Facial phenotype is influenced by genes and environment; however, little is known about their relative contributions to normal facial morphology. The aim of this study was to assess the relative genetic and environmental contributions to facial morphological variation using a three-dimensional (3D) population-based approach and the classical twin study design.MZ and rDZ, broad-sense heritability (h2), common (c2) and unique (e2) environment contributions were calculated for all traits for the monozygotic (MZ) and dizygotic (DZ) twins.3D facial images of 1380 female twins from the TwinsUK Registry database were used. All faces were landmarked, by manually placing 37 landmark points, and Procrustes registered. Three groups of traits were extracted and analysed: 19 principal components (uPC) and 23 principal components (sPC), derived from the unscaled and scaled landmark configurations respectively, and 1275 linear distances measured between 51 landmarks . The intraclass correlation coefficients, r2 ranging from 38.8% to 78.5% in the former and 30.5% to 84.8% in the latter group. Also, 1222 distances showed evidence of genetic control. Common environment contributed to one PC in both groups and 53 linear distances (4.3%). Unique environment contributed to 17 uPC and 20 sPC and 1245 distances.Heritability of 13 uPC and 17 sPC reached statistical significance, with hGenetic factors can explain more than 70% of the phenotypic facial variation in facial size, nose , lips prominence and inter-ocular distance. A few traits have shown potential dominant genetic influence: the prominence and height of the nose, the lower lip prominence in relation to the chin and upper lip philtrum length. Environmental contribution to facial variation seems to be the greatest for the mandibular ramus height and horizontal facial asymmetry. Facial morphology attracts interest from a variety of scientific disciplines dealing with craniofacial evolution (anthropology) , forensiFacial morphology is influenced by genetic and environmental factors and their complex interactions that present the greatest challenge in modern biology ,8. TradiVarious environmental influences are also responsible for specific craniofacial shape, including hormones, nutrition, diseases, trauma, surgery, dentofacial orthopaedics, lifestyle factors  and oral functions  . EnvironHeritability of craniofacial characteristics has been investigated in family (siblings or parents-offspring design) and twin studies  mainly using two-dimensional techniques  –23. The Over the last decade, a few studies have investigated 3D facial characteristics of twins –31. UsinThe aim of the present study was to assess genetic and environmental contributions to 3D facial soft tissue morphological variation within the framework of the classical twin study design . The objwww.twinsuk.ac.uk), a population-based study of twins with ongoing longitudinal data collection 56]. In tOur finding on the mandibular ramus height is in agreement with a recent cephalometric study conducted on 141 adult Lithuanian twin pairs with completed mandibular growth and DNA confirmed zygosity . The autThe present study has certain advantages and limitations, which will be discussed below. One of the merits is a large sample size, which enabled us to use additional landmarks  and contributed to the validity of heritability estimates . In addition, the sample was homogenous in terms of ethnicity and came from a well-designed population-based study. Finally, zygosity was confirmed by genetic testing, hence avoiding the misclassification of twins.On the other hand, the limitations of the study were due to: 1) limitations of the classical twin design, 2) complexity of facial morphology and 3) inclusion of only female individuals in the sample. The observed facial morphological variation is a combination of: 1) genetic contribution (encompassing additive and non-additive genetic effects as well as gene interactions), 2) environmental contribution (consisting of common and unique environment), 3) gene-environment interaction and 4) measurement errors (due to scanning and landmarking).p-values less than but close to 0.05). Genetic contributions calculated here are likely to be slightly overestimated because the model disregards gene-environment interactions. Furthermore, the imperfections of the classical twin model can lead to heritability values (h2) over 1, which are demonstrated by the following results: the heritability (h2) of sPC1 was evaluated as 1.015 (95%CI: 0.755 to 1.279), h2 of the linear distance ‘prn-men’ was 1.002 (95%CI: 0.798 to 1.217) and h2 of the ‘prn-mex’ distance was 1.048 (95%CI: 0.832 to 1.271). However, these do not diminish the importance of our findings. Instead of focusing on the actual value of an estimate, it is more important to reveal which facial traits demonstrate the most compelling evidence of heritability and use that knowledge for future genome-wide association studies of normal facial morphology.The trait correlation between twins is the result of their genetic similarity and sharing common environment. However, the classical twin design does not allow for the determination of any gene-environment interactions. In addition, the scanning and landmarking errors  may have some effect  on the heritability values of some traits, especially those that just reach statistical significance . The third limitation is due to inclusion of only females in the sample. This reflects the prevalence of females in the TwinsUK register . In the The study provides the estimates of genetic and environmental contributions to three groups of landmark-based facial traits in adult female twins. Based on the analysis of principal components, statistically significant genetic influence on the facial form was found to range from 38.8% to 78.5%, whereas that on the facial shape accounts for 30.5% to 84.8% of the total phenotypic variance. Genetic factors can explain more than 70% of the phenotypic variance in 7 principal components related to facial form, 5 principal components related to facial shape and 474 linear distances. These facial parameters represent: facial size (height), nose , lips prominence and inter-ocular distance. A few traits show potential dominant genetic influence, namely the prominence and height of the nose, the prominence of the lower lip in relation to the chin and length of the upper lip philtrum. The highly heritable traits are likely candidates for genome-wide association studies. Environmental contribution to facial variation is the greatest in the mandibular ramus height and horizontal facial asymmetry. This heritability study may inform future genetic studies which facial traits should be focused on.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


SNUPN on chromosome 15 that harbors a rare nonsynonymous variant, which was not detected by benchmark methods that did not incorporate biological information, including the T5 burden test and sequence kernel association test.With the rapidly decreasing cost of the next-generation sequencing technology, a large number of whole genome sequences have been generated, enabling researchers to survey rare variants in the protein-coding and regulatory regions of the genome. However, it remains a daunting task to identify functional variants associated with complex diseases from whole genome sequencing (WGS) data because of the millions of candidate variants and yet moderate sample size. We propose to incorporate the Encyclopedia of DNA Elements (ENCODE) information in the association analysis of WGS data to boost the statistical power. We use the RegulomeDB and PolyPhen2 scores as external weights in existing rare variants association tests. We demonstrate the proposed framework using the WGS data and blood pressure phenotype from the San Antonio Family Studies provided by the Genetic Analysis Workshop 19. We identified a genome-wide significant locus in gene  Genome-wide association studies (GWAS) have identified thousands of genetic loci robustly associated with a wide range of complex diseases and traits. However, there is a big gap between the disease heritability explained by GWAS-identified loci and that estimated from twin/family-based studies, leading to the so-called missing heritability . To fillThanks to the rapidly decreasing cost of whole genome sequencing (WGS), thousands of whole genome sequences have been generated , enablinWe used the WGS data provided by GAW19 which included more than 8.3 million variants from odd-numbered chromosomes and 959 related individuals. The longitudinal phenotype data set had 1389 individuals including all samples with WGS data. Because there were many missing observations in the baseline measurement, we selected those subjects with at least 1 blood pressure measure among 5 visits and were able to obtain 789 related individuals. We used the earliest measurement among all completed visits for each person. We analyzed the systolic blood pressure (SBP) as a quantitative phenotype.We employed a sliding window approach to group RVs with a window length of 4 kb and a step size of 2 kb, as in Morrison et al. , resultin subjects, SKAT assumes a linear mixed effects model i-th row vector of the covariate matrix; i-th row vector of the genotype matrix G coded as variant allele counts; f transforms the RegulomeDB functional categories We used T5 and SKAT as the benchmark association tests of RVs with MAFs of less than 5 %. Given a quantitative trait u et al. , the SKAentclass1pt{minimaentclass1pt{minimaWe included age at visit, sex, smoking status, and blood pressure medication use as covariates in all the association analyses. In addition, because we analyzed the family-based samples in GAW19, we applied the above described tests, including SKAT, uwSKAT, regSKAT, T5, MB, and regT5, in the family-based SKAT and T5 frameworks . As implormation . Using tAs mentioned above, in regSKAT and regT5 we transformed the RegulomeDB discrete functional categories p values lower than the genome-wide significance threshold, while the p values for these windows by other tests that did not incorporate the ENCODE/PolyPhen2 information were far less significant. On the other hand, the MB T5 burden test also identified some genome-wide significant windows on chromosomes 13 and 15. We took a closer look at the significant sliding windows identified by regSKAT and regT5 on chromosome 15. Figure p value less than SNUPN, standing for snurportin 1, which has not been reported to be associated with blood pressure. It turned out that exonic variant chr15:75913349 was not annotated in RegulomeDB, but was annotated as a probably damaging nonsynonymous variant by PolyPhen2 with a confidence score of 99.2 %, resulting in category 1 in our weighting scheme. This variant was also predicted to be highly deleterious by several other functional prediction algorithms including sorting tolerant from intolerant (SIFT), likelihood ratio test (LRT), and MutationTaster [DZIP1) and chr15:88694779 (near gene NTRK3), respectively, although neither gene was reported to be associated with blood pressure before. As all variants were assigned to low functional categories, that is, 5 and 6, in these windows, T5 and regT5 gave nonsignificant p values of similar magnitudes (see Fig. As shown in the Manhattan plots Fig. , regSKATonTaster . As showsee Fig. . A closeTo investigate if the proposed weighting scheme might increase the false-positive rate when there was no association, we used the simulated phenotypes provided by GAW19 to evaluate the type I error rate. We randomly selected 50 sliding windows on chromosome 15 that did not include any causal variants in the GAW19 simulation model. At the significance level α = 0.05, the empirical type I error rates averaged over 50 sliding windows and 200 simulated phenotype sets were 0.0551, 0.0506, 0.0513, 0.0506, 0.0535, and 0.0467 for SKAT, uwSKAT, regSKAT, T5, MB, and regT5, respectively, suggesting that incorporating external biological information into existing RV association tests did not inflate the type I error.We have proposed a general framework to exploit external biological information in the analysis of WGS data. We identified a genome-wide significant locus on chromosome 15 harboring a rare nonsynonymous variant, while other methods without leveraging biological information did not identify it. This significant locus warrants following up and replication in future independent studies.The proposed general framework can be used to incorporate other genome-wide functional annotations and conservation scores, such as CADD  and GERPIn this study, we used the Bonferroni procedure to correct for multiple testing. Considering that the neighboring sliding windows overlapped with each other, the Bonferroni correction was very conservative, leading to reduced statistical power. Further research is warranted to estimate the effective number of tests in the sliding window framework. Finally, we adjusted the treatment effect on the SBP by simply including the medication use as a binary covariate in the regression framework; alternative adjustment methods as studied in Tobin et al.  are wortIn summary, we have proposed a general framework to incorporate the ENCODE and PolyPhen2 information into association tests of WGS data. We demonstrated the potential statistical power gains with the proposed method using the GAW19 WGS genotype and SBP phenotype data. Because it remains a challenge to analyze WGS data, it would be worth capitalizing on newly available biological knowledge in the proposed and alternative frameworks to maximize the power of genomic discovery.


