Variant discovery in a QTL region on chromosome 3 associated with fatness in chickens

Identification of central regulators related to abdominal fat deposition in chickens based on weighted gene co-expression network analysis

Abdominal fat (AF) is one of the most important economic traits in chickens. Excessive AF in chickens will reduce feed utilization efficiency and negatively affect reproductive performance and disease resistance. However, the regulatory network of AF deposition needs to be further elucidated. In the present study, 300 one-day-old female Wannan chickens were reared to 17 wk of age, and 200 Wannan hens were selected to determine the abdominal fat percentage (AFP). Twenty AF tissue samples with the lowest AFP were selected as the low abdominal fat group (L-AFG), and 20 AF tissue samples with the highest AFP were selected as the high abdominal fat group (H-AFG). Eleven samples from L-AFG and 14 samples from H-AFG were selected for RNA-seq and used for weighted gene co-expression network analysis (WGCNA). Among the 25 RNA-seq samples, 5 samples with the lowest and highest AFP values were selected for differential expression gene analysis. Compared with the L-AFG, 225 and 101 genes were upregulated and downregulated in the H-AFG, respectively. A total of 20,503 genes were used to construct the WGCNA, and 44 co-expression gene modules were identified. Among these modules, 3 modules including turquoise, darkorange2, and floralwhite were identified as significantly associated with AFP traits. Furthermore, several genes including acyl-CoA oxidase 1 (ACOX1), stearoyl-CoA desaturase (SCD), aldehyde dehydrogenase 6 family member A1 (ALDH6A1), jun proto-oncogene, AP-1 transcription factor subunit (JUN), and fos proto-oncogene, AP-1 transcription factor subunit (FOS) involved in the "PPAR signaling pathway," "fatty acid metabolism," and "MAPK signaling pathway" were identified as central regulators that contribute to AF deposition. These results provide valuable information for further understanding of the gene expression and regulation of AF traits and contribute to future molecular breeding for AF in chickens.

Whole-Genome Scanning for Selection Signatures Reveals Candidate Genes Associated with Growth and Tail Length in Sheep

Compared to Chinese indigenous sheep, Western sheep have rapid growth rate, larger physique, and higher meat yield. These excellent Western sheep were introduced into China for crossbreeding to expedite the enhancement of production performance and mutton quality in local breeds. Here, we investigated population genetic structure and genome-wide selection signatures among the Chinese indigenous sheep and the introduced sheep based on whole-genome resequencing data. The PCA, N-J tree and ADMIXTURE results showed significant genetic difference between Chinese indigenous sheep and introduced sheep. The nucleotide diversity (π) and linkage disequilibrium (LD) decay results indicated that the genomic diversity of introduced breeds were lower. Then, Fst & π ratio, XP-EHH, and de-correlated composite of multiple signals (DCMS) methods were used to detect the selection signals. The results showed that we identified important candidate genes related to growth rate and body size in the introduced breeds. Selected genes with stronger selection signatures are associated with growth rate (CRADD), embryonic development (BVES, LIN28B, and WNT11), body size (HMGA2, MSRB3, and PTCH1), muscle development and fat metabolism (MSTN, PDE3A, LGALS12, GGPS1, and SAR1B), wool color (ASIP), and hair development (KRT71, KRT74, and IRF2BP2). Thus, these genes have the potential to serve as candidate genes for enhancing the growth traits of Chinese indigenous sheep. We also identified tail-length trait-related candidate genes (HOXB13, LIN28A, PAX3, and VEGFA) in Chinese long-tailed breeds. Among these genes, HOXB13 is the main candidate gene for sheep tail length phenotype. LIN28A, PAX3, and VEGFA are related to embryonic development and angiogenesis, so these genes may be candidate genes for sheep tail type traits. This study will serve as a foundation for further genetic improvement of Chinese indigenous sheep and as a reference for studies related to growth and development of sheep.

Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population

Carcass traits in broiler chickens are complex traits that are influenced by multiple genes. To gain deeper insights into the genetic mechanisms underlying carcass traits, here we conducted a weighted single-step genome-wide association study (wssGWAS) in a population of Chinese yellow-feathered chicken. The objective was to identify genomic regions and candidate genes associated with carcass weight (CW), eviscerated weight with giblets (EWG), eviscerated weight (EW), breast muscle weight (BMW), drumstick weight (DW), abdominal fat weight (AFW), abdominal fat percentage (AFP), gizzard weight (GW), and intestine length (IL). A total of 1,338 broiler chickens with phenotypic and pedigree information were included in this study. Of these, 435 chickens were genotyped using a 600K single nucleotide polymorphism chip for association analysis. The results indicate that the most significant regions for 9 traits explained 2.38% to 5.09% of the phenotypic variation, from which the region of 194.53 to 194.63Mb on chromosome 1 with the gene RELT and FAM168A identified on it was significantly associated with CW, EWG, EW, BMW, and DW. Meanwhile, the 5 traits have a strong genetic correlation, indicating that the region and the genes can be used for further research. In addition, some candidate genes associated with skeletal muscle development, fat deposition regulation, intestinal repair, and protection were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that the genes are involved in processes such as vascular development (CD34, FGF7, FGFR3, ITGB1BP1, SEMA5A, LOXL2), bone formation (FGFR3, MATN1, MEF2D, DHRS3, SKI, STC1, HOXB1, HOXB3, TIPARP), and anatomical size regulation (ADD2, AKT1, CFTR, EDN3, FLII, HCLS1, ITGB1BP1, SEMA5A, SHC1, ULK1, DSTN, GSK3B, BORCS8, GRIP2). In conclusion, the integration of phenotype, genotype, and pedigree information without creating pseudo-phenotype will facilitate the genetic improvement of carcass traits in chickens, providing valuable insights into the genetic architecture and potential candidate genes underlying carcass traits, enriching our understanding and contributing to the breeding of high-quality broiler chickens.

Genome-Wide Association Study Revealed the Effect of rs312715211 in ZNF652 Gene on Abdominal Fat Percentage of Chickens

Abdominal fat percentage (AFP) is an important economic trait in chickens. Intensive growth selection has led to the over-deposition of abdominal fat in chickens, but the genetic basis of AFP is not yet clear. Using 520 female individuals from selection and control lines of Jingxing yellow chicken, we investigated the genetic basis of AFP using a genome-wide association study (GWAS) and fixation indices (F_ST). A 0.15 MB region associated with AFP was located on chromosome 27 and included nine significant single nucleotide polymorphisms (SNPs), which could account for 3.34-5.58% of the phenotypic variation. In addition, the π value, genotype frequency, and dual-luciferase results identified SNP rs312715211 in the intron region of ZNF652 as the key variant. The wild genotype was associated with lower AFP and abdominal fat weight (AFW), but higher body weight (BW). Finally, annotated genes based on the top 1% SNPs were used to investigate the physiological function of ZNF652. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that ZNF652 may reduce AFW and BW in broilers through the TGF-β1/SMad2/3 and MAPK/FoxO pathways via EGFR and TGFB1. Our findings elucidated the genetic basis of chicken AFP, rs312715211 on the ZNF652 gene, which can affect BW and AFW and was the key variant associated with AFP. These data provide new insight into the genetic mechanism underlying AF deposition in chickens and could be beneficial in breeding chickens for AF.

