Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction.We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries.NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR.Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.The online version contains supplementary material available at 10.1186/s12711-021-00661-y. Genomic prediction using single nucleotide polymorphism (SNP) markers has been widely used for livestock, but most current methods of genomic prediction use additive SNP models and only a limited number of studies have used haplotype models –10. In gLow-coverage sequencing (LCS) of a large number of individuals has proven to be more informative than sequencing fewer individuals at higher coverage because of the use of shared stretches of the genome across the population and haplotype diversity , 13. WitIn this study, we conducted an extensive evaluation of the accuracy of haplotype models for genomic prediction of phenotypic values for eight traits in Duroc pigs, using fixed chromosome distances and gene boundaries to define haplotype blocks of SNPs from LCS. For each method of haplotype block construction, one haplotype model, two SNP models, and three models that combine SNP and haplotype effects were evaluated for prediction accuracy. We also investigated the reasons for the successes and failures of the haplotype models using relative haplotype epistasis heritability and the comparison of SNP and haplotype heritability profiles .2), loin muscle depth at 100 kg live weight , and total teat number (TN). Trait measurements began when the weight of pigs reached 30 ± 5 kg (average age 80 ± 8.4 days). Trait statistics are summarized in Additional file Animal and phenotype data used for this study were provided by the Guangdong Wen’s Foodstuff Group . The swine population consisted of 3195 Duroc boars born from September 2011 to September 2016 on a single nucleus farm, with most of the pigs (3108 out of 3195) born from September 2011 to March 2014. The eight traits analyzed included age at 100 kg live weight , average daily gain during 30–100 kg live weight , back fat thickness at 100 kg live weight , body judging score , feed conversion ratio from 30 to 100 kg live weight (FCR), loin muscle area at 100 kg live weight  was used to collect the feed intake and weight of the pigs. When a pig entered the measuring station, it was identified by radio frequency identification ear tags and the time and duration of each feeder visit, the weight of feed consumed per visit, and the cumulative feed consumed for each pig over a 24-h period were recorded. Daily feed intake was computed as the total feed intake during a 24-h period and daily weight each pig was computed as the average weight of the pig for the same 24-h period. Feeding was stopped in the afternoon of the day when the weight of each pig reached 100 ± 5 kg (average age 160 ± 9.0 days), and the final weight was recorded 12 h after feeding was stopped. Average daily gain was calculated based on daily gain from 30 to 100 kg live weight. The FCR from 30 to 100 kg was calculated by dividing average daily feed intake by ADG. A type B ultrasound scanner  was used for measurements of muscle-related traits. Measurement positions were between the penultimate third and fourth ribs and five centimeters from the midline of the back on the left side, and the direction of the scanner head was perpendicular to the pig midline. The strong echogenic bands that appeared in the ultrasonic images and that represented the skin, connective tissue, and myolemma of the loin muscle were identified and provided a reliable basis for determining BF, LMA and LMD. TN was defined as the sum of the normal left and right teats counted within 48 h after birth. BJS was based on a ten-point scoring system and was recorded by skilled technicians for four body regions: head, foot and leg, forequarters, and hindquarters. A higher comprehensive BJS score is associated with a more desirable body shape and structure and is one of the company’s breeding aims in Duroc boars.The phenotypic values of BJS had a skewed distribution and the Box-Cox transformation implemented in the R package  only chaGenomic DNA was extracted from ear tissues of 3195 Duroc boars using a DNeasy Blood & Tissue Kit (Qiagen 69506) and quantified using a NanoDrop spectrophotometer. Then, all DNA preparations were diluted to the same concentration in 96-well plates using a Qubit 2 Fluorometer (Invitrogen) and checked on a 1% agarose gel. The Tn5 transposase  was used to construct the LCS libraries. The protocol and oligonucleotides for the Tn5 based library construction were as described previously , 15. Two−5. After quality control, a clean SNP data set with 9,769,161 autosomal SNPs was subjected to further density reduction because haplotype reconstruction using nearly 10 million SNPs for many models  would be computationally too costly. Among the 9,769,161 autosomal SNPs, one SNP was selected from each 20-SNP window such that SNPs selected from adjacent windows were approximately equally spaced. Thus, 488,124 SNPs across the 18 pig autosomes were identified with a high average call rate of 98.9 ± 0.6% ],],\documeThe theoretical accuracy for predicting phenotypic values was calculated as:Depending on the prediction model, The estimate of the haplotype epistasis heritability e models . Dependieviously , i.e.:17 in Eqs.  to 16)..\documenTo assess the impact of relative haplotype epistasis heritability on the increase in prediction accuracy, the Pearson’s correlation coefficient between estimates of relative haplotype epistasis heritability [Eqs.  and 22)22)] and Here, a heritability profile is a Manhattan plot of heritability estimates for SNPs or haplotype blocks using the SNPEVG2 program , where tEquations  and 25)25) can bThe heritability size for a SNP is related to the number of SNPs in the model, i.e., the larger the number of SNPs, the smaller the heritability estimate for each SNP , 32. ConWe found that, for the eight traits included in this study, prediction accuracy was improved by using haplotypes in the prediction model, for both prediction of the original Fig. a and theFor haplotype blocks defined by fixed chromosome distance, predictions of the original phenotypic values based on haplotype additive values (Model-4) had the highest accuracy for BJS, AGW and ADG, with increases in accuracy relative to the best SNP prediction of 7.4, 7.1 and 6.6% using haplotype block sizes of 100, 500, and 500 kb, respectively Table . The fulHaplotype blocks defined by gene boundaries had the best prediction accuracy of original phenotypic values for two muscle traits (LMA and LMD) and teat number (TN), with increases in prediction accuracy of 2.7% for LMA, 1.9% for LMD and 0.3% for TN relative to the prediction accuracy of the best SNP model , the observed prediction accuracy [p of Eq. ] was lowp of Eq. ] for prefrom Eq.  decreaseThe observed accuracies for predicting the original and corrected phenotypic values were two observed correlations: the correlation between the GBLUP of genotypic values and the original phenotypic values, and the correlation between the GBLUP of genotypic values and the corrected phenotypic values after removing the fixed year-season effects in each validation population. The results of these observed accuracies showed that the haplotype models had better prediction accuracies than the SNP models for all traits for predicting the original and corrected phenotypic values Fig.  and that, slightly increased the prediction accuracy for AGW (0.4%), AGD (0.4%), LMD (0.3%), and TN (1.0%), and had no effect on the prediction accuracy for BF (Table Estimates of the SNP additive heritability ranged from 0.08 (for BJS and FCR) to 0.33 (for LMA), while estimates of SNP dominance heritability ranged from 0.00 (for LMA) to 0.05 (for BJS) (Table The relationship between heritability estimates and prediction accuracy is the basis to understand the performance of different prediction models. We examined the relationship between estimates of relative haplotype epistasis heritability [Eqs.  and 22)22)] and The differences between heritability profiles across the genome based on SNPs and haplotypes reflect the differences between the SNP and haplotype models in the genetic contributions of genes and chromosome regions to phenotypic variation. Such differences in heritability profiles provide indications about the likely reason why a haplotype model does or does not improve prediction accuracy. Our results showed that the haplotype heritability profile needs to be different from the SNP heritability profile, at least for some regions, for the haplotype models to be more accurate than the SNP models. For traits where the haplotype-only models were most accurate, haplotype effects fully accounted for the SNP effects because adding SNPs to the prediction model decreased the prediction accuracy. In these cases, the chromosomal locations with high heritability estimates should be considered as more accurately identified than those with high SNP heritability estimates but are not shared by haplotypes. For traits where the integration of SNP and haplotype additive values increased the prediction accuracy over the haplotype-only models, haplotypes likely incorrectly estimated some SNP effects and the inclusion of SNPs in the prediction model compensated the weakness of haplotypes in those cases. One trait (TN) provided an example where SNP and haplotype heritability profiles were virtually identical, and the use of haplotypes virtually provided no help for improving prediction accuracy (increased prediction accuracy by only 0.25%).COL5A2 gene as having the highest heritability  had the best prediction accuracy for AGW, ADG, BJS and LMA, and the  had the eostasis . For FCRNUDT3 gene as having high SNP and haplotype heritability estimates , which was consistent with previous results that NUDT3 had significant effects for LMA and LMD [GRIK4 with large SNP heritability estimates did not have high haplotype heritability estimates, and for BF, PIGN-CCBE1 with large SNP heritability estimates did not have high haplotype heritability estimates. These differences in SNP and haplotype heritability profiles likely contributed to the increased prediction accuracy due to the integration of SNPs with haplotypes and this integration compensated the haplotype loss for those traits. The next example was the only known example showing virtually identical SNP and haplotype heritability profiles with no accuracy increase from the haplotype model over the SNP model.The heritability profiles of SNPs and haplotypes for the three muscle and fat traits, LMA, LMD, and BF, all identified the tes Fig. . For LMAity Fig. a but theity Fig. b. This rity Fig. . The inttes Fig. c and d, ate Fig. c, and TEate Fig. d. For BFtes Fig. e and f, ate Fig. e, but thUDT Fig. f. It is  and LMD  and for  and LMD  in Duroc and LMD , which wVRTN gene on chromosome 7 (Fig. VRTN gene and its surrounding regions had the most significant effects on TN [The SNP and haplotype heritability profiles for TN were unique because they were virtually identical, with high SNP and haplotype heritability estimates within and around the e 7 Fig. g and h. e 7 Fig. . TN was ts on TN , 35–38. ts on TN . TherefoAnalysis of haplotype genomic prediction models showed that haplotype prediction models had a higher prediction accuracy of phenotypic values than SNP models in Duroc pigs. Overall, the traits analyzed in this study had different SNP and haplotype heritability profiles and required different haplotype prediction models to achieve the best prediction accuracy. Haplotype-only models were the best prediction models for some traits, whereas the integration of SNP and haplotype effects in the prediction model provided the best prediction accuracy for other traits. Gene-based haplotype blocks resulted in the best prediction accuracy for some traits, providing evidence that gene-based haplotypes contained the most important genetic information for those traits although they only covered part of the autosomes.Additional file 1: Table S1. Trait statistics of the Duroc population. This table provides basic statistics for the eight traits analyzed in the Duroc population used in this study. Figure S1. Phenotypic distributions. AGW: age at 100 kg live weight. ADG: daily gain during 30–100 kg live weight. FCR: Feed conversion ratio during 30–100 kg. LMA: loin muscle area at 100 kg. LMD: loin muscle depth at 100 kg. BF: back fat thickness at 100 kg. TN: teat number. BJS: body judging score. “FCR without outliers” means phenotypic values that were more than four standard deviations from the mean were removed. Figure S2. Heat map of the SNP density distribution across the autosomes. This figure provides a global view of the SNP coverage of the swine autosomes in the Duroc population used in this study. Figure S3. Distribution of autosomal gene sizes. This figure shows the distribution of the gene sizes on swine autosomes.


