initially performed will exaggerate statistical significance (and indeed even with this PC2 is only weakly associated with genotype). I believe that there are significant  differences in ear morphology between Intu hets and WT, as indicated by the univariate landmark analysis in 3F. If the authors wish to perform an integrated morphological analysis by genotype, they should use a generalized Procrustes analysis framework (such as the one available in the R package geomorph), which would allow them to test for overall shape changes as a function of genotype. The effects of Tbx15 KO are stronger, but the manuscript would benefit from similar analyses for Tbx15 het/KO mice as well.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). 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ROH) and common diseases in Genes & Health , UK Biobank , and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe . We estimated that autozygosity due to consanguinity accounts for 5%–18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity.Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (F  •Robust method to reduce confounding in autozygosity-phenotype association studies•Higher autozygosity associated with increased risk for common diseases such as T2D•Replication of findings including a within-sibling analysis•Consanguinity explains ∼10% of T2D cases in British Pakistanis Autozygosity resulting from consanguinity is causally associated with several complex diseases, including type 2 diabetes. For example, attempted replication of a previously detected association with schizophreniaHere, we describe the patterns of consanguinity and examine the effect of autozygosity on disease risk across the phenotypic spectrum in two cohorts: the Genes & Health (G&H) cohort, a population-based study of self-identified British Bangladeshi and British Pakistani individuals, and UK Biobank (UKB) individuals genetically inferred to have majority European and South Asian ancestries. We show that subsetting association analyses to highly consanguineous individuals better controls for social and environmental confounding. With this approach, we find significant associations between autozygosity and various diseases, several of which we replicate using a different method in a within-sibling analysis conducted in the 23andMe cohort. Via simulations, we show that these observed associations most likely stem from non-additive genetic effects. Our study quantifies the effect of autozygosity across the disease phenotypic spectrum using a robust approach that addresses confounding and highlights the possibility of widespread non-additive effects across diseases.Since consanguinity is a sensitive topic for many communities, we have prepared a “Our main analysis focuses on two cohorts, G&H and UKB, both with electronic health record (EHR) data from primary and secondary care provided by the National Health Service (NHS) in England. G&H is a community-based cohort of individuals self-identifying as British Bangladeshi (65%) and Pakistani (35%), recruited in London, Manchester, and Bradford, UK . The dataset is reasonably representative of the background population, albeit likely with some over-sampling of individuals with chronic diseases since much of the recruitment was conducted in a primary care setting.We began by classifying G&H and UKB individuals into genetically inferred ancestry (GIA) groups, as described in the section “ROH distribution,Given that G&H has high self-reported rates of consanguinityROH significantly increases with age  and one using only individuals who are inferred to be offspring of first cousin/avuncular unions and who have FROH < 0.18 (highly consanguineous cohort). . The motivation for this was that we suspected that social and environmental correlates of consanguinity may confound associations between phenotypes and FROH within the full cohort, i.e., highly consanguineous individuals might have systematically different cultural, social, or environmental exposures to those whose parents are unrelated. If we restrict to individuals whose parents had the same degree of PR and control for population structure, variance in FROH is attributable to stochastic recombination events and Mendelian segregation  as measured by the Townsend deprivation index, and having attended university. Clark et al. previously showed that FROH negatively correlated with educational attainment (EA) and alcohol and tobacco use.ROH is significantly associated with all five traits assessed in UKB EUR and UKB SAS , dietary patterns, and exercise frequency-related variables (ROH on the confounders within the full and highly consanguineous cohorts and assessed the model fit. In both full cohorts (UKB EUR and UKB SAS), the models are highly significant, with adjusted-R2 estimates greater than 0.01  that was not significantly different  from the effect in the full cohort   . Then, t< 10−10) . We consROH = 0.0625, 95% confidence interval [CI] = , p = 8.5 × 10−5). We replicated this in a set of individuals inferred to be offspring of first cousin/avuncular unions  from a cohort of Saudi Arabs, with effect size consistent with what we saw in G&H+UKB .After 5% false discovery rate (FDR) correction, we found twelve associations, with four associations passing Bonferroni correction (p < 0.05/61) in the meta-analysis of the highly consanguineous cohorts . The disROH varied linearly with respect to the log-odds within the highly consanguineous cohorts using binned FROH values to ensure model assumptions were met. We find that the increase in log-odds for the significant traits consistently appears to be approximately linear . We meta-analyzed these three regressions within each of UKB EUR, UKB SAS, and G&H and then meta-analyzed these results across cohorts , there was no evidence for a bias of within-sibling < highly consanguineous , and all had concordant directions of effect size, significantly more than expected by chance . Additionally, two were