Cyclopterus lumpus, have historically been harvested throughout Atlantic Canada and are increasingly in demand as a solution to controlling sea lice in Atlantic salmon farms—a process which involves both the domestication and the transfer of lumpfish between geographic regions. At present, little is known regarding population structure and diversity of wild lumpfish in Atlantic Canada, limiting attempts to assess the potential impacts of escaped lumpfish individuals from salmon pens on currently at‐risk wild populations. Here, we characterize the spatial population structure and genomic‐environmental associations of wild populations of lumpfish throughout the Northwest Atlantic using both 70K SNP array data and whole‐genome re‐sequencing data (WGS). At broad spatial scales, our results reveal a large environmentally associated genetic break between the southern populations (Gulf of Maine and Bay of Fundy) and northern populations (Newfoundland and the Gulf of St. Lawrence), linked to variation in ocean temperature and ice cover. At finer spatial scales, evidence of population structure was also evident in a distinct coastal group in Newfoundland and significant isolation by distance across the northern region. Both evidence of consistent environmental associations and elevated genome‐wide variation in FST values among these three regional groups supports their biological relevance. This study represents the first extensive description of population structure of lumpfish in Atlantic Canada, revealing evidence of broad and fine geographic scale environmentally associated genomic diversity. Our results will facilitate the commercial use of lumpfish as a cleaner fish in Atlantic salmon aquaculture, the identification of lumpfish escapees, and the delineation of conservation units of this at‐risk species throughout Atlantic Canada.Lumpfish,  Lawrence (GSL) (N = 108), waters around Grand Manan (N = 13), and from the Gulf of Maine (N = 83). Due to the different sampling approaches utilized, differences in the sexes and life stages collected were present. Research vessel surveys collected individuals using a bottom trawl and generally targeted adults of both sexes, commercial samples were largely adult females due to the selective nature of the fishery for roe, and coastal sites around Newfoundland  according to manufacturer's protocols. Genomic DNA was visualized by 1% agarose gel electrophoresis and quantified using Quant‐iT PicoGreen ds‐DNA Assay kits (Thermofisher) on a fluorescent plate reader. Genomic DNA was normalized to 15 ng/μL and sent to the Centre of Integrative Genomics (CIGENE) for genotyping on the lumpfish 70K SNP Affymetrix Axiom array , whole‐genome re‐sequencing (WGS) of 848 lumpfish from multiple sites across North America Figure . The sam2.2mind 0.05), sample call rates (geno 0.05), and minor allele frequency (maf 0.01). SNPs that did not align to chromosomes were filtered out using the not‐chr function in Plink. We also filtered individuals by relatedness in Plink, using the genome function and a pi‐hat threshold of 0.2 (second‐degree relative) in order to remove any false signals of population structure as the highly related individuals could cluster as distinct groups.The SNP array data was mapped to the North American lumpfish genome , with >80% (‐minInd 870) of individuals genotyped and a minimum of 1000 reads were present per locus (‐setMinDepth 1000). We also exported called genotypes for each SNP as .bcf files using the ‐dobcf flag, and converted to .vcf files  were performed using Arlequin v3.5  within each site using the ibc function in Plink.We had a total of 58 different sampling sites across the entire GSL region Figure  with onl2.5K = 10 and min.maf of 0.01, and the plot function in R  and extracted p‐values. We then corrected p‐values using the qvalue package  using the pcadapt package  present by choosing the lowest cross validation value. This analysis is sensitive to linked loci, therefore, prior to determining the optimal K value we filtered the datasets by LD in Plink using the function indep‐pairwise, with a window size of 50, a step size of 5, and a pairwise r2 threshold of 0.5. For WGS data, we used the admixture proportion estimation function in PCANGSD, and plotted individual Q values for the highest supported K inferred by PCANGSD using the MAP algorithm.We used Admixture v1.3.0 . The ape package (nj) was used to build the tree using default settings and FigTree  by comparing geographic distance between sites to pairwise FST scores generated in the step above. The geographic distance between all sampling sites were determined with the least path method implemented in package marmap v1.0.5 . To identify patterns of genomic divergence between clusters identified by PCA for the WGS data, we carried out pairwise FST scans of 50 individuals selected from each cluster using the weir‐fst‐pop function in vcftools, and filtering for a minor allele frequency of 0.05 in the reduced set of individuals. We estimated Weir and Cockerham's weighted FST . We followed up by running a variance inflation factor (vif) scores model to verify which climatic variables had a high degree of multicollinearity, where a value <10 indicated low multicollinearity  matrix composed of Moran's Eigenvectors Maps (MEM) to correct both RDAs for geographic space using the package adespatial (mem function) in R . To reduce computation time with the WGS data, we down sampled genotypes to only include SNPs from genomic regions overlapping exons  using bedtools intersect. To identify genomic regions significantly associated with environmental variation, we used the Mahalanobis distance outlier detection approach implemented in the RDadapt R function described in Capblancq et al.  as our windows and a 1 kb step size between windows using a reduced set of whole‐exome data for computational efficiency . We filtered out any windows with less than three observations and calculated the outlier cutoff for each run of the different window sizes . We generated pairwise FST values between sites in Arlequin for both datasets. We then calculated discordance between estimates of FST using a Pearson correlation (cor.test function in R). We also ran correlations between the first two PC axes, as well as between the three admixture groups that were generated previously for the entire North American dataset, also using the cor.test and a linear regression (lm function in R).Admittedly, the two independent datasets have differing tradeoffs or potential biases; the SNP array is subject to potential ascertainment bias as it was constructed using Norwegian lumpfish , 53 sites from the GSL (N = 108/106), one site from the Bay of Fundy (N = 13/11), and five sites from the Gulf of Maine (N = 70/53). Based on NAFO regions and individual‐based analyses (see Supporting information HO/HE) overall was 0.293/0.292 and ranged from 0.259/0.259 in FTN, Newfoundland, Canada to 0.357/0.356 in BIM, Gulf of Maine, USA ; however, an Admixture plot of the (3) primary north group revealed two main clusters, with all Newfoundland individuals belonging to one, and some members of the GSL , belonging to a second  +/−<0.001 (Supporting information FST comparisons across the northern sites (both the primary and secondary group) appeared to not be associated with geographic distance. Despite inconsistent isolation by distance within the north range, we observed significant isolation by distance across the entire study area , in which 69.9% of the data were explained by the distance between sites .When we re‐ran pairwise 3.3K = 2 (over 0.75 membership). We compared the areas of differentiation across the genome between the two northern groups and the south group and found highly significant, genome‐wide FST differentiation between each comparison  primary northern group was 0.026 , and (2) secondary northern group was 0.034 . The mean FST between the primary northern and secondary group was 0.012 . In particular, chromosomes 3, 9, 10, 11, 13, and 22 contained the greatest proportion of outliers , adjusted R2 = 0.007 (0.002 MEM corrected), p‐value <0.001; WGS: R2 = 0.014 (0.012 MEM corrected), adjusted R2 = 0.004 (0.003 MEM corrected), p‐value <0.001). Temperature associated variables , BO_chlomax 96/1380 loci), BO_salinity (87/1025 loci), BO_sstrange (184/391 loci), BO2_tempmax_bdmean (38/446 loci), BO2_lightbotmean_bdmin (290/1923 loci), BO2_icecoverltmax_ss (331/920 loci), and BO2_curvelmean_ss , adjusted R2 = 0.007 (0.001 MEM corrected), p‐value <0.001, and for the WGS data was R2 = 0.008 (0.008 MEM corrected), adjusted R2 = 0.001 (0.001 MEM corrected), and a p‐value <0.001. Both plots produced similar results, where all five variables were significantly correlated with the genetic data and separated each site distinctly along both the primary and secondary axes , BO_sstrange (293/996 loci), BO2_tempmax_bdmean 204/694 loci), BO2_icecoverltmax_ss (131/432 loci), and BO2_curvelmean_ss , as well as when we compared the SNP array to 50 kb WGS windows , 100 kb WGS windows , and 200 kb WGS windows . Similarly, correlations between PC axis 1 , haddock (Melanogrammus aeglefinus), and northern shrimp  have also reported genetically different inshore and offshore groups (Berg et al., Beyond the North–South subdivision, our results suggest significant non‐spatial limits to gene flow within the Northern region, reflected in the detection of two population clusters within multiple sites. Interestingly, the presence of a discrete population represented primarily by several non‐proximate juvenile samples with some adults from the around the island of Newfoundland was unexpected. Moreover, genome‐wide evaluation of differences between these groups suggests that the differences are widespread across the genome and significant. The presence of this discrete group could not be explained by relatedness or sex‐biased sampling and was present both in the SNP array and whole‐genome re‐sequencing datasets Table . As such4.3In the primary northern group, we found significant genetic structuring based on environmental variables and a positive isolation by distance. We found discretely structured sampling sites based on a variety of environmental variables, suggesting that localized adaptation could be occurring. Therefore, site fidelity of adults and their life history/local oceanographic conditions seems to allow the isolation of lumpfish across large and small spatial scales. A recent study found that sampling that took place at breeding sites or within juvenile groups, formed clear‐cut groups, while sampling of actively migrating individuals were more admixed (Jansson et al., 4.4Lumpfish are of growing commercial interest for their ability to effectively reduce sea lice in salmon farms (Deady et al., 4.5This study provided a unique opportunity to directly compare the results of SNP array and whole‐genome sequence data for the inference of population structure in a marine fish. The purpose of this comparison was to evaluate if both datasets were consistent and able to resolve similar patterns of genomic and population structure. The SNP array utilized was ascertained using European lumpfish (Knutsen et al., 5Our results suggests that previously unrecognized regional patterns of population structure of lumpfish across the Northwest Atlantic are significantly driven by environmental associations. Regional patterns include the presence of a major north–south delineation as noted for other species in the region, as well as further structuring within the northern area. The comparison of the SNP array and whole‐genome re‐sequencing data further supports the use of low‐depth whole‐genome data as a viable and effective approach for population genomics of marine species. The results directly inform the further conservation of this at‐risk species as well as its continued development as a cleaner fish in salmonid aquaculture. Despite the wide‐ranging distribution of lumpfish, the presence of discrete populations in Newfoundland and elsewhere (Davenport, We declare no conflicts of interest.Data S1:Click here for additional data file.