N = 215 NMO cases, 1244 controls). We identify two independent signals in the major histocompatibility complex (MHC) region associated with NMO-IgG+, one of which may be explained by structural variation in the complement component 4 genes. Mendelian Randomization analysis reveals a significant causal effect of known systemic lupus erythematosus (SLE), but not multiple sclerosis (MS), risk variants in NMO-IgG+. Our results suggest that genetic variants in the MHC region contribute to the etiology of NMO-IgG+ and that NMO-IgG+ is genetically more similar to SLE than MS.Neuromyelitis optica (NMO) is a rare autoimmune disease that affects the optic nerve and spinal cord. Most NMO patients ( > 70%) are seropositive for circulating autoantibodies against aquaporin 4 (NMO-IgG+). Here, we meta-analyze whole-genome sequences from 86 NMO cases and 460 controls with genome-wide SNP array from 129 NMO cases and 784 controls to test for association with SNPs and copy number variation  is a rare autoimmune condition characterized by inflammation and demyelination of the optic nerve and the spinal cord. Here, Estrada et al. identify NMO susceptibility variants in the MHC region and find that autoantibody-positive NMO genetically overlaps with lupus. Once thought to be a clinical sub-class of multiple sclerosis (MS), NMO has now been recognized as a distinct clinical entity owing to the presence of antibody biomarkers (termed NMO-IgG) found in NMO but not MS cases2. Pathogenic water channel aquaporin 4 (AQP4) antibodies were identified within NMO-IgG, and sensitive cell-based assays demonstrated reactivity in > 70% of those with NMO syndrome manifestations. The estimated prevalence is 0.3–4.4 per 100,000, equivalent to 200,000–3 million cases worldwide3. NMO occurs preferentially in women ( > 80%) and appears most often in individuals between the ages of 30 and 404. Less than 3% of NMO cases have one affected relative3. Heritability estimates have not been reported, but the small sibling recurrence rate indicates that NMO behaves more like a complex disease than a monogenic disease.Neuromyelitis optica (NMO) is a central nervous system autoimmune disorder that affects the optic nerves and spinal cord5, however, results were not significant, potentially because of the limited power and different ethnic background. To elucidate genetic factors driving NMO risk and to clarify the genetic architecture of this disease, we analyzed up to 6.8 million single-nucleotide polymorphisms (SNPs) and performed copy number variation (CNV) analysis on 215 cases and 1244 controls of European ancestry  than to MS.Just one genome-wide association study (GWAS) has been published for NMO (Korean cases)In stage I, we performed whole-genome sequencing of 149 NMO cases and 704 controls. After applying a standard next-generation sequencing pipeline and quality control (QC)  missense or loss-of-function variants. No genes passed our significance threshold of 2.5 × 10P = 5.8 × 10−12) mapped 21.5 kb downstream of HLA-DQA1 (Table P = 8 × 10−11) but had a weaker nominal association in the NMO-IgG− . Given the smaller sample size of the NMO-IgG− group compared with that of the IgG+ group, it seems likely that this signal is shared by both NMO-IgG+ and IgG− and that the result would be genome-wide significant with a larger sample size , 6,804,788 variants MAF > 1%) in 215 NMO cases and 1244 controls in the combined analysis were used for single variant analyses with logistic regression adjusted for five principal components, and we then combined the evidence using an inverse variance fixed effects meta-analysis of stage I + II results  was observed on the NMO-IgG+ Fig. . The AF nes Fig. .P = 1.90 × 10−12). The signal was driven by NMO-IgG+ , not NMO-IgG−  (Table r2 = 0.7) with the secondary signal identified by rs1150757 and poorly correlated (r2 = 0.2) with the main signal , HLA-DRB1*03:01 is much rarer in Asian populations than Europeans. The frequency of HLA-DRB1*03:01 is not reported for Koreans, however, it seems likely that DRB1*03:01 is much less common in Koreans than Europeans, and that the Kim et al. study was therefore not sufficiently powered to detect this association. The association of HLA-DRB1*03 and NMO was previously evaluated in a small study and yielded a similar effect size 7. In our study, with a model adjusting by the most significant HLA allele (HLA-DRB1*03:01), the most significant HLA allele was HLA-DRB1:15:01  . The most significant association in the combined analysis mapped to a set of common HLA alleles in high LD that contain the HLA-DRB1*03:01 allele . After QC, 22,871 CNV sites were tested for association. A significant association was identified in the NMO-IgG+ group  for reduced copy number in the region annotated by a complex structural site on the location of complement component 4 (C4), which is located 26 kb from the secondary signal rs1150757 , and therefore the combined analysis showed a reduced effect size and significance  as compared with the NMO-IgG+ analysis.To identify potential structural variation associated with NMO, we conducted genome-wide CNV analysis using GenomeSTRiP57 Figs. . There wC4A and C4B; both exist in a tandem array in the MHC class III region. The most common haplotypes contain one to three C4 genes (C4A and/or C4B). The paralogous C4A and C4B genes both have a polymorphic human endogenous retrovirus (HERV) insertion, the presence of which distinguishes long (L) from short (S) forms and allows C4 genes to be subcategorized into four C4 gene “types”: C4AL, C4AS, C4BL, and C4BS10. A method has recently been described to characterize the different C4A/C4B haplotypes using genetic data (Methods)10. We applied these methods in our stage I and stage II data sets and estimated the risk for NMO and its subtypes using haplotype analysis (Methods). The most common haplotype characterized by one copy of the long form of C4A and one copy of C4B (AL-BL) is present in 40% of the population and was used as a reference. In this analysis, the C4 BS haplotype—which carries only a single C4 gene (a short form of C4B)—was the haplotype most associated with NMO-IgG+ risk , followed by another single-gene haplotype which also carries only a single C4 gene (AL)   Fig. . The C4 P = 7.20 × 10−6; OR = 5.10, P = 8.47 × 10−15 for CN = 2 and CN = 3 respectively compared with CN = 4). Compared with the most common total C4 copy number (4), higher total C4 copy number (n > 4) tended  to be protective for NMO-IgG+ risk , P = 0.001, Supplementary Fig. We evaluated the combined effect of C4 copy number  on NMO-IgG+ risk. As expected, lower than normal total C4 (CN = 2 or CN = 3) was associated with higher risk for NMO-IgG+ . In a multivariate analysis including rs28383224, rs1150757, C4 “deletion”  and HLA-DRB1*03:01, the most significant genetic factor was C4 deletion (P = 5.0 × 10−4) followed by rs1150757 (P = 0.02); HLA-DRB1*03:01 was no longer significant (P = 0.94) (Supplementary Tables C4 gene copy number (rs1150757) was significantly associated with C4A expression in many tissues, including nerve and brain  driven by rs1150757 as confirmed by a leave one out sensitivity analysis . Next, we evaluated the causal link between MS and NMO. Interestingly, the MR effect of known MS genetic variants in NMO-IgG+ was much weaker than the MR effect of known SLE genetic variants in NMO-IgG+  using 30 genome-wide significant markers for SLE15. SLE-associated autoantibodies target damaged cell components, such as dsDNA and ribonucleoproteins. AQP4, the target of the NMO autoantibodies, was increased twofold in an animal model of muscle after exercise16. Thus, one could hypothesize that AQP4 autoantibodies arise in NMO patients because of the tendency towards reactivity to damaged cell components, similar to autoantibody production in SLE. Consistent with this hypothesis, AQP4-IgG-seropositive NMO patients are threefold more likely to develop additional non-organ-specific antibodies than AQP4-IgG-seronegative NMO patients17.Persistence of cellular debris along with impaired killing of compromised cells are proposed to stimulate chronic inflammation in interferon-producing myeloid dendritic cells, which drive B-cell autoimmunity in SLE patients with complement component deficiencyC4A/C4B locus that increase expression of C4A have previously been found to increase risk for schizophrenia10. In NMO, we found evidence for association with C4A copy number, and a non-significant trend toward association of C4B  might reflect the encoded proteins’ differential binding to relevant sites in each tissue.Alleles of the C4B Fig. ; for botC4 CNV was associated with NMO-IgG+ but not NMO-IgG−, providing a potentially novel biological hypotheses for seropositive NMO pathogenesis related to low C4 expression. Given the complexity of the MHC region, larger studies will be needed to finemap and disentangle the role of classical HLA alleles to that of C4 structural variation as well as to understand the effect of these haplotypes on the NMO-IgG negative subgroup. (3) The association of the same C4 deletions in SLE and NMO-IgG+ and the genetic link observed in the Mendelian Randomization study between SLE and NMO-IgG+ suggest a potential common strategy for therapeutic targeting of autoantibody production. Interestingly, both diseases also demonstrate separate roles for complement-mediated effector mechanisms involving the later elements in the cascade20. The associations of C4 duplication with schizophrenia highlight the remarkably pleiotropic biology of the complement system and indicate that its genetic variation will yield a rich trove of insights into disease mechanisms.In summary, we have made several relevant observations: (1) The most significant genetic marker for NMO (rs28383224) was shared between NMO-IgG+ and NMO-IgG−, suggesting that at least one shared genetic determinant exists for both conditions. Data from the GTEx project (Methods) do not suggest that rs28383224 regulates C4 expression, and the mechanism of action of rs28383224 in NMO risk remains unknown. (2) 21 that require optic neuritis and transverse myelitis plus two of the following three supportive elements: (1) longitudinally extensive lesions ; (2) magnetic resonance imaging of the brain with normal findings or with findings not consistent with MS; and (3) NMO-IgG seropositivity.De-identified samples were obtained from the University of Texas Southwestern and the Accelerated Cure Project. Both organizations stored blood specimens under IRB-approved protocols as part of longitudinal studies of NMO. Informed consent for broad genetic research was obtained, this project was approved by the UT Southwestern IRB board. Patients consented for their specimens to be stored and used in future genetics studies. We used 2006 NMO diagnostic criteriaAnti-AQP4 serostatus was determined via standardized assays, including enzyme-linked immunosorbent assay (ELISA) or cell-based assay. ELISA-based detection was obtained from one of the numerous labs that offer the test . CBAs were obtained from the Mayo Clinic Labs. This assay utilizes cells transfected with AQP4 and detects antibody binding using flow cytometry to detect the presence of anti-AQP4 antibodies. DNA was extracted from 149 NMO cases (124 NMO-IgG+ /25 NMO-IgG−). In total, 21/149 cases were males. The mean age was 45.5 years  from an extension of the Accelerated Cure Project using the same protocols and diagnostic criteria as the original ACP discovery samples (see above). Informed consent for broad genetic research was obtained, this project was approved by the UT Southwestern IRB board. The mean participant age was 46 years.In total, 929 neurologically healthy controls of European ancestry were obtained as controls from the following Coriell projects: NDPT020, NDPT079, NDPT082, NDPT084, NDPT090, NDPT093, NDPT095, NDPT098, and NDPT099. These projects contain samples of unrelated individuals of European ancestry collected from North America without neurological disorders. Of these individuals, ~ 50% are female. The ages at collection ranged from 56 to 95 years. The data collected on these subjects included Control Clinical Data Elements as outlined