experiment-wide significant: post-traumatic stress disorder (PTSD) , which is included within ICD10 subchapter F43, and T2D . We had 54% power to replicate height at p < 0.05, which did replicate at nominal significance  of the prevalence of T2D in G&H British Pakistanis is attributable to autozygosity resulting from consanguinity A–S4D. ThAs a point of comparison for T2D, we considered the population-attributable risk due to having a high polygenic risk score (PRS) for the disease. We considered the T2D PRS developed by Mars et al.ROH and traits can be induced by several underlying genetic architectures. A commonly described hypothesis is that FROH increases the risk of inheriting deleterious recessive variants, thereby increasing genetic predisposition toward disease. An alternative  explanation is that autozygosity increases the additive genetic variance of a trait in the population . These included several that are not well-defined diseases, such as shoulder lesions, which include adhesive capsulitis, a common comorbidity of T2D.Having demonstrated that restricting analyses to highly consanguineous individuals greatly attenuates confounding, we investigated associations between FROH associations that differ between British South Asians and White British people .We showed that the risk of T2D and asthma incurred by autozygosity may contribute substantially to the incidence of these diseases in British Pakistanis and, to a lesser degree, in British Bangladeshis. Our estimates of PAR assume that G&H is representative of the broader British Pakistani and Bangladeshi communities in the UK, although we note that the majority of the current G&H cohort is from London. Our recent work in a cohort collected in Bradford, a city in the north of England with a substantial British Pakistani population, reported higher rates of consanguinity than found here, with 44% of British Pakistanis inferred to have parents who are first cousins or closer,,,ROH associations likely stem from non-additive effects at low allele frequency variants and/or epistasis. It has been suggested that comparing the effect sizes of FGRM and FROH distinguishes whether associations are driven by homozygosity at common versus rare variants.GRM effect size estimates in the highly consanguineous cohorts were concordant with those of FROH, although with a weaker effect, with the ratio of effect sizes FGRM/FROH being significantly less than 1   . This conp2 individuals have informative alternative genotypes  versus n(p(1 − p) + p2)) = np under an additive model. This issue is especially exacerbated at rare variants due to the quadratic scaling but is reduced in consanguineous cohorts where the number of informative alternative genotypes for recessive loci is n((1 − F)p2 + Fp) (where F is the average FROH in the sample). Thus, large sequenced cohorts enriched for consanguineous individuals will be necessary to fully characterize the nature of non-additive genetic effects across the allele frequency spectrum on polygenic traits.Our study indicates that non-additive effects contribute to disease risk, but mapping specific loci will require large cohorts. Assuming an outbred population, detecting recessive effects requires much larger sample sizes than for additive loci, since only In conclusion, we have described patterns of consanguinity in two large UK cohorts and proposed a robust approach to control for social and environmental confounding in autozygosity association studies. We found multiple significant associations between autozygosity and common diseases that we contend are unlikely to be confounded. Our findings suggest that previous results in the field should be revisited, as they may have been driven by uncontrolled confounders. Furthermore, our results indicate that autozygosity may be an important contributing factor to the increased incidence of T2D in British Pakistanis as well as in other worldwide populations with high rates of consanguinity. Our work motivates the incorporation of genome-wide autozygosity into predictions of genetic risk as well as a search for individual non-additive-acting variants and genes influencing disease risk across the phenotypic spectrum.ROH with weaker effect sizes than we are powered to detect, or there may be other confounders that were not measured. Another limitation is that we were underpowered to replicate many of our findings in the within-sibling analysis from 23andMe; having said that, we did replicate more than we would have expected to at 80% power. Results for the diseases we did not replicate should be treated with caution unless replicated in future studies. Furthermore, our results may not necessarily generalize outside the set of highly consanguineous individuals we have studied. For example, we noted non-linear effects of FROH for individuals with extremely high values   B, which The current members of Genes & Health Research Team : Shaheen Akhtar, Mohammad Anwar, Elena Arciero, Omar Asgar, Samina Ashraf, Gerome Breen, Raymond Chung, Charles J. Curtis, Shabana Chaudhary, Maharun Chowdhury, Grainne Colligan, Panos Deloukas, Ceri Durham, Faiza Durrani, Fabiola Eto, Sarah Finer, Ana Angel Garcia, Chris Griffiths, Joanne Harvey, Teng Heng, Qin Qin Huang, Matt Hurles, Karen A. Hunt, Shapna Hussain, Kamrul Islam, Ben Jacobs, Ahsan Khan, Amara Khan, Cath Lavery, Sang Hyuck Lee, Robin Lerner, Daniel MacArthur, Daniel Malawsky, Hilary Martin, Dan Mason, Mohammed Bodrul Mazid, John McDermott, Sanam McSweeney, Shefa Miah, Sabrina Munir, Bill Newman, Elizabeth Owor, Asma Qureshi, Samiha Rahman, Nishat Safa, John Solly, Farah Tahmasebi, Richard C. Trembath, Karen Tricker, Nasir Uddin, David A. van Heel, Caroline Winckley, and John Wright.The following members of the 23andMe Research Team contributed to this study: Stella Aslibekyan, Adam Auton, Elizabeth Babalola, Robert K. Bell, Jessica Bielenberg, Katarzyna Bryc, Emily Bullis, Daniella Coker, Gabriel Cuellar Partida, Devika Dhamija, Sayantan