ST6GAL1, B4GALT1, FUT8, and MGAT3), other genetic loci have strong influences on the IgG glycosylation patterns. A novel locus on chromosome 1, harboring RUNX3, which encodes for a transcription factor of the runt domain-containing family, is associated with decreased galactosylation. Interestingly, members of the RUNX family are cross-regulated, and RUNX3 is involved in both IgA class switching and B-cell maturation as well as T-cell differentiation and apoptosis. Besides the involvement of glycosyltransferases in IgG glycosylation, we suggest that, due to the impact of variants within RUNX3, potentially mechanisms involved in B-cell activation and T-cell differentiation during the immune response as well as cell migration and invasion involve IgG glycosylation.Immunoglobulin G (IgG), a glycoprotein secreted by plasma B-cells, plays a major role in the human adaptive immune response and are associated with a wide range of diseases. Glycosylation of the Fc binding region of IgGs, responsible for the antibody’s effector function, is essential for prompting a proper immune response. This study focuses on the general genetic impact on IgG glycosylation as well as corresponding subclass specificities. To identify genetic loci involved in IgG glycosylation, we performed a genome-wide association study (GWAS) on liquid chromatography electrospray mass spectrometry (LC–ESI-MS)—measured IgG glycopeptides of 1,823 individuals in the Cooperative Health Research in the Augsburg Region (KORA F4) study cohort. In addition, we performed GWAS on subclass-specific ratios of IgG glycans to gain power in identifying genetic factors underlying single enzymatic steps in the glycosylation pathways. We replicated our findings in 1,836 individuals from the Leiden Longevity Study (LLS). We were able to show subclass-specific genetic influences on single IgG glycan structures. The replicated results indicate that, in addition to genes encoding for glycosyltransferases (i.e.,  Its gly protein . They redy (LLS) . A recendy (LLS)  used a mdy (LLS) ] in a muN-linked glycans. It has been shown that the IgG subclasses, IgG1–IgG4, not only differ in their structure, especially within the hinge region of the glycoprotein, but also in their effector functions (N-acetylglucosamine (GlcNAc)  method allows for subclass-specific quantification of unctions , 25. Besunctions . While Iunctions . IgG4, o(GlcNAc) . How thePrevious GWAS on serum metabolite levels have indicated that analyzing enzyme substrate-product ratios benefits in gain by power for detecting associated genetic loci over analyzing single metabolites . Due to n = 1,823), and a replication of the results for the same glycan panel in an additional 1,800 samples from LLS, we want to further investigate the underlying genetic control of IgG glycosylation.By means of a GWAS including these newly derived traits of the LC–ESI-MS measured IgG glycopeptides in our discovery cohort from the KORA F4 study , out of which 14,425 associations (848 SNPs and 164 traits) reached genome-wide significance . Explained variances in the discovery cohort ranged from 1.4 to 14.1%  initial IgG glycopeptides, 87 (out of 155) summarizing derived traits, 39 (out of 95) within-subclass ratios, 6 (out of 40) between-subclass ratios, and 5 (out of 36) glycan proportions. Effects for all replicated associations are in the same direction and of similar magnitude as in the discovery cohort , chromosome 3: 186,705,790–186,782,999 (ST6GAL1), chromosome 7: 50,336,551–50,355,207 (IKZF1), chromosome 9: 33,113,322–33,180,813 (B4GALT1), chromosome 14: 65,734,600–66,275,755 (FUT8), chromosome 22: 24,100,654–24,189,032 (SMARCB1/DERL3), and chromosome 22: 39,737,929–39,893,932 (MGAT3)]. An overview of the associated traits per locus can be found in Table RUNX3 (chromosome 1p36.11).The r2 ≥ 0.5) and are flanking the gene RUNX3  have significant impact on glycan traits. A multivariate analysis in KORA F4 reveals that the three SNPs describe one locus, with rs16830188 being the most influential SNP  , the associations of FUT8 and IgG1_G0FN/IgG1_G0N.Besides known pathway steps in IgG glycan synthesis and inferred reactions from network analyses , the resSubclass comparisons of meta-analyzed data revealed that 22 glycan traits are significantly different associated with 432 SNPs on 4 chromosomes , rs11158592, was significantly negative associated with these traits in IgG1 and IgG2 but not in IgG4.MGAT3 and IgG glycan traits, we detected two traits being significantly different between IgG1 and IgG2, 10 glycan traits differing between IgG2 and IgG4, and only one glycan trait being different for IgG1 and IgG4 (G1FN). Almost all of the significantly differing glycan structures contain a bisecting GlcNAc. In addition, within-subclass ratios representing the addition of a GlcNAc  are significantly differently associated with the MGAT3 locus for the IgG subclasses.Furthermore, for the association between SNPs within N-glycans, and the traits are comparable between the discovery and replication study.Our study attempts to deepen the knowledge of genetic influence on IgG glycosylation and to disclose possible subclass specificity in the synthesis pathways. We used the LC–ESI-MS-measured glycopeptides in both the discovery and replication cohort. In contrast to the UPLC and MALDI–TOF MS data used in Ref. , 23, we RUNX3, on chromosome 1p36.11. Unfortunately, we could not verify any of the additional proposed loci proposed by Shen et al. . RUNX3 and other transcription factors of the runt-family have a large impact on hematopoiesis  and IgG4 were associated  of their tryptic Fc glycopeptides in Caucasians . NeverthThe within-subclass ratios representing enzymatic pathway steps are mainly associated with the assumed genetic loci coding for known glycosyltransferases  see Fig but alsoThe comparison of IgG glycan traits across subclasses leads to the hypothesis that fucosylation catalyzed by Fut8 and the addition of bisecting GlcNAc supported by Mgat3 is realized to different extent between the IgG subclasses. Fucosylation seems to be especially different between IgG1 and IgG2 while bisection is mostly differing between IgG2 and IgG4. For more details, see the supplementary note in Data Sheet S1 Supplementary Material. Still, functional studies are needed to elucidate the underlying mechanisms, especially with regard to subclass specificity. Indeed, it has been shown that specific subclasses and their attached glycan structure are highly relevant as biomarkers for diseases and even more when used in antibody therapy –50.The obtained results help to broaden our knowledge on the pathway steps of IgG glycan synthesis in general, and, specifically the differences for each IgG subclass. While the initial glycan traits outperform the between-subclass ratios and glycan proportions, the findings from comparing SNP–glycan associations across subtypes . All individuals were of European ancestry and samples with mismatching phenotypic and genetic gender were excluded. These criteria led to a total of 18,185,628 SNPs. After the analysis, we additionally excluded SNPs with imputation quality defined by IMPUTE lower than 30%.The KORA F4 study, conducted in 2006–2008, is an independent population-based health survey  and was A total of 1,823 individuals from the KORA F4 cohort were used for discovery. The samples include 935 women and 888 men ranging from 32 to 81 years, with mean age of 62.56 years (SD = 9.89) . As for KORA F4, we excluded SNPs with imputation quality lower than 30% as provided by IMPUTE. For the current genome-wide association analysis with IgG glycopeptide measurements, 1,836 samples of offspring and their partners were available.The LLS examined long-lived siblings of European descent together with their offspring and the partners of the offspring. Families with at least two long-lived siblings  were recruited. This age category represented <0.5% of the Dutch population in 2001 . In totaAs described in Ref. , IgG wasDue to the different IgG isolation procedures for KORA F4 and LLS, we obtained subclass-specific measurements for IgG1, IgG2/IgG3, and IgG4 in KORA F4 and IgG1, IgG2, and IgG4 in LLS. IgG3 is less abundant compared with IgG2 and we thus denote the IgG2/IgG3 mixture in KORA as IgG2 only.Isolated IgG (approximately 25 µg) was resuspended in 40 µL of ammonium bicarbonate containing 200 ng of trypsin  and digested at 37°C over night. The KORA F4 samples underwent an additional purification step: resulting tryptic glycopeptides were purified by reverse phase solid phase extraction using Chromabond C18 ec beads . C18 beads were activated by 80% acetonitrile (ACN), 0.1% trifluoroacetic acid (TFA)  and conditioned with 0.1% TFA. Tryptic digest was diluted 10× with 0.1% TFA and loaded onto C18 beads. Beads were washed with 0.1% TFA and glycopeptides eluted with 20% ACN and 0.1% TFA. Tryptic glycopeptides were dried by vacuum centrifugation and dissolved in 20 µL of ultrapure water.via a capillary electrophoresis electrospray (ESI) interface . A sheath liquid  was pumped at a flow rate of 2 µL/min. Nine µL of IgG tryptic glycopeptides was loaded on Acclaim PepMap100 C8 (5 mm × 300 μm i.d.) trap column. The glycopeptides were washed 1 min with 0.1% TFA (solvent A) at a flow rate of 40 µL/min and separated on an HALO C18 nanoLC column   at 30°C, using a 3.5 min gradient from 19 to 25% solvent B (80% ACN) at 1 µL/min flow rate. Mass spectra were acquired from 500 to 2,000 m/z units with two averages at a frequency of 0.5 Hz. The quadrupole ion energy and collision energy were set to 4 eV. NanoACQUITY UPLC system was operated under MassLynx software version 4.1 and the Bruker micrOTOF-Q was operated under HyStar software, version 3.2. Data extraction was performed using an in-house Python script. In short, data were m/z recalibrated based on a subset of hand-picked analytes having a high signal-to-noise ratio and the expected isotopic distribution. Intensities for the top four isotopologues were extracted using a 10 ppm m/z window. Retention times were aligned toward the cohort median and retention time bins were determined for the analytes. All of the signals belonging to a single analyte for every sample were summed up.For the KORA F4 study, tryptic glycopeptides were analyzed on nanoACQUITY UPLC system  coupled to Compact mass spectrometer  t = 0, 3% solvent B; t = 2, 6%; t = 4.5, 18%; t = 5, 30%; t = 7, 30%; t = 8, 0%; t = 11, 0%. The LC was coupled to the MS via a sheath-flow electrospray (ESI) interface . A sheath flow, consisting of 50% isopropanol, 20% proprionic acid, and 30% MilliQ-purified water, was applied with a flow rate of 2 µL/min, along with nitrogen gas at 4 L/min. Mass spectra were acquired within an m/z range of 600–2,000 at a frequency of 0.5 Hz. LC–MS data were examined and calibrated using Compass Data Analysis 4.2 , and retention time alignment was done using Msalign. In-house developed software Xtractor 2D (see http://ms-utils.org/Xtractor/) was used to extract signal intensity data. For each type of glycopeptide, the background-subtracted signal intensity of the first three isotopic peaks in both 2+ and 3+ charge state were summed.For the LLS study, the IgG glycopeptide samples were analysed using an Ultimate 3000 RSLCnano liquid chromatography system  coupled to a Maxis Impact quadrupole time-of-flight-MS , as described previously . SamplesFor the following analyses we used the most prominent measureable 20 glycopeptides in subclasses IgG1 and IgG2  and the most prominent and identifiable 10 fucosylated glycopeptides in IgG4, since peaks belonging to afucosylated IgG4 glycans overlapped with those of earlier eluting and much higher abundant IgG1 glycans.Glycosylation is highly differing between individuals. Absolute values of peaks obtained by the LC–ESI-MS method are not comparable. We normalize glycopeptides per subclass by total area normalization as defined in the R-package “glycanr” (R-package version 0.3.0) , taking Summarizing derived traits per subclass were computed as described in S1 using the ildt function from glycanr package . Ratios For the glycan proportions, i.e., normalization per specific glycopeptide trait in