Biologia delle comunicazioni volume 6, numero articolo: 577 (2023) Citare questo articolo
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La mappatura genetica per identificare i geni e gli alleli associati o che causano variazioni dei tratti quantitativi economicamente importanti negli animali da allevamento come i maiali è un obiettivo importante nel miglioramento genetico degli animali. Nonostante i recenti progressi nelle tecnologie di genotipizzazione ad alto rendimento, la risoluzione della mappatura genetica nei suini rimane scarsa, in parte a causa della bassa densità di siti varianti genotipizzati. In questo studio, abbiamo superato questa limitazione sviluppando un pannello di aplotipi di riferimento per i suini basato su 2259 animali sequenziati dell'intero genoma che rappresentano 44 razze suine. Abbiamo valutato le combinazioni di software e la composizione della razza per ottimizzare la procedura di imputazione e abbiamo ottenuto un tasso di concordanza medio superiore al 96%, un tasso di concordanza non di riferimento dell'88% e un r2 di 0,85. Abbiamo dimostrato in due casi di studio che l'imputazione del genotipo utilizzando questa risorsa può migliorare notevolmente la risoluzione della mappatura genetica. È stato sviluppato un server web pubblico per consentire alla comunità genetica suina di utilizzare appieno questa risorsa. Ci aspettiamo che questa risorsa faciliti la mappatura genetica e acceleri il miglioramento genetico nei suini.
Il maiale domestico (Sus scrofa) è un'importante specie animale e un organismo modello per la ricerca biomedica1. Storicamente, la domesticazione e l’intensa selezione artificiale hanno creato molte razze suine geneticamente e fenotipicamente distinte le une dalle altre e dai loro parenti selvatici2,3,4. Più recentemente, le tecnologie di sequenziamento del DNA e di genotipizzazione5 ad alto rendimento hanno facilitato il miglioramento genetico dei suini. Ad esempio, centinaia di studi di associazione sull'intero genoma e di mappatura dei loci dei tratti quantitativi (QTL) hanno identificato numerose regioni genomiche associate a vari fenotipi produttivi, fisiologici e comportamentali6. Questi studi sono importanti per comprendere le basi genetiche e biologiche di tratti importanti dal punto di vista economico e biomedico come la crescita7, la fertilità8 e la resistenza alle malattie9.
La risoluzione della mappatura genetica nei suini rimane scarsa, in parte a causa della bassa densità degli array di genotipizzazione del polimorfismo a singolo nucleotide (SNP). Un approccio comprovato ed economicamente vantaggioso per superare la limitazione nella risoluzione è attraverso l'imputazione del genotipo, sfruttando il disequilibrio del collegamento per dedurre genotipi in loci polimorfici non osservati10. Con ampi pannelli di riferimento di aplotipi creati mediante sequenziamento dell'intero genoma, l'imputazione ha il potenziale per fornire genotipi a livello di sequenza11. Negli animali d'allevamento, dove l'identificazione dei QTL e la previsione genetica sono due obiettivi principali, e lo squilibrio di collegamento è ampio, l'imputazione del genotipo a livello di sequenza è stata applicata con successo con un numero relativamente piccolo di aplotipi di riferimento ma con una discreta precisione12, 13. Nei suini, in particolare, sono disponibili almeno due server pubblici di imputazione14, 15. Tuttavia, essi contenevano un numero molto limitato di animali nel pannello di riferimento14 oppure non erano adeguatamente rappresentati dalle principali razze commerciali15, limitando le loro applicazioni. Inoltre, sebbene molti studi abbiano dimostrato un miglioramento nella risoluzione della mappatura16 e nell’accuratezza della previsione genomica17, nessuno di questi è accessibile pubblicamente.
In questo studio, abbiamo prodotto dati sulla sequenza dell'intero genoma di 1530 suini appena sequenziati e li abbiamo combinati con altri 729 animali provenienti da database pubblici per identificare le varianti e sviluppare di gran lunga il pannello di riferimento di aplotipi nei suini di gran lunga più ampio e diversificato fino ad oggi. Questo aumento sostanziale del numero di genomi disponibili ci ha permesso di imputare i genotipi dell'array SNP alle sequenze dell'intero genoma in modo rapido e accurato. Abbiamo valutato l'accuratezza dell'imputazione e dimostrato l'utilità di questo pannello di riferimento dell'aplotipo nella mappatura delle associazioni dell'intero genoma. Introduciamo un nuovo server web pubblico (swimgeno.org) in cui gli utenti possono inviare genotipi di array e recuperare genotipi a livello di sequenza dell'intero genoma imputati. Questa risorsa migliorerà notevolmente l’accesso all’imputazione del genotipo ad alta precisione, facilitando la mappatura genetica con potenziale risoluzione nucleotidica nei suini.