Das, Sarah L. Elson, Nicholas Eriksson, Teresa Filshtein, Alison Fitch, Kipper Fletez-Brant, Pierre Fontanillas, Will Freyman, Julie M. Granka, Karl Heilbron, Alejandro Hernandez, Barry Hicks, David A. Hinds, Ethan M. Jewett, Yunxuan Jiang, Katelyn Kukar, Alan Kwong, Keng-Han Lin, Bianca A. Llamas, Maya Lowe, Jey C. McCreight, Matthew H. McIntyre, Steven J. Micheletti, Meghan E. Moreno, Priyanka Nandakumar, Dominique T. Nguyen, Elizabeth S. Noblin, Jared O'Connell, Aaron A. Petrakovitz, G. David Poznik, Alexandra Reynoso, Morgan Schumacher, Anjali J. Shastri, Janie F. Shelton, Jingchunzi Shi, Suyash Shringarpure, Qiaojuan Jane Su, Susana A. Tat, Christophe Toukam Tchakouté, Vinh Tran, Joyce Y. Tung, Xin Wang, Wei Wang, Catherine H. Weldon, Peter Wilton, and Corinna D. Wong.hcm@sanger.ac.uk).Further materials and requests may be directed to lead contact, Hilary Martin  and primary care clinics.The UK Biobank is a prospective cohort of over 500,000 individuals with genotype and deep phenotype data recruited across the United Kingdom between 2006 and 2010.The Saudi Arabian cohort is a cohort of 5,668 Saudi Arabian individuals recruited at the Institutional Catheterization Centre in the King Faisal Specialist Hospital and Research Centre.The 23andMe cohort is a prospective cohort of over 8 million research consented individuals, with genotype array and self-reported phenotype data. In the current study, we used research-consented individuals with at least one genetically-inferred full sibling also in the cohort see . The totParticipants in the G&H, UK Biobank and Saudi cohorts were asked their sex with at least the options of ‘female’ or ‘male’ (some cohorts include other sex options) and had their sex genetically inferred via genotype microarray data . Individuals with discordant self-reported and genetically-inferred sex were removed to prevent the chance of sample mixups. In 23andMe, sex was similarly genetically inferred via genotype microarray data.-6 in British Bangladeshi individuals alone, as done in Huang et al.We used the 2021 July data release of the G&H data, which contained 46,132 individuals genotyped on the Illumina Global Screening Array v3EAMD (GRCh38). We first removed 1,736 individuals with call rate less than 99.2% and SNPs with MAF < 1%, leaving 355,862 SNPs. To ensure we did not lose SNPs that have high quality but that fail Hardy-Weinberg Equilibrium due to high rates of consanguinity and strong population structure in British Pakistani individuals, we removed 726 SNPs that failed Hardy-Weinberg Equilibrium p-value < 1x10Genotyping and processing for the UK Biobank cohort were done centrally by the UKB group.For the Saudi Arabian cohort, genotyping and processing has been described previously.rd relatives or closer. To remove related individuals while maximising the sample size, we ranked individuals by their number of relatives, then removed the individual with the highest number of relatives iteratively until no relatives remained.In the three cohorts, we estimated the relatedness between individuals using PropIBD from KING10 frequency bins, and Fisher's exact test p<1x10-5), resulting in 348,347 variants. PLINK 1.9 LD pruning was performed with a window size of 1000kb, step size 50 and LD r2 cutoff of 0.1, then long LD regionsIn G&H, we determined genetically-inferred ancestry (GIA) by merging the data with reference sequences of unrelated individuals (determined using KING as described above) from the 1000 Genome ProjectUKB individuals were projected into the 1000 Genomes PCA space, which defined five GIA groups that corresponded to the continents from which the 1000 Genomes individuals’ recent ancestors came. UKB individuals were then assigned to the GIA group to which they had the highest genetic similarity, based on the Mahalanobis distance between their position in PC space using 6 PCs and the average position of each 1000 Genomes GIA group. Individuals with a Mahalanobis distance that deviated from each GIA group average by >6 standard deviations were excluded. 387,531 individuals of majority European GIA (UKB EUR) and 9,653 individuals of majority South Asian GIA (UKB SAS) remained after quality control.-6 in the relevant GIA group (UKB EUR or UKB SAS).For ROH calling in G&H, we filtered out SNPs with minor allele frequency <5% and used PLINK 1.9 to call ROHs on the filtered SNPs using the following parameters, following Clark et al.ROH by summing up the total length of all autosomal ROHs previously calculated (in base pairs) and dividing by 2.7 billion , following Clark et al.We calculated Fhttps://github.com/malawsky/consanguinity_simulation; specifically we included the following PR categories: unions between individuals who are siblings, avuncular pairs (including multiple generations), first cousins (including multiple generations), first cousins once removed, and second cousins, as well as between unrelated individuals. We then applied the same ROH calling procedures described above to the simulated offspring. For each simulated individual, we then calculated fifteen statistics for the purposes of classification using a neural net classifier: the total length of the ten longest ROHs (in cM), and the frequency of ROHs ranging from 10 to 150 cM binned into 14 intervals of 10 cM. Using these statistics, we trained a neural net classifier implemented in the R package nnet to assign simulated individuals to a given PR category by repeating this procedure 10 times, summing up the probabilities for each possible PR category, and choosing the one with the highest probability per individual. We then calculated the same statistics on the true samples and used the trained neural net classifier to infer the degree of PR. For most of our analyses, we group together people whose parents were inferred to be second cousins with first cousins once removed, and people whose parents were inferred to be first cousins for one/two/three generations, because of the low accuracy in differentiating