in the Coriell NINDS Genetics Repository guidelines and detailed medical and family histories. All participants were specifically asked about the following disorders, which were not present: Alzheimer’s, amyotrophic lateral sclerosis, ataxia, autism, bipolar disorder, cerebrovascular disease, dementia, dystonia, Parkinson’s, and schizophrenia. None of the patients had a first-degree relative with one of the known primary neurological disorders mentioned above. All samples and data were collected with informed consent under local IRB-approved protocols.http://picard.sourceforge.net). Polymorphic SNP and indel sites and genotypes were called with the HaplotypeCaller from GATK v3.125. The HaplotypeCaller algorithm is an assembly-based method that determines genotype likelihoods independently in each sample and then jointly considers data from all samples in the cohort to make confident genotype calls and filter low-quality sites26.Sequence reads were processed and aligned to the reference genome (hg19) with the BWA algorithm and processed with Picard , of which 19,595,281 were singletons.The raw callset contained 50,285,563 variants  in 853 samples. Biallelic SNPs represented 86.2%  of the raw callset, whereas small INDELs  and multiallelic variants  represented 9.5 and 4.3%, respectively. Variant quality was evaluated and filtered using the GATK’s Variant Quality Score Recalibration (VQSR) approachIn addition, we applied the following sample QC filters: 200 genetic outliers , which were three or more standard deviations away from the population mean of the first eight principal components  had IBD PI_HAT > 0.4 consistent with first-degree relationships. Other sample QC filters included samples with abnormally high numbers of non-reference alleles or heterozygosity or with abnormally low call rates or low concordance with prior SNP array genotypes. A breakdown of these and other sample QC filters is provided in Supplementary Table The clean callset contained 545 samples (86 cases and 460 controls) and 29,092,408 variants, of which 12,851,416 were singletons. Aggregated and per sample statistics are displayed in Supplementary Tables 27. A detailed list of sample and variant QC filters is available on the Supplementary Tables 6–7.The raw callset contained 144 cases and 929 controls. Genotypes were refined using the Affymetrix Axiom Best Practices Genotyping Analysis Workflow. Only variants passing the variant QC Affymetrix best practices implemented in the software SNPPolisher were included28. In short, twelve samples with mismatches between the genetic and reported genders were removed. Five samples failed the sample genotyping rate (missing rate > 5%) and were removed. An additional three were removed based on principal component analysis to retain only samples of European ancestry. Finally, eight samples with identities by descent > 0.40 with other samples were removed. In total, 11,758 markers with duplicated rsID and/or with duplicated chromosomal positions were excluded. An additional 25,988 variants did not pass the genotyping call rate and were excluded. A total of 24,678 variants with a differential missing genotype rate P < 0.05 between cases and controls were excluded from further analyses. the final cleaned data set contained 638,524 variants in 129 cases and 784 controls.Additional sample and variant QC was performed using PLINK 1.929, we excluded variants using the following thresholds: 691 based on a HWE P < 5 × 10−6, 938 variants with genotyping rate < 5%, 1073 variants with MAF < 0.1% and 37,603 variants of A/T or C/G genotypes. Variants were then matched to the strand reported by the HRC release 1–1. A total of 39,131,578 variants were imputed on the autosomal chromosomes. The imputation r2 of the top signals (rs28383224 and rs1150757) was 1.0.Before imputation with the Haplotype Reference Consortium (HRC)−6 to account for ~ 25,000 protein-coding genes in the human genome.For burden analysis, we grouped variants based on gene annotations. Two main categories were used: non-synonymous and loss-of-function variants. For each of these categories, we applied two MAF filters (1 and 5%). Only variants with call rates > 50% were included. Genes with at least two variants in the mentioned categories were analyzed. Five principal components were used as covariates using the Logistic Wald Test implemented as b.collapse in the EPACTS package. We used a significance threshold of 2.5 × 10P missing < 0.05, P HWE < 5 × 10−8. After these filters, 7,828,420 and 7,495,108 variants were individually tested for associations on the stage I and II data sets, respectively. We used a logistic model to estimate significance, including five principal components as covariates. In addition to analyzing combined cases, we performed stratified analyses based on the anti-AQP4-IgG status.Variants with MAFs > 1% were selected for single-variant association testing using a Wald test implemented in the software package EPACTS. To further minimize batch effects, we applied the following additional filters to minimize the amount of false positive associations: 30 on all 6,804,788 shared variants in stages I and II. For the imputed data, an additional filter using an imputation quality score of RSQ > 0.30 was applied. The standard P value threshold for common variants of P < 5 × 10−8 was used as the threshold for significance. Inflation factors were marginal: 1.036, 1.027, 1.036 for Combined, NMO-IgG−, and NMO-IgG+, respectively. These genomic inflation factors were used to adjust test statistics.An inverse variance fixed effects meta-analysis was run using METALAfter identifying the first signal, a conditional analysis on the top hit (represented by the variant rs1150753) was conducted using the genotypes from that variant as an additional covariate in a logistic regression implemented in EPACTS.31, which uses Beagle v 3.0.4 as the imputation machine28 and the T1DGC reference panel. Up to 424 HLA alleles were imputed of which 190 had a MAF > 1% in the following HLA genes: The HLA genes: HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1.We imputed classical HLA alleles using the method implemented in the SNP2HLA v 1.0 packageWe found that 90.3% of the BS haplotypes traveled together with the HLA-DRB1*03:01 allele. Conversely, 78.3% of all the HLA-DRB1*03:01 haplotype carried the BS haplotype. We found in total 126 haplotypes that had informative recombinations (having either HLA-DRB1*03:01 or BS but not both).HLA alleles were tested for association using the genotype probabilities from beagle as input and the five first principal components as covariates in a logistic regression implemented in Plink.32. In brief, Genome STRiP’s CNV pipeline genotypes segments of the genome using observed read depth-of-coverage by fitting a Gaussian Mixture Model constrained to call integer copy numbers for each segment. Read depth counts are normalized for the overall sequencing depth and observed GC bias of each library. Segments are iteratively adjusted to find the strongest model fits, and segments in which one or more samples are strongly genotyped as having a copy number differing from the expected ploidy are emitted. We filtered out deletion and mixed deletion/duplication sites under 1 kb in length and duplications under 2.5 kb.We discovered and genotyped CNVs in the Stage I WGS data using the CNV pipeline of Genome STRiP10. In brief, each haplotype can contain a variable number of copies of each of two paralogs of the C4 gene, designated C4A and C4B. Furthermore, each paralog can be found in either a long form or a short form, depending on the presence or absence of a HERV insertion. The most common haplotypes among European ancestry populations are AL-BL , AL-BS, AL-AL, and BS.Association analysis of the CNV calls produced by Genome STRiP revealed a strong association with a CNV adjacent to the C4 gene in the MHC region of chromosome 6. Recent work showed that the C4 locus occurs as complex structural haplotypes and that specific structural haplotypes are associated with risk for schizophrenia32, using a set of custom intervals diagnostic for the C4 and C4-HERV copy numbers. Reads aligning to the critical regions of exon 26 that distinguish C4A from C4B were counted and likelihoods for the C4A to C4B ratio were determined under a binomial model conditional on the total C4 copy number.To determine total copy numbers of each paralog of C4, we used the segmental duplication genotyping procedure in Genome STRiP (CNV Discovery Set 2)C4A, C4B, HERV) generated by previous step, we applied the procedure described in Sekar et al. (2016)10 to determine the likelihood of each haploid copy number for each segment, taking into account the estimated frequency of each haploid copy number in the sampled population using an expectation-maximization algorithm. We then assigned likelihoods to each of the possible structural C4 haplotypes defined by Sekar et al. (2016)10 based on the haploid copy number genotype likelihoods across the three segments. We started by examining the diploid copy numbers across the three sites and finding all combinations of haplotypes that could explain them. For example, if the diploid copy numbers are C4A:3, C4B:1, HERV:3, then the structural haplotypes AL-AL and AL-BS are a potential combination. Each structural haplotype was given likelihood equal to the product of site haploid copy number likelihoods. We also assigned a small amount of likelihood to an < UNK > haplotype allele to account for unknown haplotypes or errors in genotyping.We then determined the likelihood of C4 haplotypes with the following procedure. Given the likelihood of diploid copy numbers for each segment  consisting of genotypes taken from whole-genome sequencing data for a multi-ethnic cohort of 1265 individuals from the Genomic Psychiatry CohortPhased C4 structural alleles from the CNV calling were used in a haplotype analysis to understand the effect of the different C4 alleles in relation to NMO-IgG+. The most common haplotype (AL-BL) was used as reference for comparison to the other haplotypes.C4A + C4B) and use that as predictor for NMO-IgG+. The most common combination (C4n = 4) was used as a reference for comparison in a logistic regression using the other combinations as factors with five principal components as covariates. Statistical analyses were conducted using the R statistical software package.For each participant, we used the CNV data to count the total number of C4 alleles . Although we do not have high-coverage sequencing data for these samples, we were able to use low-coverage (~ 4×) sequencing data for 87 of these samples from the 1000 Genomes project and SNP genotypes from the HapMap project to validate several of our methods.To assess the accuracy of our computational methods for determining C4 copy numbers from whole-genome sequencing data, phasing the copy numbers into C4 structural haplotypes, and imputing C4 structural haplotypes based on genotyping data, we used a set of 111 HapMap CEU samples. The C4 structural haplotypes of these samples were fully characterized by Sekar et al.C4A and C4B paralog-specific variants. We applied our methods for assessing total C4 copy number to the HapMap low-coverage sequencing data described above and evaluated the results against the validated C4 structural haplotypes for these samples. The results are shown in the Supplementary Table C4A and C4B in this low-coverage data, likely due to the low expected number of reads at the sites of paralog-specific variation necessary to distinguish between the two forms. However, based on the performance of our phasing and imputation pipeline described below, we believe that the paralog-specific copy number performance is much better in high-coverage sequencing data.A key step in our methods for inferring phased C4 structural alleles is determining the total diploid copy number of C4, the C4 HERV element that distinguishes long from short forms of the C4 gene, and 10, we found that we accurately imputed both haplotypes into 90 of the 111 HapMap samples (81.1%), and had a correct imputation of at least one haplotype in 106/111 (95.5%). We found that we were able to