total Fc IgG glycopeptides, we also used the total area normalized traits as input. We only calculated per glycan normalization for core-fucosylated glycopeptides as others are not available for IgG4. In addition, we computed the sums per IgG subclass as ratios of these sums. Again, the ratios were log-transformed before any further analyses. For the discovery cohort, characteristics of all IgG glycan traits can be found in Table With LC–ESI-MS, 50 initial glycopeptides from different IgG subclasses were measured and quantified. For IgG1 and IgG2 (IgG2/3 in KORA), 20 initial glycopeptides are available, for IgG4, only glycopeptides with core-fucosylation (10 glycopeptides) were measured.From these 50 initial glycopeptides, summarizing traits per subclass were derived  and included the newly normalized glycopeptides, the subclass-specific sums, and ratios thereof in the analyses. These traits describe the relative abundance of the attached glycopeptide structure for the specific subclass.All traits declared as “ratios” were log-transformed before any statistical analysis.For a complete list of all 376 phenotypic traits, please see Table We performed a genome-wide association analysis in the discovery cohort, KORA F4. First, each phenotype see “ using an−8. Given that we performed 376 different GWASs, the genome-wide significant threshold for the discovery was additionally adjusted for the number of initially measured glycopeptide traits and thus set to 1 × 10−9 . All derived traits and different ratios are a function of initially measured glycan traits and are therefore dependent on these traits. We acknowledge that there might be less independent traits within initially measured glycans, but we decided to be more conservative and correct for 50 tests.The threshold determining the suggestive SNPs was set to 5 × 10http://www.lumc.nl/uh, under GWAS Software (−6 (0.05/number of replicated associations).For the LLS cohort, we computed linear regression models for the suggestive associations from the discovery only. Similar to the discovery cohort, each trait was adjusted for sex and age first, and the obtained residuals were normally rank transformed. For LLS, a score test including family information  was condSoftware ]. The Bop < 2.33 × 10−6), we additionally performed an inverse variance-weighted fixed effects meta-analysis on the summary statistics of the two cohorts. The meta-analysis was performed using the software METAL ] .For replicated SNPs on chromosome 1, we additionally performed multivariate linear models with the data from the discovery cohort, KORA F4, and settings as in the original model. For associated glycopeptide traits, models included one up to all three of the replicated SNPs on chromosome 1. We compared the significance of the single SNPs in the joint models as well as the added explained variance of the glycan traits.The KORA study was carried out in accordance with the recommendations of good clinical practice guidelines and the guidelines of the KORA study group, with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the KORA study group. The LLS study was carried out in accordance with the recommendations of good clinical practice guidelines and the guidelines of the medical ethics committee of the Leiden University Medical Center, with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the medical ethics committee of the Leiden University Medical Center.Performed wet lab experiments and data extraction: JŠ, IT-A, and GR, and RP. Performed data cleaning: LK, FV, EA, AW, JD, and MB. Performed GWAS: AW and EA. Analyzed the data: AW, EA, LK, JŠ, IT-A, GR, and EB. Contributed data/reagents/material/analysis tools: MB, JD, DH, RP, HG, JK, KS, AP, TM, MW, PS, GL, and CG. Supervised the research: M, CH, PS, GL, and CG. Wrote the manuscript: AW, EA, and LK. All the authors discussed the results and reviewed the final manuscript.GL is the founder and owner of Genos Ltd., a private research organization that specializes in high-throughput glycomic analysis and has several patents in this field. LK, JŠ, IT-A, GR, and FV are employees of Genos Ltd.


Multiparent populations (MPP) have become popular resources for complex trait mapping because of their wider allelic diversity and larger population size compared with traditional two-way recombinant inbred (RI) strains. In mice, the collaborative cross (CC) is one of the most popular MPP and is derived from eight genetically diverse inbred founder strains. The strategy of generating RI intercrosses (RIX) from MPP in general and from the CC in particular can produce a large number of completely reproducible heterozygote genomes that better represent the (outbred) human population. Since both maternal and paternal haplotypes of each RIX are readily available, RIX is a powerful resource for studying both standing genetic and epigenetic variations of complex traits, in particular, the parent-of-origin (PoO) effects, which are important contributors to many complex traits. Furthermore, most complex traits are affected by >1 genes, where multiple quantitative trait locus mapping could be more advantageous. In this paper, for MPP-RIX data but taking CC-RIX as a working example, we propose a general Bayesian variable selection procedure to simultaneously search for multiple genes with founder allelic effects and PoO effects. The proposed model respects the complex relationship among RIX samples, and the performance of the proposed method is examined by extensive simulations. Drosophila Synthetic Population Resource  mice are among the major mouse resources in biomedical and genetic research. However, traditional mouse RI lines are derived from only two inbred parental strains, with limited a number of lines available, resulting in a low percentage (15%) of genetic variation across all mouse inbred strains , a set oResource , in whicResource . The CC L RI lines, Through the generation of RI intercrosses (RIX) of RI lines, a large number of potential “outbred” RIX samples can be generated. That is, given During the past 20 yr, QTL mapping methods, including analysis of variance (ANOVA), interval mapping , composid RI see , and R/qd RI see .In this paper, for MPP-RIX data, we develop a Bayesian variable selection procedure to simultaneously map multiple genes with founder allelic effects and PoO effects. We demonstrate our method with CC-RIX data, but the method is general enough for other MPP-RIX populations. We place parameter-expanded Gaussian (PeG) priors on both the random founder allelic effects and PoO effects for variable selection.Statistical Method section, we first introduce the CC-RIX experiment, then propose a random-effects model and describe a Bayesian variable selection procedure. In the Simulation Study section, we perform extensive simulation to examine the proposed method. Summary comments are given in the Discussion section.The paper is organized as follows. In the L CC RI lines, a total of n be the total number of CC-RIX samples and p be the total number of genetic markers. Further, let the phenotype of individual i (the dependent variable) be The CC-RIX panel is the RI intercross of CC lines. For jth QTL and PoO should be included in or excluded from the model and µ is the overall mean; kth element of kth founder allele  at the jth candidate locus; n × L matrix whose i = 1, 2, ⋯, n, since each CC-RIX has two and only two parents. Therefore, kth founder allelic effect of locus j, and l. We let In order to account for the unbalanced relatedness between the CC-RIX mice and model the eight founder alleles and PoO effects, we extend the mixed additive random-effects model of ith subject at the jth locus equals the kth founder allele, and 0 otherwise. For those CC-RIX samples with a homozygous genotype at a given locus, such as AA or BB, we let jth locus.At a given locus, PoO effects can only be estimated from individuals with heterogeneous genotypes, since for those with homozygote genotypes, the parental contributions cannot be distinguished. Label the eight-founder alleles A, B, C, D, E, F, G, and H by numbers 1–8, and let the Model (1) can be expressed by the following matrix form:To enable selection of QTL and PoO effects, we reexpress the eight-dimensional vectors μ, p.In the above model (2), the observed data are s proper  for the μ as For indicator variables The specific priors above result in known marginal conditional distributions for all variables. The blockwise Gibbs sampling algorithm that we employ can be summarized as follows:First, we initiate Step 1. Updating μ: Step 2. Updating i.e., the eight-dimensional multivariate normal distribution with zero mean and covariance matrix Step 3. Updating i.e., the eight-dimensional multivariate normal distribution with zero mean and covariance matrix Step 4. Updating Step 5. Updating Step 6. Updating Step 7. Updating Step 8. Updating Step 9. Updating Step 10. Updating Step 11. Updating Step 12. Updating File S1 contains five supplemental figures. The MATLAB code used to analyze the simulated data is provided in File S2.Supplemental Material, L CC-RI lines randomly and forming them into a circle, and then mating each CC-RI line (clockwise) with the next three CC-RI lines after it  are generated, corresponding to three marker density cases. Among the total p simulated markers, we randomly select five markers, and let the first three markers have QTL effects the last three markers have PoO effects. That is, the first two genes only have QTL effects, and the last two only have PoO effects. However, the middle-selected marker has both QTL and PoO effects. The corresponding variances of QTL or PoO effects of the selected markers are all set to 1.In model (2), we set the overall mean A and a. Based on the haplotype frequencies ∗ to 3∗.To better assess the performance of our method in situations where multiple nearby SNPs jointly affect the outcome, for cases 1–3, we now let the number of causal SNPs in each of the first two QTL be 2 instead of 1. Specifically, we randomly select two nearby SNPs for each of the first two simulated QTL and denote the alleles of the two SNPs File S2).For each simulated data point, we generate a single long chain with 20,000 cycles, of which the first 10,000 cycles are discarded as burn-in, resulting in a total of 10,000 samples for post-MCMC analysis. All the analysis is done in MATLAB, and the MATLAB source code is submitted as supplemental material . For convenience, we subsequently call model (3) the “mixed model”.jth tested locus. Given the constraints that 0 to overcome the identifiability problem, and jointly test the effects of the jth locus as:H1j, some of the equations in p times, it is necessary to find an appropriate significance threshold to control the multiple testing, which can be obtained, for example, by modified permutation procedures (Here, random effects jth putative QTL with j ≤ p in Here, jth marker, we record the LOD scores, To compare the methods, ROC curves , where tFrom File S1, respectively, and show similar patterns.The CC , a panelIn this article, it is assumed that the RI lines are equally distanced from each other in terms of genetic distance which is a sensible assumption given the funnel design used for generating CC RI lines. However, we do observe that some RI lines share more or fewer founder alleles than expected. Our limited simulation shows that such genetic unbalance has a negligible effect on mapping genes, but this issue deserves further investigation. Alternatively, as genotypes of the parental RI lines are available, we could modify the design matrices p. Similar ideas have been proposed for genome-wide association study data (One of the drawbacks of Bayesian methods is their computational cost, especially for data with large numbers of samples and markers. Our model employs a block Gibbs sampler, which dramatically reduces computational time. However, further improvements may be possible. For example, instead of modeling each marker separately, we could jointly model multiple nearby markers and reduce the magnitude of www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300483/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.