between the finer-grained classifications.We used the method to infer parental relatedness which we described previously.In G&H, individuals were asked about their parental relatedness at recruitment (“Were your parents related by blood? (not just by marriage)”) with the options of “Yes”, “No”, and “Don't know”. If the individual answered “Yes”, they were asked a follow-up question of “If Yes, how were your parents related?” with the options of “First Cousins”, “Don't Know'', and “Other related by blood”. ROH on age G&H British Pakistanis, G&H British Bangladeshis, UKB EUR and UKB SAS, controlling for sex and 20 PCs. To test if overall consanguinity changed over time, we made a binary variable indicating parental relatedness  and regressed that on age, sex, and 20 PCs using a logistic regression. To test for more subtle changes in consanguinity patterns over time, we made a categorical variable indicating each of the three main inferred parental relatedness categories , and regressed it on age, sex, and 20 PCs using a multinomial logistic regression with the nnet R package.We used linear regression to regress FThe G&H EHR data consisted of SNOMED codes from primary care data for 34,712 of the participants , ICD10 codes from secondary care data for 17,132 individuals , and ICD10 codes from national Hospital Episode Statistics available on all participants. There were twelve participants with no ICD10 codes, and we removed these individuals from the analyses since it was possible that they had recently moved to the UK so may be missing any EHR data for that reason. After removal of relatives, 23,978 individuals were retained. We translated SNOMED codes in primary care data to ICD10 codes using the Interactive Map-Assisted Generation of ICD10 Codes algorithm .Our methods were designed to closely resemble those used in UK Biobank. For each ICD10 code, we determined whether the participant had any diagnostic codes equivalent to the ICD10 code, the date of the earliest diagnostic code, and the data sources which corroborated the presence of the ICD10 code. In total, we combined data from the following different sources: Barts Health inpatient and outpatient care , Barts Health inpatient and outpatient care , Bradford Teaching Hospitals inpatient and outpatient care , Bradford Teaching Hospitals inpatient and outpatient care , primary care observations from the Discovery Clinical Commissioning Group (CCG) and Tower Hamlets , NHS Digital Hospital Episode Statistics (both Admitted Patient Care and Outpatient Care), and mortality records (native formats ICD10).First, we mapped SNOMED description IDs to SNOMED concept IDs for clinician-coded SNOMED codes pertaining to participants who had healthcare encounters at Bradford Teaching Hospitals or Barts Health. The SNOMED mapping file was downloaded from the NHS Digital website on 12/05/22. We used SNOMED build SNOMEDCT2_32.12.0_20220413000001 – the 20th April 2022 minor release (fileset uk_sct2cl_32.12.0_20220413000001Z.zip). This folder contains four separate link files referring to the international SNOMED edition and three distinct UK-specific editions. These files contain mapping for SNOMED descriptionIDs to SNOMED conceptIDs. We collated them into a single mapping reference. All description IDs map onto a single conceptID. This relationship is many-to-one: each descriptionID maps to a single conceptID, but each conceptID can be referred to by several descriptionIDs (the median is three). In total we used a mapping reference consisting of 1,746,657 unique SNOMED description IDs mapping to 578,387 unique SNOMED concept IDs.-Diagnoses: 118191 out of 138235 records mapped (85.5%)-Problems: 31006 out of 31084 records mapped (99.75%)-Procedures: 3518 out of 3586 records mapped (98.1%)For the Barts Health data, we obtained three separate datasets containing records of ‘Diagnoses’, ‘Problems’, and ‘Procedures’ respectively. These files were merged with the mapping files based on the description ID. We excluded codes with a missing SNOMED description ID. Overall we were able to successfully map a high proportion of SNOMED description IDs to concept IDs:The most common unmapped code was a code for ‘Venous Thromboembolism Risk Assessment’ , an administrative code of no diagnostic relevance, referring to a standard thromboembolism risk checklist completed on patient admission within Barts Health. Exclusion of this code improved the mapping for the diagnoses dataset from 85.5% to 95.2%. We performed identical mapping for Bradford Teaching Hospitals ‘Diagnoses’ and ‘Problems’ data with a similar successful mapping percentage.Next, we mapped these codes to ICD10 using the most recent SNOMED maps from NHS digital . We combined the UK and the international map. We restricted this map to SNOMED concept IDs which mapped to a single 3-digit ICD10 code (i.e. a 1-to-1 relationship), resulting in 119,459 individual SNOMED concept IDs. We combined the derived SNOMED concept IDs from step 1 with ‘directly coded’ ICD10 data for each participant in Barts Health and Bradford data separately. 4-digit ICD10 codes were truncated to the first three characters. We then processed data from two primary care networks: the Discovery Clinical Commissioning Group (CCG) network and Tower Hamlets. These data were provided as SNOMED concept IDs and were mapped to ICD10 codes using the same 1:1 mapping approach as for primary care data. Overall between 3% and 8% of all primary care codes were successfully mapped to ICD10 codes, reflecting the large number of administrative and measurement codes recorded in primary care, e.g. ‘text message sent to patient’, ‘blood pressure recording’, and ‘body mass index’.We then combined 3-digit ICD10 codes derived from these sources  with data exports from NHS Digital . Mortality records were searched for underlying cause of death (provided in ICD10 3-digit format). HES-APC codes were used to extract all diagnostic codes recorded during an admission (provided in