0.5) in 435 Durocs, 522 Landraces, 493 Yorkshires, 36 Meishans, 24 European wild boars, and 27 Asian wild boars. c Scatter plot of first two principal components of genotype matrix for common (MAF > 0.05) and LD-pruned variants. Points are color-coded according to their reported breed information. A preliminary principal component analysis was performed to visually inspect and remove clear outliers from clusters, which indicated errors in breed information. d Ancestries of pigs were estimated with variable (K = 2, 4, 6) numbers of postulated ancestral populations using the ADMIXTURE software. Estimated ancestries were plotted as stacked bar charts with breeds annotated on the top. In addition to annotations above the bar chart, broad geographical locations are also annotated below the bar chart for K = 6./p> 0.005 to construct the haplotype reference panel. To investigate factors that influence imputation accuracy, we considered different combinations of commonly used phasing and imputation software, including SHAPEIT4/IMPUTE5, Beagle5.2/Beagle5.2, and Eagle2.4/Minimac4. We defined imputation accuracy using three metrics, the overall concordance rate between imputed and observed genotypes, non-reference concordance rate summarizing accuracy for non-reference genotypes only, and squared correlation (r2) between imputed and observed genotypes. We focused on Landrace as the target set because it has the largest number of animals in the dataset. We held out 100 Landrace pigs sequenced at high coverage (>15X) and compared observed genotypes with imputed genotypes starting from sequencing-based genotypes at sites on a 50 K SNP array (GeneSeek GGP). Regardless of breed composition in the haplotype reference panel of fixed size, SHAPEIT4/IMPUTE5 outperformed Beagle5.2/Beagle5.2 and Eagle2.4/Minimac4 in all three metrics (Fig. 2a–c). SHAPEIT4/IMPUTE5 was therefore chosen for all subsequent analyses./p>94.24%), imputation using the SWIM panel developed in the present study was consistently higher than PHARP within each breed (Fig. 4b). The improvement was much more pronounced when considering the non-reference concordance rate and r2, two metrics that more faithfully reflect the accuracy, especially at low frequency (Fig. 4c, d). The difference between SWIM and PHARP could simply be a sample size difference, especially for the breeds evaluated. The final reference haplotype panel consisting of all 2259 animals is expected to achieve a concordance rate in excess of 95.84%, a non-reference concordance rate of 88.26%, and an r2 of 0.85./p>A) has been suggested as the causative mutation21 and extensively replicated in multiple genetic backgrounds23. Furthermore, mutations in MC4R are strongly associated with early onset obesity in humans24, and its role in the regulation of energy homeostasis is well established25. Importantly, the putative causal mutation in MC4R has been included in one of the commercially available SNP genotyping arrays, the Geneseek GGP Porcine 50K SNP Chip (Neogen, Lincoln, NE). However, the same SNP is not present in the more widely used Illumina PorcineSNP60 chip. To see if genotype imputation was able to correctly impute the genotypes of this SNP, we excluded the MC4R SNP and imputed whole-genome genotypes from a population of 3769 Duroc pigs genotyped using the GGP Porcine 50K SNP arrays. Remarkably, the concordance rate and r2 between the imputed and array MC4R SNP genotypes were 99.71% and 0.9916, respectively. We performed GWAS using array and imputed genotypes; both showed a major peak on chromosome 1 (Fig. 5a, Supplementary Data 3 and 4) and a clear deviation of P-value distribution from the null (Supplementary Fig. 4a). Using imputed genotypes, the highest hit from imputed SNPs (chr1:161511936:T > C, P = 2.98 × 10−13) explained 2.85% of the total phenotypic variance (Fig. 5a). Under this peak in a 4-Mb region (158.5–162.5 Mb), there were 7138 variants within 22 genes. Linkage disequilibrium in this region was extensive, with 1050 variants in strong LD (r2 > 0.8) with the top hit, including the MC4R SNP (Fig. 5b). The highest hit was an intronic SNP in the gene CCBE1 (Fig. 5b). However, the extensive LD in this region makes it difficult to pinpoint a causative mutation by genetic data alone. Additional functional information and genetic data that break the LD are necessary to further fine-map causative genes and mutations. Nevertheless, the ability to identify the putative MC4R causative SNP as one of the top associated variants in a long stretch of high LD region