Plants are commonly exposed to abiotic and biotic stresses.Arabidopsis thaliana accessions grown under controlled conditions. We employed genome‐wide association analysis to investigate the genetic architecture and underlying loci involved in genetic variation in resistance to: two specialist insect herbivores, Pieris rapae and Plutella xylostella; and combinations of stresses, i.e. drought followed by P. rapae and infection by the fungal pathogen Botrytis cinerea followed by infestation by P. rapae.We used 350 P. rapae was limited compared with B. cinerea plus P. rapae or P. rapae alone. Resistance to the two caterpillars is controlled by different genetic components. There is limited overlap in the quantitative trait loci (QTLs) underlying resistance to combined stresses by drought plus P. rapae or B. cinerea plus P. rapae and P. rapae alone. Finally, several candidate genes involved in the biosynthesis of aliphatic glucosinolates and proteinase inhibitors were identified to be involved in resistance to P. rapae and P. xylostella, respectively.We found that genetic variation in resistance to combined stresses by drought plus This study underlines the importance of investigating plant responses to combinations of stresses. The value of this approach for breeding plants for resistance to combinatorial stresses is discussed. For example, annual control costs of P. xylostella are estimated to be nearly US$ 4–5 billion  studies in this model plant . This population was developed from a global collection of 5810 accessions with the purpose to minimize redundancy and relatedness, a common problem in GWA studies  commercial potting soil , which was mixed 1 : 1 (v/v) with autoclaved sand in Expt 1 and with pasteurized  potting soil in Expt 2. Pots were accommodated in trays that were randomly distributed within a growth chamber. Plants were watered three times per week by adding water to the tray. Once per week, entomopathogenic nematodes were included  to prevent infestation by fungus gnats.Pieris rapae L.  was reared on Brussels sprouts plants (Brassica oleracea var. gemmifera cv Cyrus) in a growth chamber at 21 ± 1°C, 50–70% relative humidity and a diurnal cycle of 16 h : 8 h, light : dark.Plutella xylostella L.  was reared on Brussels sprouts plants (B. oleracea var. gemmifera cv Cyrus) in a growth chamber at 22 ± 1°C, 40–50% relative humidity and a diurnal cycle of 16 h : 8 h, light : dark.B. cinerea, strain B0510  and streptomycin (200 μg ml−1) for 2 wk at room temperature. Spores were collected and re‐suspended in half‐strength potato dextrose broth  to a final density of 1.0 × 105 spores ml−1. After a 3‐h incubation period, the spores were used for inoculation , CS76113 (Col‐0) and CS76129 (Fei‐0)); the last block contained only 17 accessions. Within temporal blocks, plants were allocated in trays and the position of the tray in the rearing chamber was recorded as its position in one of the six racks, each with four shelves. The spatial location of each plant within a tray was recorded in terms of column C and row R. In each temporal block, accessions were exposed to the following five treatments: (1) no stress; (2) drought stress; (3) P. rapae herbivory; (4) drought and P. rapae; or (5) B. cinerea and P. rapae. Six replicates were included per accession and treatment combination; 11 400 plants were phenotyped in Expt 1: six replicates × 40 accessions  × nine temporal blocks × five treatments plus six replicates × (17 + 3 accessions) × five treatments for the last block. Plants were grown under similar conditions during the first 3 wk. Drought stress was imposed by withholding water for 7 d during the third week, whilst the rest of the plants were watered every 2 d with 1 l of water per tray. Withholding water for 7 d clearly resulted in water stress: the plants showed retarded growth and were smaller than well‐watered plants. Botrytis cinerea inoculation was carried out 24 h before P. rapae inoculation. Plants were inoculated with B. cinerea by pipetting 5 μl of spores suspended in half‐strength PDB (Difco Laboratories) at a concentration of 1 × 105 spores ml−1 on two leaves of the rosette. Plants were kept at 100% relative humidity for 24 h in order to ensure successful infection by B. cinerea. Four‐week‐old plants were exposed to herbivory by P. rapae as a single or combined stress. Plants were inoculated with two newly hatched first‐instar (L1) caterpillars that were allowed to feed for 5 d. At the time of inoculation, individual plants were placed on the inverted lid of a Petri dish and the trays were filled with water to prevent caterpillars from moving between plants. Whilst the plants were on the Petri dishes, they received the same watering regime as described above; however, watering was carried out by adding 20 ml of water to each Petri dish. Rosette fresh weight (FW) was quantified for all treatments .The experimental design and treatments have been described in detail in Davila Olivas et al. . BrieflyArabidopsis after exposure to herbivory by P. xylostella. The experiment was performed in four temporal blocks. Within blocks, accessions were randomly distributed over 39 trays with nine accessions per tray plus one tray that contained eight accessions. In this experiment, accession Col‐0 was included to control for a positional effect within the chamber. Each tray contained both control and treatment for Col‐0 and for nine other accessions. Plants were randomized within the trays. In each block, all accessions were phenotyped; one replicate per accession was phenotyped at a time. We repeated this four times , leading to four replicates per accession. Within blocks, accessions were exposed simultaneously to either (1) no stress or (2) herbivory by P. xylostella; 2800 plants were phenotyped in Expt 2: four replicates  × 350 accessions × two treatments. Some of the accessions displayed germination problems and so we did not have sufficient replicates for the experiment; this reduced the dataset from 350 to 321 accessions. Plants were 4 wk old when they were inoculated with two L2 larvae. Larvae were allowed to feed for 5 d. At the time of inoculation, individual plants were placed on the inverted lid of a Petri dish and the trays were filled with water to prevent caterpillars from moving between plants. Whilst the plants were on the Petri dishes, they followed the same watering regime as described above; however, watering was carried out by adding 20 ml of water to each Petri dish. Rosette FW was quantified for all treatments .In Expt 2, we evaluated the growth reduction in et al., et al., eml package in R , TRT is the treatment factor, BLOCK represents the temporal block, RACK, SHELF, TRAY, C and R are factors that represent the spatial location of the plants within the chamber, and e is the residual error. GEN + TRT + GEN × TRT were fitted as a fixed effect, whereas all other variables were fitted as random effects (underlined).We obtained BLUEs (best linear unbiased estimators) for all genotype–treatment combinations as described in the literature .Using BLUEs, for each stress, we estimated the percentage difference of rosette FW relative to control plants without stress. In the treatment in which plants were exposed to both drought and herbivory by et al., We initially inspected the variation in response to each stress Fig. . We obsemisc in R  between two traits indicates that exactly the same loci control these traits, and a non‐perfect genetic correlation (rg < 1) reveals a mixture of unique and common genetic effects among traits. Genetic correlations were estimated according to the multi‐trait mixed model described in Korte et al.  estimates the proportion of phenotypic variance that is caused by genetic factors. Genetic variance can be a result of additive, dominant or epistatic effects. Narrow‐sense heritability (h2) captures the proportion of genetic variance that is caused by additive genetic effects. Narrow‐sense heritability is important because it is an indicator of how a population responds to artificial or natural selection  and e∼N(0σe2I) are random effects. We tested the hypothesis β = 0 using generalized least squares (GLS), conditional on residual maximum likelihood (REML) estimates σg2 and σe2 for the genetic and environmental variance. The proportion of the genetic variance explained by each SNP was estimated using two methods: (1) the RLR2 statistic proposed by Cox & Snell (n)(L1 − L0)); and (2) 2 × (β2p(1 − p)/σ2), where β is the allele effect, p is the frequency and σ2 is the sample variance. Fast‐LMM corrects for population structure using a GRM (genetic relatedness matrix) instead of a kinship matrix as in Emmax software  each SNP test is based on a local kinship matrix that consists of the GRM based on all markers, except those that are in a window of 20 kb on each side of the tested SNP; and (2) the genetic and residual variance components are estimated for each SNP, instead of assuming that these are constant across the genome. In order to reduce the amount of spurious associations caused by rare variants, a MAF of 5% was used.Variation in growth reduction under different stresses was linked to regions in the genome that explained the observed variation using a GWA analysis, carried out employing Fast‐LMM software, as described in Cao et al. . Fast‐LM & Snell , which i10(P) ≥ 4 was considered. The same threshold has been used in other GWA studies on A. thaliana  ≥ 4 were considered for further analysis as described by El‐Soda et al. (http://1001genomes.org/), as described in Bac‐Molenaar et al. (http://bar.utoronto.ca/) . The data are summarized in Tables S2–S6.For the selection of candidate genes, an arbitrary threshold of −loga et al. . A searcr et al. . Thus, ag et al. . RNA‐seqs et al. . RNA‐seqP. xylostella (CV = 78%), whereas the lowest variation was observed for the response to Drought&Pieris (CV = 31%) . A low phenotypic correlation was observed between the responses to P. xylostella and P. rapae (ρ = 0.15). Because phenotypic correlations may arise as a result of genetic and environmental factors, a better estimate of shared genetic basis between traits is genetic correlation. The largest genetic correlation was between the responses to Botrytis&Pieris and to P. rapae (rg = 0.98), followed by the responses to drought and to Botrytis&Pieris (rg = −0.81).To explore the relationships among different traits, we performed Spearman correlation analysis on the phenotypic values Table . The resP. rapae feeding and the two multiple stress situations, Drought&Pieris and Botrytis&Pieris, we performed a GWA analysis. We used a threshold of −log10(P) ≥ 4 to declare an SNP being associated with a trait. SNPs in LD were considered in a region of ± 20 kb from a significant SNP (Tables S2–S5). A summary of the GWA analysis for each trait is presented in Fig. P. rapae. For the responses to combined stresses, the numbers of significant SNPs were greater than in the responses to single stress situations: 38 (106 SNPs in LD) for the response to Drought&Pieris and 40 (106 SNPs in LD) for the response to Botrytis&Pieris. Effect sizes for the Col‐0 allele were estimated for each trait. For most of the traits, the significant SNPs displayed low effect sizes, except for the response to Botrytis&Pieris . The maximum percentage of genetic variance explained by an SNP for the response to each stress was 7% for drought, 7% for P. rapae, 5% for Drought&Pieris and 12% for Botrytis&Pieris. Despite the moderate to high genetic correlations among traits, little overlap was observed between the significant SNPs, between the regions delimited by SNPs in LD (QTLs), and therefore also between the genes contained within QTLs  than for the response to P. rapae (34 SNPs plus 78 SNPs in LD)  was larger than that for the response to P. rapae (from −7 to 7) . No common significant SNPs, regions delimited by SNPs in LD (QTLs) and therefore also genes contained within QTLs were observed between P. xylostella and P. rapae . In general, LEA proteins have been suggested to play a protective role for other proteins under conditions of water stress in vegetative tissues . This gene was induced on drought stress and ABA application (Table S2). This suggests that this may be the causal gene for QTL 7.Eighteen QTLs were identified for biomass reduction in response to drought. Within these regions, several genes that are known to play a role in drought acclimation were identified. For example, QTL 7 on chromosome 3 contained only one gene, Arabidopsis in response to two specialist insect herbivores, P. rapae and P. xylostella. GWA allowed the linking of this variation to several regions in the plant genome. We identified a total of 18 and 32 QTLs for the responses to P. rapae and P. xylostella, respectively  and AT5G0700 (MYB76) (Table S3). The closest significant SNP (Chr5: 2454480) explained 4% of the phenotypic variance. The Col‐0 allele was rare (55 accessions including Col‐0) and was associated with a greater reduction in A. thaliana FW . Both genes were induced in response to P. rapae infestation. Furthermore, MYB76 was induced by JA and ET treatment (Table S3).For P. rapae was QTL 1 on chromosome 1, which contained AT1G10060 (BCAT‐1) and AT1G10070 (BCAT‐2) (Table S3). The closest significant SNP (Chr1: 3294935) explained 5% of the phenotypic variance; the Col‐0 allele was rare (89 accessions including Col‐0) and was associated with a greater reduction in A. thaliana FW . Furthermore, both genes were induced by P. rapae infestation and application of the phytohormones JA and ABA (Table S3).Another interesting QTL for the response to P. xylostella, more QTLs were identified than for the response to P. rapae (Table AT4G11310 (CP1) and AT4G11320 (CP2). The closest significant SNP (Chr4: 3294935) explained 7% of the phenotypic variance; the Col‐0 allele was common (159 accessions including Col‐0) and was associated with a smaller reduction in A. thaliana FW . CP1 and CP2 were induced by both P. rapae and P. xylostella infestation. In addition, they were also induced by JA application (Table S6). Both CP1 and CP2 encode CYSTEINE PROTEASE enzymes (TAIR 10). CP2 has been implicated in increasing the resistance of cotton against Helicoverpa armigera . The closest significant SNP (Chr5: 25640504) explained 9% of the phenotypic variance; the Col‐0 allele was common (178 accessions including Col‐0) and was associated with a smaller reduction in A. thaliana FW . XYP1 was induced by P. rapae and P. xylostella infestation (Table S6), and encodes a proteinase inhibitor/seed storage/lipid transfer protein. This type of protein has been implicated in anti‐nutritional defences against insect herbivores . AT1G55740 (SIP1) and AT1G55760 within this QTL displayed interesting expression patterns. SIP1 was induced by P. rapae infestation, drought and ABA application. AT1G55760 was induced by drought and ABA, but was repressed by JA application .Nineteen and 25 QTLs were identified for the responses to the combined stresses Drought&ly Table . QTL 1 fPieris, QTL 10 on chromosome 4 and QTL 19 on chromosome 5 contained the bHLH transcription factors AT4G00480 (MYC1) and AT5G50915. The Col‐0 allele in both QTLs was rare and was associated with a smaller reduction in A. thaliana FW. Both genes were induced by P. rapae infestation and slightly induced by drought (Table S4). Natural variation in trichome density in A. thaliana has been associated with genetic variation in MYC1  are well established in the literature as major regulators of JA‐ and ABA‐mediated responses, insect resistance and drought responses  and P. xylostella (AT1G12540) .For the response to Drought&Botrytis&Pieris, no bHLH transcription factors were identified. However, QTL 3 on chromosome 1 contained AT1G19210, an ERF/AP2 transcription factor (Table S5). The significant SNP with the highest effect within this QTL (Chr1: 6627245) explained 6% of the phenotypic variance; the Col‐0 allele was common (232 accessions including Col‐0) and was associated with a smaller reduction in A. thaliana FW . AT1G19210 was induced on P. rapae infection, drought, JA, ABA and ET application (Table S5). Several homologues of AT1G19210  have been implicated in tolerance to drought and freezing and resistance to necrotrophic fungi . We identified variation in resistance to both insect herbivores among A. thaliana accessions that is genetically determined, as indicated by the moderate narrow‐sense heritability estimates for the responses to both species , valine  and isoleucine (Ile)) (BCAA) metabolism  have been implicated in the production of aliphatic glucosinolates , an abiotic stress (drought) and two combined stresses . We have provided evidence using transcriptomic data from independent studies which show that these genes are differentially expressed when plants are exposed to the same stresses as addressed in this study. The genes identified here remain to be validated by, for example, allelic complementation or mutant experiments in future studies. However, it should be realized that a mutant effect is estimated in just one genetic background and tests all genetic  and genotype‐by‐environment effects for that position/gene simultaneously. A QTL allele effect in an association analysis represents the conditional difference between two groups of genotypes with alternative versions of an SNP, and so the additive effect in a more general sense than the additive effect in the mutant, where these groups are part of an association panel. Moreover, the data probably deal with quantitative phenotypic variation and small QTL effects; knockout mutants do not provide the best means to confirm such QTLs, as mutants are especially suitable for genes and QTLs with a qualitative effect. The present study, whilst revealing the genetic architecture underlying resistance to several environmental stresses, also highlights the complexity of studying combinations of stresses.In this study, we identified several candidate genes for resistance to two specialist insects  should be directed to the Fig. S1 Time scheme of treatments.Fig. S2 Single nucleotide polymorphisms (SNPs) associated with reduction in Arabidopsis thaliana rosette fresh weight in response to drought.Fig. S3 Single nucleotide polymorphisms (SNPs) associated with reduction in Arabidopsis thaliana rosette fresh weight in response to Pieris rapae.Fig. S4 Single nucleotide polymorphisms (SNPs) associated with reduction in Arabidopsis thaliana rosette fresh weight in response to Plutella xylostella.Fig. S5 Single nucleotide polymorphisms (SNPs) associated with reduction in Arabidopsis thaliana rosette fresh weight in response to drought plus Pieris rapae.Fig. S6 Single nucleotide polymorphisms (SNPs) associated with reduction in Arabidopsis thaliana rosette fresh weight in response to Botrytis cinerea plus Pieris rapae.Click here for additional data file.Table S1 Percentage reduction in rosette fresh weight in Arabidopsis thaliana on stress exposureClick here for additional data file.Table S2 Candidate genes for reduction in Arabidopsis thaliana rosette fresh weight in response to droughtClick here for additional data file.Table S3 Candidate genes for reduction in A. thaliana rosette fresh weight in response to Pieris rapaeClick here for additional data file.Table S4 Candidate genes for reduction in Arabidopsis thaliana rosette fresh weight in response to drought plus Pieris rapaeClick here for additional data file.Table S5 Candidate genes for reduction in Arabidopsis thaliana rosette fresh weight in response to Botrytis cinerea plus Pieris rapaeClick here for additional data file.Table S6 Candidate genes for reduction in Arabidopsis thaliana rosette fresh weight in response to Plutella xylostellaClick here for additional data file.


The Generation Scotland: Scottish Family Health Study (GS:SFHS) is a family-based population cohort with DNA, biological samples, socio-demographic, psychological and clinical data from approximately 24,000 adult volunteers across Scotland. Although data collection was cross-sectional, GS:SFHS became a prospective cohort due to of the ability to link to routine Electronic Health Record (EHR) data. Over 20,000 participants were selected for genotyping using a large genome-wide array.GS:SFHS was analysed using genome-wide association studies (GWAS) to test the effects of a large spectrum of variants, imputed using the Haplotype Research Consortium (HRC) dataset, on medically relevant traits measured directly or obtained from EHRs. The HRC dataset is the largest available haplotype reference panel for imputation of variants in populations of European ancestry and allows investigation of variants with low minor allele frequencies within the entire GS:SFHS genotyped cohort.Genome-wide associations were run on 20,032 individuals using both genotyped and HRC imputed data. We present results for a range of well-studied quantitative traits obtained from clinic visits and for serum urate measures obtained from data linkage to EHRs collected by the Scottish National Health Service. Results replicated known associations and additionally reveal novel findings, mainly with rare variants, validating the use of the HRC imputation panel. For example, we identified two new associations with fasting glucose at variants near to Y_RNA and WDR4 and four new associations with heart rate at SNPs within CSMD1 and ASPH, upstream of HTR1F and between PROKR2 and GPCPD1. All were driven by rare variants . Proof of principle for use of EHRs was verification of the highly significant association of urate levels with the well-established urate transporter SLC2A9.GS:SFHS provides genetic data on over 20,000 participants alongside a range of phenotypes as well as linkage to National Health Service laboratory and clinical records. We have shown that the combination of deeper genotype imputation and extended phenotype availability make GS:SFHS an attractive resource to carry out association studies to gain insight into the genetic architecture of complex traits.The online version of this article (doi:10.1186/s13073-017-0414-4) contains supplementary material, which is available to authorized users. Generation Scotland is a multi-institution collaboration that has created an ethically sound, family-based and population-based resource for identifying the genetic basis of common complex diseases –3. The SGS:SFHS can also be utilised as a longitudinal cohort due to the ability to link to routine Scottish National Health Service (NHS) data. Electronic Health Record (EHR) linkage uses the ten-digit community health index (CHI) number, a unique identifying number allocated to every person in Scotland registered with a General Practitioner (GP), and used for all NHS procedures . This unique patient identifier allows healthcare records for individuals to be linked across time and location . The popThe Haplotype Reference Consortium (HRC) dataset is a large haplotype reference panel for imputation of genetic variants in populations of European ancestry, recently made available to the research community . Within SLC2A9 alone explaining ~3% of the phenotypic variance ).SLC2A9 were found for serum urate in this project and validate this EHR data resource as a valuable method of acquiring additional phenotypes for the GS:SFHS cohort. We did not detect significant effects from other known urate loci, such as ABCG2, but this is not entirely surprising given that our sample size is much smaller than most consortium meta-analyses. However, the majority show comparable effect size and direction  and to prescribing data, providing multiple opportunities to further exploit this approach.APOC3 gene has been validated in a pioneering GWAS UK10K study that first report its association with HDL cholesterol, plasma triglycerides and VLDL levels in the ALSPAC and TwinsUK cohorts )This dataset is now available for collaborative studies and meta-analyses that are consistent with the original ‘broad’ consent .