P < 4.73 × 10–7) were detected for the six yield-related traits through GWAS. Of these, 31 associations were consistently detected in multiple environments, and 15 loci were commonly detected to be associated with multiple traits. Two major loci located on chromosomal pseudomolecules A06 and A02 showed pleiotropic effects on yield-related traits, explaining ∼20% phenotypic variations across environments. The two genomic regions were found 46 putative candidate genes based on gene annotation and expression profile. The diagnostic marker for the yield-related traits from non-synonymous SNP (Aradu-A06-107901527) was successfully validated, achieving a high correlation between nucleotide polymorphism and phenotypic variation. This study provided insights into the genetic basis of yield-related traits in peanut and verified one diagnostic marker to facilitate marker-assisted selection for developing high-yield peanut varieties.Peanut is an important legume crop worldwide. To uncover the genetic basis of yield features and assist breeding in the future, we conducted genome-wide association studies (GWAS) for six yield-related traits of the Chinese peanut mini-core collection. The seed (pod) size and weight of the population were investigated under four different environments, and these traits showed highly positive correlations in pairwise combinations. We sequenced the Chinese peanut mini-core collection using genotyping-by-sequencing approach and identified 105,814 high-quality single-nucleotide polymorphisms (SNPs). The population structure analysis showed essentially subspecies patterns in groups and obvious geographical distribution patterns in subgroups. A total of 79 significantly associated loci ( Arachis hypogaea L.) is an important oilseed crop and a key source of oil and protein. It is grown in more than 100 countries, with a world production of 45.95 m tons from an area of 28.5 m ha  were identified  size and weight have been extensively studied in some crops, leading to better understanding of the genetic basis. A large number of QTLs were identified, and dozens of important genes were cloned. In rice, the genes that controlled grain-related traits, such as r weight . In Brase length , and thre length . CompareArachis ipaensis and Arachis duranensis  method and investigated the phenotypic data for six yield-related traits in four environments. By exploiting the genomic variation from the population and performing GWAS for seed (pod) size and weight in multi-environments, the significantly associated loci were identified, and stable major associations were found. Two associated genomic regions were investigated for candidate gene discovery and making validated diagnostic markers available for breeding.A total of 250 accessions of the Chinese peanut mini-core collection were obtained from the China National Gene Bank, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, China. These materials were maintained by self-pollination for several generations. Detailed information of the accessions is listed in h2 = σ2g/(σ2g + σ2ge/n + σ2e/nr), where σ2g was the genetic variance, σ2ge was the interaction variance of the genotype with environment, σ2e was the variance of residual error, n was the number of environments, and r was the number of replications. The multi-environment joint variances were obtained using the general linear model (GLM) procedure of SAS  at the experimental fields of Nanchong, Sichuan, China, and Wuhan, Hubei, China. The four experimental environments were designated as E1 (Nanchong in 2015), E2 (Wuhan in 2015), E3 (Nanchong in 2016), and E4 (Wuhan in 2016), respectively. A random block design with three replications was adopted for generating phenotyping data across four environments. Twelve plants per accession were planted in a 2.5-m-long single row, maintaining a 30-cm distance between rows. The traits of PL, PW, SL, SW, HPW, and HSW were measured after drying the harvested pods according to the described standard procedures . The mine of SAS . Correlae of SAS .MseI , T4 DNA ligase (NEB), ATP, and the Y-adapter containing a barcode, followed by digestion at 37°C and heating at 65°C to inactivate the enzymes. Then, two additional enzymes, HaeIII and NlaI (NEB), were simultaneously added into the same tube to further digest the fragments at 37°C. The digested fragments with ligations were purified using the Agencourt AMPure XP System, followed by PCR amplifications with purified samples and Phusion Master Mix (NEB) after adding a universal primer and an index primer to each sample. The PCR reactions were purified using Agencourt AMPure XP (Beckman) and electrophoresed on 2% (w/v) agarose gel, followed by fragment isolation of 340–410 bp (including indexes and adaptors) using a Gel Extraction Kit . These fragments were then purified using the Agencourt AMPure XP System and were then diluted for sequencing on the Illumina HiSeq platform by Novogene-Beijing, China.The genomic DNA was extracted from a single plant of each accession using a modified cetyltrimethyl ammonium bromide method. For constructing GBS sequencing libraries, initially, 10 ug of genomic DNAs from each accession was incubated with To generate the genotype data for GWAS, the method was essentially according to Q), phylogenetic trees, principal component analysis (PCA), and relative kinship (K). The number of groups (k) was determined using Structure 2.2 ] ]  and deltP(D)] , MLM (PCA + K), and MLM (Q + K) were compared, and quantile (QQ) plots were used to evaluate the most suitable model for association analysis. The MLM (PCA + K) model exhibited the best performance to eliminate false positives and was selected for GWAS. We defined the whole-genome significance cutoff as the Bonferroni test threshold. The threshold was set as −log10(0.05/105814) = 6.33. Those with P < 4.73 × 10–7 were defined as significant trait-associated SNPs. The Manhattan and QQ plots were drawn using R (r2) was used to assess the LD using the Tassel 5 software . All the traits exhibited a large phenotypic variation . The traThe broad-sense heritability was 0.92, 0.94, 0.96, 0.91, 0.96, and 0.94 for PL, PW, SL, SW, HPW, and HSW, respectively . The higThe GBS library was constructed for each accession, and massively parallel sequencing was performed to generate ∼311 Gb data and 589,167,078 tags. Polymorphic reads defined by GATK software and a total of 3,070,141 SNPs were initially identified among the 250 peanut accessions. After filtering, a total of 105,814 SNPs were retained. The chromosomal pseudomolecule A03  had the largest number of SNPs, followed by A04 , while the smallest number of SNPs was detected on chromosomal pseudomolecule A08 , followed by A07  . Functior2 of SNP pairs. Decay of r2 value was analyzed across all chromosomes. Almost 25.7% r2 showed statistical significance (P < 0.05). The value where r2 dropped to half of its maximum was set as the background level. The extent of LD was found in the peanut panel at approximately 1.3 Mb, which is the point of 50% decay r2 value  value when k was increased from 1 to 2, suggesting two as the most likely number of inferred clusters. The first group, called G I, comprised 74 accessions, of which 64 accessions (86.49%) were subspecies of ssp. fastigiata. The second group, named G II, included 146 accessions, of which 95 accessions (65.07%) were subspecies of spp. hypogaea. In the mixed group, there were 15 accessions (50%) from ssp. fastigiata and 13 accessions (43.33%) from ssp. hypogaea  were identified for the six yield-related traits under four environments of E1, E2, E3, and E4, respectively  of PW in three environments and of HPW, HSW, PL, SL, and SW in two environments . The higAP2 transcription factor (Aradu.GG3LG) is the homologous gene of AINTEGUMENTA  which has been reported to regulate seed size through regulating the growth of the embryo organ and the proliferation of cell number in Arabidopsis (Aradu.K0L5G) was found to be specifically expressed in seeds and pods according to expression profile using RNA sequencing data from 22 tissues of A. hypogaea L. (CLAVATA–WUSCHEL (CLV-WUS) pathway that affected seed size by controlling the locule number  of peanut . A totalbidopsis . The Eamogaea L. . A set oogaea L. . A set oe number . Seven ge number . Two cane number . Two gene number . A single number .Aradu.K0L5G) at A06-107901527 (P < 0.001) seed (pod)-related trait value than those having TT genotype in our panel under different environments  size and weight, we investigated the correlation between nucleotide polymorphism in the candidate genes and phenotypic variation in the association panel. There were 15 non-synonymous SNPs in seven candidate genes in the genomic region underlying the A06-108577126 and 23 non-synonymous SNPs in eight candidate genes in the genomic region underlying the A02-86439145. The allelic effect of each genotypic class for the associated target trait was estimated in different environments. Unfortunately, we did not find non-synonymous SNP to correlate with phenotypic variation in the genomic region underlying the A02-86439145, but in the genomic region underlying the A06-108577126, we found the non-synonymous SNP  size and weight in peanut and would be useful for marker-assisted breeding of high-yield peanut varieties.In addition, two diverse extreme trait groups of 25 accessions with high trait values and 25 accessions with low trait values of seed (pod) size and weight were surveyed using SSR primers that amplified a fragment including the SNP. PCR product sequencing revealed a clear differentiation between the accessions with high and low values of seed (pod)-related traits, with the high value accessions having the GG genotype and the low value accessions having the TT genotype . The resA. duranensis (A genome) and A. ipaensis (B genome)  , are ava genome)  and will genome) . Finallyfastigiata and ssp. hypogaea could be clearly divided into GI and GII, respectively, but