clearly demonstrated the improvement of resolution using imputed genotypes. In our analysis, the MC4R SNP was initially removed and would otherwise be invisible without the imputation, as would be the case if the Illumina PorcineSNP60 chips were used./p> C) is indicated by a gradient of blue color. Locations of genes are indicated in the box below the plot, where blue boxes and gene names with a left arrowhead (<) indicate genes transcribed on the reverse strand, and red boxes and gene names with a right arrowhead (>) indicate genes transcribed from the forward strand. Genes that are not marked do not have gene symbols. Gene locations are based on the Ensembl Release 98 annotation./p>T, P = 3.45 × 10−39). Remarkably, this variant explained 13.65% of the total phenotypic variance, and the homozygous C/C animals were, on average, 4.01 cm longer than the T/T homozygotes (Fig. 6b, c). BMP2 has been repeatedly shown to be associated with growth traits in pigs. A recent study implicated a regulatory variant upstream of the BMP2 gene and validated its functional impact using reporter genes26. This regulatory variant was the third most significant SNP under this peak in our analysis. Whether one or both of these potentially regulatory variants are the causative mutations remains to be determined. Given the strong association, high MAF of these SNPs, and less extensive LD in this region, it is unlikely that these regulatory variants were tagging protein-coding and less common variants in the BMP2 gene. In addition to the genetic support from this Yorkshire population, the body length increasing C allele was much more prevalent in Landrace than in other breeds. A hallmark of the Landrace breed is its long body size; thus, regulatory variation of the BMP2 gene may be a major contributor to the phenotypic differentiation between pig breeds. In contrast, although the SNP chip was able to broadly identify this region, the most significant SNP (chr17:15827832:T>G, P = 1.58 × 10−25) in an SNP chip-based GWAS was about 184 kb away from the lead SNP and explained a substantially smaller variance (8.22% versus 13.65%)./p>T) are indicated by a gradient of blue color. Locations of genes are indicated in the box below the plot and according to the Ensembl Release 98 annotation. All three genes are colored in red and transcribed from the forward strand. The only gene with a symbol in this region is BMP2. c Scatter and box plots of body length (in cm) for the three genotypes of the chr17:15643342:C>T SNP. The lower and upper boundaries of the box are, respectively, 25% and 75% quantiles of the data, the midline median, and the whiskers minimum and maximum. d Allele frequencies of the chr17:15643342:C>T SNP in different breeds./p> 54.69") were removed. Variant quality score recalibration (VQSR) on SNPs was performed with truth SNP sets compiled from commercial SNP arrays, including 50K, 60K, and 80K SNP chips (prior = 15.0) on the Illumina platform and the 660K (prior = 12.0), SowPro90 (prior = 15.0) SNP chips from the Affymetrix platform. SNPs were filtered with a truth sensitivity filter level at 99.0. Without a truth set of indels, we applied hard filtering on them by excluding indels with QD < 2.0, QUAL < 50.0, FS > 100.0, ReadPosRankSum < −20.0, as recommended by GATK's best practices. Additionally, we filtered out animals with a missing rate >0.20, heterozygosity >0.20, and retained bi-allelic sites with a missing rate <0.2 and mean sequencing depth between 5 and 500. Filtering was performed using a combination of VCFtools 0.1.1332 and BCFtools 1.1333 commands./p> 0.5) and low-frequency variants (MAF < 0.05). To understand the genetic structure in the population, we retained variants with MAF > 0.05 and missing rate <0.1 and pruned SNPs with LD (r2 < 0.3, -indep-pairwise 50 10 0.3) using PLINK 1.935. Principal component analysis (PCA) was performed on the filtered list of 1,223,882 variants using GCTA 1.93.236 for all individuals. Ancestries were estimated using ADMIXTURE 1.337 on 185 individuals randomly selected according to breed representation in the dataset or at least four individuals per breed. The downsampling was necessary to properly visualize population structure./p>0.1 and MAF < 0.005 were removed. Additionally, variants with a Hardy–Weinberg equilibrium test P-value < 10−10 implemented separately in PLINK in all three of the Duroc, Landrace, and Yorkshire pigs were removed. Only autosomal variants were retained for imputation./p>
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