Applying a genome-wide scan with 46,996 variants, we identify 2 loci significantly (p < 7.14 × 10−6), and 12 loci suggestively (p < 2.13 × 10−4), associated with onset. At the most significant locus, whole-genome resequencing reveals a putative causal variant in the promoter of FAM204A. Expression quantitative trait loci (eQTL) analyses further reveal gene associations for 8/14 loci. Our results highlight the power of applying genetic mapping to hibernation and present new insight into genetics driving its onset.Hibernation in sciurid rodents is a dynamic phenotype timed by a circannual clock. When housed in an animal facility, 13-lined ground squirrels exhibit variation in seasonal onset of hibernation, which is not explained by environmental or biological factors. We hypothesized that genetic factors instead drive variation in timing. After increasing genome contiguity, here, we employ a genotype-by-sequencing approach to characterize genetic variation in 153 ground squirrels. Combined with datalogger records ( FAM204A.Katherine Grabek et al. use genotype-by-sequencing to characterize genetic variation in 153 ground squirrels, finding high heritability for hibernation onset. They find 14 loci associated with hibernation onset, and a putative causal variant in the promoter of  Ictidomys tridecemlineatus, an endogenous circannual clock entrained by light controls the timing of winter hibernation3, along with rhythms in reproductive behavior, food intake, and body mass6. These hibernators partition their year between two distinct states, homeothermy and heterothermy  could be spread across multiple scaffolds. We therefore first sought to increase the genome’s contiguity using a long-range scaffolding techniqueA single library was constructed using proximity ligation of in vitro reconstituted chromatin. After sequencing, which provided 52.6× physical coverage of the genome Table  and scaf19 to generate libraries for 153 13-lined ground squirrels from which we obtained DNA from frozen tissue. After aligning the resulting sample library reads to the HiRise genome assembly  estimates between populations were 0.47 and 0.31 for CO vs. WI and IL, respectively, and 0.30 for WI vs. IL, indicating moderate to strong genetic drift.The squirrels genotyped in this study originated from wild stock trapped in disparate geographical locales Fig. . The rec= 3 Fig. , where iK = 6, we observed separation most consistent with records about sampling  among all populations  from a PCA-based analysis recapitulated both the observed ADMIXTURE els Fig. . All popels Fig. .n = 119), for which we were able to collect the most phenotypic measurements , and we input the genetic relatedness estimates as the random effect. Unexpectedly, this model converged with no residual error in the variance components, resulting in an estimate of 100% heritability  and SNP 2  using 46,996 variants with a minor allele frequency (MAF) ≥0.05 and the fit from the linear mixed model. After applying a Bonferroni correction to adjust for 14,004 estimated independent testsp-values (Q–Q plot) also exhibited an excess of significant values well above the red line in the tail of the distribution  p-value threshold and generated an empirical null distribution of observed variant counts from 5000 permutations , the effect sizes for the 2 genome-wide significant and 12 suggestive variants were all within either the top or bottom 1% of the total distribution, being at least ±4.25 days for each additional allele  for the GWAS was 0.979, indicating we had adequately corrected for population structure. While this is often adjusted via its input as a fixed effect covariate in the linear mixed model , genetic relatedness alone  may also sufficiently account for population structure27. We observed this to be the case in our model; a plot of the cumulative distribution function of the p-values revealed no upward skew , whose function is poorly characterized  and exocyst complex component 4  and located ~500 kb from SNP 5, was located near pleiotrophin (PTN), a growth factor involved in neurogenesis, axonal outgrowth, angiogenesis and hematopoiesis of bone marrow34. Finally prohormone convertase 2 (PCSK2), an enzyme that activates hormones and neuropeptides including cleavage of proinsulin into its mature form35, was located in close proximity to SNP 14  halves. With a critical value of α = 1 × 10−3, corroboration paramater η = 0.6 and 100 resamples, the ComPaSS-GWAS corroborated both SNP 1 (η = 0.66) and SNP 2 (η = 0.77) as being significantly associated with the onset of autumn torpor, as well as four of the suggestively associated variants: SNP 3 (η = 0.76), SNP 7 (η = 0.66), SNP 8 (η = 0.66) and SNP 10 (η = 0.70). For the remaining suggestive loci, the corroboration values ranged from η = 0.18–0.54  was located approximately 13 bp upstream of the transcriptional start site of FAM204A  was the major allele in this population of ground squirrels and therefore associated with earlier torpor onset. An in silico analysis predicted this variant to flank the CCAAT box within an NF-Y(A/B) transcription factor binding site38  transcription factor binding site  analysis using the EDGE-tag transcript datasets from heart, liver, skeletal muscle (SkM) and brown adipose tissue (BAT)40, as a subset of the squirrels genotyped in this study were also assayed for transcriptome expression in these prior studies. Under an additive linear model, we identified significant cis-eQTL associations  for 8/14 variants (Table cis-eQTL (q < 4.4 × 10−8), where the minor allele, linked to the non-disruptive reference allele in the FAM204A promoter and associated with a later onset of torpor  in both heart and SkM (Table chloride voltage-gated channel 3 (CLCN3) expression in SkM, yet also correlated with increased microfibril associated protein 3 like (MFAP3L) expression in heart  to be identified as trans-eQTLs  and the large number of EDGE-tags tested (25–30 K per tissue) in each dataset. We therefore examined the top trans-eGene (>500 kb from variant) for each significant cis-eQTL, hypothesizing that we would identify genes within the same pathway or consistent with the physiology of the cis-eGene. Indeed, in heart, the angiotensin II receptor type 1, AGTR1, involved in regulation of blood pressure41, was the top trans-eGene for SNP 5  and hibernation timing have been observed47. While age did not significantly affect hibernation onset in our study, it is worth noting that 74% of the squirrels in this dataset were juveniles, and therefore hibernation-naïve prior to their first torpor bout. Juveniles likely face a far greater challenge in growing and fattening sufficiently to support winter hibernation in the wild; in contrast, in a relatively constant, resource-rich laboratory environment, hibernation onset for these animals may be particularly driven by endogenous mechanisms, thus increasing heritability estimates.Our results of heritability are consistent with those from a study that reported significant heritability in spring emergence from hibernation in wild Columbian ground squirrels48, we find that relatively few loci of large effect account for phenotypic differences in the seasonal onset of hibernation. Our results are comparable to what has been observed for adaptive traits in A. thaliana49, morphological variation in domesticated dogs50 and plate armor of threespine sticklebacks51. Similar to these studies, our results could be explained by local genetic adaptation52. Alternatively, our results may stem from limited sample size inflating the percent of explanatory variance for a single locus, a phenomenon known as the Beavis effect. Relaxing the p-value threshold and increasing the number of explanatory loci will decrease the severity of this effect53. In light of this, we did not estimate the percent of variance explained by any single locus, instead examining estimates from combinations of loci. Although lowering the p-value threshold will also increase the false positive rate, we note that we estimated a probability of just 2% for observing the same number variants randomly associated with the phenotype at our exploratory p-value threshold. Additionally, we found that both the significantly associated variants were corroborated for association using an alternative approach for independent replication. We therefore posit that despite small sample size, both the significant loci are indeed associated with the onset of torpor. For the remaining suggestively associated loci, the results of our study now warrant independent replication with a larger sample size.In contrast to complex human diseases, where often thousands or even millions of variants of relatively small effect influence complex physiological phenotypeFAM204A. Coupled with transcriptome data40, we detected that this locus was a cis-eQTL associated with FAM204A transcript abundance, which suggests that FAM204A is involved in the onset of hibernation. Intriguingly, there are few studies that have characterized FAM204A, especially in the context of hibernation. One recent study reported an increased number of regions surrounding FAM204A undergoing accelerated evolution in two species of hibernating bats and the 13-lined ground squirrel as compared to four non-hibernating mammals54. In addition, expression of FAM204A is significantly up-regulated in the brains of torpid greater horseshoe bats as compared to those that are not hibernating55. In humans and other non-hibernating organisms, not much is known about this gene’s function. While abundance is highest in the pituitary, it is also expressed in every tissue and localizes to the nucleus (www.proteinatlas.org56), where it interacts with a histone acetyltransferase and a methyltransferase57. We therefore hypothesize that FAM204A may play a role in epigenetic regulation of gene expression, possibly in response to the onset of fasting and a switch to fatty acid metabolism58, as animals prepare for hibernation in the fall.Within the most significantly associated locus, SNP 1, we identified a putative causal variant in the promoter of motilin (MLN), as well as insulin signaling and processing, including EXOC4 and PCSK2. Researchers have hypothesized that mechanisms governing food intake and metabolic suppression are linked, and that hibernation cannot begin until food intake has ceased9. Our results support this hypothesis, and present new candidates for study in hibernation.Although more research is now needed to determine the precise role in torpor immergence for each of the remaining loci, we propose candidate genes, oftentimes the closest to the marker, due to their function being closely related to the physiology of this seasonal transition. In particular, several genes are known to modulate food intake, such as the 40) have identified hundreds to thousands of genes differentially expressed among the seasonal and physiological states of the hibernator’s year. While leading to insight into the pathways involved, the results of these screens do not distinguish between genes driving vs. those responding to changes in phenotype. They are also limited to the tissue and time-points being examined and may therefore miss important regulators of phenotype. For example, several cis- and trans-eGenes identified here have clear roles in the physiology of torpor, including CHRM2 and AGTR1; yet neither of these were identified in the original differential expression study40 because the timing of the first bout of torpor was not considered. Thus, by applying complementary genetic mapping approaches, current limitations inherent to gene expression screening strategies will be addressed and enable new insight into the mechanisms driving hibernation. The approaches used here can be extended to a wide variety of hibernators and the quantifiable components that comprise this highly dynamic phenotype.Finally, our results highlight the power of integrating genome data with transcriptome and other high-throughput data to better understand the genetic mechanisms underlying hibernation. Prior omics screens  were obtained from the University of Wisconsin, Oshkosh in the summers of 2007–2010 Fig. , WI. TheTb) monitoring until tissue collection. The dataloggers recorded Tb ± 0.5 °C every 20, 30 or 60 min, while the radiotelemeters transmitted Tb ± 0.5 °C every 20 s.Upon arrival, animals were housed individually in rodent cages Fig.  under stIn late September or early October, the squirrels were moved to the hibernaculum to facilitate hibernation. The temperature was lowered stepwise over a two-week period to 4 °C. Food was removed as animals became torpid.17. All animals were exsanguinated under isoflurane anesthesia, perfused with ice-cold saline, decapitated, and dissected on ice; tissues were immediately