Introduction: This study aimed to evaluate the feasibility and necessity of using fluorescence Gap-polymerase chain reaction combined with haplotype analysis in preimplantation genetic testing for SEA-type α-thalassemia.Methods: A total of 26 preimplantation genetic testing biopsy cycles were performed in 25 families from June 2021 to February 2022. All couples were carriers of SEA-type α-thalassemia. Fluorescent Gap-polymerase chain reaction was used for detecting fragment deletion. Subsequently, according to the results of polymerase chain reaction, reference embryos were identified to establish haplotype using single nucleotide polymorphism array, and aneuploidy was screened simultaneously. In cases wherein the polymerase chain reaction results were inconsistent with the haplotype results, the reasons were investigated, either by retest of the biopsied samples or rebiopsy of the embryo.Results: Among the 172 embryos, 162 had consistent results when tested using both methods, resulting in a consistency rate of 94.2%. Conversely, 10 embryos had inconsistent results, mainly due to chromosome 16 aneuploidy (n = 7), allele dropout in Gap-polymerase chain reaction (n = 2), or incorrect haplotype due to poor sample amplification quality (n = 1). The clinical pregnancy rate of each frozen-thawed embryo transfer was 57.7% (15/26). Six families underwent prenatal diagnosis, which confirmed the results of preimplantation genetic testing.Conclusion: Fluorescent Gap-polymerase chain reaction combined with haplotype analysis is feasible and necessary for SEA-type α-thalassemia preimplantation genetic testing. It is cPreimplantation genetic testing (PGT), which is used to select unaffected embryos via genetic testing on biopsy cells from preimplantation embryos, is aimed at preventing the transmission of genetic disorders, thereby allowing couples carrying genetic diseases to have a healthy offspring. Conventional methods of PGT for SEA-type α-thalassemias include Gap-polymerase chain reaction (PCR), real-time PCR, and digital PCR . HoweverIn this study, we established a comprehensive PGT approach for SEA-type α-thalassemia by fluorescent Gap-PCR combined with SNP array, which can directly detect SEA-type α-thalassemia along with linkage analysis as well as aneuploidy screening.This study included 25 families that underwent 26 PGT biopsy cycles from June 2021 to February 2022. In these families, the average maternal age was 30.11 ± 3.30 years  and the average paternal age was 31.96 ± 3.73 years . In the 25 families, both couples were carriers of SEA-type α-thalassemia. In the biopsy cycles, routine clinical service of SEA-type α-thalassemia PGT was performed using the conventional fluorescent Gap-PCR method independent of this study.The study protocol was approved by the ethics committee of the Faculty of Medical Research Service, the First Affiliated Hospital of Sun Yat-sen University, China. All couples were given genetic counseling before treatment and were informed of the treatment protocol and procedure as well as the misdiagnosis rate of PGT. All patients were informed about relevant risks and provided written informed consent.Ovarian stimulation was performed as previously reported . RetrievWGA products were either used for fluorescent PCR identification immediately or stored at −20°C. Amplification involved the use of three α-thalassemia SEA primers. The S1 and S3 primers flank the SEA deletion, whereas the S2 primer anneals within the deleted area. The specific process and results of primer sequences and Gap-PCR are the same as those of our group’s earlier published studies  Table 1Table 1.Peripheral blood (2 mL) was collected from each couple and placed in an EDTA anticoagulant tube for genomic DNA preparation. Genomic DNA was extracted using the T Guide Large Volume Blood Genomic DNA Kit .® HD Assay Super, Manual Protocol, Illumina, San Diego, CA, United States). After termination with a series of enzymes, denatured DNA and the peripheral blood DNA obtained from the parents were hybridized with the HumanCytoSNP-12 BeadChip , which contains approximately 300,000 SNPs with an average distance of 9.7 kb. Subsequently, the bead chips were scanned using an Illumina iScan Bead Array Reader . The scanning results were processed using the B-allele frequency and the log R ratio using Illumina GenomeStudio  to analyze the copy number of the chromosomes.The peripheral blood DNA and WGA products after two rounds of amplification were treated according to the manufacturer’s instructions . Informative SNPs within a 2-Mbp region flanking the HBA1 + HBA2 genes were used to build the family haplotypes. Informative SNP was defined as a heterozygous SNP genotype in one parent (such as AB) while being homozygous in the other (such as AA/BB). Embryos with gap-PCR results showing (−/− or αα/αα) were preferentially used as reference samples and assumed to have no recombination for phasing purpose. The rest of the embryos were compared with haplotype to determine whether they carried the risk haplotype. A total of 26 biopsy cycles were performed. The average number of biopsied embryos was 6.62 ± 3.00. The clinical pregnancy rate of each frozen embryo transfer (FET) was 57.7% (15/26). One case had an early miscarriage, and the abortion product was not tested in any way. The number of healthy babies born was nine, and the rest were still in pregnancy. Amniotic fluid was extracted during gestational weeks 18–22 for prenatal diagnosis in six families; however, the rest of the families refused to the extraction. The results of prenatal diagnosis confirmed the PGT results. Details of the results are presented in The homozygous normal and homozygous affected rates detected by Gap-PCR were both 22.7% (39/172), and the rate of heterozygous carriers was 54.7% (94/172). In haplotype analysis, the homozygous normal, heterozygous carrier, and homozygous affected rates were 22.7% (39/172), 54.1% (93/172), and 23.3% (40/172), respectively. The consistency rate of the two test results was 94.2% (162/172). Among the 10 embryo samples with discordant results, 7 were found to be aneuploidy on chromosome 16 via whole-chromosome haplotype analysis and aneuploidy screening. The remaining three samples were retested by PCR. Among them, two exhibited a single peak of 280 bp (αα/αα) and 178 bp (−/−) in the first PCR, respectively, but both results showed two peaks of 178 and 280 bp (--/αα) in the second PCR. Both the results of the second PCR matched the haplotype analysis results. For these two samples, by comparing the results of haplotype analysis with the two PCR results, ADO was observed. In the first PCR, one of these two samples was diagnosed as a homozygous normal embryo, whereas the other was diagnosed as a homozygous affected embryo. However, the results of both the haplotype analysis and second PCR proved that the two samples were heterozygous carriers. Thus, the Gap-PCR ADO rate was estimated to be 1.16% (2/172).For the last embryo sample, the second PCR result still exhibited a single peak of 178 bp (−/−). Furthermore, the call rate of this sample was 0.88 after SNP array detection, suggesting that the WGA amplification quality was poor. Therefore, a second biopsy sample was taken for the embryo. The PCR result of the rebiopsied sample was consistent with that of the previous one. The result of the haplotype analysis was consistent with those of the three PCRs when the call rate was 0.95. The HBA genes are located at 16p13.3 (GRCh37 16:222846–227521) at the end of the p arm of chromosome 16. The Cyto-12 array has 407 customized SNPs in the 2-Mbp region flanking the HBA1 + HBA2 genes, including 10 in the genes, 42 in the upstream 5′ region, and 355 in the downstream 3′ region. According to the haplotype statistics of the 25 families, no informative SNPs were observed within the HBA genes. In the downstream 3′ 2Mb region of the genes, the average numbers of maternal and paternal informative SNPs were 42.52 ± 14.561 and 34.96 ± 10.907, respectively. Within the upstream 5′ 2Mb region of the genes, the average numbers of maternal and paternal informative SNPs were 2.36 ± 2.177 and 2.88 ± 2.00, respectively. In this study, we presented a comprehensive PGT approach for SEA-type α-thalassemia by fluorescent Gap-PCR combined with SNP array, which can directly detect SEA-type α-thalassemia along with linkage analysis as well as aneuploidy screening.The detection methods of SEA-type α-thalassemia PGT evolved from early PCR technologies , to STR In the present study, PGT-M was performed on SEA-type α-thalassemia. On the basis of fluorescent Gap-PCR, haplotype analysis and aneuploidy screening were additionally performed. In particular, PCR was used to directly detect the target genes (HBA1 + HBA2), and 300,000 SNPs customized by SNP array were used to perform chromosome-wide haplotype analysis and aneuploidy screening of embryos. Gap-PCR is a reliable method for prenatal diagnosis of α-thalassemia . Since 2The genes HBA1 and HBA2 of α-thalassemia are located at 16p13.3, close to the end of the p arm of chromosome 16, and less than 0.3 Mb from the upstream 5′ region to the end of the p arm of the genes. The array customized 36 SNPs in the upstream region; therefore, informative SNPs that can be used to construct haplotypes were limited. In our sample population, 2.36 ± 2.177 maternal informative SNPs and 2.88 ± 2.00 paternal informative SNPs were detected, respectively. Because there are fewer