Favored single nucleotide variants identified using whole genome Re-sequencing of Austrian and Chinese cattle breeds

Cattle have been essential for the development of human civilization since their first domestication few thousand years ago. Since then, they have spread across vast geographic areas following human activities. Throughout generations, the cattle genome has been shaped with detectable signals induced by various evolutionary processes, such as natural and human selection processes and demographic events. Identifying such signals, called selection signatures, is one of the primary goals of population genetics. Previous studies used various selection signature methods and normalized the outputs score using specific windows, in kbp or based on the number of SNPs, to identify the candidate regions. The recent method of iSAFE claimed for high accuracy in pinpointing the candidate SNPs. In this study, we analyzed whole-genome resequencing (WGS) data of ten individuals from Austrian Fleckvieh (Bos taurus) and fifty individuals from 14 Chinese indigenous breeds (Bos taurus, Bos taurus indicus, and admixed). Individual WGS reads were aligned to the cattle reference genome of ARS. UCD1.2 and subsequently undergone single nucleotide variants (SNVs) calling pipeline using GATK. Using these SNVs, we examined the population structure using principal component and admixture analysis. Then we refined selection signature candidates using the iSAFE program and compared it with the classical iHS approach. Additionally, we run Fst population differentiation from these two cattle groups. We found gradual changes of taurine in north China to admixed and indicine to the south. Based on the population structure and the number of individuals, we grouped samples to Fleckvieh, three Chinese taurines (Kazakh, Mongolian, Yanbian), admixed individuals (CHBI_Med), indicine individuals (CHBI_Low), and a combination of admixed and indicine (CHBI) for performing iSAFE and iHS tests. There were more significant SNVs identified using iSAFE than the iHS for the candidate of positive selection and more detectable signals in taurine than in indicine individuals. However, combining admixed and indicine individuals decreased the iSAFE signals. From both within-population tests, significant SNVs are linked to the olfactory receptors, production, reproduction, and temperament traits in taurine cattle, while heat and parasites tolerance in the admixed individuals. Fst test suggests similar patterns of population differentiation between Fleckvieh and three Chinese taurine breeds against CHBI. Nevertheless, there are genes shared only among the Chinese taurine, such as PAX5, affecting coat color, which might drive the differences between these yellowish coated breeds, and those in the greater Far East region.

Analysis of the Progeny of Sibling Matings Reveals Regulatory Variation Impacting the Transcriptome of Immune Cells in Commercial Chickens

There is increasing recognition that the underlying genetic variation contributing to complex traits influences transcriptional regulation and can be detected at a population level as expression quantitative trait loci. At the level of an individual, allelic variation in transcriptional regulation of individual genes can be detected by measuring allele-specific expression in RNAseq data. We reasoned that extreme variants in gene expression could be identified by analysis of inbred progeny with shared grandparents. Commercial chickens have been intensively selected for production traits. Selection is associated with large blocks of linkage disequilibrium with considerable potential for co-selection of closely linked "hitch-hiker alleles" affecting traits unrelated to the feature being selected, such as immune function, with potential impact on the productivity and welfare of the animals. To test this hypothesis that there is extreme allelic variation in immune-associated genes we sequenced a founder population of commercial broiler and layer birds. These birds clearly segregated genetically based upon breed type. Each genome contained numerous candidate null mutations, protein-coding variants predicted to be deleterious and extensive non-coding polymorphism. We mated selected broiler-layer pairs then generated cohorts of F2 birds by sibling mating of the F1 generation. Despite the predicted prevalence of deleterious coding variation in the genomic sequence of the founders, clear detrimental impacts of inbreeding on survival and post-hatch development were detected in only one F2 sibship of 15. There was no effect on circulating leukocyte populations in hatchlings. In selected F2 sibships we performed RNAseq analysis of the spleen and isolated bone marrow-derived macrophages (with and without lipopolysaccharide stimulation). The results confirm the predicted emergence of very large differences in expression of individual genes and sets of genes. Network analysis of the results identified clusters of co-expressed genes that vary between individuals and suggested the existence of trans-acting variation in the expression in macrophages of the interferon response factor family that distinguishes the parental broiler and layer birds and influences the global response to lipopolysaccharide. This study shows that the impact of inbreeding on immune cell gene expression can be substantial at the transcriptional level, and potentially opens a route to accelerate selection using specific alleles known to be associated with desirable expression levels.

Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach

BACKGROUND

Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600 K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F2 Chicken Resource population. Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations.

RESULTS

Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35 days-of-age (BW35), and 41 days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this F2 population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1-4, 6-7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens.