snap frozen in liquid nitrogen and stored at −80 °C until processed further. Telemeters were retrieved during tissue collection.Liver samples were collected at different points throughout the year for use in other biochemical studies as previously described59. Tb was averaged over 4-hour windows. Homeothermic Tb typically ranged from 34–39 °C. The first torpor bout was defined as the first point at which Tb fell to or below 25 °C  was reconstituted into chromatin in vitro and fixed with formaldehyde. Fixed chromatin was digested with DpnII, the 5′ overhangs filled in with biotinylated nucleotides, and then free blunt ends were ligated. After ligation, crosslinks were reversed and the DNA purified from protein. Purified DNA was treated to remove biotin that was not internal to ligated fragments. The DNA was then sheared to ~350 bp mean fragment size and sequencing libraries were generated using NEBNext Ultra enzymes and Illumina-compatible adapters. Biotin-containing fragments were isolated using streptavidin beads before PCR enrichment of the library. The library was sequenced on an Illumina HiSeq 2500 (rapid run mode) to produce 150 million 2 × 101 bp paired end reads, which provided 52.6× physical coverage of the genome (1–50 kb pairs).A Chicago library was prepared by Dovetail Genomics as described previously18. Shotgun and Chicago library sequences were aligned to the draft input assembly using a modified SNAP read mapper (http://snap.cs.berkeley.edu). The separations of Chicago read pairs mapped within draft scaffolds were analyzed by HiRise to produce a likelihood model for genomic distance between read pairs, and the model was used to identify and break putative misjoins, to score prospective joins, and make joins above a threshold. After scaffolding, shotgun sequences were used to close gaps between contigs. Supplementary Data The 13-lined ground squirrel draft assembly (SpeTri2.0), shotgun reads, and Chicago library reads were used as input data for Dovetail HiRise Scaffolding software, a pipeline designed specifically for using proximity ligation data to scaffold genome assembliesGene annotations from the Ensembl (Release 86) and NCBI Release 101) datasets were lifted over to the HiRise assembly using a custom Python script and Supplementary Data 01 datase19. Briefly, high molecular weight DNA was extracted from 8–15 mg of frozen liver with Gentra Puregene Tissue Kits . Digestion and ligation reactions were performed using 200 ng of genomic DNA from each sample with BglII and DdeI and 11-fold excess of sequencing adaptors. Samples were amplified by PCR for 8–12 cycles with a combination of index-containing primers. Between 50–60 samples were pooled in equal amounts according to their concentration of PCR product between 280–480 bp as measured by Tapestation . Inserts were size-selected on a BluePippin  with a target range of 380 ± 100 bp and sequenced on the Illumina NextSeq in single-end 151 bp mode using a high output kit.We used the modified ddRAD sequencing protocol previously described60. Tables of predicted BglII and DdeI restriction digest fragments were generated as previously described19, and sequencing coverage was measured at these sites. We then defined target regions for variant calling using the set of fragments between 125–350 bp long that had non-zero coverage in at least one individual. The mapping data are summarized in Supplementary Data Reads were mapped to the 13-lined ground squirrel HiRise assembly with BWA v. 0.7.1261, Platypus62 and Samtools64. In Sentieon, the pipeline algorithms indel realignment, base quality score recalibration, haplotyper and GVCFtyper were implemented with default settings. In Samtools, variants were called jointly using mpileup to first compute genotype likelihoods and then BCFtools to call genotypes with default parameters. Finally, variants were called jointly in Platypus with the following parameters: minFlank = 3, badReadsWindow = 5, maxVariants = 12, and minReads = 6. Only biallelic variants that both passed the filter flags and were identified by all three callers were retained  using KING software68. Due to the expectation that the Colorado squirrels are of a separate subspecies69, relatedness was calculated independently for this subset. We selected an unrelated subset of 54 squirrels that best represented the ancestries of all squirrels within the dataset using the GENESIS package70 in R59. Variants were pruned for LD in PLINK v. 1.971 using the parameters–indep-pairwise 50 10 0.5, which reduced the dataset to 148,870 variants. We then ran unsupervised ADMIXTURE22 for K = 3 through K = 10 with 5-fold cross-validation. To estimate the ancestries of the remaining 99 squirrels, we ran ADMIXTURE’s projection analysis using the population structure learned in the initial unsupervised analysis, here with K = 2 through K = 8 and 5-fold cross-validation.We first inferred relatedness from identity-by-state (IBS) estimates among all genotyped squirrels (n = 119), as identified by ADMIXTURE analysis. We extracted the top 20 principal components in each analysis.We performed principal components analyses (PCA) with PLINK using 90,376 LD pruned variants with MAF > 0.01 for the entire dataset and 30,356 LD pruned variants with MAF > 0.01 for the squirrels within the Oshkosh, WI, population (23. We used the resulting kinship coefficients and identity-by-descent (IBD) estimates to reconstruct and visualize pedigrees among the 1st degree relatives with PRIMUS25. We also constructed a genetic relatedness matrix from the pairwise kinship coefficients.Finally, we calculated genetic relatedness among the 119 Oshkosh WI squirrels using the GENESIS package, adjusting for both population substructure and inbreeding with the first principal component59. To identify environmental and biological factors that affected the date of first torpor, we applied a linear regression using variables available from records about the squirrels. We modeled the date of first torpor as a function of the following factors: sex, year of monitoring, date of datalogger implantation, age (juvenile vs. adult), date of placement into hibernaculum and weight (as last recorded before placement into hibernaculum). We then pruned factors using stepwise regression until we identified a minimum set that did not significantly (p < 0.05) reduce the adjusted R-squared value from the initial model while minimizing AIC value. In this final model, we retained sex, year of monitoring and date of placement into hibernaculum as fixed effects.All analyses, unless otherwise stated, were performed in R70. We first fit a linear mixed model using the fixed effects and the genetic relatedness matrix as the random effect. We then performed SNP genotype association tests with 46,996 SNPS (MAF ≥ 0.05) and the fit from the linear mixed model. To correct for multiple hypothesis testing, we first estimated the number of independent SNP-association tests using the simpleM procedure26. We then applied a Bonferroni correction based upon the number of independent tests and a significance threshold of α = 0.1. We considered any variant with a p-value ≤ 7.14 × 10−6 to be significantly associated with the phenotype. As this was an exploratory analysis, we identified additional variants suggestively associated with the phenotype by defining a lower significance threshold of p = 2.13 × 10−4, where we expected 10 variants to be significantly associated with the phenotype by random chance alone. To account for LD, we calculated the r2 values for significant SNPs within the same scaffold using PLINK71. We removed those in moderate to high LD (r2 ≥ 0.5), reporting only the most significant variant.We carried out a genome-wide association scan (GWAS) on the date of first torpor using GENESIS72. Here we input the same fixed and random effects  as in the linear mixed model. For the prior, we used an uninformative inverse-gamma distribution  on the variance components. We ran three chains, each with a total of 1,000,000 iterations, a burnin of 100,000 rounds and a thinning interval of 200 rounds. Here, all variables had Gelman-Rubin statistics of 1.00–1.01, with the absolute value of all autocorrelations <0.1 and effective sample sizes between 3682.8 and 5294.5. We combined the 3 chains in order to estimate the posterior mode and confidence intervals of the variance components.We estimated heritability of the first day of torpor from the variance components of the linear mixed model. In addition, we also estimated heritability of this phenotype using a separate Bayesian mixed model with the MCMCglmm package in RFinally, we estimated the effects of the significant GWAS variants on the onset of torpor. Specifically, we used linear regression with the phenotype as the dependent variable and a matrix of significant variant genotypes, either with or without the fixed effects, as the explanatory variables. We also performed forward stepwise regression using genotype combinations from the top 10 significant variants.p-values by rank ordering the values after each GWAS permutation. For each rank, we calculated the median p-value ± 97.5% confidence intervals. Additionally, after each round of GWAS permutation test, we counted the number of variants meeting the suggestive significance threshold (described above), and we generated an empirical distribution using the variant counts from all 5,000 rounds. The probability for observing the actual number of variants at the suggestive p-value threshold was estimated by determining the percentile at which 42 variants were detected using the Harrell–Davis distribution-free quantile estimator in the R package WRShd73.We used permutation testing to examine the empirical distribution of test statistics under the null model and to estimate the probability of detecting the observed number of variants at the suggestive threshold. We disrupted true genotype–phenotype associations by randomly reassigning the phenotype of each individual to another individual, while maintaining all other aspects of the dataset. We then performed the GWAS with permuted phenotypes using the same methods as described above. The GWAS permutation tests were repeated for a total of 5,000 rounds. We generated an empirical distribution of null 36. To compensate for the small sample size in our set, we randomly separated samples into two subsets, each two-thirds in size of the original sample pool. We allowed one-third of the samples to overlap between subsets and distributed the remaining two-thirds of samples into two complementary halves. Following the recommendations of ComPaSS-GWAS, we performed GWAS on the subsets for a total of 100 resamples, using a critical value of α = 1 × 10−3 and a corroboration parameter of η = 0.6. We marked the variants in each resample as validated if the maximum p-value among both subsets was p ≤ 1 × 10−3, and we considered variants corroborated for significance if they were validated in at least 60% of the resamples. Because the critical value recommended by ComPaSS-GWAS was identified empirically under assumptions of one million independent tests  and where samples were split into halves, we checked its appropriateness for use in our case by including 1,000 additional variants sampled uniformly across the distribution of effect sizes. At a type I error rate of 0.05, we expected no more than 50 of these additional variants to be corroborated for associated with the phenotype.To corroborate variants identified as significantly and suggestively associated with hibernation onset, we used an alternative published method based on complementary pairs stability selection for genome-wide association, denoted ComPaSS-GWAS, where samples are split into complementary halves multiple times and GWAS are performed on the halves in each resamplen = 6 squirrels carrying both major alleles and n = 6 squirrels carrying at least one minor allele for the most significant variant, SNP 1, were sequenced with an average coverage of approximately 9× on an Illumina HiSeq 2500. Library preparation and sequencing were performed by Genewiz . Read mapping and variant calling were performed as described above. We retained variants in moderate to high LD (r2 ≥ 0.5) with SNP 1; these were annotated and their effects predicted using SnpEff74 with a custom database consisting of the Ensembl and NCBI gene annotations, as well as regions conserved across vertebrates, which were obtained using the UCSC Table Browser and lifted over from the hg38 assembly. Variants located within conserved elements were prioritized by their individual GERP++ scores37, while those within or <1 kb from a gene annotation were prioritized by their predicted effects, with greatest priority being given to variants falling within both categories. We next