SNPs at the upstream 5′ region of the HBA genes, for haplotype analysis, gene recombination is a risk factor for analysis errors. Bjarni et al. reported that there were 173 and 38 paternal and maternal recombination hotspots, respectively, within 2 Mb upstream and downstream of the HBA1 + HBA2 genes (CRGh37 16:0–2227521), but they all occurred in the near-centromeric region downstream of the HBA genes.al. They used samples from the Icelandic population . We alsoThe advantages of our comprehensive PGT approach were as follows: additional experimental operations are not needed, and aneuploidy can be screened only via quantitative analysis of the customized 300,000 SNPs distributed on 23 chromosomes. At present, uncertainties still exist regarding the efficacy of PGT-A routinely applied in IVF population . In 2019The biggest difference between haplotype analysis based on SNP array and NGS-based SNP haplotyping is that the former is a genome-wide haplotype analysis whereas the latter only includes haplotypes in a region or a gene and its near region . Because SNP array constructs the haplotype of the whole genome, the range of haplotype analysis in the target region can be changed if necessary. Genome-wide haplotype analysis can also illustrate the whole picture of chromosomes. In recent years, a general method based on NGS, such as OnePGT, has also emerged, which can automatically complete haplotyping and copy number analysis of the whole genome . The impIn conclusion, Gap-PCR combined with haplotype analysis based on SNP array for SEA-type α-thalassemia is feasible and necessary. This approach combines direct detection of target genes and haplotype analysis to minimize the risk of misdiagnosis caused by ADO and gene recombination. No additional experimental operation was required for aneuploidy screening. However, a larger sample size is needed to examine the impact of recombination near the telomeric region upstream of the HBA genes on the detection results.


The wide use of genomic information has enabled the identification of lethal recessive alleles that are the major genetic causes of reduced conception rates, longer calving intervals, or lower survival for live-born animals. This study was carried out to screen the Nellore cattle genome for lethal recessive haplotypes based on deviation from the expected population homozygosity, and to test SNP markers surrounding the lethal haplotypes region for association with heifer rebreeding (HR), post-natal mortality (PNM) and stayability (STAY). This approach requires genotypes only from apparently normal individuals and not from affected embryos. A total of 62,022 animals were genotyped and imputed to a high-density panel . Expected numbers of homozygous individuals were calculated, and the probabilities of observing 0 homozygotes was obtained. Deregressed genomic breeding values [(G)EBVs] were used in a GWAS to identify candidate genes and biological mechanisms affecting HR, STAY and PNM. In the functional analyses, genes within 100 kb down and upstream of each significant SNP marker, were researched. Thirty haplotypes had high expected frequency, while no homozygotes were observed. Most of the alleles present in these haplotypes had a negative mean effect for PNM, HR and STAY. The GWAS revealed significant SNP markers involved in different physiological mechanisms, leading to harmful effect on the three traits. The functional analysis revealed 26 genes enriched for 19 GO terms. Most of the GO terms found for biological processes, molecular functions and pathways were related to tissue development and the immune system. More phenotypes underlying these putative regions in this population could be the subject of future investigation. Tests to find putative lethal haplotype carriers could help breeders to eliminate them from the population or manage matings in order to avoid homozygous. The widespread use of a few Nellore bulls has fixed long different homozygous segments in the genomes of more than 70% of the animals in a population2. These homozygous segments indicate the occurrence of inbreeding that leads to an increased accumulation of deleterious alleles, increasing the frequency and expression of harmful recessive genes5. In specialized beef cattle  and dual-purpose , where artificial insemination is extensively implemented, autosomal recessive lethal loci at moderate frequencies that significantly impact economically traits, have been recently found6.Nellore is the most important beef breed raised in Brazil. The use of artificial insemination has allowed great diffusion of superior genetic material across different production systems, but it has also increased the excessive use of specific paternal lineages7. With the availability of a large number of SNPs, combining genotypes with individuals pedigreed information enables to unraveling the genetic background of rare disorders in the cattle population8. Using genomic information, recessive deleterious variants have been identified using a statistical depletion test or by the absence of determined haplotypes in a homozygosity state9. This method represents a powerful tool to identify deleterious haplotypes that are common in the population without using any phenotypes, whereby its power for detecting is directly dependent on the number of genotyped animals9.The traditional methodology for identifying genetic factors related to lethal recessive variants causing defects or death, is to trace the common ancestors of the affected animals using pedigree information. As this method requires information for the target phenotype, it is not able to detect the deleterious genetic variants that cause embryonic losses, which is generally not observed12, beef cattle13, horse14, pigs15 and dairy sheep16.Growing interest has been directed toward identifying marker variants linked to lethal genomic elements by genotyping a large number of animals using SNP chips and applying a haplotype-based approach. This approach is useful due to its efficiency in capturing deleterious variation linked to some of the SNP markers, while most markers are expected to be in low linkage disequilibrium with the causal variant. Several authors have implemented and adapted this approach to identify and track lethal recessive haplotypes in different populations such as dairy breeds17. In this framework, reproduction inefficiency has an important effect on reductions of profitability in cattle18. Over the last few decades, increased attention has been directed toward reproduction efficiency and sexual precocity traits in Nellore cattle breeding goals19. In addition, advances in high-density genotyping have enabled the implementation of genomic selection jointly for production and reproduction traits in Nellore cattle20, but screening for recessive lethal alleles has not yet been applied in Nellore cattle.Identifying and monitoring lethal recessive haplotypes in a population can help breeders to anticipate problems caused by disadvantageous variants. Through the implementation of selection and mating strategies and, potentially, gene editing, problems which compromise post-natal survival and even reproduction into adulthood, could be avoid6. If the recessive lethal allele in a heterozygote stage is associated with a positive effect on phenotypic expression of economic traits, it would lead to the selection of animals carrying the recessive lethal allele for the next generation, causing its propagation across the population21. Hence, this study was carried out to map lethal recessive haplotypes based on expected population frequencies of homozygous haplotypes; and detect the significant SNP markers surrounding the lethal haplotypes region for heifer rebreeding (HR), post-natal mortality (PNM) and stayability (STAY) in Nellore cattle using the genome-wide association study approach (GWAS). In addition, we in silico identified candidate genes and biological mechanisms that could influence these traits.Recessive lethal alleles show an impact on reproduction efficiency by embryonic death when an embryo is homozygous for it or result in reduced viability of embryos. Moreover, markers associated with lethal genetic element can also be related to reduced conception rates and longer calving intervals in adult cows, besides abortions and stillbirthswww.gensys.com.br). The animals, born between 1984 and 2019, were from 276 commercial herds widely distributed in the Midwest, Southeast, and Northeast of Brazil with high connectedness by the common sires intensively used through artificial insemination (AI), with more than 50% of the calves born from AI.The Nellore data base belonged to three commercial breeding programs , which are part of Alliance Nellore database  for 60 days, with heifers being exposed to the first mating at around 16 months of age (irrespective of body weight and body condition score). Heifers that don't conceive in the anticipated breeding season have a second chance in a regular breeding season (November to January), approximately 8 months later, at the age of ~ 24 months. This sexual precocity test, began in the 1990sThe reproduction efficiency related trait evaluated was heifer rebreeding (HR), which was determined attributing a value of 1 (success) or 0 (failure) to females that presented or not a second calf, respectively. Stayability (STAY) was determined by attributing a value of 1 (success) to cows with at least three calving's up 76 months and, otherwise, a value