it was hard to clearly distinguish the botanical types because many accessions in this population harbored features of mixed morphological types. The phenomenon may be due to the genetic exchange between varieties from different botanical types. Further analysis found that G I contained two subgroups of ssp. fastigiata accessions mainly from southern China and countries other than China, while G II contained four subgroups of ssp. hypogaea accessions mainly from northern China, southern China, central China, and countries other than China, suggesting that obvious geographical distribution patterns existed in the subgroups. A similar phenomenon was observed in many other crops, such as rice, soybean, and sorghum  and qSL2 for SL in four environments (PVE ranged from 42.43 to 61.74%) in Huayu36 × 6-13 population  was identified. Its homologous gene (ANT) had been well studied in Arabidopsis, and it controlled seed size through regulating the growth of the embryonic organ and the proliferation of cell number. However, information on the functional role of this gene in peanut is limited, making seed (pod) development a good concern in future research. In the same genomic region, the gene Aradu.K0L5G was found to be specifically expressed in seeds and pods. It was worth noting that the sequence polymorphism of the non-synonymous SNP located at A06-107901527 in Aradu.K0L5G was highly correlated with the phenotypic variation of the six yield-related traits. The SNP changed the base from G to T and resulted in the amino acid from Ala to Ser in Aradu.K0L5G. The homologous gene of Aradu.K0L5G in Arabidopsis (At4g32140) was reported to be a cell cycle-regulated gene, showing a significant fluctuation and a peak expression in the S phase  size and weight was successfully validated in this study. Its sequence variation was correlated with phenotype variation and could be used to distinguish peanut accessions with corresponding high or low trait values. The allelic effect of the SNP was stable in multi-environments. This character allows breeders to avoid many difficulties in checking its stable performance in different places for higher field suitability. The diagnostic marker developed in this study could be applied to accelerate the marker-assisted breeding program of high-yield peanut varieties.One of the main advantages of sequencing-based trait mapping approaches is the development of markers for target traits . DiagnosIn conclusion, the results provided a large data set of genomic variation for diverse varieties and a comprehensive presentation of associated loci for six yield-related traits in peanut. Putative candidate genes underlying stable major associated loci were discovered, and the correlation was investigated between nucleotide polymorphism and phenotype variation. The diagnostic marker was successfully validated and could be deployed in GAB of high-yield peanut varieties.PRJNA687812 and PRJNA695811.The datasets presented in this study can be found in online repositories. The names of the repositories and accession numbers can be found below: XZ and HJ conceived and designed the study. XZ and JG analyzed the data. XZ, LH, HL, NL, WC, YL, and BL performed the field trials and phenotyping. XZ wrote the manuscript. MP and RV revised and improved the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


OXR1, RSPO2, and PKHD1L1 on chromosome 13 (CFA13). We identified the previously reported 167 bp insertion in RSPO2 3’ untranslated region as a causative mutation for hair length variations. The analysis of 118 dog breeds and wolves revealed the selection signature on CFA13 in long-haired breeds. Haplotype analysis showed the association of only a few specific haplotypes to the breeds carrying the 167 bp insertion. The genetic diversity in the neighboring region linked to the insertion was higher in Sapsarees than in other Asian and European dog breeds carrying the same variation, suggesting an older history of its insertion in the Sapsaree genome than in that of the other breeds analyzed in this study. Our results show that the RSPO2 3’ UTR insertion is responsible for not only the furnishing phenotype but also determining the hair length of the entire body depending on the genetic background, suggesting an epistatic interaction between FGF5 and RSPO2 influencing the hair-length phenotype in dogs.Genetic analysis of the hair-length of Sapsaree dogs, a Korean native dog breed, showed a dominant mode of inheritance for long hair. Genome-Wide Association Study (GWAS) analysis and subsequent Mendelian segregation analysis revealed an association between  R-spondin-2 (RSPO2), has been identified to maintain the anagen phase by forming a positive feedback loop of Wnt signaling by inhibiting the Znrf3/Rnf43 complex that antagonizes the Wnt signaling pathway in the hair matrix [fibroblast-growth-factor-5 (FGF5) induces the catagen phase by terminating the positive feedback loop by suppressing R-spondin expression in DP through the Fgf signaling pathway [Mammalian hair length is regulated by the activation and inhibition of the Wnt signaling pathway in the hair dermal papilla (DP) and hair matrix, which determine the hair growth cycle divided into anagen, catagen, and telogen ,2,3,4,5.r matrix ,7. Addit pathway ,9. Canis lupus familiaris) breeds is tremendously diverse and is an essential characteristic of each breed. Previous studies have identified mutations in three genes, namely FGF5, RSPO2, and keratin-71 (KRT71), to be associated with hair length, furnishing, and curl shape, respectively, in dogs [FGF5 and KRT71 were nucleotide substitutions, but the RSPO2 variant was caused by the 167 bp insertion of a 9-nucleotide (ATAATGAAC) repeat located in the 3’ UTR of RSPO2 transcripts. The hair phenotype of modern domestic dog  [Most domestic dog breeds have been developed in the last two hundred years as a result of direct selection designed to meet working or aesthetic requirements ,13,14,15er 2021) .RSPO2 gene. We also analyzed selection signatures for the hair length phenotype and analyzed the phylogenetic relationships of the identified haplotypes for the region in domestic dogs. We also investigated the mode of inheritance of FGF5 and RSPO2 for hair length phenotypes in domestic dogs.In this study, we showed that the hair phenotypes of Sapsarees were caused by the same mutation previously identified in the Blood samples and pedigree information of 61 long-haired and 53 short-haired Sapsaree dogs were provided by the Sapsaree Research Foundation in Gyeongsan, Korea. Genomic DNA was isolated from 1 mL of peripheral blood using the QIAamp DNA Blood Midi Kit  according to the manufacturer’s protocol. Animal welfare and experimental procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Konkuk University and conducted according to the accepted protocol.p < 0.000001) were removed. Single nucleotide polymorphism (SNP) typing was performed using an Illumina 170 K CanineHD BeadChip  according to the manufacturer’s protocols. Genotype calls were generated using an Illumina GenomeStudio Genotyping Module (Illumina). Pairs of individuals with genetic similarities above 99% were regarded as twins by the identity-by-state test implemented using PLINK v1.07 [b vector containing the fixed effects associated with the traits, while gender was fitted as a fixed effect and age as a covariate; X is the design matrix containing the fixed effects related to the trait, p is the vector of the effect of SNP effect, u represents the vector of the random effects related to the animal effects for the trait, Z is the design matrix that corresponds to the animal effects of the trait, and e is a vector containing the residual. The analysis was conducted using the SAS software version 9.1 . SNPs linked genomic regions were estimated using PLINK 1.9  > 0.7 were used for linkage analysis.The hair length phenotype (long versus short) data were modeled using a generalized linear mixed model. The logistic regressioner 2021; ). The fihttps://www.ncbi.nlm.nih.gov/bioproject/; accessed on 26 September 2021). In addition, the genome sequencing data of 151 dogs, including five breeds of Northeast Asian breeds (PRJNA782070), were retrieved from the NCBI Short Read Archive database, and variant calling was conducted  data of 722 dogs was retrieved from NCBI  [TM Terminator Cycle Sequencing Kit  following the manufacturer’s protocol. The reactions were analyzed using an ABI 3730 Genetic Analyzer . The sequencing chromatograms were analyzed using CLC main workbench 7.8.1 software .Genotyping PCR was conducted using 15 ng of DNA to determine the presence and absence of the 9-nucleotide repeat insertion in the 3’ UTR of ATGC-3′) . PCR proATGC-3′) . SequencTajima’s D was calculated using the Tajima’s D option in vcftools 0.1.17 , with a RSPO2 3’UTR insertion homozygotes) and control (RSPO2 3’UTR wildtype homozygotes) for each individual. The genotype information of RSPO2 has been reported previously [http://www.fci.be/en/; accessed on 26 September 2021).Haplotype blocks were determined using PLINK v1.9 by assigning the case  clustering and neighbor-joining trees were constructed using VCF2poptree  with a pR script  using a R script : (2)FST STR estimation, dog breeds with data of more than four individuals were selected. For Fhttps://github.com/BGI-shenzhen/VCF2Dis; accessed on 26 September 2021), and the result was displayed in the MDS plot using the cmdscale function of R .Multidimensional scaling (MDS) was performed using the informative SNPs in the CFA13:8,530,000-8,730,000 bp region from 363 dogs. A pairwise genetic distance matrix was calculated according to the ratio of the nucleotide difference (p-distance) between individuals using VCF2Dis  with the hair length variation of Sapsarees (Canis familiaris chromosomes (CFA) with the greatest number of SNPs (n = 8) on CFA 13. However, none of the significant SNPs were found in the coding region.Genome-wide SNP typing of 114 