analyzed for transcription factor binding site motifs that could be disrupted by the conserved (GERP++ >2) noncoding variants using Tomtom motif comparison software38. Here, the 15-bp sequences surrounding each variant were input as motifs and searched against the JASPAR 2018 database. To further identify overlap within known regulatory elements, these variants were lifted over to hg37 and visualized using the ENCODE tracks in the UCSC browser. Finally, conserved noncoding variants that overlapped functional elements in hg37 were assessed for pathogenicity using FATHMM-MKL39.The whole genomes of 40. The squirrels assayed in these prior transcriptome studies were also genotyped in this study: heart (n = 22), liver (n = 23), SkM (n = 22) and BAT (n = 43). Due to small sample sizes, we limited our eQTL association tests to significant and suggestive GWAS SNPS with MAF ≥ 0.2 in heart, liver and SkM and ≥0.1 in BAT, which ensured that a minimum of 9 samples contained at least one minor allele.We applied an eQTL analysis to identify transcripts whose expression levels were significantly affected by the GWAS variants. Here, we used the EDGE-tag datasets from heart, liver, skeletal muscle (SkM) and brown adipose tissue (BAT) as previously publishedcis- (±500 kb) and trans-eQTLs were performed with Matrix eQTL75 under an additive linear model. As the purpose of the original EDGE-tag studies was to identify differentially expressed transcripts among distinct physiological states within the hibernator’s year, we included physiological state as a covariate . In BAT, sequencing platform was also included due to the observed count bias16. Finally, to control for outliers and following recommendations by Matrix eQTL, the counts for each Edge-tag were ranked and quantile-normalized before testing.Tests for both 59. Sample size was defined as number of ground squirrels genotyped by ddRAD sequencing in this study (n = 153); each squirrel served as an independent biological replicate. For the genome-wide association scan and heritability estimates, sample size was limited to the number of genotyped squirrels that also had phenotypic records of hibernation onset (n = 72). For the eQTL analyses, sample size was limited to the number of genotyped squirrels with published transcriptome sequence data, which varied by tissue . Technical replicates were not used, although a subset of the genotyped squirrels (n = 12) were resequenced using whole-genome sequencing. Here, genotypes were assessed using an independent analysis.Details about the different statistical analyses can be found in each of the subsections of the methods above. Unless otherwise stated, statistical analyses were performed in RFurther information on research design is available in the Supplementary InformationDescription of additional supplementary itemsSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting SummaryPeer Review File


Rana temporaria) exhibit a high degree of variability in metamorphic traits. However, environmental factors alone cannot explain this phenotypic variability, and the influence of genetic factors remains to be determined. Here, we tested whether the maternal genotype influences developmental time, body size, and body condition of offspring in a forest pond in Germany. We collected fertilized eggs from all 57 clutches deposited in the pond. We used multilocus genotypes based on seven microsatellite loci to assign metamorphosed offspring to mothers and to determine the number of fathers for a single matriline. We tested the influence of genetic effects in the same environment by comparing variability of metamorphic traits within and between full‐sib offspring grouped to matrilines and tested whether multiple paternity increases the variability of metamorphic traits in a single matriline. The variability in size and body condition was higher within matrilines than between them, which indicates that these traits are more strongly influenced by environmental effects, which are counteracting underlying genetic effects. The developmental time varied considerably between matrilines and variability increased with the effective number of fathers, suggesting an additive genetic effect of multiple paternity. Our results show that metamorphic traits are shaped by environmental as well as genetic effects.Successful reproduction is an important determinant of the fitness of an individual and of the dynamics of populations. Offspring of the European common frog ( Under optimality theory, the timing of specific life‐history events, such as reproduction or metamorphosis, will evolve to an optimum due to natural selection and local adaptation  can differ profoundly within and between populations  is one of the most widespread amphibians in Central and Northern Europe , near the village of Fabrikschleichach . From the 1970s, 120 small artificial ponds were constructed in this 28 km2 area for conservation purposes by the state forestry department. Our study pond has a surface of 12 m2 and is located in a 28 km2 beech grove and mixed forest which has been monitored for R. temporaria breeding sites since 2005 , and average daily values are given in Supporting Information Figure R. temporaria were found on 1st April 2013, the last clutches were deposited on 12th Apri . We sampled 10 eggs each from all clutches and kept them in small plastic containers  for 48–72 hr at 8°C until the embryos reached Gosner stage 17–20  or 300 µl 99% EtOH. All metamorphs were released in a wet area outside the fence. If more than 50 metamorphs emerged per day, measurements were taken from 50 randomly chosen individuals, and all other metamorphs were only counted and released immediately.To intercept all emerging metamorphs, a fence was installed at the beginning of June 2013 encircling the pond entirely. As soon as metamorphs began leaving the pond, the fence was controlled twice daily (from 8th July to 29th August 2013). Up to 50 metamorphs were captured each day and two measurements were taken: (a) body mass, measured with an electronical balance to the nearest 0.05 g (VOLTKRAFT PS 250) and (b) snout‐vent length (SVL), measured on scale paper with millimeter grid to the nearest 0.5 mm. Two DNA samples were taken by gently swabbing the skin with cotton buds. DNA samples were stored in 1.5 ml reaction tubes containing either 300 µl Cell Lysis Solution  and from 1,176 metamorphs (a maximum of 30 per sampling day) using the PUREGENE® DNA Purification Kit (Qiagen) and stored at −20°C until further use. Individuals were genotyped using microsatellite markers. The microsatellite DNA was amplified via polymerase chain reaction  and revised manually.PCR products were analyzed via polyacrylamide gel electrophoresis with a LI‐COR 4300 DNA Analyser (LI‐COR Biosciences). Alleles were scored with micro‐checker version 2.2.3  were used for the detection of scoring errors and overall homozygote excess.colony version 2.0.6.3  was calculated  was used as a measurement of variability of these traits within a matriline. The CV of SVL, BCI, and developmental time for metamorphs from main father and all fathers were calculated for each matriline and compared with a paired t test. Single‐mated matrilines were excluded from this analysis. To correct for the different number of metamorphs from main and all fathers of the same matriline, which could affect detected changes in mean or CV due to larger sample size in the all father dataset, we randomly subsampled the same number of metamorphs from main and all fathers 10 times.We used a paired 2.4.3p‐value correction for multiple testing  differ between matrilines using the Kruskal–Wallis rank sum test. If metamorphic traits showed significant differences, we performed a post hoc analysis using the Dunn test with 2.4.4Due to resource partitioning  left within the first 3 weeks of the migration period until day 112 (31 July 2013). The last 28% (n = 676) left within the last 4 weeks of the migration period with daily numbers of metamorphs continuously decreasing . The maximum number of individuals leaving the pond per day was 118 Figure  and that3.22, df = 1,940, p < 0.001, adjusted R2 = 54%). The calculated GAM for metamorphic size as response to developmental time showed a significant, but nonlinear influence of time . The relationship of size and mass was following a nonlinear relation and can be described best by a raw quadratic polynomial function of size on mass  and used them as known maternal sibs. From the 2,414 metamorphs that emigrated from the pond, we genotyped 1,176 (maximum 30 per day). In total, 706 metamorphs with five , six , and seven  scored polymorphic microsatellite loci were used for sibship assignment in colony software and the number of alleles ranged from 14 to 25 alleles per locus . An additional seven clusters were excluded because samples of two or more clutches were clustered together, which could be due to relatedness of spawning females. Of the remaining 50 clusters, 23 were generated without clutch samples. As defined above, a cluster without a clutch sample contained at least six metamorphs from the same father genotype to be designated as a matriline. Thus, 10 of these 23 clusters were excluded. The remaining 40 clusters were defined as matrilines and were used for further analyses (n = 439 metamorphs). More details can be found in Appendix The 3.4me) that had a mean value of 1.8 and was smaller than the mean mating frequency. This shows that not all fathers sired an equal number of offspring per matriline. The main father per matriline sired 1 to 19 offspring , and all fathers together per matriline sired 1 to 29 offspring   for both datasets.SD: 7.8 ± 3.0) than for offspring from main father . Additionally, we detected a positive correlation of effective number of fathers and the CV in developmental time , but not in the other metamorphic traits.The variability in all metamorphic traits was increased by multiple paternity when comparing main and all fathers Table , but onl3.52 = 84.89, df = 39, p < 0.0001), indicating a genetic or maternal effect. Overall, SVL of individuals ranged from 12 to 17 mm. The median of all individuals was 14 mm. The median SVL of the different matrilines ranged from 12.5 to 15 mm . The median SVL of metamorphs differed significantly between matrilines . Overall, BCI of individuals ranged from 0.11 to 0.30 with a median of 0.20. The median BCI of the different matrilines ranged from 0.16 to 0.23 , indicating a genetic or maternal effect. Overall, developmental time of individuals ranged from 89 to 140 days, with a median of 106 days. The median developmental time of the different matrilines ranged from 91 to 135 days  = 2.909, p = 0.04771, R2 = 0.13, suggested that developmental time did not influence SVL , but that number and size of offspring was positively related . This indicates that some matrilines had more metamorphosing offspring with larger SVLs , indicating a higher BCI per matriline with longer pond development. The summary of the GAM can be found in the Appendix  had an influence on the mean BCI of matrilines, when included as smoothed terms. The developmental time showed a positive albeit not significant linear trend  sitting under freshly laid clutches, feeding on the embryos. Therefore, we think that predation pressure in the early development could have been high, which could lead to smaller size and lower size variability of progeny (van Buskirk & Relyea, Due to an inverse relationship between reproductive investment per offspring and number of offspring (Charnov & Ernest, Still, we found a higher number of offspring with bigger body size in single matrilines. In anurans, the egg size correlates strongly with body size Cummins,  and has 5R. temporaria population and increases the variability in metamorphic traits, especially in developmental time of offspring from the same matriline. This increase seems to be due to additive genetic effects of multiple fathers on developmental time. These findings suggest that SVL and BCI are more influenced by environmental factors.In our study, we could show that metamorphic traits differ between matrilines in the same environment, which indicates that there are underlying intrinsic genetic effects from the parents. However, SVL and BCI differed only marginally between matrilines, an indication for strong environmental effects that are counteracting the intrinsic growth rates. These environmental effects could be predation pressure and the amount of food resources, as well as temperature and desiccation risk. We show that multiple paternity is very common in this None declared.MOR and HF designed the research; JH performed the research; CD and JH analyzed the data; CD, JH, MOR, and HF wrote the manuscript. Click here for additional data file.