impute the dosage of the four most common haplotypes with varying levels of accuracy .We analyzed the independent effects of the three highly correlated genetic factors associated with NMO-IgG+ in the HLA region: the C4 deletion and the presence of the classical HLA allele DRB1*03:01 and the SNP rs1150757 using a multivariate logistic regression implemented in the R statistical package (function glm). C4 deletion was defined as having a combined This analysis was conducted both separately on each stage and in combination. In the combined analysis, an additional dummy variable identifying the stage was added. The top five principal components were also included in the multivariate analysis.P = 0.007). Conversely, when we restrict the analysis by excluding samples with one or more deletions of C4 in 44 NMO-IgG+ and 920 controls, the HLA-DRB1*03:01 allele is no longer significant (P = 0.20).A sensitivity analysis was performed by excluding all individuals with the HLA-DRB1*03:01 allele in a logistic regression testing effect of the presence/absence of the C4 deletion in 46 NMO-IgG+ and 816 controls. The C4 deletion was still significant in those individuals without the HLA-DRB1*03:01 allele . The genetic variant rs1150757, which is highly correlated with the C4 CNV, was associated with C4A expression in all tissues to different degrees. Regression analyses and plots were obtained from the GTEX portal .The rank-normalized expression levels of 14. Markers were pruned to remove LD (r2 > 0.05) and only markers that could be harmonized in terms of alleles with the NMO GWAS were kept. The following SNPs could not be harmonized for SLE  and MS . Inverse variance weighted MR and MR Egger were used to estimate the causal effect of SLE and MS on NMO.MR analysis was performed using the Two-Sample MR package on 30 SLE and 39 MS genetic markers from previous meta-analysis of genome-wide association studieshttps://github.com/Biogen-Inc/statgen) and will be also posted at the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics).Reasonable requests for the data sets generated and analyzed during the current study are available from the authors of this paper. Summary statistics are available from the Biogen statistical genetics site (Supplementary Information


Accurate measurement of the marginal healthcare costs associated with different diseases and health conditions is important, especially for increasingly prevalent conditions such as obesity. However, existing observational study designs cannot identify the causal impact of disease on healthcare costs. This paper explores the possibilities for causal inference offered by Mendelian randomization, a form of instrumental variable analysis that uses genetic variation as a proxy for modifiable risk exposures, to estimate the effect of health conditions on cost. Well-conducted genome-wide association studies provide robust evidence of the associations of genetic variants with health conditions or disease risk factors. The subsequent causal effects of these health conditions on cost can be estimated using genetic variants as instruments for the health conditions. This is because the approximately random allocation of genotypes at conception means that many genetic variants are orthogonal to observable and unobservable confounders. Datasets with linked genotypic and resource use information obtained from electronic medical records or from routinely collected administrative data are now becoming available and will facilitate this form of analysis. We describe some of the methodological issues that arise in this type of analysis, which we illustrate by considering how Mendelian randomization could be used to estimate the causal impact of obesity, a complex trait, on healthcare costs. We describe some of the data sources that could be used for this type of analysis. We conclude by considering the challenges and opportunities offered by Mendelian randomization for economic evaluation. Accurate estimates of the marginal medical healthcare costs that are incurred as a consequence of specific health conditions are important. Estimates of cost are fundamental to the economic evaluation of healthcare technologies, whether undertaken alongside randomized controlled trials (RCTs)  or as anNeither observational studies nor RCTs offer a wholly satisfactory means of estimating the impact of different health conditions on cost. Observational studies can estimate the correlations between healthcare costs and health conditions but generally cannot identify causal relationships . It is pMany of these problems cannot be resolved in RCTs. Trials are rarely powered to detect differences in cost-related outcomes . It may We describe recent developments in genetic epidemiology that offer a new way of estimating the causal impact of health conditions on healthcare cost. The methodology of Mendelian randomization, which uses genetic variants as instrumental variables, offers a means of addressing the limitations of existing study designs. In particular, the ethical and feasibility issues that would prohibit the conduct of an RCT are avoided, but some of the advantages of interventional studies in relation to causal inference are retained.We illustrate how robust estimates of causal effects of health conditions on costs could contribute to economic evaluation, and health economics more generally, by considering the relationship between obesity and cost as a motivating example throughout the paper. Obesity is an increasingly prevalent condition  that is Improved estimates of the causal relation between health conditions (such as obesity) and healthcare cost could offer important new evidence to at least three important areas of health economics. The first area is decision-analytic modelling. Decision-analytic models, which synthesise information from a variety of sources, including observational studies and RCTs, are increasingly recommended as the most appropriate vehicle for cost-effectiveness analysis . For exaThe second area is health system management. For example, in the absence of accurate information on the cost consequences of obesity, how should healthcare funders react to information that indicates the prevalence of obesity is expected to continue to increase?The third area relates to targets for intervention. For example, if an apparent association of BMI with cost is actually confounded by an association of obesity with mental health status, then an intervention targeted solely at reducing adiposity is likely to be neither effective nor cost effective. Improved knowledge of causal relationships will help avoid wasteful research effort and facilitate the setting of research priorities .The objective of this paper is to provide an overview of the potential role of Mendelian randomization in estimating the causal effect of health conditions on healthcare cost. We begin by briefly describing the need for improved methods for causal inference when analysing observational data, then set out the genetic basis for Mendelian randomization and its relationship to instrumental variables analysis. We describe the key conditions that must be met for this form of instrumental variable analysis to produce valid causal estimates of effect, with particular regard to the specific issues and biological contexts that arise when analysing genetic variants as instrumental variables.To make this concrete, we describe the methodological issues that would arise when attempting to use Mendelian randomization to estimate the causal effects of obesity on healthcare cost. We use this example throughout the paper to link together the methodological issues. We then consider the data sources that could facilitate this type of analysis, which would represent a novel use of the large linked genomic cohort datasets that are now being developed and made available to researchers.We conclude by summarising some the challenges and opportunities offered by Mendelian randomization for the causal analysis of cost.The rationale for undertaking causal analysis of the form described below is that the relationship of some exposure (such as BMI) to an outcome  is known or suspected to be confounded. Figure Figure In the next section, we describe how instrumental variable analysis using genetic variants can offer a means of identifying the causal effect of an exposure (such as obesity or BMI) on an outcome . We begin by providing some biological context.The human genome is made up of 23 pairs of chromosomes located in the nucleus of almost every cell in the human body. Chromosomes are made up of molecules of deoxyribonucleic acid (DNA), which is constituted (in part) by nucleotides, themselves comprising nucleobases: cytosine (C), guanine (G), adenine (A) and thymine (T) .The region of the chromosome at which a specific genetic variant in a DNA sequence is located is called its locus . Each loIndividual SNPs can affect different observable traits such as disease status or health condition. This introduces the distinction between heredity (the genotype) and the consequences of that heredity (the phenotype). The phenotype can be thought of as an ‘outward’ characteristic or trait that can be observed and/or measured, while the genotype is the underlying genetic structure associated with a specific phenotype .Mendelian randomization is founded on Mendel’s first and second laws. The first law—the principle of segregation—states that, during the formation of sex cells (gametes), there is random segregation of alleles from parent to child. The second law is the independent assortment of genetic variation at conception. This ‘allocation’ of genetic variation at the time of conception is approximately random, conditional on parental genotype. Genetic variants that do not affect an outcome of interest other than through the (phenotypic) risk exposure/health condition with which they are known to be associated can serve as instruments to allow researchers to infer the causal effects of health conditions on outcomes of interest, such as healthcare cost . IndividIn the language of econometrics, the effects of the endogenous variable  can be identified by the exogenous variation induced by the genetic variants. The genetic variants are assumed to be orthogonal to a regression error term because of the approximately random allocation (no confounding or endogeneity) at the time of conception  and the absence of measurement error in the (more precisely measured) genetic variant instruments. Genome-wide association studies (GWAS) increasingly provide robust evidence concerning the association of individual genetic variants and phenotypes. Thousands of such associations have now been identified , and resAn analogy may be drawn with an RCT. In Mendelian randomization, allocation to ‘treatment’ is indicated by the genotype, which is known to be associated with the health condition of interest. Differences in outcomes in people with different genotypes may then be investigated in a manner equivalent to an intention-to-treat (ITT) analysis in an RCT, in which participants are analysed irrespective of their compliance with the intervention .An important difference between Mendelian randomization and RCT analysis is that the genotype of interest represents a lifelong difference in the health condition or risk factor concerned, rather than the effects of a short-term administered intervention. Mendelian randomization can potentially estimate the effects of a health condition when intervention studies would be unethical or impractical, e.g. the assignation of individuals to alcohol dependence or to obesity.Many reviews of Mendelian randomization methodologies are available , 22–25. The three core instrumental variable assumptions are 1) relevance (the instrument must be associated with the exposure), (2) independence (the instrument is not associated with confounders) and (3) exclusion (the instrument does not directly affect the outcome). relevancFTO gene [Figure FTO gene , 30, whiFTO variant) is related to the exposure (BMI), indicated by the arrow pointing from FTO to BMI, and thus the first IV assumption holds. The confounding variable does not influence the instrument (or