Sapsarees using the 170 K canine chip was carried out for a GWAS analysis to identify hair length phenotype-associated regions in the Sapsaree genome. Low-quality SNPs were filtered out, and logistic regression analysis was performed using GLMM. We found that 18 out of 79,229 informative SNPs showed significant associations  to further define the regions of candidate genes (r2 > 0.7) using PLINK against the VCF of 151 dogs of 20 selected breeds according to geographical origins and hair phenotypes  in the 3′ untranslated region (UTR) caused the furnishing phenotype of dogs [RSPO2-repeat in long-haired Sapsarees by sequence analysis of the RSPO2 3′ UTR-specific amplicons (RSPO2-repeat (nine repeats of ATAATGAAC) in only long-haired Sapsarees. The annotation of the eight SNPs on CFA13 showed that one SNP was located in the non-coding RNA (ncRNA) LOC111098552), two SNPs in the intronic region of c region . Howeverc region . We thenthe SNPs . Interes1098552, mplicons  and founRSPO2-repeat was reported as the causative mutation for the furnishing phenotype in dogs, we investigated if the RSPO2-repeat was responsible for the phenotype of long-haired Sapsarees using RSPO2-repeat typing against the Sapsaree pedigree. The genotyping PCR amplification produced 817 or 676 bp amplicons depending on the presence or absence of the RSPO2-repeat, respectively. We confirmed the complete correlation between the long-hair phenotype and the presence of 817 bp products  among long-haired dog breeds, we analyzed the allele frequency and distribution pattern of 1,213 SNPs identified from a 1 Mb region on CFA 13—500 kb upstream and downstream each of RSPO2 3′ UTR—using two different datasets, that is, 18 breeds of 56 RSPO2-repeat homozygous individuals and 118 breeds of 369 dogs regardless of RSPO2 genotype  from 92 individuals of 24 breeds selected based on their geological origins  based on the number of nucleotide differences (n = 22.67) was much larger than the average genetic distance between the other breeds in the cluster (n = 13.93), suggesting a possibly longer history of the long-hair phenotype of Sapsarees (Canis lupus chanco) (HIW) constituted a separate cluster. Phylogenetic relationships of the RSPO2 region from short-haired dog breeds were more diverse and distantly related than those of the long-haired breeds. The MDS plot using the pairwise distance matrix generated from the CFA 13: 8,530,000−8,730,000 bp region, identical to the phylogenetic analysis from 363 dogs , which showed a selection signature. A total of 224 individuals−55 belonging to 19 breeds homozygous for RSPO2-repeat and 169 belonging to 35 breeds homozygous for RSPO2-wildtype allele−were used to define the haplotype blocks associated with the RSPO2-repeat allele , genotype-undefined individuals (n = 126), and homozygotes (n = 224), was conducted using the 57 SNPs, and the 12 major haplotypes (HapA to L) were defined (n = 60). Hap J was the most distant from the other haplotypes. HapD was associated with the RSPO2-repeat insertion. The distribution of haplotypes in block 20 did not have any geological associations.The haplotype block associated with the t allele . Among tt allele . For hapt allele  that cou defined . HapA waRSPO2-repeat allele from the wildtype allele was not achieved by haplotype analysis using 57 SNPs, we conducted an additional haplotype phasing using all SNPs (n = 178) present in the haplotype block 20, which increased the number of haplotypes to 93  shared two common SNPs at positions 8,610,423 (T) and 8,610,424 (A), in contrast to G and C, respectively, of the remaining haplotypes corresponding to short-haired breeds. Consequently, the occurrence of 93 haplotypes divided the animals into 278 RSPO2-wildtype homozygotes, 77 RSPO2-repeat homozygotes, and 8 RSPO2 heterozygotes  in 57 RSPO2-repeat homozygotes belonging to 13 breeds with at least three animals per breed  and highest with German Shepherd Dog (GSD). Northeast Asian dog breeds with relatively low FSTR with Sapsarees were Jindo, DongGyeongi, and Shiba Inu (We compared nucleotide diversity (π) of the genomic region linked to er breed . The meaer breed , which iividuals . The paihiba Inu , consistRSPO2 across diverse breeds (n = 172) of domestic dogs . Probing several other chromosomal regions showing significance also did not lead to the identification of any hair growth-related genes. However, the presence of multiple markers on CFA13 showing strong associations with the long-hair phenotype was critical for the identification of the causal variants underlying hair length variation in Sapsarees.Our initial GWAS analysis mapped the region of association to the long-hair phenotype of Sapsarees on CFA 13, but with less precision. This may be due to the dominance of the long-hair allele and the small sample size , and their hair length variations have been explained by the FGF5 and RSPO2 variations [RSPO2-repeat allele among the RSPO2-associated long-haired dog breeds, including Sapsarees, suggests a common origin of the allele or an ancestral effect in long-haired dog breeds. This suggests that the genetic origin of hair length phenotypes could be different from the genetic background estimated from whole genomes [Sapsarees, a native dog breed, have existed in the Korean peninsula for at least a few hundred years and carry an identical allele responsible for the hair phenotype of the European dog breeds  despite riations ,39. The  genomes ,42,43,44RSPO2-repeat and wildtype alleles in a single breed has been reported on several occasions, including in the Portuguese Water Dog, Brussels Griffon, Dachshund, and Sapsarees [FGF5 have been reported as causal mutations for the long-hair phenotype in diverse dog breeds with the FGF5:p.Cys95Phe mutation as the most common among the breeds [RSPO2 variant has been found in all the breeds analyzed to date, however, the repeat number of the nine-nucleotide repeat in the RSPO2 3′ UTR insertion allele differs slightly in some breeds [RSPO2 could be harmful, considering its role in Wnt signaling, which is critical for animal development [The presence of both apsarees ,35. The e breeds . In contelopment ,46,47. RSPO2 3′ UTR, we blasted 150 bp each of the upstream and downstream sequence of the 3′ insertion junction of RSPO2 as a query against the reference genome of dogs using NCBI nucleotide BLAST . Although the results showed several matches on chromosomes other than CFA 13, we were unable to draw any conclusion on the mechanism of simple repeat insertion.To understand the possible mechanisms involved in the insertion of the nine-nucleotide repeat in RSPO2-associated region showing a high FST [RSPO2-repeat allele in Sapsarees. The higher genetic diversity in the 200 kb genomic region linked to the RSPO2-repeat allele in Sapsarees than the other dog breeds suggests that the age of the RSPO2 3′ UTR insertion in Sapsarees may be older or less affected by intensive selection than in the other breeds analyzed in this study despite the bottleneck effect of breed rebuilding. However, the haplotype diversity in the neighboring region of the RSPO2-wildtype allele in 119 dog breeds showed much more diversity (Hap6–93) than that in the neighboring region of the RSPO2-repeat allele (Hap1–5) (STR) against the sequences of the 200 kb region of Sapsarees was the lowest (0.0594) with Jack Russel Terrier, a European breed, followed by other Korean native breeds, namely Jindo and DongGyeongi  . UnexpecgGyeongi . This maRSPO2-repeat allele has been described as a causal mutation for the furnishing phenotype. However, the combined genotype of FGF5 and RSPO2, in which the presence and absence of mutant alleles were described as “+” and “-”, respectively, showed that the hair length of Sapsarees was also significantly affected by the RSPO2-repeat allele  showed allelic combinations of the two genes identical to those of Shih Tzu and Tibetan Terrier. This pattern also appeared in European breeds such as the Old English Sheepdog, Maltese, and Portuguese Water Dog [FGF5 (−) and RSPO2 (+) genotype showed the long-hair phenotype. The hair length of the short-haired Sapsarees  was significantly shorter than that of other breeds with the same genotype, such as Pekingese and Tibetan Mastiffs. Such phenotypic variation could be due to the interaction of RSPO2 with other hair cycle regulatory genes or the regulatory sequence variation of RSPO2. These results suggest that hair length in dogs is regulated by the epistatic interaction between FGF5 and RSPO2. In previous studies, the t allele . Among Nater Dog . In contater Dog . HoweverRSPO2 as the causative mutation determining the short hair length phenotype of Sapsarees and provided evidence for the role of the epistatic interaction between RSPO2 and FGF5 in hair length phenotype in domestic dogs. We also showed the presence of the RSPO2 variant-associated selection sweep and haplotypic diversity of the region. The presence of a single RSPO2 variant in all dog breeds strongly suggests a common origin of the mutation. The significantly higher genetic diversity within the RSPO2-repeat allele-linked haplotype block in Sapsarees than in the other long-haired dog breeds may suggest that the integration of the RSPO2 variant in the genome of Sapsarees happened earlier than in that of other long-hair dog breeds carrying the variant. However, the difference in breeding history, including the intensity of inbreeding, should also be considered. Previous studies have suggested the RSPO2-repeat allele to be responsible for the long-hair phenotype in various dog breeds. However, this study further explored the phylogenetics of the RSPO2 mutation in domestic dogs and its genetic interactions by also analyzing the genetic data of previously undescribed breeds.In this study, we identified


Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium  and generalized our findings in 9 additional studies . We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability  in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification. Large-scale genetic analyses of diverse populations hold great potential for advancing the field. In our sample of Hispanic/Latino adults, and when combined with publicly available results from other ancestries, we identified, described, and characterized 42 novel genomic findings. We additionally illustrated that novel biologic insights can be garnered from studying ancestrally diverse populations. Overall, our findings indicate the added value of building large, more diverse genome-wide association studies in the future. In the 1000 Genomes Project samples, rs200342067 is only present in two Latin American samples , and yet the authors reported that this missense variant shows subtle evidence of positive selection in the Peruvian population.Ancestry may also play a role in anthropometric-related health disparities in Hispanic/Latino populations. Previous studies have described the historical contexts leading to admixture in Latin American populationsIn the US, as in other high-income nations, both the population size and diversity in national origins (backgrounds) of Hispanic/Latinos have been increasing over the past several decades,23,Thus, there is a need to study Hispanic/Latino populations to fully address these disparities.The Hispanic/Latino Anthropometry (HISLA) Consortium is comprised of 27 studies/consortia of adult participants. First, HISLA stage 1 includes 17 studies and one consortium . Adult height was measured or self-reported using either metric or US units and then converted to meters. Waist-to-hip ratio (WHR) is used to capture central fat deposition and is derived from the circumference of the waist at the umbilicus compared with the circumference of the hip at the maximum protrusion of the gluteal muscles.BMI is a commonly derived index of obesity risk and is calculated as the ratio of body weight to height squared . For WHR, we also adjusted for BMI when creating the residuals to isolate the central deposition of fat from overall body mass. Residuals were then used to create inverse normalizations of BMI and WHRadjBMI, and Z scores of height . In family-based studies, the residuals were calculated in women and men together, adjusting for age, sex, and other study covariates including PCs. Descriptive statistics on the covariates and anthropometric measures are provided for each study’s analytic sample in Residuals were calculated by sex and/or case status, adjusting for age, ageZ scores were calculated in children/adolescents aged 5–18 years from the US and Chile  genotypes and one study that blended genotypes from multiple platforms  and S6. 2 < 0.3), minor allele count (MAC) < 5, or sample size < 100 were removed. We meta-analyzed effects across all studies using a fixed-effect inverse variance weighted meta-analysis with genomic control in METAL.The studies of the HISLA Consortium were meta-analyzed in two stages: discovery (stage 1) and replication (stage 2). Stage 1 included a total sample of 59,771 individuals with data on BMI, 56,161 with height, and 42,455 with WHRadjBMI. All stage 1 studies/consortia provided full genome-wide analysis results. All SNPs that met our significance criteria were brought forward for replication in stage 2, which included 10,538 individuals with data on BMI, 8,110 with height, and 4,393 with WHRadjBMI. All reported association results passed our quality control criteria; i.e., variants with low quality (info score < 0.4 or R−8) that were independent of each other. In cases where stage 2 studies did not have the lead variant, we selected two proxies per lead variant with a linkage disequilibrium (LD) r2 ≥ 0.9 using 1000 Genomes AMR. Stage 2 studies provided a list of the requested lead variants and/or their proxies from stage 1 for replication. Stage 2 studies were meta-analyzed and subsequently combined with stage 1 using METAL.2 across all 27 HISLA adult studies/consortia by entering each study separately into the meta-analysis, irrespective of stage. The characteristics of the final SNP array data used in the HISLA adult studies and the children/adolescent Hispanic/Latino studies are summarized separately in Regional plots of all GWAS significant HISLA stage 1 findings were plotted using LocusZoom. From stage 1, we selected lead variants for replication that met genome-wide significance  software. For HISLA analyses, we used our stage 1 discovery results with the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) as the LD reference dataset. For the approximate conditional trans-ancestral analyses, we used our trans-ancestral results from HISLA stage 1, AAAGC, or GIANT and a trans-ancestral LD reference dataset of Europeans and African Americans from the Atherosclerosis Risk in Communities (ARIC) cohort, and Hispanic/Latinos from the HCHS/SOL cohort, as a representation of the ancestry distribution of our meta-analysis. In both conditional analyses , we first identified all independent SNPs using the --cojo-slct command. Then, we conditioned each of these independent SNPs on all known SNPs from GWAS  or reported as part of targeted-array analyses published through December 2019  refers to the observation of the same signal in an independent sample but with a distinct ancestral background, or distinct period of the life course. Furthermore, SNP associations were then defined as either newly discovered or established, depending on their location. An established locus was defined as an SNP association within ±500 kb of at least one previously identified index SNP, otherwise the association was considered a newly discovered locus.−8 in HISLA stage 1 and directionally consistent in the stage 2 independent sample, and (2) the addition of stage 2 samples improved the estimated p value of the stage 1 + 2 meta-analysis. For the trans-ancestral analyses, the designation of a signal as novel was based on SNPs that were: (1) associated at p < 5 × 10−8 in the combined HISLA, AAAGC, and GIANT meta-analysis, and (2) directionally consistent with the trans-ancestral meta-analysis and associated at p < 5 × 10−2 in an independent sample of Hispanic/Latino children/adolescents  or in the British subsample GWAS from the UKBB (replication).We designated our Hispanic/Latino SNP associations within either newly discovered or established loci as “novel” if they met the following criteria for replication: (1) were associated at p < 5 × 10−2, and (3) had a heterogeneity of I2 < 75% in the Hispanic/Latino children/adolescent lookups, the adult AAAGC, or the adult GIANT GWAS lookups. SNP effects of variants previously associated with anthropometric traits in non-Hispanic/Latino populations  were considered to be transferable  to Hispanic/Latinos only if they were: (1) directionally consistent, (2) displayed a p < 5 × 10−2, and (3) had little to moderate effect heterogeneity (I2 < 75%) in stage 1.Hispanic/Latino SNP effects were considered to transfer  to Hispanic/Latino children/adolescents or to African or European ancestry adults if they were: (1) directionally consistent, (2) associated at p < 5 × 10,,,44,,,,,,,,,,We used FINEMAPFor trans-ancestral fine-mapping of the novel loci or new signals identified in the trans-ancestral meta-analysis of HISLA, AAAGC, and GIANT, we used a 1 Mb region defining each locus using the summary statistics of the given meta-analysis. We calculated the LD for Hispanic/Latino samples using the HCHS/SOLAll regions allowed up to a maximum of 10 causal variants, as defined by FINEMAP. The cumulative 95th percentile credible set was calculated from the estimated posterior probabilities. Convergence failed for three regions  using the stochastic approach. For these three regions, we used the conditional approach to determine number of causal variants.We performed association analyses of measured whole blood gene expression in 606 individuals from the Cameron County Hispanic Cohort.To gain further insight into the possible functional role of the identified variants and to assess their relevance to other phenotypes, we conducted bioinformatic queries of our potentially novel loci and new signals within known loci in multiple publicly available databases, including PhenoScanner,2 > 0.2. We further removed SNPs in regions of long-range LD.We demonstrated the degree to which the present trans-ancestral meta-analysis could lessen the bias induced by population stratification, using height from HISLA as an example. We first conducted PCA on the four European populations  from 1000 Genomes. We excluded the Finnish population because of its known unique demographic history that could drive or dominate the top PCs in a limited sample.We performed linear regressions of individual PC values on the allelic genotype count for each polymorphic variant in the four European populations from 1000 Genomes and used the resulting regression coefficients as the estimate of the variant’s PC loading. For each PC, we then computed Pearson correlation coefficients of PC loadings and effect sizes (of variants with MAF > 1%) from each GWAS summary statistic. We estimated p values based on Jackknife standard errors, by splitting the genome into 1,000 blocks with an equal number of variants. If the GWAS summary statistics are not biased by residual stratification , the correlation coefficients would be expected to be zero. If there was significant correlation in either the GIANT dataset or the HISLA stage 1, AAAGC, and GIANT trans-ancestral meta-analysis, we then further evaluated the improvement of bias due to stratification in trans-ancestral meta-analysis by comparing the correlation coefficients in the trans-ancestral meta-analysis with those in GIANT. Restricting to variants shared between GIANT and the trans-ancestral meta-analysis, we computed their difference in correlation coefficients of PC loadings and effect sizes, and estimated p values again based on Jackknife standard errors from 1,000 equal sized blocks.The