Mendelian randomization is a method for exploring observational associations to find evidence of causality.To apply Mendelian randomization between risk factors/phenotypic traits (exposures) and PD in a large, unbiased manner, and to create a public resource for research.We used two‐sample Mendelian randomization in which the summary statistics relating to single‐nucleotide polymorphisms from 5,839 genome‐wide association studies of exposures were used to assess causal relationships with PD. We selected the highest‐quality exposure genome‐wide association studies for this report (n = 401). For the disease outcome, summary statistics from the largest published PD genome‐wide association studies were used. For each exposure, the causal effect on PD was assessed using the inverse variance weighted method, followed by a range of sensitivity analyses. We used a false discovery rate of 5% from the inverse variance weighted analysis to prioritize exposures of interest.We observed evidence for causal associations between 12 exposures and risk of PD. Of these, nine were effects related to increasing adiposity and decreasing risk of PD. The remaining top three exposures that affected PD risk were tea drinking, time spent watching television, and forced vital capacity, but these may have been biased and were less convincing. Other exposures at nominal statistical significance included inverse effects of smoking and alcohol.https://pdgenetics.shinyapps.io/MRportal/). Alongside, we report further evidence to support a causal role for adiposity on lowering the risk of PD. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.We present a new platform which offers Mendelian randomization analyses for a total of 5,839 genome‐wide association studies versus the largest PD genome‐wide association studies available ( Although monogenic forms of Parkinson's disease (PD) are responsible for approximately 5% of cases, the vast majority of disease is considered to be sporadic and attributable to a range of genetic and nongenetic risk factors.exposure) and a disease outcome.Mendelian randomization (MR) is an epidemiological method that can be used to provide support for causality between a modifiable exposure/risk factor/phenotypic trait (henceforth collectively termed MR Base (http://www.mrbase.org), which enables targeted (hypothesis‐driven) exploration of causal associations or hypothesis‐generating approaches to MR.A common approach to MR involves the use of summary statistics from published GWASes of exposures and the summary statistics of a GWAS of an outcome; an approach known as two‐sample MR. Recently, the summary statistics from GWAS for a large range of exposures have been curated in MR Base is an online resource which, at the time of the analysis, contained summary results from 7,956 GWASes across multiple exposures, encompassing a wide range of physiological characteristics and disease phenotypes. MR Base was accessed on January 14, 2019. Each exposure was tested separately to determine whether it altered risk of PD. All analyses were performed using the R package, TwoSampleMR . The instrumental variables used for each binary exposure consisted of the per‐allele log‐odds ratio (or the beta estimate for continuous exposures) and standard errors for all independent GWAS significant SNPs.P values <5.0 × 10–8 for their association with a given exposure; (2) these SNPs or their proxies  had to be present in both the exposure and outcome (PD) data sets; and (3) these SNPs were independent signals that were generated through a process called “clumping.” In order to “clump,” index SNPs were identified by ranking exposure associations from the smallest to largest P value . Clumped SNPs were those in linkage disequilibrium (LD) with index SNPs (R2 threshold of 0.001) or within 10,000 kb physical distance. Hence, each index SNP represented a number of clumped SNPs that were all associated with or near to the index SNP, and the index SNPs were all independent of one another (according to the stringent parameters defined here). A total of 5,839 exposure GWASes surpassed these criteria and were tested against the outcome. Then, we further expanded our filtering approach as follows: (4) in order to use MR sensitivity analyses designed to identify pleiotropy and its effects, each GWAS had to include a minimum of 10 associated SNPs; (5) the number of cases was >250 for GWASes of a binary exposure or > 250 individuals for GWASes of a continuous exposure; and (6) both the exposure and the outcome data were drawn from European populations. A total of 401 exposures met our filtering criteria , consisting of 175 published GWASes and 226 unpublished GWASes from the UK Biobank . For UKB GWASes, some of the exposures are reported in an ordinal fashion, but treated as continuous when calculating betas for the effect allele at each SNP. This means that some of the effect estimates that arise are difficult to interpret quantitatively, both in the GWAS and in the subsequent MR analysis.We used the following stringent criteria for any exposure GWAS to be included in our analysis: (1) the GWAS had to report SNPs with Summary statistics from the largest, published PD GWAS meta‐analysis involving 26,035 PD cases and 403,190 controls of European ancestry were used as the outcome data for the primary analysis. Recruitment and genotyping quality‐control procedures were described in the original report.A newer PD GWAS included a total of 37,688 cases, 1,417,791 controls, and 18,618 “proxy cases” from the UKB .Harmonization was undertaken to rule out strand mismatches and to ensure alignment of SNP effect sizes. Wald ratios were calculated for each clumped SNP in the exposure GWAS by dividing the per‐allele log‐odds ratio (or beta) of that variant in the PD GWAS data by the log‐odds ratio (or beta) of the same variant in the exposure data.P value of <0.05 to define exposures of interest as showing potential evidence of a causal effect. The IVW estimate is valid when the three core assumptions that underpin MR are upheld (see Supporting Information). However, simulation studies show that up to 90% of MR analysis may be affected by pleiotropy, which, in turn, may bias the IVW estimate.First, the inverse‐variance weighted (IVW) method was implemented to examine the relationship between the individual exposures and PD. In this method, the Wald ratio for each SNP was weighted according to its inverse variance and the effect estimates were meta‐analyzed using random effects. This approach is equivalent to plotting SNP‐exposure/SNP‐outcome associations on scatter plot and fitting a regression line (IVW regression), which is constrained to pass through the origin. The slope of the linear regression represents the pooled‐effect estimate of the individual SNP Wald ratios.Heterogeneity in the IVW estimates was tested using Cochran's Q test, quantified using the IMR‐Egger was used, in which the regression line fitted to the data is not constrained to pass through the origin and a nonzero intercept indicates whether there is a net horizontal pleiotropic effect which may bias the IVW estimate.For effect estimate directionality, odds ratios were scaled on a standard deviation increase in genetic risk for the exposure from that population mean. We evaluated the possibility that the overall estimate was driven by a single SNP using leave‐one‐out (LOO) analyses for each of the exposures associated with PD. Finally, we tested for reverse causation by using SNPs tagging the independent loci described in the latest PD GWAS as exposure instrumental variables and exposure GWASes as the outcomes. Note that this analysis measures the causal effect of genetic liability toward PD on each of the exposures included in the main analysis, which is independent of PD actually occurring (in a case‐control setting such as this).https://pdgenetics.shinyapps.io/MRportal/. Here, we explore some of the top results to assist users in understanding how to interpret these data , tea drinking (positive), and forced vital capacity . In each case, to explore the possibility that results were biased because of the violation of core MR assumptions, we looked for heterogeneity in the individual Wald ratios and performed the three additional MR sensitivity analyses . A unit increase in arm fat percentage (right and left) yielded a pooled odds ratio (OR) of 0.65  and a pooled OR of 0.66 , respectively. Although the individual Wald ratios showed significant heterogeneity , the sensitivity analyses did not suggest significant bias in the causal effect estimate from the IVW analysis . Given the results for adiposity, we expected that a unit change in impedance would result in a lowering of PD risk. In contrast to the results for adiposity, the impedance exposure gave rise to an IVW OR of 1.25 . There was significant heterogeneity in the individual Wald ratios , but the sensitivity analyses did not suggest bias in the IVW effect estimate , and there was no significant heterogeneity in the individual Wald ratios . The sensitivity analyses did not imply bias in the IVW estimate. Of note, coffee intake was not causally associated with increased risk of PD , but similar to the tea drinking exposure, the direction of effect was also not consistent with the recognized negative observational effect.The “tea drinking” exposure was captured by an instrument containing 35 SNPs. The IVW OR was 1.74 , suggesting that more time spent watching television caused a lower risk of PD. However, the MR Egger sensitivity analysis gave a very different pooled effect estimate , suggesting that the IVW result may have been biased by directional pleiotropy. The intercept term can be used to test for net directional pleiotropy , but this test is generally underpowered. The difference in the slope from the IVW analysis and the MR Egger analysis is shown graphically in Supporting Information Figure “Time spent watching television” was inversely linked to PD risk in the IVW analysis , but dropping this SNP from the analyses did not affect the overall results. Similarly, the most precisely estimated Wald ratio in the impedance instrument was for a different SNP in the FTO gene (rs62048402), but again dropping this SNP did not affect the overall results. Importantly, and in support of the observations relating to negative genetic correlation between percentage fat and impedance described above, the direction of the Wald ratio for the FTO SNP in adiposity exposures was negative and for impedance was positive.The LOO analysis showed that none of the results described for the 12 exposures were explained by a single SNP in each of the instruments (Supporting Information Table AHR and CYP1A2 gene loci respectively), which are known to be strongly associated with caffeine consumption.AHR) was removed (the OR 1.74 changed to 1.61). In the forced vital capacity analysis, SNPs