vice versa) since there are no arrows between mental health status and the FTO variant. Thus, the second assumption is satisfied. Finally, the only arrow leading from the instrument is to BMI—the instrument influences costs only via this path and does not otherwise affect the outcome, as required by the third IV assumption.In Fig. Genetic variants must have a robust association with the exposure of interest to be valid instrumental variables. This is known as the relevance assumption . InstrumThe relevance assumption can be tested by estimating the association of the variants and the exposure . These tThe independence assumption refers to the independence of the instrument from all confounders. Intuitively, this can be understood as the variants being ‘as good as’ randomly assigned to different individuals.FTO are known to vary by ethnic group. If these ethnic groups also have systematically different healthcare costs for reasons other than obesity, the independence assumption is violated. This can be accounted for by stratification of the population according to the subgroup, limiting analysis to groups with similar ancestral backgrounds or adjusting for ancestry-informative principal components [Population structure can induce associations between genetic variants and outcomes (e.g. cost) that are not due to the effect of the exposure of interest (e.g. BMI). This can occur because of population stratification, by which population subgroups differ in their relationship between the exposure and outcome. For example, allele frequencies of mponents .Assortative mating, the preferential mating of like genotypes (driven by mating of like phenotypes), will also tend to isolate alleles in certain population subgroups . AssortaThe exclusion restriction is so called because a valid instrument can be thought of as ‘excluded from’ or ‘exogenous to’ the causal relationship of interest by virtue of having no direct effect on the outcome or by being unrelated to any other determinant of the outcome .The exclusion restriction can be violated in the presence of canalization , which rThe interpretation of analysis in this context is similar to ITT analysis in an RCT, with canalization playing a role comparable to non-adherence to an intervention. Gene-by-environment interactions could provide evidence that particular exposures are affected by canalization. This is because development will not usually occur in the presence of a modifiable risk factor; hence, no compensation could have occurred . HoweverPleiotropy refers to the phenomenon in which a single locus directly or indirectly affects more than one phenotypic trait , 22, 38.Pleiotropy may violate the exclusion assumption. For example, assume a hypothetical gene separately influences both obesity and depression. A Mendelian randomization analysis using this gene to assess the causal effects of obesity could be confounded inadvertently by depression if both traits affect the outcome of interest.This type of scenario is summarised in Fig. Note that, even if a variant is pleiotropic, it need not violate the exclusion restriction, provided that the other trait does not affect the outcome  that itself affects the outcome [FTO tends to be co-inherited with a variant that predicts a mental health condition, which independently affects healthcare costs, the exclusion restriction is violated.Co-inheritance of traits, against Mendel’s second law, may also violate the exclusion restriction , 35. One outcome . For exaThe testing of suspected associations and recourse to evidence on genetic function and known linkages can offer protection against violations of the exclusion restriction caused by linkage disequilibrium. For example, ‘maps’ of the human genome can provide information on linkage disequilibrium . LinkageThe exclusion restriction can be examined indirectly by establishing whether the proposed genetic variants are associated with potential confounding factors, or alternative mediating variables, though one cannot directly test whether the exclusion restriction is valid. If either or both of these associations are observed, then the exclusion restriction is unlikely to hold. However, inference can still be undertaken even if some instruments are invalid, as discussed below in Sect Genetic variants are generally not related to confounders that affect observational studies. For example, Davey Smith et al.  found thHowever, dynastic effects, in which a genetic trait of a child is affected by a parental exposure caused by the parental genotype, can confound variants . For exaThe Wald estimator, or the ratio method, involves calculating the ratio of estimated coefficients obtained from a regression of the outcome on the instrument to the coefficient obtained from a regression of the exposure on the instrument. The intuition here is that a unit change in the outcome for a unit change in the exposure is given by the ratio of an ‘effect’ of the instrument on the outcome to an ‘effect’ of the instrument on the exposure. The familiar two-stage least squares (2SLS) estimator will give the same estimated causal effect as the ratio method when using a single instrument .Mendelian randomization can be conducted using likelihood-based estimators, with Bayesian methods, and semi-parametric methods such as the generalized method of moments (GMM) and structural mean models (SMM). Burgess et al.  argued tThe precision of IV estimates will generally be less than that of corresponding observational estimators, since the size of IV standard errors is inversely related to the strength of the association between instrument and exposure. The greater consistency of an IV estimator in the presence of confounding is associated with wider confidence intervals around estimated effect size.Recent methodological developments offer the possibility of obtaining unbiased estimates of the causal effect of exposure, even when some or all of the proposed variants are invalid.For example, Bowden et al.  proposedPhenotypic data could be drawn from medical records or other sources such as routinely collected data –53. PhenEvidence from PWAS can identify associations not already known from GWAS  but can IV analysis in Mendelian randomization needs to reflect underlying biological relationships and understanding of gene function. Results need to be interpreted in a manner that reflects the functional biological context and the broader population from which data are drawn. Glymour et al.  encouragIn this section, we illustrate some of the practical and methodological issues that might affect a Mendelian randomization analysis of the effects of obesity on healthcare costs.The prevalence of obesity has increased in recent decades , 63. It If obesity is a notable trait on these grounds, it is also a challenging one. Obesity is a complex trait—many genetic variants affect BMI . The useThe standard errors of IV estimators are related to the strength of association between instrument and exposure. Multiple instruments—such as the many genetic variants known to be associated with obesity —can imprHowever, multiple weak instruments will bias the IV estimates toward the observational estimate , 76. BurAll the variants in the score must be valid instruments for an allele score to meet the IV assumptions; even minor violations of the exclusion restriction can introduce bias into approaches using single allele scores. Davies et al.  suggesteThe data on which estimation is performed need not come from a single sample . Data onAll of the assumptions described concerning validity of analysis continue to apply, and particular care needs to be taken to ensure that the populations in each study are comparable . The absSome relationships of interest to health economists, such as between BMI and healthcare costs, are likely to be non-linear . Where eIf the shape of the exposure–outcome relationship is of interest, and its association between exposure and genetic association is the same at different levels of the exposure, then stratification within different quantiles of the exposure can be performed to examine the local impacts of the exposure on the outcome, although stratification should not be directly on the exposure itself to avoid inducing an association between the IV and confounders , 79. SilLinking healthcare costs to a specific health condition can be complicated. For example, Lehnert et al.  noted thThis gives rise to a conceptual question: should the causal analysis of the cost consequences of obesity focus on total healthcare costs or on ‘obesity-related’ costs only? Casting the net widely to encompass total costs allows for unknown and unexpected influences on cost causally related to the variant and exposures of interest to be included in the analysis. Consider an example of an individual who experiences a car accident, to which diabetic retinopathy associated with obesity contributed, and who undergoes an expensive inpatient hospital stay. A focus on ‘obesity-related’ costs that excluded consideration of this type of emergency admission could overlook these costs, even though they are caused by obesity in the scenario described. Both a total cost approach and an obesity-specific approach could be undertaken if information on overall resource use and resource use by diagnostic code is available.An ideal data source for the type of analysis proposed in this paper would contain extensive genotypic information on as large a group of individuals as possible, linked to longitudinal medical records and/or routinely collected administrative claims or reimbursement data. We focus on the UK Biobank project as an example dataset.The UK Biobank is a prospective study of approximately 500,000 participants aged between 40 and 69 years at recruitment between 2006 and 2010 . DetailePart of the UK Biobank’s motivation in recruiting individuals aged at least 40 years was to ensure a sufficient number of incident outcomes during the early years of follow-up. There is likely to be a minor selection effect in observing the health outcomes of individuals who have survived to at least 40 years of age.A more severe issue of selection relates to participation in the study itself. Participants in the UK Biobank face lower mortality risks than the general population. This gives rise to the potential for selection bias (a form of collider bias) , wherebyStudies with wide population coverage include the Age, Gene/Environment Susceptibility-Reykjavik Study, which contains linked genetic, phenotypic and medical records data for a large Icelandic cohort . The EstThe UK 100,000 Genome Project plans to sequence 100,000 genomes by 2017. The project has a focus on rare diseases and on cancer , 85. ThiThe eMERGE  network in the USA  offers lThere is scepticism that Mendelian randomization can offer anything to the study of economic outcomes  in spiteAs with RCTs, the generalizability of a Mendelian randomization analysis is not secured merely by conducting a well-designed study. For example, genetic variants tend to have modest effects on the exposures of interest, albeit that they influence lifelong exposure and not the short-term exposures often observed in RCTs, which may also produce small effect sizes .RCTs and well-designed prospective cohort studies will continue to be an important source of evidence. However, there is little or no prospect of obtaining robust causal cost estimates associated with long-term exposure to many medical conditions . In circA comparison is sometimes drawn between the human genome and a book —the 23 pSubstantial progress has been made in Mendelian randomization-based analyses , 37. ThiMendelian randomization analysis is potentially a valuable technique for health economists. Contextual reasoning, large sample sizes (including multi-sample designs), a focus on SNPs with material functional consequences, evidence from a variety of sources, information on biological plausibility, and sensitivity testing could form elements of a well-designed Mendelian randomization study. The outputs of these kinds of study could support the development of more robust evidence for economic evaluations and for healthcare priority setting more generally.