Formulae to predict the precision or accuracy of genomic estimated breeding values (GEBV) are important when modelling selection schemes. Simple versions of such formulae have been proposed in the past, based on a number of simplifying hypotheses, including absence of linkage disequilibrium and linkage between loci, a population made up of unrelated individuals, and that all genetic variability of the trait is explained by the genotyped loci. These formulae were based on approximations that were not always clear. The objective of this paper is to offer a unique framework to derive equations that predict the precision of GEBV from the size of the reference population and the heritability of and number of QTL controlling the quantitative trait.The exact formulation of the precision of GEBV involves the expectation of the inverse of a linear function of the genomic matrix, which cannot be calculated from simple algebra but can be approximated using a Taylor polynomial expansion. First order approximations performed better than the initial prediction equations published in the literature. Second order approximations produced almost perfect estimates of precision when compared to results obtained when simulating situations that agreed with the assumptions that were required to derive the precision equations. Using this proposed framework, we present several generalizations, including multi-trait genomic evaluation.Although further improvements are needed to account for the complexity of practical situations, the equations proposed here can be used to derive the precision of GEBV when comparing breeding schemes a priori.The online version of this article (10.1186/s12711-017-0366-6) contains supplementary material, which is available to authorized users. After the seminal work of Meuwissen et al. , who proMathematical models to describe and evaluate breeding plans can be useful to decide whether a breeding scheme based on genomic evaluations should be implemented or not e.g. . These mA very simple formula to obtain the precision of GEBV was given by Daetwyler et al. , based oIn the present paper, using the simple situation that was initially studied by Daetwyler et al. , we propM biallelic SNPs, with alleles Bk at locus k, the frequency of allele Bk being fk. All SNPs are assumed to be in linkage equilibrium. The reference population, which is considered to be a random subset of a larger population, is made up of N unrelated individuals, which are genotyped and phenotyped. We are interested in the precision of the GEBV of a selection candidate that is not related to individuals in the reference population, but belongs to a selection population that is another subset of the larger population. The GEBV is derived as a SNP best linear unbiased prediction (BLUP) based on SNP genotypes.A list of abbreviations is in Table The random elements of the prediction model βk effects are supposed to be uncorrelated.nik ∊ {0, 1, 2} is the number of Bk alleles carried by individual i at locus k. We assume that allele frequencies fk are known. The expectation of Xik is null, and its variance is All genetic variability is assumed to be explained by the SNPs.For a given set of genotypes g) and their prediction .This equation differs from formula  of DaetwEX[Pkk] simplify to δk = σk2(N + Mλ),  simplifies to:When the size of the reference population is limited, elements of matrix n of Eq.  partly cn of Eq. , matricementclass2pt{minimfmin, the minimum minor allele frequency of genotyped SNPs (MAF), and 1 − fmin (i.e. a probability density function This expression includes genetic variances fmin) was 0.025, 0.05, 0.075 or 0.10. One hundred replicates were simulated for each scenario, with allele frequencies generated for each. Computations were made in FORTRAN with the help of the Nag library [Equations , 2), ,  and 7)  7) were  library .M = 500, fmin = 0.05 and h02 = 0.3 are in Fig. fmin on the results. They showed consistently that (i) the prediction proposed by Daetwyler et al. [The results obtained when r et al.  is closer et al.  in most The framework that we propose is flexible to accommodate alternative situations without major problems, as illustrated in the following.C constant must be estimated from the constraint α and 1 − α are the bounds of the fk domain. Hayes et al. [α = 1/2Ne, with Ne being the effective size of the reference population, leading to C = 1/ log (2Ne). Alternatively, we could set α = fmin and In our a priori approach leading to Eq. , a unifos et al. , a U-shas et al.  argued tentclass1pt{minimaNe) by Me = M, we compared the two approaches by using simulated data, as explained in the previous section. Results in Table Goddard  also derσβ2 is linked to genetic variance by In previous developments, it was assumed that each SNP had its own distribution of effects with a variance of mentclass2pt{minimn et al. . Alternamentclass2pt{minimcument}π . The assN ≥ 5000.In this case, approximations of Eq.  for the mentclass2pt{minimnc traits are recorded in the reference population and this information is used to predict the global genetic value of the candidate: nc economic weights and nc genetic values, i.e. M SNP effects on trait j  term. Those approximations differ by the way the error term variance is defined when a single SNP effect is estimated. In Eq.  genetic origin, i.e. when  our Eq.  predicts our Eq. . With ththe Eqs.  and 7)  h02 = 1b of the genetic variance explained by the markers should be taken into account through a reduction in the heritability (from h2 to bh2) in the equations used and, using path coefficient theory, through a regression of precision of GEBV by b (from r2 to br2). This is easily implemented in the equations provided in this paper.The hypotheses that underlie the equations derived here are strong and efforts should be made to overcome these constraints. First, it was assumed that all genetic variability is explained by the SNPs included in the evaluation. Although this is increasingly true as the size of SNP chips grows towards a full knowledge of genomes by resequencing and imputation, other polymorphisms, including copy number variations (CNV), may play a role and the genotype information obtained is still far from sufficient to fully explain genetic variability. It has been suggested , 22 thatk and l. Equations can be derived for this situation, based on principles similar to the theory given here, but they are cumbersome, e.g. [A second central hypothesis was independence between SNPs. With the current sizes of SNP chips, which will be even larger in the future, close SNPs are in LD and cannot be considered to be independent. This dependence means that non diagonal terms of me, e.g. . The conme, e.g.  and formme, e.g.  as an alme, e.g. . Then, tme, e.g. . Solutiome, e.g. , 21, 37 me, e.g. ).A third assumption was the absence of relationships between individuals in the reference population and between candidates in the reference population. A formalisation of situations where individuals are related was proposed  but onlyThe predicted variances of SNP effects calculated by Eq.  in the mi.i.d. of QTL effects, and the use of GBLUP. As long as the objective is to model and optimise breeding plans, then only relative values will be of interest and we assume that these assumptions are not critical for those comparisons.A few other assumptions are used in the current paper, including the additivity and The objective of this paper was to provide a clear framework to derive predictive equations to estimate the precision of GEBV. Such equations can generate results in a second and thus enable the optimisation of a breeding program design through intensive numerical exploration. Not the entire complexity of practical breeding programs was included in the simple formulae derived here and in previously published papers. The purpose was to support the a priori comparison of breeding schemes, rather than to evaluate actual breeding schemes. The exact formulation of precision involves the expectation of the inverse of a linear function of the genomic relationship matrix, which cannot be calculated from simple algebra but can be approximated by a Taylor series development, as was already suggested by Goddard . Second Additional file 1. Diagonal elements of Additional file 2. Algebra when the SNP effect variance Additional file 3. The multivariate case. This file provides details about the algebraic derivation in the multivariate case.