rs1991556 and rs13146142 were most precisely estimated and appeared to influence the magnitude of the causal effect. Closer examination revealed that rs1991556 is in the MAPT locus and rs13146142 is in the LCORL locus, and both are known to be associated with PD, likely biasing the causal effect of FVC on PD.For tea drinking, the two Wald ratios with the greatest influence on the pooled effect estimates came from SNPs rs4410790 and rs2472297 , current tobacco smoking , increased alcohol intake , and increased education . The FDR‐adjusted P values for each of these were < 0.1 and unadjusted P values were all <0.004.Of interest, the next seven exposures with the strongest associations in the IVW analysis, but not surpassing the FDR‐adjusted P value, but the top hit was a marker of adiposity (hip circumference). When an even earlier iteration of the PD GWAS was used (~13.5k cases and ~95k controls),P‐value threshold and all 11 were related to adiposity  from the 2019 PD GWAS dataset as the outcome  and risk of PD—a genetically estimated 5 kg/mP = 0.037), using the same BMI instrument that we have previously published on.Of note, although BMI was not one of the top 12 exposures, the causal effect of BMI on PD was also negative in this analysis  have consistently suggested a negative association with PD, and caffeine has been explored as a potentially therapeutic option for PD.In contrast to tea drinking, there was some evidence to support a true protective effect of smoking, which fell below the threshold to be discussed as a top result. The remaining two exposures  that appeared to be causally linked to PD should be regarded with caution. Television watching failed in one of the sensitivity analyses and the effect of forced vital capacity appeared to be driven, in part, by two recognized PD SNPs, rendering these two results potentially invalid.MR Base. However, as highlighted in this report and in a warning message in the portal, we advise caution with the interpretation of results for exposures that do not surpass considered filtering criteria . These included GWASes for which the case numbers were small and the number of variants in the exposure instruments was too few to permit the use of the sensitivity analyses.Alongside a standard method used to pool ratio estimates from individual SNPs (the IVW method), we also demonstrated the use of three sensitivity analyses that provide more valid estimates when core instrumental variable assumptions have been violated .P values that arise from MR hypothesis tests are often not small and, in the various sensitivity analyses, often border on or exceed nominal statistical significance. Given the approach, traditional methods of adjusting for multiple comparisons  render many potentially causal associations obsolete and limit the conclusions that one can draw. In general, we recommend using this tool to confirm or add to evidence to associations identified in observational studies or toward a particular intervention. It is generally recommended that emphasis should be placed on the confidence intervals and consistency of point estimates across the IVW estimate and the sensitivity analyses, rather than just the P values per se. In addition, we recommend the calculation of F‐statistics for determining instrument strength and post‐hoc power calculations, particularly in the instance of null results.no evidence to support a causal association . These additions, and others, are planned for future versions of the portal.The general limitations of MR have been covered extensively elsewhere,For all analyses presented here, the outcome is “risk of PD” because the outcome data come from a GWAS of PD cases versus controls. In order to make causal inferences about the effect of various exposures on disease progression, one would need to have an outcome GWAS of PD progression. As mentioned in the Materials and Methods, an important source of bias in MR studies can occur when there is overlap in the control groups in the exposure and outcome data. For this reason, all UKB data were removed from the outcome PD GWAS summary statistics.In summary, we present a new portal for use by the research community that will help assess causality where observational associations exist and prioritize (or deprioritize) interventions aimed at reducing risk of PD.(1) Research Project: A. Conception, B. Organization, C. Execution; (2) Data Generation: A. Experimental; (3) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (4) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique.A.J.N.: 1A, 1B, 1C, 3A, 3B, 4A, 4BS.B.‐C.: 1A, 1B, 1C, 3A, 3B, 4A, 4BJ.K.: 1A, 1B, 1C, 3A, 3B, 4BK.H.: 3C,4BD.K.: 3C,4BG.H.: 3C,4BA.X.: 3C,4BD.A.L.: 3C,4BG.D.S.: 3C,4BR.D.: 3C,4BZ.G‐O.: 3C,4BC.B.: 3C,4BJ.R.G.: 3C,4B23andMe Research Team: 2AIPDGC: 2AD.A.H.: 3C,4BJ.Y.: 3C,4BP.V.: 3C,4BJ.C.: 3C,4BH.M.: 3C,4BJ.H.: 3C,4BN.W.W.: 3C,4BM.A.N.: 2A,3C,4BA.B.S.: 3C,4BAJN reports grants from Parkinson's UK, Barts Charity, Leonard Wolfson Experimental Neurology Centre, UCL Movement Disorders Centre and the Virginia Kieley Benefaction; honoraria or consultancy fees from Britannia, Global Kinetics Corporation, Profile Pharmaceuticals, Guide point, Biogen and Roche. KH and DAH are employees of 23andMe and hold stock or stock options in 23andMe. DAL reports grants from the Medical Research Council, numerous charitable funders,Medtronic and Roche. ZG‐O reports consultancy fees from Inceptions Sciences,Idorsia, Denali, Lysosomal Therapeutics inc. HM reports reports consultancy from Biogen, UCB, Abbvie, Denali, Biohaven; lecture fees/honoraria from Biogen, UCB,C4X Discovery, GE‐Healthcare, Welcome Trust, Movement Disorders Society; Research Grants from Parkinson's UK, Cure Parkinson's Trust, PSP Association, CBD Solutions, Drake Foundation, Medical Research Council. Dr Morris is a co‐applicanton a patent application related to C9ORF72 (PCT/GB2012/052140).Figure S1. Forest plots showing point estimates of the exposures of interest.Figure S2. Funnel plots showing point estimates of the exposures of interest.Table S1. Genetic correlations between fat related traits and impedance related traits.Table S2. Leave‐one‐out analyses of exposures of interest.Table S3. Reverse causality analyses.Table S4. Mendelian Randomization analyses of exposures significantly linked to PD at IVW p‐value <0.05 vs Nalls et al., 2019 .Table S5. Mendelian Randomization analyses of exposures significantly linked to PD at IVW p‐value <0.05 vs Nalls et al., 2014.Click here for additional data file.


Partitioned heritability analysis showed an enrichment for central nervous tissue‐associated genes for BF%, which was more prominent in females than males. Genetic correlations of BF% and FFM with the largest GWAS of AN by the Psychiatric Genomics Consortium were estimated to explore shared genomics. The genetic correlations of BF%male and BF%female with AN differed significantly from each other , suggesting that the female preponderance in AN may, in part, be explained by sex‐specific anthropometric and metabolic genetic factors increasing liability to AN.Anorexia nervosa (AN) occurs nine times more often in females than in males. Although environmental factors likely play a role, the reasons for this imbalanced sex ratio remain unresolved. AN displays high genetic correlations with anthropometric and metabolic traits. Given sex differences in body composition, we investigated the possible metabolic underpinnings of female propensity for AN. We conducted sex‐specific GWAS in a healthy and medication‐free subsample of the UK Biobank ( SNPs were excluded if they had a minor allele frequency (MAF) smaller than 1%, if no call was made in more than 2% of samples following imputation, if they were imputed with low confidence (INFO<0.7), or if they deviated substantially from Hardy–Weinberg equilibrium . Individuals were excluded if they showed inbreeding coefficients >0.2, or evidence of DNA contamination. Ancestry outliers were removed based on plotting of the first two principal components (PCs). The analysis was performed using imputed variant dosages and an additive model. The SNP‐based heritability (SNP‐h2) of AN calculated using these data was 17% (SE = 1%), suggesting that a substantial fraction of the heritability of AN stems from common genetic variation across all autosomes  for every participant. This body composition analyzer calculates FFM and FM from raw bioelectrical impedance data, using standard formulas including sex, age, height, and athleticism. Individuals whose hydration status might be compromised  were excluded Table . Bioelec2.4http://csg.sph.umich.edu/abecasis/metal/; Willer, Li, & Abecasis, p < 5 × 10−8) were considered as potential index SNPs. SNPs in LD (r2 > 0.2) with a more strongly associated SNP within 3,000 kb were assigned to the same locus using Functional Mapping and Annotation  were compared with entries in the NHGRI‐EBI GWAS catalog  in either females or males while not showing at least suggestive significance in the opposite sex (5 × 10−6) with differences in beta estimates that remain significant after Bonferroni correction for the total number of significant genomic loci.We calculated sex‐specific GWAS on residualized BF% and FFM, using BGENIE v1.2 . We included all genotyped and imputed autosomal variants for BF% and FFM and used the LD score reference files provided with the software. We tested for differences between the heritabilities by calculating SE using a block jackknife method implemented into the software. To identify tissue types associated with BF% and FFM, we performed a partitioned heritability analysis in LDSC v.1.0.0, ranking 10 cell type groups based on contribution to heritability after controlling for the effects of 53 functional annotations  across the autosomes to examine the genetic overlap between AN and metabolic and psychiatric GWAS summary statistics. First, we calculated SNP‐rgs between anthropometric traits, namely our BF% and FFM GWASs with GWASs of childhood BMI  with glycemic traits, such as insulin sensitivity assessed by the insulin resistance homeostatic model assessment (HOMA‐IR), fasting glucose, and insulin concentrations  in the SNP‐h2 estimates between males and females using a block jackknife approach (Supporting Information) and tested whether the SNP‐rgs between females and males were different from 1 to identify potential genetic differences related to sex. We calculated the SNP‐rg of the female and male GWASs with AN separately to investigate the differences in the relationship of these sex differences with the risk for AN. To test the statistical significance of all estimates, we calculated their SE and corresponding p value by applying a block jackknife method, as described and implemented in LDSC v1.0.0 by Bulik‐Sullivan et al.  between 1.18 and 1.59 (Table p < 5 × 10−8) after LD‐based and distance‐based clumping  entries 