of 0 (failure). Post-natal mortality (PNM) was determined considering a value of 1  for parents of dead calves that have not reached weaning and 0 (survived) for animals that were evaluated at weaning.Contemporary groups (CG) for HR and STAY were defined considering information regarding the weaning of the first calf, considering the year and birth season, sex, herd, management group (at birth and weaning) and weaning date. For PNM the CG were formed by year and season at birth, farm, management group at birth and for animals evaluated at weaning were include also the variables sex, management group at weaning and weaning date. The season of birth was divided into two from August to January and from February to July. The CG with less than five animals or in which all scores for HR, STAY and PNM were the same, i.e., groups without variability, were eliminated. The descriptive statistics of the phenotypic data are shown in Table 23. By default, the program first examined haplotypes of 2,000 markers on the same chromosome, then 632 markers, and finally identified haplotypes with up to 200 markers, used for further analyses. The haplotypes with the highest frequencies (> 2%), which were never observed in a homozygous state, were prioritized for examination due to their potential significance and to enhance the likelihood of detection23.The haplotypes were constructed using the sliding windows method implemented in findhap.f90 software v323, two methods were used to calculate the expected number of homozygous individuals for each haplotype: (1) Simple method—it was assumed that all members of the population were on random mating over time and the formula used to calculate the expected number of homozygotes was to divide the number of genotyped individuals by 4 and then multiply it by the square of the carrier frequency of haplotypes; and (2) Mating method—the actual mating pattern  generating the genotyped individuals in the population. In this case, the number of carrier mating sire × carrier maternal grandsire pairs divided by 4 was used to obtain the expected number of homozygous. Allele frequencies for maternal granddams and maternal grandsire were assumed to be equal.Following VanRaden et al.n is expected were obtained by 2 analogous formulas that were used to obtain above expectations, following VanRaden et al.9 and Jenko et al.6. The first test, for the simple method the probability is: The probabilities of observing 0 homozygotes when 8: (1) the haplotype carrier frequency had to be higher than 2%, (2) the number of expected homozygous individuals for the haplotype had to be higher than 1 (3) the probabilities of observing 0 homozygotes had to be < 0.6. All the haplotypes satisfying these conditions were selected.Due to thousands haplotypes that could have 0 homozygotes by chance, to define a putative recessive lethal haplotype region, we set the following conditions, following Wu et al.22 considering the following general model:GWAS were performed to identify significant SNP's within or close to the regions of the candidate's lethal haplotype, and were performed in two steps: in the first step the single-trait threshold animal model was used to estimate the breeding values (EBVs) for HR, PNM and STAY using gibbsf90 + program of blupf90 familyThe random effects of animal and residual were assumed to be normally distributed: 28, and only the animals presenting deregressed EBVs (dEBV) with a minimum accuracy of 0.40 (based on prediction error variance), were used in the GWAS analysis. The GWAS was performed using the GCTA program29 for HR , STAY  and PNM , following the model:e is the vector of residual effects. For statistical tests, the SNP effects were standardized as follows: p-values for the SNP effects were computed as p-value) > 6 (Bonferroni correction). To identify genes, a region of 100 kb down and upstream of each significant SNPs was considered within or close to the region of candidate lethal haplotypes , considering the ARS-UCD1.2 Bos taurus genome assembly30  the gene set for all traits; 2) the gene set found commonly for the three traits. Gene ontologies (GO) biological process terms, immune system, molecular function and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used to find the gene-networks annotations , School of Agricultural and Veterinary Science Ethical Committee (protocol number 18.340/16). Furthermore, all the data sampling was performed following the CEUA/ FCAV-UNESP guidelines and regulations according to Regulations for the Administration of Affairs Concerning Experimental Animals . In addition, we confirmed the statement that the study was conducted following the ARRIVE guidelines.Analyzing phased haplotypes data for 62,022 genotyped animals and 612,154 SNP markers using sliding windows approach, the maximum number of SNPs per segment was 200 with minimum segment length of 194 to match chromosome length resulting in a total of 3,073 segments. On average, 900 haplotypes were found within each segment. Out of these, 30 haplotypes had no homozygous genotypes  with a frequency for animals carrying recessive markers varied from 2.98% to 12.21%  on 17 haplotypes harboring possible recessive lethal alleles. On the other hand, we observed a possible lethal haplotypes carrier frequency 3.9 times lower than that found by Jenko et al.6 of 17.22% (1.2%–33.5%) in five beef cattle breeds. Those authors suggested that these high haplotype frequencies may come from their substitution effect with pleiotropy or linked selection on target traits goals of breeding programs.In general, the observed average for the frequency of potentially lethal haplotypes of 4.41% shows a slight difference from those reported by Sahana et al.9.The expected homozygous number obtained through the method considering random mating was overall greater , BTA 7 , BTA 8 (1184.1 and 1192.1) and BTA 9  , can reach an important economic impact by reducing fertility. Testing animals for lethal haplotypes can help mate allocation to avoid animals that carry lethal alleles in the same locus and maximize the genetic gain rate towards a determined breeding objectivep-value) > 6 for HR, PNM, and STAY, respectively  and PNM in Nellore cattle Fig. . A totalely Fig.  and S3. ely Fig. .Figure 1MTUS2, NFIA, A2M, A2ML1, KLRG1, M6PR, OVOS2, PHC1, BVES, and LIN28B; and 2 RNAs: U6 and 5S_rRNA , 186 with post-natal mortality (PNM), and 398 with stayability (STAY) , heart development (GO:0007507), and hemopoiesis (GO:0030097). According to Mathew and BordoniLIN28B (BTA 9) and NFIA (BTA 3) impacts reproductive functions by regulating adipogenesis and embryonic development41. LIN28B gene is associated with puberty and inhibits gonadal steroids production, while NFIA indirectly affects reproductive functions by regulating cellular differentiation and activating adipogenesis processes43. The markers surrounding these genes have a significant effect on reproductive functions  production45  is related to activating the glucose-sodium cotransporters (SGLTs) that may significantly affect the energy homeostasis of the animal by a reduction in glucose supply49. Such findings for HR support the hypothesis that genomic regions with harmful effects surrounding genes related to body energy aspects delayed the return to reproductive activity due to lower body energy reserves and metabolic status. On the other hand, the favorable effect of these genomic regions is related to maintaining the energy body homeostasis, whereby provides a link between nutritional status and the gatekeeper signals to heifer attain reproductive function, which provides the longevity of the reproductive life of the female.A total of 26 genes surrounding biological processes related to energy metabolites and immune systems are shared by HR and STAY Fig.  and S8. ACTR8, CACNA1D, CACNA2D3, CHDH, IL17RB, and SELENOK), related to calcium transport and immune response (https://www.genecards.org/) , mediating the immune response in dairy cattle in post-partum metabolic distress51.On BTA22 (46.96–47.09 Mb) including the gene set  secretion53. Genes affecting physiological changes in lipid and glucose blood levels can affect reproductive functions because glucose levels represent a key biological link between metabolic factors and the endocrine axis to maintain the reproductive functions affecting the oocyte development and quality54.Specific genomic regions affecting HR were identified on BTA 1, 3, 5, 7, 8, 9, 17, 18, 22 and 29, suggesting different physiology mechanisms leading to reproductive functions and adaptation to stress . The GWAS pointed out significant SNP markers that implies different physiology mechanisms leading to harmful effects on heifer rebreeding (146 SNPs), post-natal mortality (268), and stayability (482) in Nellore cattle. The functional analysis showed 26 genes enriched for 19 GO terms . We observed that most GO terms found for biological processes, molecular function, and pathways were related to tissue development and the immune system. Tests to find lethal haplotype carriers could help breeders to implement selection actions to eliminate these haplotypes from the population or manage mattings to avoid lethal allele dispersion. In addition, future studies with fine-mapping approaches, region sequencing, and expression analyses to find the causal mutation in candidate regions may also contribute to the decrease in the frequency of lethal alleles.Supplementary Information 1.Supplementary Tables.