ICD10 format). We used the admission date as the date of the report. HES-OP data were used to extract all diagnostic codes recorded in relation to the appointment, also provided in ICD10 format. The appointment date was used as the date of report. All ICD10 codes were truncated to 3-digit codes. We excluded ICD10 codes describing generic symptoms rather than disease entities (codes beginning R-Z). For each ICD10 code and each participant, we determined the presence/absence of the ICD10 code , the data sources supporting the presence of the code, and the earliest recorded code. When determining the earliest reported code we excluded codes which encode ‘special dates’ in electronic healthcare records (placeholders for missing data) - 1/1/1860, 30/12/1899, 31/12/1899, and 1/1/1900. Similarly to UKB, we derived the ‘source of first report’ field by taking the earliest reported source for the ICD code and specifying whether other data sources supported the code. e.g. if an individual has a diagnostic code for G35 in primary care records and Barts Health data, with the first primary care code being recorded earlier, their ‘source of first report of G35’ value would be ‘Primary care and other sources’. For simplicity, we grouped data sources into ‘secondary care’, ‘primary care’, and ‘mortality’.Overall, we successfully mapped data for 46,279 unique NHS numbers, 1,926 unique 3-digit ICD10 codes, and 2,976,436 individual diagnoses.-Whether a G&H individual had primary care data from an inner London borough, outer London borough, and/or Bradford (3 binary variables)-Whether a G&H individual had at least one secondary care code from Barts Health or Bradford University Hospitals NHS trust (2 binary variables)Since we suspected that coding practices might be different in different areas, and since missing EHR data could otherwise affect our results, in the analyses described below we included indicator variables to account for:•Religiosity (field 100328) indicates whether an individual reported attending a religious group at least once a week.•Townsend deprivation index (field 189) was used as a proxy for socioeconomic status.•Educational attainment (field 6138) was binarised into ‘having attended university’ or not when used as an outcome phenotype . We used logistic regression in base R for binary variables.We considered two subsets of individuals in each cohort  to identify associations between FROH, sex, age, age2, age∗sex, genetic PCs 1–20, (has primary care code from outer London primary care data), (has primary care code from inner London primary care data), (has primary care code from Bradford), , and .For G&H, as covariates in the regression we included FROH, sex, age, age2, age∗sex, genetic PCs 1-20, array (UKB field 22000), batch (UKB field 22000), recruitment centre (UKB field 54), and whether primary care data were available (UKB field 42040).In UKB, slightly different covariates were used, including FFor the meta-analysis of disease phenotypes, we used inverse variance-weighted fixed effect meta-analysis of estimates obtained from regressions in the UKB EUR and UKB SAS cohorts and in G&H.ROH quintiles , we used an inverse variance-weighted linear regression using SE estimates for each log(OR) estimate. We followed the same procedure in the full cohort , [0.001, 0.002), [0.002, 0.003), [0.003, 0.004), [0.004, 0.005), [0.005, 0.006), [0.006, 0.007), [0.01, 0.02), [0.02, 0.03), [0.03, 0.18), [0.18, 1). The [0.03, 0.18) bin contains >95% of individuals in the highly consanguineous cohorts.For the log(OR) estimates by residualised Fuintiles , we regrl cohort B, but inWe also conducted a sensitivity analysis in which we stratified each highly consanguineous cohort into three groups: individuals inferred to have 1, 2, or 3 generations of first cousin parental relatedness. We used the same covariates as before, and, within each cohort , conducted a variance-weighted fixed effect meta-analysis to derive a PR-stratified effect size estimate for each cohort. We then meta-analysed these values across the three cohorts.∗PowerROH on a given putative confounder in a logistic regression in the highly consanguineous cohorts, assuming the effect size estimates observed in the full cohorts  independently:To analyse the variance in F2 and adjusted-R2) and model fit of the following regression:We then assessed the variance explained ∗Power2 as that observed in the full cohorts, or half of this R2.We used GROH-T2D association in an independent cohort of 5,668 Saudi Arabian individuals that has been previously described.ROH < 0.18. We conducted logistic regression, regressing T2D status on FROH, genetic PCs 1-20, age, age2, sex, sex∗age in the full and highly consanguineous cohorts.We replicated the Fhttps://www.versiticlinicaltrials.org/salusirb).23andMe participants provided informed consent and volunteered to participate in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent (E&I) Review Services. As of 2022, E&I Review Services is part of Salus IRB . For quantitative phenotypes, we regressed the phenotype on the same covariates and family membership fixed effect using the plm R package.As 23andMe does not have electronic health records, we used self-reported phenotypes as proxies to replicate our significant findings from the meta-analysis of G&H and UKB. The results and lists of equivalent phenotypes are shown in p: Np = round((average clique size - 1) ∗ # cases in discordant cliques)To conduct a power calculation for each disease tested, we considered the number of discordant sibling pairs in 23andMe. At the time of completing this power calculation, we were not able to directly access the distribution of cases and controls across cliques, so we used a slightly conservative approach to appropriate sample sizes for power calculations across phenotypes. To derive an estimate of number sibling pairs to simulate, we sought to identify the number of unique intra-clique case-control pairs that were in the within-sibling