20–25. Summary statistics are available for download www.topherhuebel.com/GWAS.After quality control, we performed sex‐stratified association analyses on the continuous outcomes of BF% and FFM. Minimal inflation due to population stratification or other systematic biases was indicated by LDSC intercepts between 1.02 and 1.10 and lambda median statistic inflation values . However, neither the SNP‐h2 estimates for BF% nor for FFM measured by LDSC differed significantly between the sexes.The % Figure . The SNPSNP‐h2 that can be attributed to different cell type groups. BF%female showed an significant enrichment for the CNS cell type group with 14% of SNPs explaining an estimated 40% of the SNP‐h2 (p = .004), whereas BF%male was significantly enriched for the “other” cell type group that contains adipose tissue with 20% of SNPs explaining an estimated 57% of the SNP‐h2  and between BF%meta and BMIadolescence/young adulthood was 0.48 . Similarly, FFMchildhood and FFMadulthood showed a significant SNP‐rg of 0.69  and FFMchildhood also correlated genetically with FFMmeta in our UK Biobank sample .The significant meta and FFMmeta correlated genetically . The SNP‐rg between BF%female and BF%male was significantly less than 1 , indicating heterogeneity in the genomic architecture between females and males , whereas that between BF%male and AN was −0.26 . These SNP‐rg were significantly different from each other . AN showed a significant genetic correlation with FFMmeta  Physical activityfemale showed a significant SNP‐rg with AN , but physical activitymales did not . However, this difference was not statistically significant . Fasting insulinfemale was genetically correlated with AN , but not fasting insulinmale . However, this difference in SNP‐rg between sexes did not reach significance  after multiple testing correction. Sex‐ and age‐adjusted insulin resistance (HOMA‐IR) correlated significantly with AN  and anxietymeta . However, the difference between the male and female SNP‐rg with AN was not significant in MDD . While the SNP‐rg between education years in females and males was significantly different from 1 , indicating sex differences, the SNP‐rg of education years with AN did not differ between females and males .AN was significantly correlated with MDD4SNP‐rgs of AN with a variety of metabolic phenotypes, including body composition, lipid metabolism, and glycemic traits  than male‐specific effects on BF% . This suggests that a specific set of genomic variation may be differentially active in females and may increase the liability for AN. The partitioned heritability analyses of SNP‐h2 showed that BF%female was significantly enriched for CNS tissue while BF%male was enriched for adipose tissue, recapitulating prior findings in sex‐combined samples  of body mass index (BMI) across the lifespan as calculated by ACE models from Silventoinen et al. Blue values represent males and red females with error bars depicting 95% confidence intervals. After the age of 19, heritabilities are represented for a whole decade  QQ plot for the body fat percentage (BF%) genome‐wide‐association study (GWAS). (b) QQ plot for the fat‐free mass (FFM) GWASFigure S4 (a) Manhattan plot of the meta‐analyzed genome‐wide association study (GWAS) of body fat percentage (BF%). The red line represents the genome‐wide significance threshold of 5 × 10−8. Chr = chromosome. (b) Manhattan plot of the meta‐analyzed genome‐wide association study (GWAS) of fat‐free mass (FFM). The red line represents the genome‐wide significance threshold of 5 × 10−8. Chr = chromosomeFigure S5 (a) Partitioned heritability by 10 cell type groups for body fat percentage in females. The black dashed lines at −log10(P) = 2.3 is the cutoff for Bonferroni significance. CNS = central nervous system SNP = single nucleotide polymorphism. (b) Partitioned heritability by 10 cell type groups for body fat percentage in males. The black dashed lines at −log10(P) = 2.3 is the cutoff for Bonferroni significance. CNS = central nervous system, SNP = single nucleotide polymorphism. (c) Partitioned heritability by 10 cell type groups for body fat percentage in the meta‐analyzed GWAS. The black dashed lines at −log10(P) = 2.3 is the cutoff for Bonferroni significance. CNS = central nervous system; SNP = single nucleotide polymorphismFigure S6 (a) Partitioned heritability by 10 cell type groups for fat‐free mass (FFM) in females. The black dashed lines at −log10(P) = 2.3 is the cutoff for Bonferroni significance. CNS = central nervous system, SNP = single nucleotide polymorphism. (b) Partitioned heritability by 10 cell type groups for FFM in males. The black dashed lines at −log10(P) = 2.3 is the cutoff for Bonferroni significance. CNS = central nervous system, SNP = single nucleotide polymorphism. (c) Partitioned heritability by 10 cell type groups for fat‐free mass (FFM) in the meta‐analyzed GWAS. The black dashed lines at −log10(P) = 2.3 is the cutoff for Bonferroni significance. CNS = central nervous system; SNP = single nucleotide polymorphismClick here for additional data file.Table S1 Twin‐based (twin‐h2) and single nucleotide polymorphism‐based heritability (SNP‐h2) estimates derived from genome‐wide association studies (GWAS) for anorexia nervosa (AN) and anthropometric traits measured by bioelectrical impedance analysis of fat‐free mass (FFM), and body fat percentage (BF%)Table S2. Sex data for the anorexia nervosa (AN) genome‐wide association study (GWAS) datasetsTable S3. Exclusion criteria by International Statistical Classification of Diseases (ICD‐10), British National Formulary (BNF), and UK Biobank variableTable S4. Inflation statistics and heritability estimates on the observed scale of the genome‐wide association studies (GWAS). Attenuation ratio as calculated by Loh et al. (2017)Table S5 (a) Genome‐wide significant loci of the body fat percentage genome‐wide association study (GWAS) including heterogeneity measures of the meta‐analysis and a z‐test to test for significant differences between the sexes. Sex‐specific loci are defined as reaching genome‐wide significance (5 × 10−8) in either females or males, not reaching suggestive significance in the opposite sex (5 × 10−6) and the differences in beta estimates is significant after Bonferroni correction for the total number of significant genomic loci. (b) Genomic loci associated with anthropometric traits in published genome‐wide association studies (GWAS). Previous associations have been retrieved from the GWAS catalog (https://www.ebi.ac.uk/gwas/). Data extracted from Functional Mapping and Annotation of Genome‐Wide Association Studies Table S6. (a) Genome‐wide significant loci of the fat‐free mass genome‐wide association study (GWAS) including heterogeneity measures of the meta‐analysis and a z‐test to test for significant differences between the sexes. Sex‐specific loci are defined as reaching genome‐wide significance (5 × 10−8) in either females or males, not reaching suggestive significance in the opposite sex (5 × 10−6) and the differences in beta estimates is significant after Bonferroni correction for the total number of significant genomic loci. (b) Genomic loci associated with anthropometric traits in published genome‐wide association studies (GWAS). Previous associations have been retrieved from the GWAS catalog (https://www.ebi.ac.uk/gwas/). Data extracted from Functional Mapping and Annotation of Genome‐Wide Association Studies Table S7. Heritability as estimated by BOLT‐LMM, v2.3.2, on genotyped single nucleotide polymorphisms (SNPs) on anthropometric traits in the UK Biobank. Variance explained by BOLT‐LMM's linear predictor—using the default mixture‐of‐Gaussians prior on SNP effect sizes, which accounts for larger‐effect SNPs—and variance theoretically explained by an optimal linear predictor, that is, SNP heritability (h2g). Additionally, linkage disequilibrium score regression (LDSC) estimates are presented and the difference between BOLT‐LMM and LDSC estimatesTable S8. Genetic correlations for sex‐combined anorexia nervosa genome‐wide association study and genetic correlations across body composition, physical activity, and psychiatric and behavioral traits. (a) Genetic correlations for female‐only anorexia nervosa genome‐wide association study and genetic correlations across body composition, physical activity, and psychiatric and behavioral traitsClick here for additional data file.Appendix S1 Supporting InformationClick here for additional data file.Appendix S2 Supporting InformationClick here for additional data file.Appendix S3 Supporting InformationClick here for additional data file.


Whole-genome sequence (WGS) data could contain information on genetic variants at or in high linkage disequilibrium with causative mutations that underlie the genetic variation of polygenic traits. Thus far, genomic prediction accuracy has shown limited increase when using such information in dairy cattle studies, in which one or few breeds with limited diversity predominate. The objective of our study was to evaluate the accuracy of genomic prediction in a multi-breed Australian sheep population of relatively less related target individuals, when using information on imputed WGS genotypes.Between 9626 and 26,657 animals with phenotypes were available for nine economically important sheep production traits and all had WGS imputed genotypes. About 30% of the data were used to discover predictive single nucleotide polymorphism (SNPs) based on a genome-wide association study (GWAS) and the remaining data were used for training and validation of genomic prediction. Prediction accuracy using selected variants from imputed sequence data was compared to that using a standard array of 50k SNP genotypes, thereby comparing genomic best linear prediction (GBLUP) and Bayesian methods (BayesR/BayesRC). Accuracy of genomic prediction was evaluated in two independent populations that were each lowly related to the training set, one being purebred Merino and the other crossbred Border Leicester x Merino sheep.A substantial improvement in prediction accuracy was observed when selected sequence variants were fitted alongside 50k genotypes as a separate variance component in GBLUP (2GBLUP) or in Bayesian analysis as a separate category of SNPs (BayesRC). From an average accuracy of 0.27 in both validation sets for the 50k array, the average absolute increase in accuracy across traits with 2GBLUP was 0.083 and 0.073 for purebred and crossbred animals, respectively, whereas with BayesRC it was 0.102 and 0.087. The average gain in accuracy was smaller when selected sequence variants were treated in the same category as 50k SNPs. Very little improvement over 50k prediction was observed when using all WGS variants.Accuracy of genomic prediction in diverse sheep populations increased substantially by using variants selected from whole-genome sequence data based on an independent