TSPAN18, NRG3 and NOVA2, respectively. These genes have previously described roles related to angiogenesis and osteoblast, osteoclast and chondrocyte proliferation and differentiation, which suggests the possibility for their involvement in the pathogenesis of osteochondrosis. Functional consequences of SNPs were evaluated through transcription factor binding site analysis, which predicted binding sites for transcription factors of known importance to bone growth, including SOX6, SOX9 and RUNX2. The identification of genetic risk factors for ALD may help to improve animal welfare and production in Rambouillet, a breed known to be at risk for ALD development. This study proposes genes TSPAN18, NRG3 and NOVA2 as targets for further research towards understanding the etiology of ALD in Rambouillet sheep.Angular limb deformity (ALD) affects many species of livestock and companion animals. The mechanisms of ALD development are not well understood, but previous research suggests the involvement of genetic risk factors. A case-control genome-wide association study (GWAS) was conducted with 40 ALD-affected and 302 unaffected Rambouillet rams and 40,945 single nucleotide polymorphisms (SNPs). Forelimbs of 6 ALD-affected rams were examined and diagnosed with osteochondrosis. Genome-wide or chromosome-wide significant SNPs were positioned exonic, intronic or within the 3′UTR of genes  To the best of our knowledge there is no single disease pathway responsible for ALD. Congenital ALD has been associated with perinatal factors such as maternal nutrition, teratogen exposure, premature birth, twin pregnancy or placentitis2. The causes of acquired ALD are likely multifactorial and may involve the interaction of trauma, genetics, rapid growth rate, endocrinological and/or nutritional factors4. Dietary mineral imbalance, such as of vitamin D, calcium, phosphorus, copper, zinc or magnesium, have also been known to contribute to ALD5. Evidence in multiple species strongly suggests a genetic component to ALD etiology, which may function in conjunction with other predisposing environmental influences7.Angular limb deformity (ALD) is a welfare and longevity concern for livestock and domestic animals. The term ALD describes deviations in the frontal plane of the limb that can be medial or lateral, unilateral or bilateral and congenital or acquired9. Osteochondrosis is a non-infectious disorder characterized by focal disturbance of endochondral ossification that may affect the articular cartilage (osteochondrosis dissecans) or the physes  of the long bones of animals and humans10. These disturbances may be caused by hypertrophic chondrocytes that fail to progress or a lack of chondrocyte organization in the proliferative zone, which ultimately lead to areas of retained cartilage promoting lameness and leg weakness12. The development of osteochondrosis is strongly associated with high-energy diets and rapid growth, and it is thought to involve the interaction of genetic, nutritional and mechanical factors, with no one factor being entirely responsible for disease progression12. Previous studies have reported genetic regions associated with osteochondrosis in horses and pigs, although specific genetic mechanisms remain undetermined14.Osteochondrosis is frequently described as the underlying pathology of acquired ALD in sheep, cattle, horses, pigs, dog and poultryIncidence of acquired ALD have been observed in Rambouillet rams during central performance ram testing at North Dakota State University (NDSU) and University of Wyoming (UWY). In the present study, a subset of affected rams were examined with gross and microscopic pathology to better understand the cause of ALD in these animals. The relatively low rates of disease development suggest the contribution of genetic risk factors; therefore, genotype data were collected in order to test for SNPs associated with ALD. Genome-wide association study (GWAS) was conducted with 40 ALD-affected and 302 unaffected Rambouillet rams who were enrolled in NDSU and UWY ram tests over a three-year period. Three different models were evaluated for comparison and interpretation of GWAS results, and the significant GWAS markers and corresponding genes were investigated for known biological roles related to bone growth and development.Rams were 7 ± 3 months of age at the time of enrollment in the NDSU and UWY central performance ram tests. Of the 342 Rambouillet rams collected from ram tests in 2019, 2020 and 2021, 40 rams developed ALD and 302 rams remained unaffected by conclusion of the 140-day testing period. Rams were considered to be affected by ALD upon presentation of abnormal conformation  of one or both forelimbs, as determined by trained UWY and NDSU sheep center staff who had daily interactions with the animals Fig. . A consiThe examined forelimbs of six ALD-affected rams displayed varying degrees of carpal valgus with occasional slight deviation of the fetlock. The humerus, olecranon, radius, metacarpal and phalanges were sectioned and examined. Gross changes consisting of mild to moderate irregular physeal thickening primarily affected the distal radial physes, although some mild changes were observed in the olecranon physis of two rams and the distal metacarpal physis of one ram.The histologic changes were similar amongst the different animals and varied only in the degree of severity. Changes were primarily characterized by irregular thickening of the physis (growth plate) consisting of both the proliferating and hypertrophic zones that extend down into the metaphysis Fig. . OccasioPopulation structure and its relationship to phenotype, test location and test year were evaluated through principal component analysis (PCA). The first principal component (PC1) had an eigenvalue of 9.62 and explained 11.64% of the total genetic variance and the PC2 had an eigenvalue 7.56 and explained 9.15% of the total genetic variance. There did not appear to be any specific grouping of ALD-affected animals apart from control animals by either PC1 or PC2 Fig. a. SimilaP-values < 1.08e−05) and/or permutation genome-wide  significance thresholds: rs160736723 located on chromosome 15, rs427563170 on chromosome 25, and rs416810983 on chromosome 14  for the fastGWA, SAIGE, and chi-square models were 1.05, 1.02, and 1.06, respectively  R package, and a 1 degree of freedom (df) chi-square allelic test in plink software. In all models, the same three SNPs were significant by Bonferroni chromosome-wide  and neuregulin 3 . Additionally, the gene tumor protein p53 inducible protein 11 (TP53I11) and the uncharacterized ncRNA LOC121816715 were located within the region defined by ± 100 kb of SNP rs160736723. The chromosome-wide significant marker rs416810983 was located in a gene-rich region containing 10 genes within ± 100 kb (Supplementary Table NOVA2). Although the 3′ UTR of NOVA2 is annotated as 352 bp in length in the sheep reference genome (ARS-UI_Ramb_v2.0), the assemblies of human and mouse contain much longer annotations; the Mus musculus NOVA2 3′ UTR is 6,701 bp (GRCm39 C57BL/6J genome) and the Homo sapiens NOVA2 3′ UTR annotation is 6,129 bp (GRCh38.p14 genome). Reference RNA tracks imply that the NOVA2 3′ UTR is a similar length in sheep, as RNA signal extends from the gene to roughly 5552 bp downstream according to the “RNA-seq exon coverage” track in the NCBI Genome Data Viewer. Therefore, it is likely that SNP rs416810983 is located within the Ovis aries NOVA2 3′ UTR.Genome-wide significant SNPs were located within genes tetraspanin 18  motifsHomo sapiens orthologs of TSPAN18, NRG3 and NOVA2 were queried and results revealed five protein interactions with TSPAN18 and eight protein interactions each with NRG3 and NOVA2 , matrix metalloproteinase 13 (MMP13) and osteopontin to direct angiogenesis and the invasion of blood cells into the cartilage matrix. These vessels facilitate the delivery of osteoclasts, osteoblasts, and bone marrow cells to the ossification front, which orchestrate cartilage resorption and the deposition of mineralized bone46. Vascular invasion of the cartilage-bone interface is critical for matrix calcification and successful endochondral ossification12.Osteochondrosis has previously been described as a cause of ALD in sheep, horses, pigs and other speciesTSPAN18  channel pore-subunit ORAI148. Experiments in mice have suggested that Orai1 has a critical role in