analysis per phenotype. We first calculated the average clique size per phenotype, which was 2.03-2.08. We then calculated the number of cliques with n=2 siblings, which we call NWe subsequently used this assumed number of sibling pairs in the simulation below. Intuitively, this approach accounts for the fact that in a clique with n=2, only one case-control comparison can be made, while in cliques with n=3, two unique case-control pairs exist.1.ROH value for the index sibling in a clique, which we call FROH,sib1, sampling this from the FROH distribution of the UKB EUR full cohort .We first randomly chose an F2.ROH value for their sibling from a normal distribution with mean = FROH,sib1 and standard deviation = 0.002, the average within-sibling standard deviation of FROH values.We then simulated the F3.ROH values, we simulated a binary phenotype with an odds ratio and prevalence equivalent to that estimated in the highly consanguineous cohorts from G&H+UKB.Using the F4.p simulated sibling pairs that were discordant for case status, and fitted a fixed effect logistic regression model using the bife package (as described above) by regressing the phenotype on FROH with a family membership as a fixed effect.We randomly selected N5.ROH association at a Bonferroni adjusted p-value (p<0.05/7).We conducted 1,000 simulations for each disease, and calculated the power to detect an FWe then simulated a dataset in the following way:To calculate the power to replicate at least one association, we used the following formula:To calculate the power to replicate at least two associations, we used the formula:c, and simulated a dataset in the following way:1.ROH value for the index sibling in a clique, as described above in the simulation of disease phenotypes.We first randomly chose an F2.ROH value for their sibling, as described above in the simulation of disease phenotypes.We then simulated the F3.ROH values, we simulated a quantitative phenotype with effect size equivalent to that estimated in the highly consanguineous cohorts from G&H+UKB and normally distributed random error with mean 0 and standard deviation 9.4 (root of the variance of height minus the variance explained by FROH).Using the F4.c simulated sibling cliques, and fitted a linear mixed model model using the plm package (as described above) by regressing the simulated phenotype on FROH with a family membership as a fixed effect.We randomly selected NTo conduct a power calculation for height, we considered the number of sibling cliques, NROH association at p = 0.05.We conducted 1,000 simulations, and calculated the power to detect an FTo calculate population attributable risk for risk factor ROH into the values corresponding to the expectation for offspring of first cousins (FROH = 0.0625) and offspring of second cousins (FROH = 0.01562). We took the prevalence of these classes of consanguinity  We then simulated 6,000 individuals  to have FROH values either uniformly drawn from 0 to 1 or from the FROH distribution of the G&H highly consanguineous cohort (as shown in ∗FROH) SNPs were assigned to be autozygous. We then simulated genotypes for each individual with the genotype in non-autozygous segments drawn from Binomial and from autozygous segments from 2∗Binomial where p is the frequency of the effect allele at the locus. Genetic risk was then calculated by multiplying each individual’s genotypes with their corresponding effect sizes, summing them up, and then normalising the values across the cohort.We simulated the architecture of an additive binary trait by first assigning an effect size pd) where pd is the probability an individual has the disease given their genetic risk score G, calculated as follows:We then simulated a binary phenotype by drawing random values from Binomial is the probability density of genetic risk score G with respect to a distribution D and d is the mean shift in the distribution of genetic risk among cases, ranging from 0.5-1.5 in increments of 0.1. The heritability was calculated using Nagelkerke pseudo-R2 in a logistic regression of the phenotype on G. We then carried out a logistic regression of simulated phenotype on FROH. We repeated this for 100 simulations, and then calculated power as the fraction of simulations in which the FROH effect size was positive and its p-value was less than a given cutoff (p<0.05 or p<0.05/61).where L(


Objective: To elucidate the bidirectional correlation of sarcopenia with coronary heart disease (CHD), as well as to investigate the mediating role of cardiometabolic factors and inflammatory biomarkers, a bidirectional two-sample, two-step Mendelian randomization (MR) study was conducted. Methods: Summary statistics were obtained from genome-wide association studies (GWAS). In our bidirectional two-sample MR, genetic variants associated with sarcopenia-related traits and CHD were instrumented for the estimation of bidirectional correlations. Besides, genetic variants associated with thirteen cardiometabolic factors and six inflammatory biomarkers were selected for further mediation analyses. To confirm the consistency of the results, several sensitivity analyses were carried out. Results: Genetically predicted higher appendicular lean mass , left hand grip strength , right hand grip strength , and walking pace  reduced CHD risk, while genetic predisposition to CHD did not affect any of the sarcopenia-related traits. Seven mediators were identified for the effects of appendicular lean mass on CHD, including waist-to-hip ratio, hip circumference, systolic blood pressure, low-density lipoprotein cholesterol, total cholesterol, triglycerides, and fasting insulin. The mediation proportion ranged from 10.23% for triglycerides to 35.08% for hip circumference. Hip circumference was found to mediate the relationships between both left (mediation proportion: 24.61%) and right-hand grip strength (24.14%) and CHD, and the link between walking pace and CHD was partially mediated by waist-to-hip ratio (31.15%) and body mass index (26.66%). Conclusion: Our results showed that higher appendicular lean mass, hand grip strength, and walking pace reduced CHD risk, but the causal relationship was not bidirectional. Several mediators were found to mediate the causal pathways between sarcopenia-related traits and CHD, and intervention of these factors may be helpful in terms of CHD prevention in sarcopenia patients. Cardiovascular diseases (CVD), including coronary heart disease (CHD), continue to have a significant impact on disease burden and mortality rates across the globe . From 19There are many observational studies investigating the correlation of sarcopenia with CHD. Campos et al. noted that low muscle mass and walking speed correlate with a high atherosclerotic burden . DecreasMendelian randomization (MR) analysis employs genetic variations to evaluate the causality between exposures and outcomes . MR is rCHD is known to be driven by an altered cardiometabolic profile, encompassing elevated systolic blood pressure, increased low-density lipoprotein cholesterol, heightened fasting plasma glucose, and a high body mass index . AssociaMR, a specialized analysis based on genetic instrumental variables (IVs), employs single nucleotide polymorphisms (SNPs) as IVs to evaluate the effects of risk factors on various outcomes, including diseases . SNPs asSarcopenia-related traits, including appendicular lean mass (ALM), hand grip strength (HGS), and walking pace (WP), were chosen as indicators of muscle mass, muscle strength, and physical performance, respectively. Using GWAS summary data, a bidirectional two-sample MR method was used to reveal the correlation of sarcopenia-related traits with CHD. To identify potential mediators, a two-step MR analysis was conducted. Specifically, 13 cardiometabolic factors and 6 inflammatory biomarkers were included as potential mediators. We first examined the causal effects of sarcopenia-related traits on each mediator, and then we further assessed the causal effects of possible mediators on CHD. The mediation effects were assessed using the product method . Figure n = 461,026; right, n = 461,089) and WP  from the UK Biobank cohort. GWAS summary statistics for CHD originated from the CARDIoGRAMplusC4D  consortium. It is a meta-analysis comprising 48 GWAS studies, and over 60,000 CHD cases were included [ALM was regarded as a pivotal measure of muscle mass in elderly individuals . ALM-relincluded .n = 681,275) [n = 245,746) [n = 225,487) [n = 224,459) [n = 151,013), fasting glucose , and hemoglobin A1C (HbA1c)  [n = 187,365), low-density lipoprotein cholesterol , total cholesterol , and triglycerides  [n = 757,601) and diastolic blood pressure  came from the International Consortium of Blood Pressure [Our investigation included a set of 13 cardiometabolic factors that can be categorized into anthropometric measures, glycemic traits, lipid traits, and blood pressure factors. GWAS summary-level data for anthropometric measures were acquired from the Genetic Investigation of Anthropometric Traits (GIANT) consortium, including body mass index  , waist c245,746) , hip cir146,806) . GWAS stFive common interleukins (ILs), including interleukin 1 receptor antagonist (IL-1Ra), IL-6, IL-8, IL-18, and IL-27, as well as C-reactive protein (CRP), were included in our study as inflammatory biomarkers. Genetic variants for cytokines were from a GWAS study involving 30,931 individuals , and sump < 5 × 10−8 for sarcopenia-related traits, CHD, and cardiometabolic factors; p < 5 × 10−6 for inflammatory biomarkers). Second, SNPs that displayed linkage disequilibrium (LD) with an r2 greater than or equal to 0.001 were excluded. Third, SNPs that were unavailable for each outcome were excluded, and SNPs that displayed a significant relationship (p < 5 × 10−8) with the outcome were excluded to avoid violating the aforementioned third assumption. Finally, the F-statistics were computed to assess the potency of each SNP, utilizing the following formula: F = R2 (N − K − 1)/(K (1 − R2)), in which R2 stands for the variance in exposures accounted for by the genetic variance, K represents the number of SNPs, and N represents the sample size. SNPs were chosen based on the commonly cited rule that the F-statistic should be greater than 10 to prevent weak instrument bias in MR analysis [The genetic instrument selection followed the following principles: First, SNPs were included if they exhibited a significant association with exposure at the genome-wide level  method under the assumption that all genetic variants in the analyses are valid IVs. For sensitivity analyses, first, the weighted median method was chosen, as it provides a reliable estimate of the causal link, assuming that over 50% of the IVs are valid ; second,p values  to assesp values ; finallyp values . In addiBy means of R programming (version 4.1.3), MR analyses were conducted. Two major packages, “TwoSampleMR” and “MRPRESSO”, were used for the estimation of causal effects and the detection of outliers. Results were presented as an odds ratio (OR) along with a 95% confidence interval (CI) per standard deviation. The mediation proportions were calculated according to the formula: (β1 × β2)/β, β stands for the total effect obtained from the primary analysis, β1 stands for the effect of sarcopenia-related traits on mediators, and β2 stands for the effect of mediators on CHD. Standard errors and CIs were calculated using delta methods .Our findings revealed that genetic variants linked to an elevated ALM , left HGS , right HGS , and WP  were correlated with decreased susceptibility to CHD. The reverse MR analysis found that the genetic predisposition to CHD had no impact on any of the sarcopenia-related traits. The causal effects of ALM on thirteen cardiometabolic factors and six inflammatory biomarkers using the IVW method are presented in Our results found that right HGS was positively associated with WC  and HC , and it was negatively related