Sterna hirundo), we examined phenotypic plasticity and the evolutionary potential of breeding phenology, a key reproductive trait. We found that laying date was strongly heritable (0.27 ± 0.09) and under significant fecundity selection for earlier laying. Paradoxically, and in contrast to patterns observed in most songbird populations, laying date became delayed over the study period, by about 5 days. The discrepancy between the observed changes and those predicted from selection on laying date was explained by substantial phenotypic plasticity. The plastic response in laying date did not vary significantly among individuals. Exploration of climatic factors showed individual responses to the mean sea surface temperature in Senegal in December prior to breeding: Common terns laid later following warmer winters in Senegal. For each 1°C of warming of the sea surface in Senegal, common terns delayed their laying date in northern Germany by 6.7 days. This suggests that warmer waters provide poorer wintering resources. We therefore found that substantial plastic response to wintering conditions can oppose natural selection, perhaps constraining adaptation.A major question for conservationists and evolutionary biologists is whether natural populations can adapt to rapid environmental change through micro‐evolution or phenotypic plasticity. Making use of 17 years of data from a colony of a long‐distant migratory seabird, the common tern ( As resource productivity and climatic conditions at the west‐central African wintering grounds may impact both migration and breeding ground population demographic events , because first‐time breeders, which are mostly three or 4 years of age (on average 3.5 ± 0.8 SE), exhibited a strongly different distribution of arrival and laying dates  because it could be involved in the birds' phenological response to environmental conditions. We called this variable the “prelaying interval.” Analyses were thus based on multivariate models including both prelaying interval and laying date.We ran linear models with year and age as continuous fixed effects on prelaying interval and laying date, in order to estimate the temporal changes in the variables between 1996 and 2010, while controlling for changes in the age structure of the population.2.2In order to estimate additive genetic variances for laying date and the prelaying interval, as well as phenotypic and additive genetic correlations between these traits, we used restricted maximum‐likelihood mixed models  of laying date or prelaying interval in experienced females was partitioned into the following components:σpe2 was the permanent environmental variance, that is, persistent influence of female identity over time with environmental origin, σa2 the additive genetic variance, and σr2 the residual variance.The total phenotypic variance  using univariate linear models for each phenotypic trait , followed by (2) quadratic selection differentials (c2t) from models that included both a linear and a quadratic term  standardized linear selection gradients for prelaying interval (βPLI) and for laying date (βLD) and (4) nonlinear quadratic gradients for prelaying interval (γPLI) and for laying date (γLD) including correlational (γPLI/LD) selection gradients:We obtained the relative annual sum of fledglings, relative survival, and relative annual fitness, by dividing each individual measure by the appropriate annual means , we tested whether the laying date of females showed a plastic response to this climatic factor. This analysis included only experienced females with 2 or more years of breeding. We used a generalized mixed model with age as fixed effect, the identity of the female, and the interaction of female identity with SST in Senegal as random effects to quantify phenotypic plasticity. We additionally examined whether individual females exhibited plasticity in laying date by testing for variation in laying date both within and among individuals, using the techniques described by van de Pol and Wright .3SE; t = 7.52; p < .0001; Figure SE; t = 6.7; p < .0001; Figure SE; t = 1.58; p = .12).Across the 15‐year study, the onset of reproduction was significantly delayed over time (β = 0.35 days/year ± 0.05 3.1SE) in laying date being accounted for by individual identity (Table h2 = 0.00 ± 0.05). A long prelaying interval was associated with a later laying event at the phenotypic level . Prelaying interval and laying date were not under significant survival selection , but significant additive genetic variance for the annual sum of fledglings in experienced females . Additive genetic variance for laying date in this sample was significant as well . The genetic correlation between annual sum of fledglings and laying date in females was negative and approached significance .Quantitative genetic analyses of the fitness components revealed nonsignificant additive genetic variances for survival and annual fitness . For each increase of 1°C of SST temperature in December on the wintering grounds, laying date was delayed on average 6.7 ± 0.5 days .For experienced common terns that bred in at least 3 years, we used a mixed model to examine the influence of SST in Senegal in December on laying date. Age and age‐squared were included in the model as fixed factors to control for the effects of aging , but there was no significant interaction between these terms to suggest that females with a different average laying date differed in their level of plasticity. In support of this, a test of random slopes of individual females (R2 ± 1.96 SD = 0.02 ± 0.03) indicated that less than 1% of the variation in the data could be explained by inclusion of the interaction term . These analyses indicated that the slopes of the individual and population‐wide reaction norms were quite similar.We further tested the pattern of plasticity using a partitioning of within and among individual effects of December SST on female laying dates, with female identity a random variable  and substantial negative genetic correlation between laying date and fecundity (rG = −0.65 ± 0.35), which suggested that selection was not acting solely on the environmental component of the trait.For laying date to be delayed via microevolutionary change, significant heritability and fitness differences in reproductive timing are required. As is commonly found in avian species , and the prelaying interval exhibited virtually no heritability. It thus seems more likely that the apparent fecundity selection on prelaying interval was influenced by its correlation with laying date, rather than vice versa. All in all, the significant delay in reproductive timing, therefore, must have been primarily due to phenotypically plastic changes in laying date, which is the most general result found in bird studies (Charmantier & Gienapp, We therefore examined the prelaying interval, which became significantly longer during the study. We also found significant fecundity selection on prelaying interval in our univariate analyses, but its sign was negative (4.2Phenotypic plasticity is now recognized as a widespread response to climate changes in birds (e.g., Charmantier & Gienapp, While the mechanism underlying this pattern is not well understood, it likely reflects poorer foraging conditions in warmer tropical waters (Szostek & Becker, Parus major (Nussey et al., Sensitivity to environmental cues to adjust breeding timing displayed a significant genetic variance in the great tit None declared.