bone homeostasis through the regulation of osteoblasts and osteoclasts47. The lack of SOCE in MC3T3-E1 pre-osteoblastic cells had an inhibitive effect on mineralized nodule formation and Orai1−/− stromal cells showed impaired mineral deposition in in vitro experiments. Similarly, osteoclastogenesis was impaired in Orai1−/− due to defects in the fusion of pre-osteoclasts47. In mouse and zebrafish, Tspan18 expression was noted in the vasculature of developing embryos and in the adult during wound healing. Deficiency of Tspan18 was found to result in the downregulation of VEGFR2, suggesting Tspan18 as either a direct or indirect regulator of the VEGF pathway49. Additionally, TSPAN18 has an experimentally determined interaction with ADAM10  has been shown to impair osteoblast differentiation and osteoclastogenesis22. The gene TBX5 was identified as a candidate for osteochondrosis development in pigs through GWAS and differential transcript abundance of healthy versus defect cartilage13. Haploinsufficiency of TBX5 is known to cause Holt-Oram syndrome in human patients, which is characterized in part by skeletal malformations54.The expression of NRG3 . In each study year, ram lambs of 7 ± 3 months of age from more than 30 regional  seedstock producers were brought to the University of Wyoming-Laramie Research and Extension Center  or North Dakota State University-Hettinger Research and Extension Center . Rams were managed as a single cohort in a dry-lot management system at each testing location and were provided fully balanced ad libitum textured diets that met requirements for all phases of ram growth, containing 15% to 17% crude protein and 68% to 76% total digestible nutrients on a dry matter basis. Each ram test began in the fall and concluded in the spring and lasted for a total duration of 140 days. Ram test protocols were approved by North Dakota State University Institute for Animal Care and Use Committee (IACUC). All methods were carried out in accordance with relevant IACUC guidelines and regulations. The methods of this study have been reported in accordance with the ARRIVE guidelines.Nine forelimbs from six ALD-affected rams (three rams each from NDSU and UWY) were evaluated for gross and histological pathology. Each limb was evaluated grossly and the humerus, olecranon, radius, metacarpal and phalanges were sectioned. Decalcified, 5 µm thick, hematoxylin and eosin stained frontal section of the distal radius and the sagittal section of the olecranon of each submitted forelimb and the frontal section of the distal metacarpus of two forelimbs were evaluated. Images were taken on a BX 43 microscope, 2 × 0.08 NA plan apo lens on a DP 23 camera at 3088 × 2076 resolution.56. DNA was isolated from blood at the University of Idaho using the phenol–chloroform method57 and tissue samples were provided to AgResearch for DNA extraction. The DNA samples were genotyped with the Applied Biosystems™ Axiom™ Ovine Genotyping Array (50K) consisting of 51,572 single nucleotide polymorphism (SNP) markers  or the AgResearch Sheep Genomics 60K SNP chip consisting of 68,848 SNP markers . Duplicate markers within a panel were filtered to retain the marker with the highest call rate (CR). Compatible markers between panels were matched by marker name and genome position and plink v1.9 was used to merge genotype array data58 (http://pngu.mgh.harvard.edu/purcell/plink/). Markers were filtered with plink v1.9 to remove non-autosomal markers and those with CR < 0.90, minor allele frequency < 0.01, and Hardy-Weinberg Equilibrium P-value < 1e−06. A total of 40,945 SNP markers were carried through to final analysis. All rams had a CR of 95% or greater.Whole blood or tissue samples were collected from 342 Rambouillet rams over a three-year period. Rams were sampled from NDSU in 2019 (n = 44), 2020 (n = 60), 2021 (n = 55), and from UWY in 2019 (n = 74), 2020 (n = 43) and 2021 (n = 66). The genotype data of 313 of these rams have been analyzed in previously published work61. Each plot was constructed with the first principal component (PC1) on the x-axis and the second principal component (PC2) on the y-axis. Rams were color-coded by phenotype (ALD-affected or unaffected), test location (NDSU or UWY) and test year . The proportion of variance explained by each PC were calculated by dividing the PC eigenvalue by the sum of eigenvalues.Principal component analysis (PCA) was conducted to visualize the population structure and phenotypic distribution among ALD-affected and unaffected rams. Analysis was conducted with plink v1.9 and visualized with the packages ggplot2 and patchwork in R version 4.2.262. For this reason, three models with the ability to handle imbalance between study groups were chosen for analysis and comparison. The data were analyzed through a generalized linear mixed model (GLMM)-based fastGWA-GLMM in GCTA version 1.93.2 software63, a Scalable and Accurate Implementation of GEneralized (SAIGE) mixed model v0.45 available as a R package62, and a 1 degree of freedom (df) chi-square allelic test in plink v1.9 software64. In the fastGWA-GLMM, the genetic variance statistic (Vg) was not significant, likely due to the lack of highly related individuals within the dataset. Therefore, the program implemented the linear regression (fastGWA) model instead of GLMM65. The fastGWA model used a sparse genomic relationship matrix (GRM) to account for population stratification and was run with default parameters. The SAIGE model was run using the R scripts “step1_fitNULLGLMM.R” and “step2_SPAtests.R” with default minor allele count and frequency thresholds, and the leave one chromosome out (LOCO) method was used to construct a different GRM for each chromosome. In plink v1.9, the flags ‘–assoc’, ‘–adjust’, and ‘–ci 0.95’ were used to run the chi-square test and to report 95% confidence intervals for estimated odds ratio (OR) statistics. The GWAS results were visualized with the package CMplot and OR confidence intervals were visualized with gridextra and ggplot2 in R67.In total 342 Rambouillet rams, including 40 ALD-affected and 302 unaffected, were examined in three genome-wide association study (GWAS) models. The case-to-control ratio of the study was 1-to-7.55, which approached but did not exceed the threshold of 1-to-10 generally used to define an imbalanced GWAS68. Permutation testing was carried out in plink v1.9 . Each SNP underwent a minimum of 500 permutations and up to 1,000,000 permutations under default conditions for the pruning alpha threshold, pruning beta, initial pruning interval and the interval increase rate. The most significant P-value achieved by chance after 1,000,000 permutations was used to define the threshold for genome-wide significance. Chromosome-wide significance was calculated using the Bonferroni adjustment for the largest chromosome .To avoid over interpretation of results and to limit the possibility of Type I or Type II error, the genome-wide significance threshold for this study was determined by adaptive permutation69 was used to identify the variant type of significant SNPs. In order to understand the genomic contexts of SNPs, the reference genome ARS-UI_Ramb_v2.070 was searched using the GenomeBrowser tool in NCBI71. Genes or noncoding elements located within ± 100 kb of significant SNPs were recorded for further investigation.The database Ensemblth position, were queried through the online software FABIAN72. Sequences were tested against detailed transcription factor flexible models (TFFM) compiled within the JASPAR 2022 database73. The proteins of implicated genes were queried through the STRING database (using Homo sapiens orthologs) for known and predicted protein-protein interactions74. Interacting proteins and predicted transcription factors were investigated through literature review for potential relevance to bone health and growth.Markers located within a gene or within the 3′ UTR were evaluated for predicted transcription factor binding site (TFBS) matrix score differences between reference and alternate SNP alleles. Reference genome sequences comprised of 11 bp total, with the SNP allele  in the 6All ram test protocols were approved by North Dakota State University Institute for Animal Care and Use Committee (# 20210012). Informed consent was collected from all animal owners upon enrollment of their rams in the central performance ram test.Supplementary Information.