CONCLUSIONS

The use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs.

Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens

BACKGROUND

Poultry breeding programs have been focused on improvement of growth and carcass traits, however, this has resulted in correlated changes in internal organ weights and increased incidence of metabolic disorders. These disorders can affect feed efficiency or even cause death. We used a high density SNP array (600 K, Affymetrix) to estimate genomic heritability, perform genome-wide association analysis, and identify genomic regions and positional candidate genes (PCGs) associated with internal organ traits in an F2 chicken population. We integrated knowledge of haplotype blocks, selection signature regions and sequencing data to refine the list of PCGs.

RESULTS

Estimated genomic heritability for internal organ traits in chickens ranged from low (LUNGWT, 0.06) to high (GIZZWT, 0.45). A total of 20 unique 1 Mb windows identified on GGA1, 2, 4, 7, 12, 15, 18, 19, 21, 27 and 28 were significantly associated with intestine length, and weights or percentages of liver, gizzard or lungs. Within these windows, 14 PCGs were identified based on their biological functions: TNFSF11, GTF2F2, SPERT, KCTD4, HTR2A, RB1, PCDH7, LCORL, LDB2, NR4A2, GPD2, PTPN11, ITGB4 and SLC6A4. From those genes, two were located within haplotype blocks and three overlapped with selection signature regions. A total of 13,748 annotated sequence SNPs were in the 14 PCGs, including 156 SNPs in coding regions (124 synonymous, 26 non-synonymous, and 6 splice variants). Seven deleterious SNPs were identified in TNFSF11, NR4A2 or ITGB4 genes.

CONCLUSIONS

The results from this study provide novel insights to understand the genetic architecture of internal organ traits in chickens. The QTL detection performed using a high density SNP array covered the whole genome allowing the discovery of novel QTL associated with organ traits. We identified PCGs within the QTL involved in biological processes that may regulate internal organ growth and development. Potential functional genetic variations were identified generating crucial information that, after validation, might be used in poultry breeding programs to reduce the occurrence of metabolic disorders.

Identification of selection signatures involved in performance traits in a paternal broiler line

BACKGROUND

Natural and artificial selection leads to changes in certain regions of the genome resulting in selection signatures that can reveal genes associated with the selected traits. Selection signatures may be identified using different methodologies, of which some are based on detecting contiguous sequences of homozygous identical-by-descent haplotypes, called runs of homozygosity (ROH), or estimating fixation index (FST) of genomic windows that indicates genetic differentiation. This study aimed to identify selection signatures in a paternal broiler TT line at generations 7th and 16th of selection and to investigate the genes annotated in these regions as well as the biological pathways involved. For such purpose, ROH and FST-based analysis were performed using whole genome sequence of twenty-eight chickens from two different generations.

RESULTS

ROH analysis identified homozygous regions of short and moderate size. Analysis of ROH patterns revealed regions commonly shared among animals and changes in ROH abundance and size between the two generations. Results also suggest that whole genome sequencing (WGS) outperforms SNPchip data avoiding overestimation of ROH size and underestimation of ROH number; however, sequencing costs can limited the number of animals analyzed. FST-based analysis revealed genetic differentiation in several genomic windows. Annotation of the consensus regions of ROH and FST windows revealed new and previously identified genes associated with traits of economic interest, such as APOB, IGF1, IGFBP2, POMC, PPARG, and ZNF423. Over-representation analysis of the genes resulted in biological terms of skeletal muscle, matrilin proteins, adipose tissue, hyperglycemia, diabetes, Salmonella infections and tyrosine.

CONCLUSIONS

Identification of ROH and FST-based analyses revealed selection signatures in TT line and genes that have important role in traits of economic interest. Changes in the genome of the chickens were observed between the 7th and 16th generations showing that ancient and recent selection in TT line may have acted over genomic regions affecting diseases and performance traits.

Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken

Excessive fat deposition is a negative factor for poultry production because it reduces feed efficiency, increases the cost of meat production and is a health concern for consumers. We genotyped 497 birds from a Brazilian F₂ Chicken Resource Population, using a high-density SNP array (600 K), to estimate the genomic heritability of fat deposition related traits and to identify genomic regions and positional candidate genes (PCGs) associated with these traits. Selection signature regions, haplotype blocks and SNP data from a previous whole genome sequencing study in the founders of this chicken F2 population were used to refine the list of PCGs and to identify potential causative SNPs. We obtained high genomic heritabilities (0.43-0.56) and identified 22 unique QTLs for abdominal fat and carcass fat content traits. These QTLs harbored 26 PCGs involved in biological processes such as fat cell differentiation, insulin and triglyceride levels, and lipid biosynthetic process. Three of these 26 PCGs were located within haplotype blocks there were associated with fat traits, five overlapped with selection signature regions, and 12 contained predicted deleterious variants. The identified QTLs, PCGs and potentially causative SNPs provide new insights into the genetic control of fat deposition and can lead to improved accuracy of selection to reduce excessive fat deposition in chickens.

A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens

Background

Excess fat content in chickens has a negative impact on poultry production. The discovery of QTL associated with fat deposition in the carcass allows the identification of positional candidate genes (PCGs) that might regulate fat deposition and be useful for selection against excess fat content in chicken’s carcass. This study aimed to estimate genomic heritability coefficients and to identify QTLs and PCGs for abdominal fat (ABF) and skin (SKIN) traits in a broiler chicken population, originated from the White Plymouth Rock and White Cornish breeds.

Results

ABF and SKIN are moderately heritable traits in our broiler population with estimates ranging from 0.23 to 0.33. Using a high density SNP panel (355,027 informative SNPs), we detected nine unique QTLs that were associated with these fat traits. Among these, four QTL were novel, while five have been previously reported in the literature. Thirteen PCGs were identified that might regulate fat deposition in these QTL regions: JDP2, PLCG1, HNF4A, FITM2, ADIPOR1, PTPN11, MVK, APOA1, APOA4, APOA5, ENSGALG00000000477, ENSGALG00000000483, and ENSGALG00000005043. We used sequence information from founder animals to detect 4843 SNPs in the 13 PCGs. Among those, two were classified as potentially deleterious and two as high impact SNPs.

Conclusions

This study generated novel results that can contribute to a better understanding of fat deposition in chickens. The use of high density array of SNPs increases genome coverage and improves QTL resolution than would have been achieved with low density. The identified PCGs were involved in many biological processes that regulate lipid storage. The SNPs identified in the PCGs, especially those predicted as potentially deleterious and high impact, may affect fat deposition. Validation should be undertaken before using these SNPs for selection against carcass fat accumulation and to improve feed efficiency in broiler chicken production.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4779-6) contains supplementary material, which is available to authorized users.

Genome-wide characterization of genetic variants and putative regions under selection in meat and egg-type chicken lines

Background

Meat and egg-type chickens have been selected for several generations for different traits. Artificial and natural selection for different phenotypes can change frequency of genetic variants, leaving particular genomic footprints throghtout the genome. Thus, the aims of this study were to sequence 28 chickens from two Brazilian lines (meat and white egg-type) and use this information to characterize genome-wide genetic variations, identify putative regions under selection using Fst method, and find putative pathways under selection.

Results

A total of 13.93 million SNPs and 1.36 million INDELs were identified, with more variants detected from the broiler (meat-type) line. Although most were located in non-coding regions, we identified 7255 intolerant non-synonymous SNPs, 512 stopgain/loss SNPs, 1381 frameshift and 1094 non-frameshift INDELs that may alter protein functions. Genes harboring intolerant non-synonymous SNPs affected metabolic pathways related mainly to reproduction and endocrine systems in the white-egg layer line, and lipid metabolism and metabolic diseases in the broiler line. Fst analysis in sliding windows, using SNPs and INDELs separately, identified over 300 putative regions of selection overlapping with more than 250 genes. For the first time in chicken, INDEL variants were considered for selection signature analysis, showing high level of correlation in results between SNP and INDEL data. The putative regions of selection signatures revealed interesting candidate genes and pathways related to important phenotypic traits in chicken, such as lipid metabolism, growth, reproduction, and cardiac development.