to FI  and DBP . The results are presented in As illustrated in According to the results, nine out of thirteen cardiometabolic factors and four out of six inflammatory biomarkers were chosen as possible mediators, and further MR analysis was conducted to explore the influences of these possible mediators on CHD.The estimation of the impact of possible mediators on CHD is shown in Based on the results of MR analyses, 11 mediators were selected as potential mediators, of whom the mediation proportions and the 95% CI were calculated. The detailed results are listed in Finally, we found eight mediators mediating the correlation of sarcopenia-related traits with CHD. The mediation proportions are listed in p values to assess the heterogeneity in our estimates. Although significant heterogeneities were found in some of our results, the significance of the IVW estimates remained after adjustment using a random-effects model. Besides, to detect potential pleiotropic effects, we used the p-value corresponding to the MR-Egger intercept. Moreover, we employed the weighted mode, simple mode, MR-Egger, and weighted median to evaluate the impacts of genetically predicted ALM on CVD outcomes, and the results showed relatively high robustness. We calculated Cochrane’s Q and Q-derived We performed a comprehensive bidirectional two-sample and two-step MR investigation to examine the unclear bidirectional causal association and potential mediators between sarcopenia-related traits and CHD. The results revealed that genetically predicted elevations in ALM, left HGS, right HGS, and WP significantly reduced the CHD risk. The causal relationships were not bidirectional because our results found no evidence that CHD had effects on sarcopenia-related traits. Our two-step MR analysis showed that the excess risk for CHD brought by low ALM was partially mediated by HC, WHR, FI, LDL-C, TC, TG, and SBP, and the most important mediator was HC (35.08%). HC was also found to be a mediator for the causal effects of left and right HGS on CHD, and the causal relationship between WP and CHD was partially mediated by BMI and WHR. Overall, the IVW estimates consistently aligned with those of the sensitivity analyses in terms of both magnitude and direction.2) and ASMI , which represent muscle mass, reduced the risk of CHD in young adults [At the observational level, lower muscle mass has been considered an independent CHD risk factor . Additiog adults . In our g adults ,14,15. Tg adults , giving g adults . Severalg adults ,12,13. Sg adults ,12, whilg adults , and a sg adults . ConsistThe mechanism underlying the progression of CHD has been well recognized, and cardiometabolic risk factors, such as elevated SBP, increased FG, elevated LDL-C, and increased BMI, played a detrimental role in it . ALM is From a mechanistic perspective, the role played by inflammation may contribute to the relationship between sarcopenia and CHD. Inflammation is well recognized to have a crucial role in the progression and initiation of CHD ,53. AccoOur study comprehensively estimated the genetic relationship between sarcopenia-related traits and CHD, and the results revealed that decreased muscle mass, low muscle strength, and impaired physical performance are risk factors for CHD in sarcopenia patients, suggesting that monitoring muscle mass and strength, along with physical activity, is a critical element in the prevention of CHD among high-risk individuals. In addition, we found that the changes in cardiometabolic factors in sarcopenia patients may increase the risk of CHD, as they were found to mediate the causal pathways between sarcopenia and CHD. These findings suggested that greater emphasis ought to be placed on the levels of these cardiometabolic factors in individuals with sarcopenia, and early interventions with these factors may reduce the incidence of CHD. In a word, our findings provide a theoretical foundation for clinical CHD risk prediction, and monitoring these traits can help lower the prevalence of CHD and thus lower cardiovascular mortality.Our study has several strengths. Firstly, we proposed a new lens on the causal association and potential mediation between sarcopenia-related traits and CHD using MR. Secondly, by using a bidirectional two-sample MR analysis, the bias caused by reverse causation and confounders, the main limitations of traditional observational studies, was minimized. Thirdly, the statistical power can be increased using summary-level data from GWAS in a two-sample MR approach, especially in assessing effects on binary disease outcomes . Moreove2) is more appropriate when measuring muscle fitness [Our investigation still has several limitations. First and foremost, although we employed several sensitivity analyses to minimize the bias, the bias would unlikely be eliminated. Secondly, it is worth noting that the GWAS statistics employed in our study were mainly derived from individuals of European descent, which may reduce the impact of ethnic variation, but caution is warranted in applying our conclusions to individuals with different ethnic backgrounds. Thirdly, age and sex are strongly associated with ALM, but we failed to investigate the associations between ALM and CHD in subpopulations because of the lack of individual-level data. Subsequent analyses are warranted to further investigate the correlations stratified by age and sex. Last, although ALM has been chosen as the indicator of muscular mass to avoid the bias caused by non-fat tissue in the measurement of lean mass, and it has been reported that relative muscle mass (ALM/height fitness .In conclusion, this study showed that higher ALM, HGS, and WP were associated with reduced CHD risk, but the causal relationship was not bidirectional. Besides, several cardiometabolic factors were found to mediate the causal pathways between sarcopenia-related traits and CHD, and intervention of these factors may be helpful in terms of CHD prevention in sarcopenia patients.