ZmSBE genes encode important starch branching enzymes in the starch biosynthesis pathway. In this study, we re-sequenced the genomic sequences of ZmSBEI, ZmSBEIIa, ZmSBEIIb, and ZmSBEIII in three lines called 335 inbred lines, 68 landrace lines, and 32 teosinte lines. Analyses of nucleotide polymorphisms and haplotype diversity revealed differences in the selection patterns of ZmSBEI, ZmSBEIIa, ZmSBEIIb, and ZmSBEIII during maize domestication and improvement. A marker–trait association analysis of inbred lines detected 22 significant loci, including 18 SNPs and 4 indels significantly associated with three maize starch physicochemical properties. The allele frequencies of two variants (SNP17249C and SNP5055G) were examined in three lines. The frequency of SNP17249C in ZmSBEIIb was highest in teosinte lines, followed by landrace lines, and inbred lines, whereas there were no significant differences in the frequency of SNP5055G in ZmSBEIII among the three lines. These results suggest that ZmSBE genes play an important role in the phenotypic variations in the starch physicochemical properties in maize. The genetic variants detected in this study may be used to develop functional markers for improving maize starch quality.Elucidating the genetic basis of starch pasting and gelatinization properties is crucial for enhancing the quality of maize and its utility as feed and industrial raw material. In maize,  Maize starch is one of the highest-quality starches and accounts for 80% of the global starch yield . MoreoveStarch is an important storage polysaccharide comprising amylose and amylopectin, which are structurally diverse polymers. The content ratio of these two components influences the function, quality, and utility of starch. Changing the structure of starch may enhance how it can be used. The basic methods for synthesizing starch in plants include synthesizing soluble precursors, followed by coordinated reactions: firstly, the glucan chain is connected to α-1→4) and elongating, and then, branching, and debranching at α-(1→6) positions . AGPase synthesizes the nucleotide diphosphate sugar precursor ADP-Glc through the catalysis of ATP and Glc1P. The α-(1→4)-linked glucan chains are formed by reactions catalyzed by ADP-Glc-dependent transferases, which are called starch synthases (SSs), whereas α-(1→6)-linked branch points are introduced via reactions mediated by starch branching enzymes (SBEs) and debranching is catalyzed by debranching enzymes (DBEs) . Thus, t and elonSBEI genes are expressed later than SBEII genes . Th. Th13]. ZmSBEI (GRMZM2G088753), ZmSBEIIa (GRMZM2G073054), ZmSBEIIb (GRMZM2G032628), and ZmSBEIII (GRMZM2G005298) gene in all tested lines were sequenced using the target sequence capture sequencing technology on the NimbleGen platform [ZmSBEs gene sequences in the background of the B73 inbred line (The genomic DNA of each line was extracted from three mixed leaves (approximately 15 days after the emergence of maize) using the CTAB (cetyl trimethyl ammonium bromide) method ,15. The platform ,17 by BGred line . At the maturity stage, the ears for each inbred line were harvested, and approximately 50–60 grains were stripped from the middle of the corn ear and immediately frozen in liquid nitrogen, and preserved at −75 °C for analysis and determination of starch. After drying and weighing, the granules were crushed and passed through a sieve  to determine the starch content. The crude starch content of corn (KSC) kernel was estimated using a near-infrared analyzer .temp).A rapid visco analyser (RVA)  was used to determine the pasting properties. A total of 3 g of starch taken from each inbred line was dispersed into 25 mL of distilled water in the viscometer test canister. Then, the test was performed with RVA. The set speed was 160 rpm/min. Viscosity values were recorded in centipoise (cp). The pasting properties were peak viscosity (PV), trough viscosity (TV), breakdown viscosity (BD), final viscosity (FV), setback viscosity (SB), peak time (PT), and pasting temperature (PgelH).The gelatinization properties of maize starches were analyzed using a differential scanning calorimeter DSC 200F3 Maia . Firstly, 5 mg starch samples (dried starch basis) were weighed in a small crucible. Secondly, 10 mL of distilled water was added to mix it. Finally, it was sealed and stored at 4 °C. The samples were used the next day to perform DSC determination. The gelatinization properties were onset temperature (To), peak temperature (Tp), conclusion temperature (Tc), and the enthalpy of gelatinization .A total of 163,931 SNPs were obtained by genotyping using a sequencing strategy . Among tSBE isozyme-encoding genes revealed ZmSBEI, ZmSBEIIa, ZmSBEIIb, and ZmSBEIII differ substantially in terms of length  , coding region, and downstream genomic region (approximately 500 bp) in three tested lines were sequenced. For ZmSBEI, 1102 polymorphisms were found, consisting of 916 SNPs and 186 indels. On average, the SNPs and indels occurred every 9.9 and 50 bp, respectively. The average length of each indel was 5.4 bp. A total of 1519 sequence variations were identified in ZmSBEIIa, including 1302 SNPs and 217 indels. On average, the SNPs and indels were detected every 9.7 and 58.8 bp, respectively. The average length of each indel was 4.9 bp. Of the 1616 sequence variations detected in ZmSBEIIb, 1353 were SNPs and 263 were indels. On average, the SNPs and indels occurred every 14.5 and 76.9 bp, respectively. The average length of each indel was 3.9 bp. In contrast, ZmSBEIII included 1044 polymorphisms, including 903 SNPs and 141 indels. On average, the SNPs and indels were detected every 7.6 and 47.6 bp, respectively. The average length of each indel was 4.7 bp  . Both Zm-amylase . To idens 4.7 bp .ZmSBE genes  in all tested lines, sequence variations were analyzed. Compared with the genes in the teosinte lines, those in the landrace lines and inbred lines were more conserved (CT < CL < CI), less diverse (πT > πL > πI), and had fewer nucleotide sequence polymorphisms (θT > θL > θI)  were performed to analyze the tested sequences. Tajima’s D and Fu and Li’s F* values for ZmSBEI and ZmSBEIIb were significantly less than 0 in all test lines, showing that these two genes were chosen during evolution. For both ZmSBEIIa and ZmSBEIII, Fu and Li’s D* and F* values were significantly less than 0, but Tajima’s D value was not. Hence, these two genes may have undergone neutral evolution (To survey the variety among the four θL > θI) . The π vsequence . To furtvolution .ZmSBEI, LD was less extensive in the teosinte lines than in the landrace lines and inbred lines, implying that this gene may have been chosen during maize domestication. For ZmSBEIIa, the extent of LD in the teosinte lines was similar to that in the landrace lines, but lower than in the inbred lines. Accordingly, ZmSBEIIa may have been chosen during maize improvement. In contrast, ZmSBEIIb may have been chosen during both maize domestication and improvement, whereas the likelihood that ZmSBEIII was chosen during maize domestication and improvement was relatively low (Linkage disequilibrium (LD) decay varied among the analyzed genes. For vely low .ZmSBE sequences were associated with starch physicochemical properties, an association analysis was performed on the basis of the variants with a minor allele frequency ≥ 0.05. Twenty-two significant variants of ZmSBEIIa, ZmSBEIIb, and ZmSBEIII were associated with three traits  consisted of two SNPs and two indels. These loci were in intronic regions. The analysis of these variants detected strong LD for SNP403 and SNP568. The inbred lines were divided into five haplotypes according to the significant variants. The ANOVA results indicated that the differences in ΔgelH among the five haplotypes were significant (p = 1.1 × 10−3). We also analyzed the frequency of indel 5969  in teosinte, landraces, and inbred lines. The results indicated that the frequency of indel 5969C was lower in the teosinte lines (12.9%) than in the landrace lines (23.2%), and inbred lines (35.8%). Accordingly, indel 5969 may have been chosen during maize domestication and improvement . The LD analysis showed that SNP17249 and SNP17786 are completely linked; the LD value (r2) of the remaining loci was approximately 0.8. Five major haplotypes were identified for the four loci across the 335 inbred lines. The differences in SB among these haplotypes were significant according to ANOVA (p = 1.1 × 10−6). The frequency of SNP 17249C was higher in the teosinte lines (87.5%) than in the landrace lines (66.2%), and inbred lines (46.3%), which indicated that SNP 17249C may have been gradually selected during maize domestication and improvement (Four variants of rovement .ZmSBEIII (4817 and 5055 bp) were significantly associated with ΔgelH. Strong LD was observed between these two loci. In addition, significant differences in ΔgelH were detected among the three haplotypes for these two loci (p = 5.2 × 10−9). The frequency of SNP5055G did not differ significantly among the three populations, indicating that SNP5055 is not exposed to obvious selection during maize domestication and improvement  of these four genes were higher than previously reported in the whole genome [ZmMADS69, which functions as a flowering activator, only retained 18.4% of the nucleotide diversity of teosinte [ZmSBEIIb had the most variant sites and the highest variant frequency. Candidate gene association mapping on the basis of LD is a powerful method for identifying elite alleles for target traits [crtRB1, which is associated with the maize kernel β carotene concentration, for the provitamin A biofortification of crops [ZmSBEIIa, ZmSBEIIb, and ZmSBEIII were related to KSC, ΔgelH, and SB. The elite variations and the best haplotypes of ZmSBEIIa, ZmSBEIIb, and ZmSBEIII were identified, which may be used to generate inexpensive markers useful for enhancing the starch quality and quantity by molecular breeding in maize.Significant phenotypic differences and nucleotide polymorphisms are important for genetic mapping through linkage or association analyses ,23. Maize genome . The inbteosinte . In riceteosinte ,30. Amont traits . This apt traits ,31,32,33ZmSBEI gene was chosen in both rice and maize [ZmSBE genes in our study have different selection patterns during maize domestication and improvement. Specifically, ZmSBEI may have been chosen during maize domestication, while ZmSBEIIa may have been chosen during maize improvement. In contrast, it is possible that ZmSBEIIb was chosen during both maize domestication and improvement, whereas there was likely minimal selection of ZmSBEIII during maize domestication and improvement. Differences in the evolution of z2 genes, which influence the maize seed zein content, have been reported. The results revealed that teosinte and landrace lines may be crucial sources of genetic variation relevant for maize improvement [Compared with teosinte, cultivated maize has undergone considerable phenotypic changes  . It is end maize . The fourovement .ZmSBE genes were re-sequenced in 335 inbred lines, 68 landrace lines, and 32 teosinte lines. Analyses of nucleotide polymorphisms and haplotype diversity revealed differences in the selection patterns of ZmSBEI, ZmSBEIIa, ZmSBEIIb, and ZmSBEIII during maize domestication and improvement. A total of 22 significant variants of ZmSBEIIa, ZmSBEIIb, and ZmSBEIII were identified as associated with KSC, ΔgelH, and SB. The effect and selection of elite variations of ZmSBEIIa, ZmSBEIIb, and ZmSBEIII were identified. These results suggest that the genetic diversity of ZmSBE genes plays an important role in the variations of the starch physicochemical properties in maize. These variants may be applicable for increasing the maize kernel starch quality and quantity.In conclusion, maize