multi-breed GWAS, when compared to genomic prediction using standard 50K genotypes. Accuracy of genomic prediction of genetic merit of farm animals depends on factors such as heritability, the size of the reference population and the effective number of chromosome segments, which in turns depends on the effective population size (mentclass2pt{minimment}Ne) . In multed value , 5, whicGenetic variants at or in high LD with causal mutations are likely to be only a small subset of the whole set of sequence variants. This means that a large part of the whole-genome sequence data is in low LD with causal mutations and can be excluded from the prediction model. Earlier work in dairy cattle showed that WGS data used in genomic prediction gave no to limited improvement in prediction accuracy when all variants were used –9 and onReasons for the limited improvement from using WGS data in previous studies may be the high levels of LD and relatively low genetic diversity in the dairy cattle populations used in these studies. With high LD, the difference in predictive ability between markers from standard 50k SNP-panels and other markers closer to the actual QTL is likely to be smaller. Indeed, it will be more difficult to distinguish between these when selecting variants from GWAS in such populations. Improvements in prediction accuracy were more notable when variants were selected from a multi-breed GWAS .Another important aspect of genomic prediction is the genetic relatedness between the training set and the target animals for which we want to predict the breeding value. In efficient breeding programs, it is likely that most selection candidates have close relatives in the training set, i.e. animals with both genotype and phenotype information are often available from previous rounds of selection. Prediction from closer relatives is less likely to be improved by markers that are closer to the QTL, and therefore WGS data provides limited benefit. However, prediction of more distant target individuals is also relevant, for example when applying genomic prediction to more diverse populations and populations of multi-breed nature, such as in beef cattle and sheep, or when special reference populations are formed for a limited set of individuals measured for traits that are not widely recorded, such as slaughter traits or feed efficiency. Relatedness between training and target individuals may vary considerably in such cases. Another application of genomic prediction could be for genetic benchmarking in commercial herds or flocks in which information on previous breeding history is limited, and the target individuals may be less related to the reference population. Using markers that are closer to the actual QTL could make genomic prediction more robust and applicable to a wider range of individuals with lower genetic relatedness, including crossbreds.The objective of our study was to evaluate the accuracy of genomic prediction in a multi-breed Australian sheep population for some key lamb production traits using variants from imputed WGS data. We used selected variants obtained via a multi-breed GWAS in a separate discovery dataset and compared best linear unbiased prediction (BLUP) and a Bayesian method to evaluate the potential change in accuracy and bias of genomic prediction when adding these variants to a standard 50k SNP genotype array.http://www.sheepgenetics.org.au) for flocks with animals with genotypes. Both research and industry datasets were from a multi-breed/crossbred sheep population. Most animals were either purebred Merino or crossbreds of Border Leicester (BL), Poll Dorset (PD) or White Suffolk (WS) rams mated to Merino dams or BL × Merino dams.Phenotypes for key meat and wool production traits were extracted for animals in the “Sheep Cooperative Research Centre” database (“Research data”), which includes the “Information Nucleus Flocks” (INF), “Resource Flocks” (RF) and “the Sheep Genomics Flock” (SGF). INF consisted of eight flocks located across different regions of Australia that are linked to each other by the use of common sires through artificial insemination between 2007 and 2011  and the For each trait, animals with both phenotype and genotype data were divided into three non-overlapping groups: 1) a QTL discovery set for a GWAS with imputed WGS data to find variants at or very near putative causal mutations; (2) a reference or training set for genomic prediction based on estimated marker effects; and (3) a validation set to evaluate accuracy and bias of genomic prediction. These three sets were purposefully chosen as being independent of each other to avoid bias in the evaluation of prediction accuracy. The data were divided into three subsets independently for each trait, because the numbers of animals recorded differed for each trait. The QTL discovery and training sets were selected to have a broad representation of breeds and crosses  half-sibs families were not split across the training and validation sets and no sires of validation animals were included in the training set; and (2) relationships between the training and validation sets were further restricted at a fixed threshold using the genomic relationship matrix (GRM) constructed from 50k SNP-chip genotypes . The appThe contributions of the MER and BL × MER breeds in the discovery, training and validation sets for each trait are in Table Phenotypic records of eight traits related to weight, carcass and meat quality and wool production were used in this study; post weaning weight (PWT), post weaning eye muscle depth  from ultrasound scanning of live animals, carcass eye muscle depth , carcass fat depth at the C site , intra muscular fat  and shear force at 5 days after slaughter , yearling clean fleece weight  and yearling wool fiber diameter .First, phenotypes from the research data were edited for possible outliers , for records with no pedigree or breed composition information or for records with missing age or weight at measurement. Phenotypes were then pre-corrected for fixed and random effects in a univariate mixed model analysis, similar to the model used by Sheep Genetics . The fixPhenotypes from the industry dataset were taken from the national Sheep Genetics OVIS database , which iThere were 31,937 animals with genotypes that had either previously been imputed from 12k to the 50k SNP panel  or were real 50k genotypes  and the breed representation in these datasets was similar and relative to their occurrence in industry flocks  was removed using the PLINK software [In the discovery population for each trait, we ran a GWAS using the WGS 31,154,082 imputed genotypes. The GWAS results were used to select the ‘top SNPs’ from those associated with variation in the traits of interest. The “top SNPs” were defined as the most significant SNPs below an association threshold of p-value < 10software . Some ofThe model used in GWAS for each trait using Wombat software  is as foWe applied genomic BLUP (GBLUP)  and the mentclass2pt{minimFive different genotype sets were tested in the models: (1) top sequence variants (top), 2) 50k panel, 3) 50k plus top sequence variants (50k + top), and (4) HD panel and 5) WGS data. However, due to the large number of SNPs in WGS data, we did not run a Bayesian model for that set. The SNPs with a minor allele frequency (MAF) lower than 0.005 in the genomic prediction training populations were excluded from the genotype sets. In the GBLUP models, top, 50k, 50k + top, HD and WGS genotype sets were fitted in the model with the GRM as a covariance structure of one random additive genetic effect  the accuracy of genomic prediction increased in some cases compared to either the 50k or HD GBLUP, but more often decreased with no clear pattern among traits. Compared to using 50k, the WGS set resulted in a change in accuracy, averaged across traits, of + 0.016 for Merinos and − 0.014 for the crossbreds.Accuracy of GBV based on the HD SNP genotypes and GBLUP was only slightly increased relative to GBLUP (50k) (0.028 accuracy increase for Merinos and no difference for the crossbreds when averaged across traits), but increases were higher and more consistent with BayesR (0.081 for Merinos and 0.039 for the crossbreds). When using all the variants in WGS data . When including top SNPs using the BayesR method, the accuracy increase, when averaged across traits, was 0.079 for Merinos and 0.059 for crossbreds, compared with GBLUP (50k), and these increases were 0.102 and 0.087 with the BayesRC method, in which top SNPs and the 50k set were treated, separately.The increase in accuracy due to adding top SNPs to the 50k array varied among traits, between methods within traits, and also with the validation set Fig. . With GBUsing just the set of selected variants alone improved accuracy more compared with GBLUP (50k) when averaged across all traits; 0.053 and 0.029 for GBLUP in Merino and crossbred validation sets and 0.069 and 0.043 for BayesR in these two validation sets  was based on performing an association study on imputed sequence data in an independent set of animals to avoid probable bias of prediction , 13. TheCompared to using 50k genotypes, using the complete set of sequence data (~ 31 million variants) resulted in a higher estimated additive genetic variance and heritability. We compared the sum of diagonal terms and off-diagonal terms from y cattle , 12 thatEstimation of heritability is mainly driven by (higher) relationships between training animals, in which case markers that are not linked to causal variants could still be helpful in estimating these more accurately. This argument can also be used to explain that the heritability based on using top SNPs alone was generally lower than the heritability based on the SNP sets with more markers, while prediction accuracies based on these selected marker sets were generally similar and sometimes even higher than the accuracy based on the 50k set. A similar result was found by Raymond et al.  who arguFitting the selected sequence variants (3924 to 8654 for different traits) that were derived from the association study along with the standard 50k genotypes often resulted in a notable increase in prediction accuracy in both purebred and crossbred animals, particularly if these selected markers were fitted as separately defined groups, either in GBLUP  across milk traits in different scenarios for selecting sequence variants. By including selected sequence variants from GWAS in GBLUP (with a separate variance component to the 50k genotypes) Brøndum et al. [Our results were based on a multi-breed dataset and, generally, the improvement in prediction accuracy from using selected variants was larger than that obtained in studies using data on a single breed .Additional file 1: Table S1. Summary statistics of phenotypes for different traits. Table with summary statistics for each trait in the study.


The genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation as it assumes a common variance for all single nucleotide polymorphism (SNP). However, this is unlikely in the case of traits influenced by major SNP. Hence, the present study aimed to improve the accuracy of GBLUP by using the weighted GBLUP (WGBLUP), which gives more weight to important markers for various carcass traits of Hanwoo cattle, such as backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Linear and different nonlinearA SNP weighting procedures under WGBLUP were evaluated and compared with unweighted GBLUP and traditional pedigree-based methods (PBLUP). WGBLUP methods were assessed over ten iterations. Phenotypic data from 10,215 animals from different commercial herds that were slaughtered at approximately 30-month-old of age were used. All these animals were genotyped using customized Hanwoo 50K SNP chip and were divided into a training and a validation population by birth date on 1 November 2015. Genomic prediction accuracies obtained in the nonlinearA weighting methods were higher than those of the linear weighting for all traits. Moreover, unlike with linear methods, no sudden drops in the accuracy were noted after the peak was reached in nonlinearA methods. The average accuracies using PBLUP were 0.37, 0.49, 0.40, and 0.37, and 0.62, 0.74, 0.67, and 0.65 using GBLUP for BFT, CWT, EMA, and MS, respectively. Moreover, these accuracies of genomic prediction were further increased to 4.84% and 2.70% for BFT and CWT, respectively by using the nonlinearA method under the WGBLUP model. For EMA and MS, WGBLUP was as accurate as GBLUP. Our results indicate that the WGBLUP using a nonlinearA weighting method provides improved predictions for CWT and BFT, suggesting that the ability of WGBLUP over the other models by weighting selected SNPs appears to be trait-dependent. The Hanwoo (Korean native cattle) is well known for extensive marbling, tenderness, juiciness, and the characteristic flavor of its beef . ProgresThe genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation because it is easier to implement and less computationally demanding than the Bayesian Alphabet methods. However, GBLUP usually assumes that all single nucleotide polymorphisms (SNPs) explain the same fraction of genetic variance ,7, whichThe objective of this study was to investigate linear and different nonlinearA SNP weighting methods under the WGBLUP models with the hope of increasing the accuracy of genomic breeding value (GEBV) prediction for carcass traits in the commercial Hanwoo breeding population. The accuracies of the predicted breeding values derived from the aforesaid method were then compared with GBLUP and the traditional pedigree-based method (PBLUP).The phenotypic and genotypic data employed in this study were derived from 10,215 Hanwoo cattle born between April 2006 and March 2016 from different commercial herds located across South Korea. The animal population was composed mostly of steers (9856), castrated at about 4 months of age. Animals were reared in sheds containing multiple pens (8–10 head/pen), were fed a concentrate mixture and a rice straw-based ration, and were slaughtered at approximately 30 months of age. All these animals were cared for according to the appropriate animal health and welfare guidelines approved by the Animal Care and Use Committee of the National Institute of Animal Science, Rural Development Administration, Korea. Ethics approval for this study was given by the Animal Care and Use Committee of the National Institute of Animal Sciences under the code 2018-293 (from 21 May 2018). The total number of animals in the pedigree was 29,176, from seven (7) generations with 428 bulls and 13,925 dams. Thirty-six (36) animals were found to be inbred, with an average inbreeding coefficient of 0.04. Pedigree structures and inbreeding coefficients were obtained using WOMBAT software [The traits analyzed in this work were backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). BFT, EMA, and MS were measured in the rib eye muscle at the 12th and 13th rib junction, while cold CWT was weighed after chilling for about 24 h. Marbling score was graded using a 9-point scale following the Korean Beef Marbling Standard . The stap < 10−6 were excluded from the genotype data set. Parent-progeny pairs were tested for discrepant homozygous SNP, and the progeny was removed when the conflict rate reaches greater than 1%. After quality control, 37,549 SNPs remained for further analyses.Meat tissue samples collected from the same muscle were genotyped using the customized Hanwoo 50K SNP Chip . The SNPs with call rates less than 0.9, minor allele frequencies less than 0.01, and deviations from the Hardy–Weinberg equilibrium with a Here, the traditional GBLUP method with a pedigree-based relationship matrix, the GBLUP method, and WGBLUP methods with a genomic relationship matrix constructed from marker information were used to predict breeding values. GBLUP and WGBLUP assume equal and unequal weights for all SNPs, respectively. The animals were split into training and validation datasets according to year of birth, and corrected phenotypes in the validation dataset were used to compute the accuracy of predictions.y is a vector of observations; b is the vector of fixed effects of herds (162 herds), year-month of birth (82 levels), slaughter year (9 levels), slaughter place (52 levels), and the sex and age as a covariate; a is the vector of additive genetic effects following a normal distribution A being the pedigree-based relationship matrix of individuals. X and Z are indices matrices associating b and a with y, respectively. e is the vector of the random residual effect that follows a normal distribution I an identity matrix. A multi-trait animal model was used to estimate the covariance components for calculating genetic correlations between traits.For traditional pedigree-based genetic evaluations, the following single-trait animal model was fit:y − Xb). The GBLUP model uses a genomic relationship matrix (G) created from the SNP markers instead of the pedigree-based relationship (A). Following VanRaden (2008) [G matrix was created asM is a matrix of centered genotypes; m is the number of SNPs, and i-th SNP.Here, we used corrected phenotypes (Yc) as response variables, which were computed using the pedigree-based model presented above (Yc = n (2008) , the G mG was constructed differently. The creation of the G matrix shown above assumes that each SNP explains the same amount of genetic variance. In the WGBLUP approach developed by Wang et al. (2012) [G (G*). This genomic relationship matrix G* is constructed as follows:M, ip, and m are the same as G and D in a diagonal matrix, where each element of the diagonal corresponds to SNP weights. Wang et al. (2012) [i was calculated as In the weighted GBLUP, model and inferences were identical as the GBLUP with the matrix . (2012) , major S. (2012)  derived Set parameters to t = 1, Calculate GEBVusing the GBLUP approach;Compute SNP effects as i is the i-th SNP.Calculate SNP weight as Normalize SNP weights to keep the total genetic variance constant:t = t + 1 and loop to step 2.The iterative steps in the algorithm of the WGBLUP approach following Wang et al. (2012)  are as fHere, GEBV was replaced by direct genomic values (DGV) in step 3. According to Lourenco et al. (2015) , DGV is sd is the standard deviation. A limit was used on the exponent of CT to avoid extreme values. VanRaden (2008) [In step 4, SNP weights were calculated as n (2008) , where Cn (2008)  used 1.2−15. All analyses were conducted using the BLUPF90 software family [The abovementioned procedures were run for ten iterations. The convergence criterion was set to 1 × 10e family . Followie family .The predictive abilities of all the methods were evaluated by splitting the records into training and validation dataset by the date of 1 November 2015. The validation set included 1652 animals born after the cut-off date, while the training set was composed of 8563 animals. Phenotypes of animals under the validation set were assumed to be unknown. The accuracy of genomic prediction was evaluated by the correlation of the EBV or GEBV from the validation and the corresponding phenotypes corrected for fixed effects divided by the square root of the heritability of the trait.In addition, the bias of genomic prediction was assessed using the regression of corrected phenotypes on GEBV. Regression coefficients close to one are considered the most desirable.The heritability estimates for carcass traits in Hanwoo cattle were medium to high, as presented in Being an iterative weighting method, WGBLUP was performed in ten iterations where the SNP weights were equal to 1 in the first iteration corresponding to that of the GBLUP. Even after iteratively recomputing the SNP effects and the GEBV, there were still no improvements in the accuracy using the linear weighting method for all carcass traits, except for BFT. The highest accuracy using the linear weighting method was at the first iteration for CWT, EMA, MS, and the second iteration for BFT. However, the accuracy for BFT deteriorated over the succeeding iterations. Zhang et al. (2016)  purporteThe nonlinearA weighting method proposed by VanRaden (2008)  was usedThe accuracies and biases of genomic prediction for carcass traits using PBLUP, GBLUP, and WGBLUP models are presented in The genomic selection in Hanwoo beef cattle has already been evaluated in small populations, although it has been not fully implemented in practical breeding. After a comparison of the accuracies of genomic prediction using GBLUP, BayesC, and BayesL in 1183 genotyped Hanwoo cattle, Mehrban et al. (2017)  reportedIn this study, WGBLUP outperformed GBLUP for BFT and CWT, where SNPs have large or moderate effects. This is mainly due to the fact that the latter only rely on average relationships among animals across the genome and is thus less sensitive to the genetic architecture of the traits under study . The perThe present work aimed to improve the accuracy of GBLUP through the use of SNP weighting iterative methods in WGBLUP. Our results indicate that the WGBLUP using a nonlinearA weighting method provides improved but minimal predictions for BFT (4.84%) and CWT (2.70%) in Hanwoo cattle in contrast to the unweighted GBLUP method. Particularly, the WGBLUP using a nonlinearA weighting method with a CT value of 1.25 and exponential limit of 20 at four iterations reached the maximum prediction accuracy for BFT and CWT. In conclusion, the gains from a WGBLUP analysis are still dependent on several factors, such as the method used to obtain the weights, the genetic architecture of the traits , and the reference population size.


Pinus contorta, based on associations with environments.Linkage among genes experiencing different selection pressures can make natural selection less efficient. Theory predicts that when local adaptation is driven by complex and non-covarying stresses, increased linkage is favored for alleles with similar pleiotropic effects, with increased recombination favored among alleles with contrasting pleiotropic effects. Here, we introduce a framework to test these predictions with a co-association network analysis, which clusters loci based on differing associations. We use this framework to study the genetic architecture of local adaptation to climate in lodgepole pine, We identify many clusters of candidate genes and SNPs associated with distinct environments, including aspects of aridity and freezing, and discover low recombination rates among some candidate genes in different clusters. Only a few genes contain SNPs with effects on more than one distinct aspect of climate. There is limited correspondence between co-association networks and gene regulatory networks. We further show how associations with environmental principal components can lead to misinterpretation. Finally, simulations illustrate both benefits and caveats of co-association networks.Our results support the prediction that different selection pressures favor the evolution of distinct groups of genes, each associating with a different aspect of climate. But our results went against the prediction that loci experiencing different sources of selection would have high recombination among them. These results give new insight into evolutionary debates about the extent of modularity, pleiotropy, and linkage in the evolution of genetic architectures.The online version of this article (10.1186/s13059-018-1545-7) contains supplementary material, which is available to authorized users. Pleiotropy and linkage are fundamental aspects of genetic architecture . PleiotrHere, we aim to characterize the genetic architecture of adaptation to the environment, including the number of separate components of the environment in which a gene affects fitness  using a previously published exome capture dataset  and E[E2]) represent the centroid or location of the ellipse in multivariate space. The geometry of the two-dimensional (1 − α) × 100% prediction ellipse on the multivariate normal distribution can then be approximated bylj  represents the lengths of the major (j = 1) and minor (j = 2) axes on the ellipse, respectively, λj represents the eigenvalues of the covariance matrix, and χ2df = 2,α represents the value of the χ2 distribution for the desired α value [α = 0.05) corresponding to the volume within which 95% of points should fall assuming the data is multivariate normal, using the function ellipsoidPoints in the R package cluster [A prediction ellipse was used to visualize the genome-wide pattern of covariance in allelic effects on a galaxy biplot. For two variables, the 2 × 2 variance-covariance matrix of Cov had the lowest variance, followed by demographic expansion from a single refuge (1R), and demographic expansion from two refugia 2R had the highest variance. The landscape size was 360 × 360 demes, and migration was determined by a discretized version of a Gaussian dispersal kernel. Carrying capacity per deme differed slightly for each scenario to give the same overall neutral FST = 0.05. IBD was run until equilibrium at 10,000 generations, but 1R and 2R were only run for 1000 generations in order to mimic the expansion of lodgepole pine since the last glacial maximum [s = 0.001) to strong (s = 0.1), see [The simulations used in this study are identical to a subset of those previously published by , 63. Bri maximum . All sel.1), see , 63 for ρ) between allele frequency and environment. The 23rd computer-generated environment was not included in analysis, as it was meant to represent the hypothetical situation that there is a single unmeasured (and unknown) environmental variable that is the driver of selection. The 23rd environment was correlated from 0 to 0.2 with the other 22 variables.The simulations were then expanded in the following way: for each of the 22 environmental variables for lodgepole pine populations, we used interpolation to estimate the value of the variable at the simulated locations. This strategy preserved the correlation structure among the 22 environmental variables. For each of the 22 variables, we calculated the uncorrected rank correlation ) and (ii) a log-10 Bayes factor (BF) > 2 . Using both criteria is more stringent and both were used in the lodgepole pine analysis. In the simulations, however, we found that using both criteria resulted in no false positives in the outlier list  a see the “” sectionAdditional file 1: Supplementary Figures. Fourteen supplementary figures associated with the manuscript. (PDF 12466 kb)Additional file 2:Table S1. Results from GO analysis for all top candidate genes and for each group. (XLSX 9 kb)Additional file 3:Table S2. Top candidate genes and their annotations. (XLSX 72 kb)