first goal of this study was to conduct a genome-wide meta-analysis of anthropometric traits in Hispanic/Latino adults to identify loci in an under-studied population . All regPAX3 on chromosome 2 in the HISLA stage 1 sample (2 = 0.46 in 1000 Genomes AMR)   and liesARRDC3 on chromosome 5 associated at genome-wide significance in HISLA stage 1 (−4). LD patterns at ARRDC3 appear to be similar across the three ancestries , meaning that lack of generalization may be related to frequency differences, haplotype effects, or a false positive (Another BMI SNP (rs1505851-T) near  stage 1 , but did stage 1 . Howeverpositive A.DOCK2 and TAOK3 at genome-wide significance in HISLA stage 1 after excluding the Brazilian and Native American samples (DOCK2 association for WHRadjBMI observed among women only in stage 1 (2 ≤ 1) than for AFR and EUR references , but the associations at this variant were not directionally consistent across stages (2 = 45%); evidence of moderate heterogeneity remained (I2 = 52%) in the combined meta-analysis of HISLA stage 1 and 2 samples , and 0.11 for rs73420913 (ILRUN), respectively. Interestingly, however, the best candidate for causality at PAX3 and ADCY5 loci were the lead SNPs from the HISLA meta-analysis; for ILRUN, the FINEMAP and HISLA SNPs were in high LD (rs73420913 had an r2 = 0.96 with the lead HISLA SNP rs148899910), providing greater support for the prioritization of these SNPs for functional interrogation. For the B4GALNT3 height locus, FINEMAP revealed six causal configurations. Four of the variants  with each configuration had a posterior probability >0.99 and contained only the variant itself in the 95th percentile credible set. One variant (rs215223) had a posterior probability of 0.93 and thus included two variants in the 95th percentile credible set. The sixth 95th percentile credible set had a lead variant with a posterior probability of 45% but contained a total of 1,621 additional variants, all of which had very small posterior probabilities .We fine-mapped the al 0.89, . Howeveral 0.89,  and S23.ADCY5 locus (rs17361324) transferred to both AAAGC and GIANT with directional consistency (beta = 0.13–0.23) and at nominal significance (p < 5 × 10−2). The lead SNP (rs148899910) representing the BMI signal near ILRUN was not available in GIANT; the signal only appeared to be transferable to GIANT  was directionally consistent and nominally significant in AAAGC only. In all cases, the effect sizes observed in GIANT and AAAGC were attenuated compared with the effect sizes from HISLA stage 1.To assess how well the effect estimates are transferable to other populations, we looked up the BMI and height findings from Hispanic/Latinos in the AAAGC and GIANT meta-analysis results . The BMIZ scores, as well as a case-control study of childhood obesity. Two of the three novel BMI signals were directionally consistent with the anticipated effect on the odds of obesity during childhood/adolescence, one of which was nominally significant . None of the HISLA findings generalized at nominal significance with the BMI/height-for-age Z scores, but were directionally consistent with the corresponding effect in adulthood (We looked up our novel HISLA findings in Hispanic/Latino children/adolescents using BMI-for-age and height-for-age dulthood . This ma2 ≥ 0.52 in AMR) with the reported index SNP  for the HOXC13 signal . Four trans-ancestral SNPs were associated at nominal significance in the child/adolescent sample, each having been already replicated in UKBB  with the lead GWAS SNP. Two of these five regions had strong prioritization given high posterior probabilities (≥0.8) and small 95th percentile credible sets: (1) for BMI, the CHD1-DT region had a posterior probability of 0.88 for rs150992 with three SNPs in the credible set, and (2) for height, the ANKRD36BP1 region had a posterior probability of 0.93 for rs10737541 with five SNPs in the credible set. From the functional annotations , there was one SNP within the configuration with the highest posterior probability. For the height locus near otations  and S23,FGGY in osteoclast tissue . Diseases associated with FGGY include autosomal recessive lateral sclerosis and spastic paraplegia type 7, which are known to affect height.For the other trans-ancestral loci, the posterior probabilities were lower, between 0.09 and 0.42, yet four loci  still had relatively few SNPs (<10) in the 95th percentile credible sets, suggesting a narrow window (combination of variants) around the causal variant. For example, functional annotations of rs17375290, the lead GWAS SNP in the NFIA locus associated with height, show it to have promoter markers in muscle, CADD score of 13.29 , and an eQTL with t tissue  and S23.2 > 0.75) with the lead GWAS SNP, suggesting causality for this signal in general, although perhaps maybe not initially described at the most-putatively causal SNP(s). For these putatively causal SNPs, the posterior probabilities ranged from 0.09 to 1. Twenty-two of these SNPs had 95th percentile credible sets that contained <10 SNPs and 15 also had posterior probability ≥0.8.Within the 33 trans-ancestral signals in known loci, 31 had configurations with more than 1 putative causal SNP . This made sense given these are loci with multiple independent signals, as described by our earlier conditional analyses. Among the putative causal SNPs within each locus, there were a number of SNPs that represented known signals . We found that, for many of these, the credible sets contained <10 SNPs. Among the 33 signals in known loci, 26 included a putative causal SNP that is the lead GWAS SNP reported here or an SNP in high LD (r2 > 0.75) with it, have <10 SNPs in the credible set and have eQTLs in relevant tissues, including muscle, thyroid, adipose, lung, and osteoclasts. Some also have promoter or enhancer marks in some of the same tissues. For the two WHRadjBMI signals, both have three SNPs in the most probable causal configurations. One of these causal SNPs for each region is either the lead GWAS SNP (rs7975017) or an SNP in high LD , has a posterior probability ≥0.95, and is the only SNP in the credible set. Furthermore, for rs7975017, we find eQTLs in thyroid for multiple genes  and enhancer marks in multiple tissues including those related to the WHRadjBMI trait, e.g., thyroid, muscle, fat, bone, and adrenal gland. Overall, across many of the loci and secondary signals, FINEMAP revealed SNPs with somewhat strong prioritization (posterior probability ≥0.8) and, at some loci, putatively causal SNPs in small 95th percentile credible sets, thus demonstrating the utility of trans-ancestral approaches to fine-mapping GWAS loci.Many have functional annotations that support the fine-mapping results  and S23.,,−94 in GIANT; rho = 0.105, p = 3.4 × 10−70 in meta-analysis). The correlation was much lower in AAAGC and HISLA , consistent with our hypothesis. Other traits were not a priori known to be as differentiated across Europe as height, and thus the degree of correlation between effect sizes and PC loadings are much lower in GIANT  and ADAMTS9-AS2  are both nominally associated with childhood obesity status aged between 2 and 18 years. Three of our trans-ancestral signals in established height loci also displayed association with height-for-age Z scores in children/adolescents aged between 5 and 18 years. These observations support the premise that diverse and trans-ancestral studies represent a valuable tool for leveraging ancestral differences and similarities both within and across populations to identify multiple signals in established association regions, identify putative variants that may account for some of the missing heritability of complex diseases, or reveal promising genes and SNPs for functional follow-up.Our conditional and fine-mapping analyses revealed 36 signals in established anthropometric loci, which independently replicated in HISLA stage 2 or the UKBB British subsample. In addition, our lead SNPs for the BMI signals discovered at IGF2BP2 I2 = 78.7; MYO6 with I2 = 84.4, In light of the notable ancestral, geographical or environmental diversity of the samples analyzed in our meta-analyses, we observed evidence of allele frequency differences for many of our Hispanic/Latino  and tranResidual uncorrected stratification in GWAS could result in biased estimates of effect sizes.rho| = 0.042 in GIANT on WHR using only males on PC1). The decrease in bias in the trans-ancestral meta-analysis was also obvious in height. The correlation with PC1 was non-significant in HISLA (rho = 0.007) and statistically significant but weak in AAAGC (rho = 0.012), consistent with a decreased impact of European population stratification on the estimate of effect size in AAAGC and HISLA. This decreased correlation could be due to large non-European ancestries in these populations  that make these populations affected by population stratification in Europe; it could also be that, by using European ancestry-based loadings, we are less likely to detect non-European based population stratification patterns or that smaller sample sizes in these cohorts result in greater noise in effect size estimates. Regardless of the reason, compared with GIANT alone, trans-ancestral meta-analysis of the three cohorts showed less impact of uncorrected stratification on effect size estimates, even though the sample sizes in AAAGC and HISLA are comparably small. For other traits, the conclusions are qualitatively similar: that trans-ancestral meta-analysis lessened the bias due to stratification, even though the bias in GIANT was not as strong in the first place.Of the three traits studied here, height is the most stratified in Europe. The correlation coefficient between effect sizes on height and PC loadings reached 0.125 in the GIANT only for PC1, while it was much smaller for other traits  in European ancestry samples.SNRPC for extremes of the BMI distribution.2 = 0.01–0.05 in 1000 Genomes AMR). More