RBFOX1 associated with reduced SBP. In a 17-member CFS family, carriers of the missense variant rs149974858 are normotensive despite being obese (average BMI = 60 kg/m2). Gene-based association test of rare variants using SKAT-O showed significant association with SBP  and DBP  in the CFS participants and the association was replicated in large independent replication studies . RBFOX1 is expressed in brain tissues, the atrial appendage and left ventricle in the heart, and in skeletal muscle tissues, organs/tissues which are potentially related to blood pressure. Our study showed that associations of rare variants could be efficiently detected using family information.Many large genome-wide association studies (GWAS) have identified common blood pressure (BP) variants. However, most of the identified BP variants do not overlap with the linkage evidence observed from family studies. We thus hypothesize that multiple rare variants contribute to the observed linkage evidence. We performed linkage analysis using 517 individuals in 130 European families from the Cleveland Family Study (CFS) who have been genotyped on the Illumina OmniExpress Exome array. The largest linkage peak was observed on chromosome 16p13 (MLOD = 2.81) for systolic blood pressure (SBP). Follow-up conditional linkage and association analyses in the linkage region identified multiple rare, coding variants in  RBFOX1 were associated with lower systolic  and diastolic  blood pressures, and explained significant amount of observed linkage evidence. We replicated the identified variants in four independent cohorts  and further observed consistent evidence that rare RBFOX1 variants are protectively associated with blood pressure traits. Our study clearly shows that family-based designs are powerful for identifying rare, coding variants underlying complex diseases.Hypertension is a risk factor for cardiovascular disease and the most important risk factor for stroke. Family studies suggest that hypertension related traits are heritable. Previous genome-wide association studies (GWAS) have identified multiple common blood pressure (BP) variants but these variants do not overlap with the linkage evidence observed from family studies. Rare variants have been suggested to play a substantial role and contribute to missing heritability of BP. In this study, linkage analysis identified 16p13 linked to SBP in a cohort of 517 white individuals in 130 families from the Cleveland Family Study (CFS). By combining linkage and association analyses, we searched for rare, coding variants that can explain the linkage evidence. Rare, coding variants within  High blood pressure (BP) is a common condition associated with multiple health outcomes, including heart, brain, and kidney diseases , 2. PrevFor complex traits such as BP, rare variants are suggested to play a greater role in heritability than anticipated in the common disease-common variant hypothesis . A FramiRecently, many statistical approaches for rare variant association analyses have been developed for unrelated samples –34 and f2 threshold of 0.1 or modeling marker-marker linkage disequilibrium resulted in a slight decrease of LOD score in the same region , which suggests that rs149974858 and rs145873257 account for a portion of the observed linkage evidence.The CFS was genotyped by an exome array, with most of the variants being coding variants. Within the linkage region, there are 454 exonic variants defined by the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium that are genotyped on the exome array . We iden2, noncarrier average = 31 kg/m2). A single SNP association test revealed that rs149974858 is also significantly associated with BMI in CFS . The exonic variant rs145873257 segregates within a different family with family-specific LOD score of 0.215. A c.1072G>A base change resulted in a glycine to serine substitution (p.Gly358Ser). Six members of this family are heterozygous for the AT genotype. The estimated effect of this variant is protective in CFS, although it is not statistically significant  in CFS. The minor allele frequency of rs149974858 is 0.0036 and only segregates within a 17-member family with family-specific LOD of 0.697 . This minificant .RBFOX1 genotyped on the exome array. Two exonic variants, rs151214012 and rs145873257, and one rare, intronic variant rs2345080 are available in the exome array. These three variants show protective effect despite not satisfying the filtering criteria  of whites for the traits SBP, DBP and PP. We specifically looked at the five rare variants of RBFOX1 found in CFS and their associations with BP traits. Within the ARIC data, 4 of the 5 variants were present and all the 4 variants showed a consistent protective effect. For WHI, all 5 variants were present but only 1 variant (rs145873257) had a protective effect size. BioUV contained 4 out of 5 variants found in CFS; 2 variants were protective for SBP and 3 variants were protective for DBP. HRS contained all 5 variants found in CFS; 4 variants were protective for SBP and 3 variants were protective for DBP. Among the total 23 tests  conducted across all cohorts, 16 of them were protective , suggesting a consistent protective effect.We next sought the replication of the rare variants in RBFOX1 and BP traits using exome array data from the CFS with weights Beta . Burden, SKAT, and SKAT-O tests were performed using the 5 rare variants of RBFOX1 found in CFS  and SKAT-O , but not by SKAT . All three gene-based tests for DBP were statistically significant . When we conducted gene-based analyses using only the 4 coding variants  identified in CFS, the associations are also significant for SBP and DBP .We next conducted a gene-based association analyses for d in CFS . In CFS,The same gene-based analysis for rare variants was done for all four replication cohorts separately and the results were meta-analyzed . Individ2, and PC1. We did not observe linkage evidence in this region (LOD = 0.721). The gene-based analysis of BMI using the same set of variants was only significant in CFS but not in any of the replication cohorts , and show a consistent protective effect for SBP in CFS. Gene-based analysis of the four available exonic variants and one rare intronic variant in RBFOX1 are significantly associated with SBP and DBP  using SKAT-O. Replication analysis of the rare variants at the gene level (but not at the variant level) is also significant for SBP and PP in the meta-analysis of four large cohorts of whites with a total replication sample size N = 57,234.We searched among published large GWAS studies of BP traits , 7–10 anRBFOX1 are protective for BP traits among obese individuals. Among individuals of European ancestry within the CFS, 5 individuals within the same family carried the minor allele for rs149974858, the variant showing significantly protective effect with SBP by single SNP association test. All of the 5 individuals are morbidly obese (with average BMI of 60). However, their SBP (mean = 117) and DBP (mean = 78) are within the normotensive range. Ma et al. conducted a GWAS of BMI in Pima Indians using Affymetrix 100K array and identified two common variants in RBFOX1 associated with BMI [RBFOX1 that are significantly associated with BMI but the association evidence could not be replicated , suggesting a consistent protective effect. Assuming a causal rare variant with an effect size equal to one quarter of the BP standard deviation, we estimate the probability of observing an opposite direction in a study to be 40.1%, which is consistent with 7 opposite directions among 18 single SNP replication tests in 4 replication cohorts.RBFOX1 are either monomorphic or extremely rare in African ancestry populations , will be necessary to identify the genes contributing blood pressure variation in this region.Under the linkage region of 16p13, there are 11 additional variants that either have an association test p-value less than 0.1 or an effect size larger than 5 mmHg in CFS. When we used the risk scores of these 11 variants as a covariate in linkage analysis, the MLOD dropped to 1.932, suggesting that there may be additional variants that contribute to linkage evidence in this region. However, the current exome array data is limited for further dissection of genes or variants contributing the linkage evidence. Whole genome sequencing data, including the sequencing data from the Trans-Omics for Precision Medicine (TOPMed) Program . Further biological studies are needed to establish the direct role of RBFOX1 in regulating blood pressure.Gene expression data and previous studies have demonstrated that RNA splicing factor function –48. RBFOe models . Gao et g hearts . We searRBFOX1 that have a protective effect on BP traits, which can be important in searching new drugs for cardiovascular disease. However, it should be pointed out that association analysis was performed using variants available in the exome array of this study. The variants identified in RBFOX1 may still be reflecting in LD with the causal variants to BP. While RBFOX1 is expressed in multiple tissues that may relate to blood pressure, the mechanism underlying how this gene contributes to BP variation needs to be further studied. The identification of these rare coding variants will facilitate precision medicine in treating cardiovascular disease.Our study suggests that family-based linkage evidence can be extremely successful in searching for rare variants contributing to complex traits. In summary, we identified rare, exonic variants in The CFS is a family-based longitudinal study comprised of patients with laboratory-diagnosed sleep apnea, their family members, and neighborhood control families, as described before . The dathttps://www2.cscc.unc.edu/aric/). There were 18,050 unrelated white participants from the Women’s Health Initiative (WHI), a study of postmenopausal women focused on strategies for preventing heart disease, breast and colorectal cancer, and osteoporotic fractures [The samples used for replication analysis include five independent cohorts. We included 10,864 unrelated white participants from the Atherosclerosis Risk in Communities (ARIC) Study. The ARIC study is a prospective epidemiologic study designed to investigate the natural history and etiology of atherosclerosis  was calculated as the difference between imputed SBP and DBP [2, body mass index  and the first principal component of genotype values in CFS separately. The residuals of these regressions were used for linkage analysis using the software MERLIN [2 threshold of 0.2. We also required a minor allele frequency (MAF) ≥ 0.2. This resulted in 56,992 autosomal SNPs using PLINK [2 threshold to 0.1 and by modeling the marker-marker LD using MERLIN [We calculated residuals of SBP, DBP, and PP after adjusting for gender, age, agee MERLIN . The prie MERLIN . Since tng PLINK . Becauseg MERLIN . Linkagey = β0 + β1g + δ + ε, where g is a genotype value vector, β0 is the intercept, β1 is the regression coefficient, Φ is the kinship coefficient matrix and  where H0: β1 = 0. For each of the variants, we first performed an association analysis with a BP trait using ASSOC and identified variants with either p-value ≤ 0.1  or absolute regression coefficient beta ≥ 5 (large effect). We next estimated family-specific LOD scores and identified families with LOD score ≥ 0.1. We kept the variants with association p-value ≤ 0.1 or absolute regression coefficient ≥ 5, and that were present at least twice in at least one family with family-specific LOD score ≥ 0.1.We examined exonic variants genotyped in the exome array in the region of 2-LOD score drop from the linkage peak. We performed the family-based association analysis for the exonic variants only in the 2-LOD drop region using the ASSOC package in S.A.G.E . The famβ is the regression coefficients of the SNPs, and xi is a vector of the number of risk alleles carried by individual i for these SNPs. Linkage analysis was further performed conditional on the risk scores.We defined the risk score as We performed family-based burden and SKAT tests for CFS using the software famSKAT and for the replication cohorts using the R package SKAT , 32, 62.S1 Fig(TIFF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig2 threshold of 0.2; MAF ≥ 0.2. Further pruning (purple): pairwise LD pruning with a window size of 50 kb, step size of 5 variants, and R2 threshold of 0.1; MAF ≥ 0.3. Cluster (red): modeling marker-marker LD using “—cluster” option with R2 threshold of 0.1; all other parameters are the same as the original linkage analysis.Original (blue): pairwise LD pruning with a window size of 50 kb, step size of 5 variants, and R(TIFF)Click here for additional data file.S1 Tablea Include both founders and nonfounders(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Tablea Include both founders and nonfounders(DOCX)Click here for additional data file.S4 Tablea Include both founders and nonfounders.b Calculated by the weighted average of Cleveland Family Study, Atherosclerosis Risk in Communities, Women’s Health Initiative, Vanderbilt University Biobank, and Health and Retirement Study.c Calculated by the weighted average of Atherosclerosis Risk in Communities, Family Blood Pressure Program, Africa America Diabetes Mellitus Study, and Howard University Family Study .d Calculated based on exome array of Nigeria data .(DOCX)Click here for additional data file.