This family is an important breeding source for developing new mother palms for superior oil yield and bunch characters. The traits of interest selected for this study were fruit-to-bunch (F/B), shell-to-fruit (S/F), kernel-to-fruit (K/F), mesocarp-to-fruit (M/F), oil per palm (O/P) and oil-to-dry mesocarp (O/DM). The marker systems evaluated were simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). RR-BLUP, Bayesian A, B, Cπ, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the traits.Genomic selection (GS) uses genome-wide markers as an attempt to accelerate genetic gain in breeding programs of both animals and plants. This approach is particularly useful for perennial crops such as oil palm, which have long breeding cycles, and for which the optimal method for GS is still under debate. In this study, we evaluated the effect of different marker systems and modeling methods for implementing GS in an introgressed The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods.dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation.Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for The online version of this article (10.1186/s12863-017-0576-5) contains supplementary material, which is available to authorized users. Genomic selection was first implemented successfully in cattle, doubling the rate of genetic gain in breeding programs [Genomic selection (GS) is a form of marker assisted selection (MAS) using markers distributed across the entire genome. Unlike conventional MAS, GS does not require programs , 2. Thisprograms , 4. In cprograms .dura x AVROS pisifera crosses, selected for high oil yield. The potential reduction of genetic variation in the Deli dura due to founder effects is a concern for oil palm breeders. In addition, self-pollination (“selfing”) and sib-mating, which are commonly practiced to concentrate the desired agronomical traits in Deli dura populations, has further reduced the genetic variation. This has resulted in various symptoms of inbreeding depression, such as abortive bunch formation, poor fruit set and oil yield depression in Deli dura trials [dura populations acquired from Nigeria to widen the genetic base of the commercial Deli dura materials. Deli x Nigerian progenies have improved fresh fruit bunch (FFB) and oil-to-bunch ratio (O/B) values, with useful trait variation observed, resulting in a new series of mother palms for future commercial material. Still, the emphasis placed on the introgression program was low because of the slow nature of breeding progress in oil palm, which is typically 10–12 years per selection cycle [As the most efficient oilseed crop in the world, oil palm is able to produce up to 10 times more than other leading oil crops, and has surpassed soy oil since 2008  as the wa trials . To addrIn oil palm, GS was first evaluated using simulated data and a limited number of QTLs . Anothertenera progenies [dura palms is of more importance for oil palm breeding programs. Before implementation of GS in oil palm dura breeding programs, it is crucial to evaluate different marker systems and GS modeling methods. For this purpose, a full-sib family, consisting of 112 individuals, derived from Deli x Nigerian origin was selected for development and assessment of GS models. In total, eight methods, including two machine learning approaches, and two marker systems, SSRs and SNPs were evaluated. Prediction models built for the important yield-related traits were assessed. In general, individual populations available for introgression or further crossing are often small in oil palm. The development of accurate models to guide future crossing and introgression is essential for efficiency development of traits into commercially-relevant parental stock.A whole-genome OP200K SNP genotyping array  capable rogenies , the selUnopened spear leaves from 112 twenty-year-old palms derived from a Deli x Nigerian family, planted in a randomized complete block design trial and maintained at Sime Darby Plantation R&D, Malaysia were sampled . The fouTotal genomic DNA was isolated from 100 mg of each leaf sample using the DNAeasy Plant Mini Kit . For SSR genotyping, 135 markers from our previous study were selected . In the randomForest package [SNP genotyping on the same family was carried out using the OP200K array  . The pro package  in R.related [Genomic heritabilities for the traits were calculated using an in-house R script, based on a linear model using all informative SNP markers , 26, 27.related  using 50related .This study was performed using an IBM System ×3850 X5 HPC, with 40 Intel (R) Xeon (R) CPU E7–8850 @2.00 GHz processors and 1 Tb of RAM. The methods selected to perform GS in oil palm were RR-BLUP , Bayes ARR-BLUP assumes that the marker effects are normally distributed and that they explain the same amount of variance . Bayes ArrBLUP [BGLR package [−4 and shape parameter as 0.55.For all methods, a 5-fold cross validation was carried out across all traits, where a single subsample was used as the validation set, and the remaining four samples were used as the training set. This step was carried out until all subsamples had been used for both training and validation steps. RR-BLUP was implemented using the R package rrBLUP , and Bay package . For Baye1071. Optimization of the parameters was carried out for each iteration during cross validation using the tune.svm function. Within the training set, another 10-fold cross-validation was used to estimate the optimal parameters. The gamma and cost factors for the SSR dataset were optimized using an exponential range of 2−15 to 215 [−30 to 215. Random forest was carried out using the randomForest package [mlr library for each cross validation step. Like SVM, 10-fold cross validation was used on the training set. The range of mtry optimization range was set from 1 to 221, nodesize from 1 to 50 and ntree was from 500 to 1500 for SSRs. For SNPs, the mtry optimization range was set at 1 to 500, ntree was set from 500 to 3000.SVM was carried out using R package 5 to 215 , epsilon package . OptimizFor each iteration, accuracy was defined as the predictive ability, which was calculated as the Pearson correlation coefficient of predicted trait value of the validation set, versus its observed trait value. The overall accuracy was calculated as the mean of the predictive abilities from all iterations. Genomic selection was performed using SSRs, followed by SNPs. Representative regression boxplots were generated for each trait using SNPs for the best method. In order to test for linear assumption made under RR-BLUP and Bayesian models, residuals were calculated from the difference between the predicted trait values and the observed trait values. The residuals were then standardized using the “rstandard” function in R. The residual and Quantile-quantile (QQ) plot were generated using the “plot” and the “qqnorm” functions.The 4-year recordings for each trait were averaged, with the mean for F/B being 67.37 (±3.74), S/F 33.89 ±2.61), K/F 9.89 (±1.55), M/F 56.23 (±3.37), O/P 28.84 (±7.00) and O/DM 75.44 (±2.54). Since these traits were all yield related, many of them correlated with each other , followed by S/F (0.25), K/F 0.19) and O/DM (0.19), O/P (0.18) and M/F (0.18). Averaging across all traits, machine learning methods out-performed all other methods, albeit only slightly. SVM having the highest accuracy for F/B (0.30), S/F (0.30) and O/P (0.28), and RF having the highest accuracy for K/F (0.24), M/F (0.34) and O/DM (0.28). However, SVM also has the lowest accuracy for K/F (0.14), RF has the lowest accuracy for F/B (0.19) and O/P (0.14)  are predominantly planted, together with a small number of mother (dura) palms, which are used solely for seed production for new planting materials. The small training population size in this study therefore reflects the reality of dura mother palm breeding. Even with this condition, we have found the application of GS in dura breeding programs to be promising.When genome-wide SNPs were introduced into the GS models, accuracy for all traits improved, with the greatest improvement for S/F, M/F and O/DM. This can be attributed to the fact that the OP200K array provides high genomic resolution . With motability . Therefotability , the durFor outcrossing perennial crops such as oil palm, the conventional breeding selection of mother palms for progeny testing is partially random and is phenotype-dependent. The ability to select for the best mother palms will expedite the production of high-yielding progenies as future commercial planting materials. Despite having a small training population, which is a usual case for mother palms, GS using SSRs has reasonable accuracy, which can be much improved by using dense SNPs that afford better coverage of the genome and hence the desirable QTL. From our results, the differences in accuracies between the methods evaluated for SSRs and SNPs were small. In general machine learning methods slightly outperformed the other methods, and with sufficient parameter optimization machine learning could become a powerful tool to support selection of optimal materials for breeding programs of oil palm and other perennial crops.


Prunus persica L.) is a highly valuable crop species and is recognized by molecular researchers as a model fruit for the Rosaceae family. Using whole-genome sequencing data generated from 129 peach accessions, here we perform a comprehensive genome-wide association study for 12 key agronomic traits. We show that among the 10 qualitative traits investigated, nine exhibit consistent and more precise association signals than previously identified by linkage analysis. For two of the qualitative traits, we describe candidate genes, one potentially involved in cell death and another predicted to encode an auxin-efflux carrier, that are highly associated with fruit shape and non-acidity, respectively. Furthermore, we find that several genomic regions harbouring association signals for fruit weight and soluble solid content overlapped with predicted selective sweeps that occurred during peach domestication and improvement. Our findings contribute to the large-scale characterization of genes controlling agronomic traits in peach.Peach ( Peach is both an economically important crop species and a model for Rosaceae fruit development research. Here, the authors perform genome-wide association analysis in peach and find candidate genes associated with variation in agronomically important fruit phenotypes. Prunus persica L.) is an economically important deciduous fruit, only exceeded by apple, grape and pear in worldwide production quantitywww.rosaceae.org), but only a few genes were clearly identified as related to qualitative traits such as flesh adhesionPeach  was performed using 1,580 peach accessions and genotype data for 5,378 polymorphic SNPs (single-nucleotide polymorphisms) derived from the 9K SNP array developed by the International Peach SNP Consortium910Here we present a GWAS for 12 agronomic traits by exploiting natural variation in 129 peach accessions and using high-throughput resequencing technology. Several genomic loci underlying these agronomic traits are identified for the first time. We also find that the linkage disequilibrium (LD) values of peak GWAS signals in peach exhibit different patterns from those reported for annual crops. These findings may help inform peach breeding as well as future sequencing studies and GWAS of peach and other fruit crops.P. persicaIn this study, a total of 129 peach accessions were used for resequencing and subsequent GWAS analysis . Among tPrunus mumeP. persica  and another to its closely related wild species (red line). Accordingly, population structure analysis indicated that the LnP(D)  increased significantly when K was increased from 1 to 2  naive model: general linear model (GLM) without any correction for population structure (GLM-no PCA); (2) GLM-PCA model: GLM with PCAs as correction for population structure; (3) MLM model: mixed linear model (MLM) with PCAs and Kinship as correction for population structure. For the 10 qualitative traits, all three models were tested  whereas 10Arabidopsis, meaningful GWAS results were obtained from the analysis of only 107 accessionsGenetic structure and the type of phenotypic variance of a population can greatly influence the power of GWAS10P values of the selected SNPs for each trait were all obtained in the GLM-PCA model  located on scaffold_6 . Cloning of the GDR (Genome Database for Rosaceae)-annotated mRNA sequence in multiple tissues