recently, rs148899910 has been associated with height in Korean women.2 = 0.9 in 1000 Genomes AMR with rs148899910), and with BMI-adjusted waist circumference in individuals of European ancestry (rs202228093 and rs2780226 each have an r2 > 0.7 with rs148899910 in 1000 Genomes AMR).,C6orf1 (p = 3 × 10−7) and not any other genes in the region  and rs215226 (B4GALNT3) have enhancer and promoter histone marks and eQTLs in the respective genes in relevant tissues. For BMI, there is an eQTL for rs17361324-ADCY5 in thyroid, and ADCY5 has been previously associated with type 2 diabetes,ADCY5 intronic variant  that is highly conserved across species (Haploreg v.4.1). For height, there is an eQTL for rs215226-B4GALNT3 in aortic (coronary) and tibial nerves. The lead SNP for the height signal in B4GALNT3, rs215226, has enhancer histone marks in bone and muscle, and promoter marks in muscle tissue. In addition, the variant rs215226 (B4GALNT3) has a posterior probability of 1 in FINEMAP analyses  based on CADD and FATHMM-XF scores . Yet, alyses see . Other iC11orf63 associated with height led by rs11605693, which showed pathogenic scores for CADD and FATHMM-XF (CADD score = 17.1 and FATHMM-XF score = 0.87). This lead SNP has an eQTL in C11orf63 for adipose, tibial nerve, and testis. C11orf63, junctional cadherin complex regulator, is responsible for ependymal cells that line the brain and spinal cord.The lead SNPs in our trans-ancestral loci were mainly located in intronic and intergenic regions  and wereRNH1 was led by rs10540 (posterior probability of 0.82), and is an eQTL for a wide range of tissues and genes  discover six additional loci with a notably smaller analytic size than other anthropometric consortia, such as GIANT. We also (2) discovered 36 signals in established loci in HISLA or our trans-ancestral meta-analysis, and (3) generated trans-ancestral effect estimates with better control for population structure. Taken together, these findings indicate the added value of building large, more diverse GWAS in the near future.Large-scale analyses of diverse populations hold great potential for advancing the field of genetic epidemiology.


A central question in evolution is how several adaptive phenotypes are maintained within a species. Theory predicts that the genetic determination of a trait, and in particular the amounts of redundancy in the mapping of genotypes to phenotypes, mediates evolutionary outcomes of phenotypic selection. In Mediterranean wild thyme, numerous discrete chemical phenotypes (chemotypes) occur in close geographic proximity. Chemotypes are defined by the predominant monoterpene produced by individual plants in their essential oil. In this study, we analyze the ecological genetics of six chemotypes nested within two well‐established chemical families (hereafter ecotypes). Ecotypes, and chemotypes within ecotypes, are spatially segregated, and their distributions track local differences in the abiotic environment. By combining population genomic, phenotypic, and environmental data from 700 individuals, we show how the genetics of ecotype determination mediates this evolutionary response. Variation in three terpene‐synthase loci explains variation in ecotype identity, with one single locus accounting for as much as 78% of this variation. Phenotypic selection combined with low segregating genotypic redundancy of ecotypes leaves a clear footprint at the genomic level: alleles associated with ecotype identity track environmental variation despite extensive gene flow. Different chemotypes within each ecotype differentially track environmental variation. Their identity is determined by multiple loci and displays a wider range of genotypic redundancy that dilutes phenotypic selection on their characteristic alleles. Our study thus provides a novel illustration of how genetic redundancy of a phenotype modulates the ability of selection to maintain adaptive differentiation. Identifying the precise genetics of the chemical polymorphism in thyme is the next crucial step for our understanding of the origin and maintenance of a polymorphism that is present in many aromatic plants. Thymus vulgaris (wild thyme) harbors several distinct chemotypes whose spatial variation is associated with a variation in highly localized climatic conditions. The predominant monoterpene is either a phenolic or a nonphenolic type. These two types are ecotypes with adaptation to sites that can experience either mild or, in contrast, early, extreme winter freezing. Chemical variation also exists within phenolic (two chemotypes) and nonphenolic (four chemotypes) ecotypes and six chemotypes frequently coexist on a landscape scale. We studied the ecological genomics of these chemotypes within an area of 25 km2 in the South of France. To understand how selection shapes the distribution of this polymorphism, it is essential to know the genetic basis underlying chemical variation. Here, we show that genetic variants located in genes of the monoterpene pathway explain a large fraction of chemotype variation, and that the genetic determination of ecotypes can explain why close populations contain distinct ecotypes despite extensive gene flow. This can also explain how ecotypes quickly responded to recent climate change. Identifying candidate genes underlying chemotype variation in thyme will provide the opportunity to dissect how this crucial chemical polymorphism has arisen and diversified in aromatic plants.Mediterranean aromatic shrubs in the Lamiaceae family are famous for the diversity of their scents, and are used both as culinary herbs and in the pharmaceutical industry. Molecules—primarily monoterpenes—contained in their essential oil confer the different scents. These monoterpenes can provide a range of ecological functions including protection against herbivores and temperature and drought stress via their antioxidant properties and their ability to maintain functional cell membranes under stress. These protective properties vary among monoterpenes and the identity of the predominant monoterpene produced by a plant defines distinct chemical types (chemotypes). In its natural distribution, How to maintain discrete stable phenotypes within a single species is a long‐standing conundrum for ecological and evolutionary theory. Theories for the maintenance of different phenotypes abound and besides trivially re‐introducing new types by mutation several mechanisms have been proposed  determines the likelihood of local selection maintaining phenotypic variation in the face of gene flow. Theory suggests that the number of genotypes that, at a given time, segregate in a population and are phenotypically redundant, so called segregating redundancy, is a critical parameter governing both the predictability of evolution  has been studied extensively for almost 50 years. Thymus vulgaris contains at least seven distinct chemical phenotypes   independent loci with strong epistatic interactions  was determined as the identity of their predominant monoterpene  determined from GC‐MS. In brief, methanol extracted samples were analyzed on a Shimadzu GCMS‐QP21010 fitted with a flame ionization detector and a fused silica capillary column SLB‐MS . Helium was used as a carrier gas. The compounds were identified by comparing retention time and mass spectra with standards from Mass Spectral Library.To increase information on chemotype frequency within and among sites, we added available data on individual plants sampled from the same sites that were chemotyped just few years prior to our sampling. Our dataset representing chemotype frequency at the 21 sites thus consists of chemotype identity of 702 individual thyme plants: 252 individuals sampled in 2016 in this study, and 450 individuals from 2013 , the mean daily minimum and maximum temperature in winter , minimum temperature in the coldest month (“mintjan”), the number of days where moderate to strong freezing was recorded , the mean daily minimum and maximum temperature in the summer , the number of days exceeding 40 or 45°C , and the mean summer daily temperature amplitude (“meansumamplit”). For summer temperature, we omitted the data recorded between 12:00 p.m. and 04:00 p.m., as direct sunlight may cause data loggers to record unusually high temperatures not representative of the site. We checked that omitting these time intervals did not alter the ranking of the warmest to coldest sites.T. vulgaris or close relatives , along with 300 anonymous targets that were genes identified by transcriptome sequencing but not expected to be associated to phenotypes.We used a locus‐targeted genotyping by sequencing procedure. A set of 20,000 DNA baits  was designed to target candidate genes previously identified by transcriptome sequencing .Plant DNA was extracted from 15 mg of fresh young leaves. Genomic library preparation for multiplexed individuals and enrichment step by bait capture follow published protocols  on the genotypes scores of all common SNPs. Each individual SNP genotype score was centered and reduced before performing a PCA. A probabilistic PCA, as implemented in the R package Fis and Fst via Weir and Cockerham estimators using the R package NAM . If two SNPs had at least 98% identical genotypes across all individuals called, they were considered as redundant.To quantify genetic differentiation between population, ecotype, and chemotypes, we estimated 0) where five PCs of SNPs variation are used to predict chemotype:We tested for associations between the nonredundant 3920 SNPs and chemotype (or ecotype) identity across the sample of individuals of the 21 sites. We evaluate association by comparing the fit of two logistic regression models. The first is a background model  composition.1) that also uses a single SNP genotype  as predictor:The second model is a SNP model (M1: binary chemotype ∼ PC1 + PC2 + PC3 + PC4 + PC5 + SNP.MMethods in the Supporting Information).Models were fitted by maximum likelihood for each