With the availability of high-throughput sequencing data, identification of genetic causal variants accurately requires the efficient incorporation of function annotation data into the optimization routine. This motivates the need for development of novel methods for genome wide association studies with special focus on fine-mapping capabilities. A penalty function method that is simple to implement and capable of integrating functional annotation information into the estimation procedure, is proposed in this work. The idea is to use the prior distribution of the effect sizes explicitly as a penalty function. The estimates obtained are shown to be better correlated with the true effect sizes (in comparison with a few existing techniques). An increase in the positive and negative predictive value is demonstrated using Hapgen2 simulated data. Detection and estimation of the genetic causal variants associated with a particular phenotypic trait is one of the most challenging problems in modern day statistical genetics. Mathematical techniques are formulated with primary focus on fine-mapping studies, phenotype prediction, and heritability estimation  are used to localize additional causal variants (using raw genotype data) that might have been gone undetected due to their small effect size, lower posterior probability, or possibly due to small sample size.With the understanding that GWAS significant SNPs harbor more than one causal variant, few researchers have attempted to utilize multivariate methods to detect additional association signals   to effect sizes and use either maximum likelihood estimation method or Markov Chain Monte Carlo (MCMC) simulations to determine the posterior effect sizes (effect sizes conditioned on the measured phenotypic data). The various methods that fall under this category differ in the specification of the prior pdf . Elements of X are typically coded as 0, 1, or 2 . We aspire to select the causal variants and determine their effect sizes, X β − y)T (Xβ − y) is minimun. Due to the correlated and sparse nature of the SNPs, the univariate results often end up being erroneous estimates   + (1 − π1)p0(β)  denoting pdf of the causal SNPs and p0(β) denoting the pdf of the null SNPs, the posterior pdf of β is given asModeling β as a mixture pdf, β ~ πy〉, where 〈•〉 denotes the mathematical expectation operator. Irrespective of the distribution of β, the likelihood function, p(y|β) can be shown to be Normal with mean Xβ, and variance Σε  and p0(β)  ~ N is expressed asThe likelihood function, C that has the ability to capture the causal nature of SNPs:We construct a cost function, jth SNP is given aswhere the cost associated with the n × 1 vector of non-null prior probabilities associated with the functional annotation of the SNPs. That is, if the jth SNP is highly likely to be causal, then a higher value (say 0.5) is specified to that SNP. pj1(•) and pj0(•) denote the pdf of causal and null SNPs, respectively. The cost function, thus, acts as a medium to incorporate the enrichment details of individual SNPs. Typically, we use a normal pdf, 1 denotes the true unknown probability. Denoting by L the negative log-likelihood function, the function to be minimized is written asHere withF. A step-wise implementation of the optimization procedure is given in Supplementary Material. We show that when the NCG method is used for optimization, the evaluation of whole n × n Hessian matrix can be avoided. This significantly reduces the computational cost whilst not compromising the accuracy of the solution (Equation S12).The nonlinear conjugate gradient method (NCG)  is conceptually similar to the penalty function proposed by Ročková and George . The autL if it is known that the genetic architecture is highly polygenic, and low if only a few genetic causal variants influence the phenotype under consideration.The likelihood ratio and the cost function are weighed equally, so that the minimum error solution is sparse. Unequal weights can be specified, say higher for Variants of the proposed method could be obtained by changing the pdfs used in constructing the mixture model—for example, Laplace or non-local pdfs .Hapgen2 —analytical solution with 0 values.For whole genome analysis, due to computational issues, the analysis can be carried out using SNP windows such that no two potential causal SNPs in LD are separated  are used to measure the accuracy of different methods Figure . PPV is The MM-CP, Infinitesimal, LASSO, and Univariate methods do not use functional annotation information. Thus any improvement in the effect size estimates obtained using MM-CP method, in comparison with the other three, is deemed significant. Though a sparse structure is imposed on the penalty function in the MM-CP method, the method essentially does not incorporate any enrichment factors. A slight improvement in the effect size correlation can be observed in Figure The correlation between estimated and true effect sizes, PPV and NPV values have been obtained for a grid of 1 value for the SNPs in the MHC region . The MM-DAP results are obtained using this strategy, i.e. use prior information to improve the performance of the estimation procedure.From Figures y. This leads to the underestimation of the effect sizes due to the distribution of the true signal among several SNPs. The sparse representation, say The correlation plotted in Figure 1) is shown to equip the optimization routine with better fine-mapping capabilities, and result in estimates that are at least as good as the LASSO and Univariate methods.It is straightforward to note that the amount of shrinkage achieved depends on the characteristics of the penalty function used. Using the mixture model pdf explicitly as a penalty function places a probabilistic sparse constraint on the effect sizes, as opposed to the distance based constraints used typical in penalty function based method. Specifying a sparse structure without any knowledge about the underlying genetic architecture (i.e., specifying an arbitrary πThe non-linear conjugate gradient method is used to solve the optimization problem efficiently by harnessing the structure of the objective function's Hessian matrix . Thus, tVS: Wrote manuscript, performed analyses, contributed to study design, interpretation of results, and critical revision of manuscript; C-CF and DH: Contributed to data preparation, interpretation of results, and critical revision of manuscript; AD: Performed analyses, contributed to study design, interpretation of results, and critical revision of manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Each trait was tested with 313,340 SNVs. Tests of association were performed with simple linear regression and average type I error rates were determined for minor allele frequency classes. Rare SNVs  showed inflated type I error rates for non–normally distributed traits that increased as the minor allele frequency decreased. The inflation of average type I error rates increased as the significance threshold decreased. Normally distributed traits did not show inflated type I error rates with respect to the minor allele frequency for rare SNVs. There was no consistent effect of transformation on the uniformity of the distribution of the location of SNVs with a type I error.In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error rates were investigated in the Genetic Analysis Workshop (GAW) 19 whole exome sequence data. To test the distribution of the type I error rate, 5 simulated traits were considered: standard normal and gamma distributed traits; 2 transformed versions of the gamma trait (log Recent advances in sequencing technologies have made it more affordable to sequence whole exome data. In next-generation sequencing data, the proportion of rare variants  is substantially larger than the proportion of more common variants (MAF ≥ 0.05) typically used in genome-wide association studies (GWAS). However, these rare sequence variants present a challenge because there are often too few rare alleles for traditional statistical tests, making it more difficult to identify rare variants that are associated with the trait of interest. Also, the increased density of next-generation sequence variants makes it difficult for traditional methods to identify independent associations in a region of interest because of multicollinearity.−4. In this study, we used the Genetic Analysis Workshop (GAW) 19 whole exome sequence data [Although it is known from statistical theory that comparing error rates from non-normal distributions to normal distributions results in inflation of type I error , 2, the Furthermore, Papanicolaou et al.  noted anVCFtools  was used10 transformation and the rank-based inverse normal transformation (RIT). Trait Q1, provided by GAW 19, was also tested. A total of 200 replications for each of these 5 null traits were generated.To investigate the average type I error rate, 2 quantitative traits were simulated under the null hypothesis of no genetic effect: one from a standard normal distribution (with mean 0 and variance 1) and one from a gamma distribution, using the “rgamma” function in R with shape parameter 3 and scale parameter 20. In addition, 2 transformations were performed on the gamma-distributed trait to satisfy the normality assumption in regression analysis: the log-5 were considered as thresholds for defining a type I error.Tests of association between each SNV and each null trait were determined with simple linear regression as implemented in PLINK . Type I Two Chi-squared goodness of fit tests were used to determine whether the SNVs with any type I errors in the 200 replicates were uniformly distributed. The uniformity of type I errors was tested among groups defined as (a) chromosomes and (b) 10 Mb intervals.Table Figure -5 by MAF of the SNVs for all 5 traits. There was inflation of type I error rate for a given critical level based on the MAF of the SNVs and on the degree of departure from normality of the trait. For the non–normally distributed traits (gamma and log10-transformed gamma), there was a substantial inflation of type I error rate for rare and extremely rare SNVs (MAF < 0.05), but not for common SNVs (MAF ≥ 0.05); the type I error rates increased as the MAF decreased. The inflation of the type I error rate was greatest for the gamma trait and somewhat smaller for the more normally distributed log10-transformed gamma. The type I error rates for the normal and rank-based inverse normal transformed gamma traits were not inflated and had the nominal type I error rate (10-5). Inflation was greatest for the gamma trait for singleton SNVs, more than 150 times the nominal level. However, the amount of inflation depended on the critical value used. For example, with the critical level of 10-3 the type I error rate for the gamma trait for singleton SNVs was 8 times the nominal value (results not shown). It is important to note that there was no inflation of type I error rates for normally distributed traits at any critical level, regardless of the MAF. Trait Q1 was nearly normally distributed, and it did not show inflated type I error rate for rare or extremely rare SNVs. However, type I error rates for common SNVs were higher than expected.Figure p values for both tests for each of the 5 traits with type I errors defined using a 10-3 threshold. Overall, the results were neither consistent nor conclusive in terms of the transformation.Table In this study, the effects the MAF of the SNVs, the degree of departure from normality of the trait and the position of the SNVs on type I error rates were investigated on 5 simulated “null” traits, each with 200 replicates, and the genotypes from the GAW19 whole exome sequencing data in the unrelated samples.10-transformed gamma) increased over the nominal level with increasing departure from normality, with decreasing MAF of the SNVs and with decreasing critical level. However, observed type I error rates for normally distributed traits were close to the nominal level regardless of the MAF of the SNVs. Trait distributions with differing degrees of departure from normality made a substantial difference in the type I error rate for the test of association with simple linear regression with rare SNVs. The gamma-distributed trait showed the largest differences between observed and expected type I error rates. When the gamma trait was log10-transformed to be more normal, the difference became smaller. When a more extreme transformation (RIT) was used, the trait was effectively normally distributed and did not show inflated type I error. This indicates that transforming non–normally distributed traits helps to control type I error rate. No inflation of type I error rate was observed for common SNVs (MAF ≥ 0.05) for the 2 non–normally distributed traits considered (gamma and log10-transformed gamma).Observed type I error rates for rare and extremely rare SNVs (MAF < 0.05) for non–normally distributed traits . Trait Q1 was generated under a different null hypothesis from the other 4 traits that were simulated for this study.The results of the tests of uniformity of the distribution of the SNVs with type I errors showed no obvious positional effect with respect to trait transformation. It is relevant to note that Papanicolaou et al.  reportedIn summary, both rare and extremely rare SNVs produced more type I errors than the nominal rate for traits with departures from normality. This effect was ameliorated by transforming the trait to be more normal. Common variants seemed to be protected from this increase in type I error for most of the tested traits.