was not achieved, implying that the original annotation was incorrect and that PpCAD1 might have an additional exon 6PpCAD1 (ppa003772m) expression increased earlier in flat peach than in round peach development (N=20) compared with flat peaches (N=12)  . These rppa006413m and ppa006339m  than in non-acidic fruits (15 accessions) a006339m . We founa006339m  and signa006339m . The resessions) , furtherMYB gene (ppa026143m) on scaffold_5, located in the 15,897,836 to 15,899,002 bp interval10P value of 121.92 and a minor allele frequency of 0.22, corresponding to 1.7 Mb distance of ppa026143m . In humans, SPF27 is a ubiquitously expressed nuclear protein, identified as part of the Prp19 complexArabidopsis thaliana and tomato. For example, GLABROUS1 (GL1) belongs to the large family of MYB transcription factors plays a central role in trichome initiationArabidopsis lack trichomes. A gene encoding a MYB-like protein (ppa000733m) was found among the 13 annotated genes located around the peak association signals for fruit hairiness . More research is required to verify if peach genes are indeed contributing to trichome cell elongation.Another major trait of interest was fruit hairiness; variation in this trait leads to two different fruit types, nectarine and peach. The fruit glabrous trait (nectarine) was recently associated with a recessive transposable element insertion in an a026143m . AccordiY) controls fruit flesh colour and the gene for white flesh is dominant over that for yellow fleshccd4, scaffold_1: 25,639,445..25,641,500 bp) was responsible for determining flesh colour. The yellow phenotype originated from mutations disrupting ccd4 function, which prevented carotenoid degradation. In this study, the peak signal in scaffold_1 , and relatively distant from the gene. The gene ppa027093m, which is involved in the function of flavanone 3-hydroxylase and NAD(P)H-dependent 6'-deoxychalcone synthase, was found in that associated region in our study. Although NAD(P)H-dependent 6'-deoxychalcone synthase plays a role in anthocyanin biosynthesisA single locus (PpMYB10 (regulator of anthocyanin content), was also located on scaffold_3 PpMYB10, other modifier genes might be involved in variation in anthocyanin biosynthesis. The weak association near the PpMYB10 locus might have resulted from the limited sample size or from complex regulation involving numerous genes.The presence of anthocyanin pigments is a fruit quality trait that potentially effects on human health. Linkage analysis showed anthocyanin pigments control the flesh colour around the stone trait, which is located in linkage group 3 ref. . Earlierppa015634m, which encoded the transcription factor bHLH14.Kernel taste was mapped to linkage group 5  and the 10P value on scaffold_2  was only 20.64, which is lower than the value found for other traits (www.rosaceae.org/), where showy flower is the dominant character. In this study, a clear association signal was located at 13,740,117 bp on scaffold_8 Flower shape-DoubleF has been mapped to linkage group 2 (www.rosaceae.org), with single flowers being the dominant trait. In this study, we found three significant peaks on scaffold_2 and scaffold_6 . Using tr traits . Flower affold_8 , with a P<1 × 10−6) and in 2010 (P<1 × 10−4), as well as significant SSC-related loci (P<1 × 10−4), as listed in FW and SSC are two economically important quantitative traits in peach, but we know little about their genetic basis and the molecular mechanism by which they are controlled. Previous studies revealed that SSC-related QTLs are mainly located in scaffold_1, _2, _4, _5 and _6 refs , 28, whiFW and SSC are also two frequently considered in peach breeding. To investigate the effects of artificial selection on the loci controlling FW and SSC, the association signals of these two traits  were comAmong all identified SSC association signals, three overlapped with domestication sweep regions  , encompappa021446m and ppa022308m, predicted to encode pectinesterase or pectinesterase inhibitor proteins, a set of three repeat genes  predicted to encode β-fructofuranosidase/hydrolase proteins and ppa021277m, which is similar to AtGRF9, which encodes the growth-regulating factor (GRF) 9 protein in Arabidopsis. After calculating the allele frequency of the three lead SNPs in different populations, the percentage of the most frequent allele was 99.09% on the first locus  of improved varieties while this value decreased to 83.96% in landraces. The most frequent variant on scaffold_2: 24,271,176 bp was present in 99.09% of improved varieties and 90.57% of landraces. The homozygous genotype of the most frequent allele of the third SNP  was more frequent in improved varieties (100.00%) than in landraces (84.91%).In addition to these genes, four FW association signals (three loci) overlapped with the improvement sweep  , encompaInterestingly, three SSC-associated loci appeared to have experienced selection during domestication, but this does not appear to be the case for FW-associated loci . HoweverArabidopsisppa003772m) and non-acid fruit (ppa006339m) were identified. These results reveal that GWAS can be employed to find candidate genes for specific qualitative traits in peach, even using relatively few accessions. However, this may have only been possible in peach due to its low rate of polymorphism, resulting from self-pollination and grafting44Association mapping has identified QTL in annual crops, such as ppa021277m, is annotated as encoding a GRF protein. GRF proteins and their effects on cell proliferation have been studied intensively in riceArabidopsis38Arabidopsis, most GRF genes are strongly expressed in actively growing and developing tissues. Overexpression of AtGRF1  were found for the peak signals of fruit shape  being related to domestication regions 13 kb13et aThe present findings help clarify the genetic basis for major agronomic traits in peach, potentially allowing for more efficient breeding. Future GWAS analysis, in conjunction with deep sequencing, can be expected to further examine the genetic basis for agronomic traits in peach given it high resolution despite the low level of LD.P. persica were included. The well-known breeding parents were also present in the improved varieties groups, such as Shu Guang, Jing Yu and Zhong You Tao 4# of Chinese accessions, as well as in the ones outside China, Okubo, Bailey, May Fire and Elberta. All plant materials were collected and maintained at the National Fruit Tree Germplasm Repository, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, in China. During the growing period, all cultivars were equally managed. Eighty-four accessions had been previously introducedWe used 129 peach accessions: 12 wild accessions, 62 landraces and 55 ancient and modern improved varieties . Among tAgronomic traits under investigation for association analysis were divided into three categories: fruit characteristics , seed characteristics (kernel taste), and flower characteristics (flower shape-DoubleF and flower shape-ShowyF). The fruit and seed characteristics were evaluated using fully matured fruits. All qualitative traits were analysed in at least five fruits or flowers in 2010, which were randomly picked from two trees for each accession. Some traits, such as fruit flesh colour, flesh adhesion, flesh texture, flesh colour around the stone and non-acid fruits were verified in two successive years, from 2011 to 2012. All agronomic traits considered here were characterized based on previously published plant genetic resources' evaluation criteriag for 15 min. The resulting supernatant was filtered through a 0.22-μm mesh before being subjected to an ion chromatography  analysis. The different acids were detected using a Dionex ED-40 detector and an Inertsil AS-11 column . The column was maintained at 35 °C. Samples were eluted with 0.022 mol l−1 KH2PO4 solution with pH 2.4 and injected at a flow rate of 0.8 ml min−1. Eluted compounds were detected by UV absorbance at 210 nm. All samples were prepared in triplicate. Citric acid, malic acid and quininic acid were identified via comparisons of their retention times and UV spectra with external standards. Acid concentrations were calculated from calibration curves obtained from the corresponding external standards and expressed in g l−1.In addition, the acid content was regarded as a quantitative character when analysing its changes during the fruit development period. Hakuho (non-acid) and Tianjinshuimi peaches were picked in the field, during fruit development period, from April 24 to July 8, 2014 representing 30, 45, 60, 75, 90 and 105 days after full bloom and brought to the laboratory. The mesocarp was immediately removed and placed in liquid nitrogen; after that, the flesh was frozen at −80 °C until analysis. Samples were homogenized in 80% ethanol and centrifuged at 12,000DNA was extracted from the fresh leaves of the added 45 peach accessions using the CTAB (hexadecyl trimethyl ammonium bromide) methodTo minimize the negative influence of sequencing errors in gene identification, two SNP-calling software packages were used simultaneously. SNP detection was performed using SAMtoolsK in Structure software ranged from 2 to 8, representing the simulated number of groups in ancient populations. Population genotypes were also transformed into a matrix containing 0, 1 and 2. The homozygous and heterozygous genotypes aligning with the reference genome were indicated with 0 and 1, respectively, while a homozygous genotype for the non-reference was indicated with 2. Sample covariance was then calculated from this matrix containing information for all individuals' information. A PCA of whole-genome SNPs was performed with the EIGENSOFT 4.2 softwareUsing the neighbour-joining method and a distance matrix calculated in PHYLIP 3.68 ref. , a phyloThe genotyping error associated to heterozygotes was inevitable given the low sequencing depth (5 × ). Therefore, we treated the loci which GQ ≤3 as missing in the raw VCF file. For imputation, we used the function infer sporadic missing genotype data of Beagle software−8). In the association analysis of the additional 345 accessions, only GLM-no PCA was used to obtain final results. The specific commands to run each model were as follows: (1) GLM-no PCA:./tassel4.0_standalone/run_pipeline.pl -fork1 -h./genotype.txt -fork2 -r./phenotype.txt -combine4 -input1 -input2 -intersect -glm -export glm_output -runfork1 -runfork2; (2) GLM-PCA:./tassel4.0_standalone/run_pipeline.pl -fork1 -h./genotype.txt -fork2 -r./phenotype.txt -fork3 -q./PCA.cov -combine5 -input1 -input2 -input3 -intersect -glm -export glm_output -runfork1 -runfork2 -runfork3; (3) MLM:./tassel4.0_standalone/run_pipeline.pl -fork1 -h./genotype.txt -fork2 -r./phenotype.txt -fork3 -q./PCA.cov -fork4 -k./kinship.txt -combine5 -input1 -input2 -input3 -intersect -combine6 -input5 -input4 -mlm -mlmVarCompEst P3D -mlmCompressionLevel None -export mlm_output -runfork1 -runfork2 -runfork3 -runfork4.In these study, three models . The value of π was calculated on the basis of the genotypes of each line at the SNP positions using BioPerl. Windows in the top 5% of π values were considered candidate regions for domestication and improvement sweeps.Under domestication sweeps, genetic regions in landraces of r2 of several non-overlapping 50 kb sliding windows across the peach genome, using PLINKD′ value in a random chromosome was also measured to compare r2 and D′ distributions across the genome.To evaluate the effects of mutation on LD, we calculated the ppa003772m) and non-acid fruit (ppa006413m and ppa006339m) traits were downloaded from the Genome Database for Rosaceae . Gene-specific primers were designed using Primer-BLAST software . We performed qRT–PCR using the LightCycler System , following the manufacturer's protocol. Relative expression levels were estimated by the 2−ΔΔCT method.Using an extraction kit , RNA was isolated from the fruit flesh of the following three groups: the first comprised two accessions with different fruit shapes . All other relevant data is contained within the paper and its All raw sequences of the 129 peach accessions have been deposited in the Sequence Read Archive (SRA) of National Center for Biotechnology Information with accession codes SRA073649 and SRA261655. The SNPs have also been deposited into Figshare database .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures 1 - 24, Supplementary Tables 1 - 16, Supplementary Note 1 and Supplementary ReferencesThe SNP validation by MassArray sequenome technology.