SNP assuming a binomial distribution for the binary phenotype identity (T (possibly multilocus) genotypes iG, i = 1, …, T, segregate with genotypic frequency pi ( ∑i=1Tpi=1). A set of K discrete phenotypes Pj, j = 1, …, K, are “encoded” by the set of T genotypes G1, …, TG. We define a coefficient wij that, for each pair , measures how strictly phenotype Pj is determined (encoded) by genotype Gi. The genotype to phenotype map (encoding) can be genetically strict or loose depending on the genetic architecture of the K phenotypes. We use the conditional probability that genotype Gi will produce phenotype Pj to measure how strict is the genotype to phenotype map for a given jP:A set of wij=1 captures the case where individuals with a specific genotype iG always produce phenotype jP.Note that wij when calculating the effective number of genotypes.We measure segregating redundancy using a statistic analogous to the effective number of genotypes . We use the phenotype‐specific weighting Pj asψij=piwij∑ipiwij.We calculate a (weighted) genotypic diversity that is segregating for determining each phenotype ψijs are estimated as the frequency of each genotype Gi weighted by probability wij that it will produce an individual with phenotype Pj. If all possible genotypes have same probability to produce Pj, then ψij=pi.The Pj, SRj, is the effective number of genotypes encoding Pj:The segregating redundancy for phenotype T genotypes can encode phenotype Pj, SRj will be maximal and equal to T when all T genotypes are equally frequent and encode Pj with identical probability .If Gi encode Pj with high probability or genotypes have unbalanced frequencies, then SRj will be lower. The minimum SRj is 1: this is the limit situation where despite the fact that T genotypes coexist in the population only one genotype is effectively “encoding” phenotype Pj. For a given amount of phenotypic selection affecting phenotype Pj, SRj measures how much phenotypic selection “percolates” at the genotype(s) level. The SNPs‐ecotype and the SNP‐chemotype associations detected were used to define multilocus genotypes. We use observed frequency of genotypes and discrete phenotypes to estimate pis, the wij, ψij, and SRjs. Resampling  was used to obtain SEs around SRj estimates.If few genotypes To characterize environment variation, we performed a PCA on the centered and reduced environmental variables measured at the 21 sites. All environmental variables were centered and normalized (to unit variance) before PCA. Principal component (PC) 1, PC2, and PC3 account for 45%, 28%, and 12%, respectively, of the total variation measured across sites. PC1 correlated with variables measuring winter freezing and PC2 strongly correlated with the number of days exceeding 40°C Figure .P degrees of freedom if models differ for P fitted parameters). To quantify the amount of variation accounted for by each model, we used the pseudo R2 computed as 1 – Dev(Mi)/Dev(M0), where Dev(Mi) is the deviance of model Mi and Dev(M0) is the deviance of a model with no predictors (merely fitting an intercept).We used a  logistic regression where counts of individuals in each ecotype  or all six chemotypes  at each site  are used as response variables and the PCs of environment variation used as predictors .We used a similar logistic regression framework as the one described above for SNP‐phenotype associations. Instead of using phenotypes as response variables, we use variation of SNP frequency from site to site as a response variable and test whether SNPs covary with the environment measured using Environmental PC1 and PC2 (Our genomic data were produced by the resequencing of target genomics regions (after enrichment of genomic DNA) in 252 individuals. The target regions are candidate genes of the monoterpene biosynthesis pathway (14 genes) and a larger set of anonymous genes (384 genes) that were previously characterized by transcriptome sequencing , these SNPs were shown to be highly reproducible genetic variants (Fst between sites: 0.021 ± 0.003), and is even lower between ecotypes .Genetic differentiation among sites, measured by average genome differentiation at the 3920 SNPs, is very low  and PCs of abiotic variation as predictors of environmental variation. A first model (Genetic Drift) is fitted using only SNPs PCs as predictor of phenotypes composition. A model featuring both SNPs and environment PCs (Genetic Drift + Environment) is then fitted and the extra fit provided by this model is our measure of the importance of the environment . Examining how these two sets of predictors explain variation across populations on ecotype distribution showed a large effect of environmental variation Table . RepeatiP < 0.05/3920 at individual SNPs), we show that ecotype identity (phenolic vs. nonphenolic) strongly associates with variation at three genetic loci.Using a logistic regression framework accounting for the underlying weak population structure Figure , and a cP < 10−8), but with more modest apparent effects . All pairwise genotype disequilibria involving these three SNPs are statistically significant , suggesting physical linkage between the three genes; although we do not have a whole genome assembly to estimate the actual physical distance between these genes. The presence of mild to strong statistical associations between the top three SNPs precludes us from parsing the associations we detect into reliable SNPs‐specific phenotypic effects and estimating how many causal variants (at least one and up to three) may be responsible for these SNP‐ecotypes associations. Alternatively, the maintenance of strong genotypes disequilibrium we observe could also reflect epistatic selection acting to maintain associations between these three SNPs genotypes.An SNP in one single gene—homologous to a linalool synthase—explains 78% of the observed ecotypic variation between individuals. SNPs located within two additional genes (homologous to two gamma‐terpinene synthases) are also strongly associated with ecotypes , we detected the same three variants already associated with ecotype differences, as well as additional SNPs located in three independent genes that strongly associate with individual chemotypes. The SNPs that display the strongest associations with individual chemotype identity are located within six genes all identified as homologs for previously reported enzymes of the monoterpene biosynthetic pathway . We use the observed phenotype‐specific weighting of each genotype when calculating the effective number of genotypes Table .Very few distinct genotypes account for ecotype variation Figure . AccordiIn contrast, the number of effectively segregating genotypes of each chemotype is considerably higher than for ecotypes Figure ; Table 3As ecotypes, and chemotypes nested within ecotypes, track environmental variation, we expect genetic variants underlying this phenotypic variation to be associated with the environmental variation. We thus examined the extent to which SNPs were associated with environmental variation. The SNP‐environment covariation measures the efficacy of ongoing selection to segregate alleles spatially despite the homogenizing effect of gene flow. To do so, we tested whether SNPs frequencies covary with the PC1 and PC2 axes of environmental variation that account for 45% and 28%, respectively, of the total abiotic environmental variation we measured Figure . Using aAmong the six major SNPs determining eco‐ and chemotype identity Table , the thrOur study relies on environmental variation at 21 sites and lacks statistical power to detect weaker SNP‐environment associations. It may further be argued that some SNP‐chemotype associations could be caused by population structure not accounted for by the five SNP PCs. However, these associations still hold even when accounting for population structure by using up to 10 SNP PCs Figure . MoreoveIn contrast to results for ecotypes, the frequency of SNPs displaying strong associations with chemotype identity had much weaker associations with environmental variables. Theory predicts that the effect of selection on a locus controlling phenotypic variation for a fitness related trait can be influenced by selection jointly acting on other loci (Chevin and Hospital We demonstrate in this study how ecotypic differentiation can be maintained in the face of considerable gene flow. Ecotype identity is under oligogenic control and variation at a single gene explains the majority of ecotype variation. This has consequences for how selection operates on phenotypic variation to maintain adaptive differentiation. Loci accounting for most of the heritable variation in a fitness‐related trait undergo the strongest selection at the genotype level (Chevin and Hospital Thymus and other Lamiaceae genera. The ecological roles of monoterpenes are manifold and include deterrence of herbivores (Linhart and Thompson The chemotypes that we studied are also found in many other species of Fig. S1. A: Projection of individual environmental variables on principal components of environmental variation. B: Correlation between PC1 and the minimum temperature registered in January. C: Correlation between PC2 and the number of days with temperature exceeding 40 degrees C.Fig. S2. SNPs frequency clines along environment gradient at 6 SNPs explaining ecotype and chemotype identity. Environmental variation is measured using the first principal component of environmental variation.Fig. S3. Robustness of PCs correction for population structure when detecting associations.Fig. S4. SNPs PCs colored by site of origin of individuals.Table S1. Repeatability of SNPs genotypingTable S2. sample size, Fis and Fst at the top 6 candidate lociTable S3. Effect of environment on change in allele frequency at 6 SNPs with the strongest associations to ecotype and chemotype identity.Table S4 Model comparison for genotype‐ ecotype associations using 5 or 10 PCs.Click here for additional data file.