Geographic patterns of taurine and indicine mitochondrial and Y-chromosomal DNA diversity largely agree with the autosomal cline. The geographic distribution of the genomic admixture of different bovine species is proposed to be the combined effect of prehistoric immigrations, gene flow, major rivers acting as genetic barriers, local breeding objectives and environmental adaptation. Whole-genome scan for genetic differentiation and association analyses with both environmental and morphological covariables are remarkably consistent with previous studies and identify a number of genes implicated in adaptation, which include TNFRSF19, RFX4, SP4 and several coat color genes. We propose indigenous Chinese cattle as a unique and informative resource for gene-level studies of climate adaptation in mammals.Indigenous Chinese cattle combine taurine and indicine origins and occupy a broad range of different environments. By 50 K SNP genotyping we found a discontinuous distribution of taurine and indicine cattle ancestries with extremes of less than 10% indicine cattle in the north and more than 90% in the far south and southwest China. Model-based clustering and  It is commonly referred as yellow cattle and divided into 53 indigenous breeds raised in various agro-ecological environments3. Their diversity and unique species composition emerged from a complex history. Domestic cattle spread to East Asia by at least two routes. Taurine cattle migrated from north Eurasia to northern China and northeast Asia between 5000 and 4000 BP4. This is supported by the evidence that ancient cattle from northern China, dated 4500 to 2300 BP, carried only taurine mtDNA haplotypes5. A unique mtDNA haplotype, T4, observed in East Asian cattle breeds9 is a subtype of the common haplogroup T310, suggesting a founder effect in Chinese taurine cattle11.China harbors around 10 million of indigenous cattle12. Southeast Asian and southern Chinese cattle are morphologically and genetically recognized as zebu14. Yue et al. provided evidence for an additional southwestern immigration route of zebu from India into northwest China15.Indicine cattle (zebu) migrated eastward from their domestication center in the Indus valley and entered China from the south since 3000 BP3. This has been confirmed by genetic studies using mtDNA17 and Y-linked markers18. A genetic diversity study using microsatellite markers clustered Chinese indigenous cattle breeds into one taurine and four indicine groups19.The taurine and indicine cattle migrations resulted in a morphological gradient from humpless taurine cattle in the north to humped indicine cattle in southern and southwestern ChinaBos javanicus), gaur (Bos gaurus) or gayal , which may have been the dominant cattle species until 4500 BP12. Gayal in Yunnan province of China carried indicine or taurine mtDNA but gaur Y chromosome, indicating its hybrid origin20. Meanwhile, in Tibetan Autonomous Region (TAR) of China, bidirectional introgression between yak and cattle has also been reported24. Genetic admixture has been identified between zebu and Bali cattle (domestic banteng) in Indonesia25. A previous study on hair color and blood protein polymorphism provided evidence of banteng introgression into Hainan cattle in southeastern China26, which was confirmed by genomic SNP array data25.In addition to taurine and indicine cattle, several other bovine species have been living in southern China and Southeast Asia, including banteng  with the zebu ancestry. A similar trend has previously been observed in West-African cattle39. Bali cattle (0.026), gayal (0.059) and yak (0.029) also have relatively low levels of heterozygosity as normally observed with SNP panels designed for a different species40.Observed heterozygosity Table  ranged fFive methods were implemented in this part to explore the population structure.Figure In an analysis of only Asian cattle Fig. , the firFigure 41 and yak introgression into Tibetan cattle, but also suggests minor banteng and gayal into southeastern China  suggests gene flow from Bali to indicine populations in southeastern China and Southeast Asia. This gene flow has clearly been more consequential for the Indonesian cattle breeds, MAD, BRE and PES as well as the southern Chinese breeds LP, HN, WN and WL. For the Indonesian breeds this confirms previous results41. Although Bali cattle are relatively closely related to gaur, replacing Bali cattle by gaur as source of admixture generates only a moderately negative value for the Indonesian breeds  values for the southern Chinese breeds. In combination with the supervised model-based clustering  plot  is negative for all test breeds, but clearly more negative for indicine than for taurine breeds  is positive but f4 is not   and to Group 9B in the Felius classification43.Cattle from Manchuria, Inner Mongolia and northwestern China with 4 to 8% indicine admixture. This group corresponds to the taurine type 119 and belonging to the Huanghuai group of central Chinese cattle (Group 10A)43.Taurindicine cattle in TAR and northern China above the Yellow or Wei River with 28 to 35% indicine admixture, denoted as zebu type 219. The northernmost LX, JX and NY breeds belong to the Huanghai group (Group 10A)40, but the other breeds to the Changzu group (Group 10B)43.Taurindicine cattle below the Yellow River with 61 to 73% indicine genome, corresponding to indicine types 1 and 419, Group 10B43).Predominant indicine cattle in southern China with an indicine genomic component of 79 to 87% .Indicine cattle with a >90% indicine genome 9, northern China8 , which are found in the west of the Mekong River, are more closely related to Thai cattle than to southeastern Chinese cattle. This is also supported by the PCA and admixture patterns Figs  and 3. T43, and banteng, represented by its domestic relative Bali cattle.  and gaur  with model based clustering  and association with three synthetic environmental covariables and one synthetic morphological covariable  regions53. Notably, Porto-Neto et al.32 identified a 20 Mb region on BTA5 with effects on parasite resistance, yearling weight, body condition score, coat color and penile sheath score. We found a significant signature of selection for XtX and the environmental PC1 and PC3 as well as the morphological PC1 on BTA5 region 69.5–71.0 (Table RFX4. This gene is a member of Regulatory Factor X (RFX) family of transcriptional regulators that influence MHC class II expression54 and play a critical role in brain development55. It was also found to affect heifer fertility in tropical composition breed Brangus56.In other studies, strong signals of selection in tropical cattle have been detected on BTA5KITLG (near SNP BTA-74300-no-rs on BTA5)58, and LEF159 (here indicated by a peak in the XtX GWAS on BTA6). These genes and another candidate gene MCM6  overlap with pigmentation QTL regions underlying UV-protection60. The environmental PC1 signal near IGFBP7 and the combined XtX-morphological signal near ADRA1D  as novel candidate gene on BTA4. Interestingly, this signal is overlapped with a selection signature region in African cattle34. SP4 is a member of the Sp1-family of zinc finger transcription factors and is required for normal murine growth, viability, and male fertility63. In cattle, SP4 was suggested to have effect on body size and testicular growth from birth to yearling age64. In human, diseases associated with SP4 include bipolar disorder65 and schizophrenia66.We further detected an environmental PC1 association signal near 34. It may be anticipated that in both regions adaptation to agro-ecological constraints is mediated by recruiting and combining gene variants from taurine and indicine origins with possible original contributions in Chinese indigenous cattle from the indicine mtDNA, and the minor gayal, and banteng and yak genomic ancestry.It is interesting to note that Chinese and African cattle have developed independently a variable taurine-indicine ancestry following a gradient from tropical to temperate climates. An attractive opportunity is a detailed comparison of gene variants involved in climate adaptation by using whole genome sequence dataThe protocols for collection of the blood and hair samples of experimental individuals were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at China Agricultural University. All experiments were performed in accordance with approved relevant guidelines and regulations.67. Genotypes are accessible via the WIDDE repository (http://widde.toulouse.inra.fr/widde/).We collected samples of 437 animals from 24 populations Table , twenty 68, we removed SNPs with call rates <90% or with minor allele frequencies <0.001 and discarded individuals with 10% missing genotypes. The resulting data set contained 36,872 SNPs and 736 animals from 44 populations representing taurine cattle, zebu, and three species related to cattle . Gaur (Bos gaurus)69 was used instead of gayal in f4 analysis (see below) of the species composition of Chinese cattle because of the indicine zebu introgression into gayal.We compared these newly generated data with published genotypes of some European and Asian cattle breeds, Bali cattle and gaur Table . The com68 to calculate the observed homozygosity for each population. Three complementary methods were used to analyze the genetic diversity among populations. First, a Principal Component Analysis (PCA) was carried out to investigate the pattern of genetic differentiation among populations and individuals using the R package SNPRelate70, which performs eigen-decomposition of the genetic covariance matrix to compute the eigenvalues and eigenvectors. Second, population structure was evaluated by unsupervised and supervised model-based hierarchical clustering implemented in the Admixture software71. The results were visualized using the program Distruct72. Third, a NeighbourNet network was constructed using Reynold’s distances between populations using Splitstree 4.1373.We used PLINKf4-statistics) implemented in ADMIXTOOLS74. Additionally, taurine and indicine ancestries in Chinese cattle populations were quantified via the f4 ratio estimation in ADMIXTOOLS, which allows inference of the admixture proportions without access to accurate surrogates for the ancestral populations74. The proportion of taurine ancestry was then computed as74.To investigate species composition of Chinese cattle, we used the four-population test 76, which differentiates Y1 (dominant in north Europe) and Y2 (dominant in other taurine cattle) and Y3 (indicine cattle) type Y chromosome77.A 445-bp mtDNA control region was amplified and sequenced as described previously (GenBank accession codes KY682307-KY682687)es Table  with the57. The underlying models explicitly account for the covariance structure among the population allele frequencies, which make the approach particularly robust to complex demographic histories57. Identification of overly differentiated SNPs was based on the XtX statistics78 estimated under the core model of BayPass. To calibrate the XtX’s, a pseudo-observed data set (POD) containing 250,000 SNPs simulated under the inference model with hyperparameters equal to those estimated on the real data set was generated and further analyzed under the same conditions following the procedure described in Gautier57. In particular, we ensured that the posterior estimate of the scaled covariance matrix of population allele frequencies (Omega) obtained with the POD was similar to that obtained on the real data since the FMD distance between the two matrices was found equal to 0.2857. Similarly, the posterior means of the two hyperparameters a and b for the Beta distribution of across population allele frequencies obtained on the POD (a = b = 1.02) were almost equal to the ones obtained in the original data (a = b = 1.00). Taken together, these sanity checks indicated that the POD faithfully mimics the real data set, allowing us to define a 0.1% significance threshold on the XtX statistics (XtX = 32.3) to identify genomic regions harboring footprints of selection.Whole genome-scans for adaptive differentiation and association with population-specific co-variables were performed with BayPass 2.1http://data.cma.cn/). Values for 10 morphological covariables, i.e. male body weight, female body weight, male height, female height, male body length, female body length, male heart girth, female heart girth, male fore-shank circumference, and female fore–shank circumference were provided for 17 out of the 20 studied indigenous breeds in National Commission of Animal Genetics Resources3, but these data were not available for the populations LZ, BN and HH. We carried out a PCA on scaled variables for environmental and morphological co-variables separately. The first three environmental PCs and the first morphological PC were retained as uncorrelated co-variables for association studies  obtained on the real data set as described above. This model allows to account explicitly for multiple testing issue by integrating over (and estimating) the unknown proportion of SNPs actually associated with a given covariable. The support for association of each SNP with each co-variable was evaluated by computing Bayes Factor (BF) and a BF > 15 is considered as decisive evidence for association57. Briefly, the UMD3.1 bovine genome assembly79 on which all the SNPs were mapped was first split into 4718 consecutive 1-Mb windows (with a 500-kb overlap). For each window, we counted the number ns of SNPs that were either significantly differentiated at the 0.1% threshold  or associated (BF > 15) with at least one of the four population-specific covariables. Windows with ns > = 2 were deemed significant and overlapping windows were further merged.As a matter of expedience, we applied a sliding window approach to identify the main genomic regions of interest as described in previous studySupplementary Figures S1-S6 and Tables S1, S2, S5, S6.Supplementary Table S3Supplementary Table S4