Conclusions

In this study, Fst method was applied to identify high confidence putative regions under selection, providing novel insights into selection footprints that can help elucidate the functional mechanisms underlying different phenotypic traits relevant to meat and egg-type chicken lines. In addition, we generated a large catalog of line-specific and common genetic variants from a Brazilian broiler and a white egg layer line that can be used for genomic studies involving association analysis with phenotypes of economic interest to the poultry industry.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4444-0) contains supplementary material, which is available to authorized users.

Genome-wide association study for performance traits in chickens using genotype by sequencing approach

Performance traits are economically important and are targets for selection in breeding programs, especially in the poultry industry. To identify regions on the chicken genome associated with performance traits, different genomic approaches have been applied in the last years. The aim of this study was the application of CornellGBS approach (134,528 SNPs generated from a PstI restriction enzyme) on Genome-Wide Association Studies (GWAS) in an outbred F₂ chicken population. We have validated 91.7% of these 134,528 SNPs after imputation of missed genotypes. Out of those, 20 SNPs were associated with feed conversion, one was associated with body weight at 35 days of age (P < 7.86E-07) and 93 were suggestively associated with a variety of performance traits (P < 1.57E-05). The majority of these SNPs (86.2%) overlapped with previously mapped QTL for the same performance traits and some of the SNPs also showed novel potential QTL regions. The results obtained in this study suggests future searches for candidate genes and QTL refinements as well as potential use of the SNPs described here in breeding programs.

High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing

Chicken genotyping is becoming common practice in conventional animal breeding improvement. Despite the power of high-throughput methods for genotyping, their high cost limits large scale use in animal breeding and selection. In the present paper we optimized the CornellGBS, an efficient and cost-effective genotyping by sequence approach developed in plants, for its application in chickens. Here we describe the successful genotyping of a large number of chickens (462) using CornellGBS approach. Genomic DNA was cleaved with the PstI enzyme, ligated to adapters with barcodes identifying individual animals, and then sequenced on Illumina platform. After filtering parameters were applied, 134,528 SNPs were identified in our experimental population of chickens. Of these SNPs, 67,096 had a minimum taxon call rate of 90% and were considered 'unique tags'. Interestingly, 20.7% of these unique tags have not been previously reported in the dbSNP. Moreover, 92.6% of these SNPs were concordant with a previous Whole Chicken-genome re-sequencing dataset used for validation purposes. The application of CornellGBS in chickens showed high performance to infer SNPs, particularly in exonic regions and microchromosomes. This approach represents a cost-effective (~US$50/sample) and powerful alternative to current genotyping methods, which has the potential to improve whole-genome selection (WGS), and genome-wide association studies (GWAS) in chicken production.

SIFT missense predictions for genomes

The SIFT (sorting intolerant from tolerant) algorithm helps bridge the gap between mutations and phenotypic variations by predicting whether an amino acid substitution is deleterious. SIFT has been used in disease, mutation and genetic studies, and a protocol for its use has been previously published with Nature Protocols. This updated protocol describes SIFT 4G (SIFT for genomes), which is a faster version of SIFT that enables practical computations on reference genomes. Users can get predictions for single-nucleotide variants from their organism of interest using the SIFT 4G annotator with SIFT 4G's precomputed databases. The scope of genomic predictions is expanded, with predictions available for more than 200 organisms. Users can also run the SIFT 4G algorithm themselves. SIFT predictions can be retrieved for 6.7 million variants in 4 min once the database has been downloaded. If precomputed predictions are not available, the SIFT 4G algorithm can compute predictions at a rate of 2.6 s per protein sequence. SIFT 4G is available from http://sift-dna.org/sift4g.