The 1,000 wheat exome project captured the single nucleotide variants in the coding regions of a diverse set of 890 wheat accessions to analyse the contribution of introgression to adaptation of wheat. However, this highly useful single nucleotide polymorphism (SNP) dataset is based on RefSeq v1.0 of the International Wheat Genome Sequencing Consortium (IWGSC) assembly of the bread wheat genome of Chinese Spring. This reference sequence has recently been updated using optical maps and long-read sequencing to produce the improved RefSeq v2.1. Our objective was to develop a reliable high-density SNP dataset positioned onto RefSeq v2.1 because it is the current standard reference sequence used by wheat researchers.The 3,039,822 SNPs originally positioned on RefSeq v1.0 were projected to v2.1 using Liftoff with four different flanking regions, and 2,946,536 SNPs were consistently lifted to the same location irrespective of the flanking region lengths. Of these, 2,799,166 were located on the ‘+’ ve strand. The distribution of the SNPs across the 21 chromosomes on RefSeq v2.1 was similar to that of RefSeq v1.0. Among the SNPs that were based on unanchored scaffolds in RefSeq v1.0, 11,938 were projected to one of the 21 pseudomolecules in the upgraded assembly. This SNP dataset constitutes a much-needed standardized resource for the wheat research community.The online version contains supplementary material available at 10.1186/s13104-023-06496-8. Triticum aestivum L.) is an allohexaploid species with a genome estimated at ~ 16 Gb that contains the A, B and D subgenomes [As of January 2021, reference sequences had been generated for 798 land plants [bgenomes . The firbgenomes , providebgenomes . Using oTriticum turgidum L. ssp. dicoccum) and goatgrass (Aegilops tauschii Coss.) [The current day hexaploid wheat evolved ~8000 years ago through chance hybridizations between domesticated emmer wheat (i Coss.) , 8. The i Coss.) . Here a i Coss.) . This pui Coss.)  and preshttps://wheat-urgi.versailles.inra.fr/Seq-Repository/Variations). The 3,039,822 SNPs in the VCF file were split into 16 parts for ease of computational analyses, and the result files were merged after processing. The VCF files were converted to GFF files using a custom R script. For each SNP, the GFF file contained the parent feature, i.e., the flanking region start and end coordinates, and the child feature, i.e., the SNP coordinates per se. Each SNP and the flanking regions were assigned an ID, which associated the SNPs with their corresponding flanking regions. Strand orientation was not included in the 1,000 wheat exome VCF file. Since providing the plus strand alleles is the standard convention for publicly available SNP datasets [The Liftoff tool was originally developed for projecting annotations from one reference genome assembly to another without the use of chain files . For thidatasets , the strA flanking region of 1,980 bp on either side of the SNP was initially used because the average transcript length in the annotation RefSeq v1.1 was 3,961 bp; hence, this length can be argued to represent the average length of conserved regions in the genome. To test the ability of Liftoff to project the SNPs from v1.0 to v2.1, the following flanking region lengths were also assessed: 500 bp, 1,000 bp and 1,250 bp, and only the consensus set of SNPs lifted to the same location with all four flanking region lengths was retained. Liftoff uses Minimap2  for sequThe new v2.1 SNP coordinates corresponding to the v1.0 SNPs of the 1,000 exome VCF file were assigned using their corresponding SNP IDs. The preparation of input files, merging and formatting of files and output summarization steps were performed using custom R scripts and bash. The functional annotation of the SNPs was performed using IWGSC RefSeq v2.1 on SnpEff . The datThe number of SNPs lifted-over using the four flanking region lengths is summarized in Supplementary Tables A total of 35,284 out of the 39,822 SNPs originally positioned on the unanchored scaffolds (chrUn) in RefSeq v1.0 were lifted to the same loci regardless of the flanking region lengths. Of those, 11,938 were assigned to a position and projected onto one of the 21 assembled chromosomes. The vast majority of the SNPs were lifted to the same location in both versions, but 5,949 SNPs ended up projected to a different chromosome from v1.0 to v2.1 .Below is the link to the electronic supplementary material.Supplementary Material 1


Christian Schönbach, Chair of the Department of Biology, School of Sciences and Humanities at Nazarbayev University, Kazakhstan, unexpectedly passed away on 15 April 2023. Christian’s contributions to science were immense and span four thematic areas: immunoinformatics, computational and functional genomics, data integration and biological knowledge discovery from databases, and environmental microbiomics. In addition to his research, while serving in leadership roles at the Asia Pacific Bioinformatics Network (APBioNet) and the International Society for Computational Biology (ISCB), he built bridges between diverse scientific communities and enabled the dissemination of scientific knowledge. Christian’s contributions to furthering bioinformatics education and training, in particular in the Asia Pacific region, cannot be overstated. Christian was also a mentor to many students and young researchers.Christian Schönbach graduated in 1990 from the Julius-Maximilians University Würzburg, Germany, with a master’s degree in biology (Diplom-Biologe) and went on to postgraduate studies in genetics at the Eberhard-Karls University Tübingen, Germany. His contributions to immunology can be traced back to his Ph.D. thesis on the organization of major histocompatibility complex (MHC) class II genes in orangutans . ChristiIn the context of the large, international ‘big data’ research endeavors, Christian’s contributions were critical in the effort to identify all mouse mRNA transcripts  Figs 1 .While continuing his work in immunoinformatics and computational and functional genomics, Christian moved into academic roles with an added teaching and mentoring component, first as Associate Professor, Division of Genomics and Genetics, School of Biological Sciences, Nanyang Technological University, Singapore (2006–8), and then as a tenured professor again in Japan at the Department of Bioscience and Bioinformatics, School of Computer Science and Systems Engineering, Kyushu Institute of Technology (2009–13). In 2013, he accepted tenure at the Department of Biology, School of Science and Technology, Nazarbayev University in Kazakhstan. Nazarbayev University had been established only 3 years prior, and Christian was very excited about the rare opportunity to contribute to shaping an entire department and school, and critically influencing the research agenda and curricula, almost from scratch. His research also branched out into a new direction, environmental microbiomics  and then President 2016–20) of APBioNet and as an elected member of the Board of Directors of ISCB since 2018 and tirelessly served as a section editor of raining’ , 2016, s–20 of APhttps://www.youtube.com/watch?v=udedu-So_Hc). Christian thrived conducting his research in these diverse cultural backgrounds, a very rare skill.Christian’s path took him across East Asia (Japan), Southeast Asia (Singapore), and Central Asia (Kazakhstan), and he moved between these countries multiple times. He took on the difficult tasks of learning Japanese as well as Russian and spearheaded the application of Bioinformatics in the context of the intersection of biology and technology within the Digital Kazakhstan initiative  first met Christian in 2005 when doing an internship in his Immunoinformatics Research Team at RIKEN Yokohama. Anton very vividly remembers how Christian clearly identified any issues or roadblocks and immediately set out to find and implement a solution, not only when conducting research but also regarding any organizational issues. Interacting with Christian profoundly influenced his career trajectory. He encouraged Anton to apply for graduate school and set him on the path to become a scientist. After leaving his team, Anton and Christian stayed connected as friends through the years.https://2022.igem.wiki/nu-Kazakhstan/attributions).Christian’s nurturing of students and young minds is amply evident from the attributes of the 2022 iGEM team from Kazakhstan: ‘We also thank our Department Chair Dr. Christian Schoenbach for ensuring the necessary supervision of the team’s activities, and the safety of our working conditions. Under his guidance, we received generous lab support from our Teaching Assistants Kamilya Sydykova, Nurzhanna Bakuova, and Yerkebulan Yesbolatov who conducted safety sessions with us, prepared all the necessary materials, reagents, and equipment available from the Department, even on weekends and holidays’ (https://nu.edu.kz/news/in-memory-of-professor-christian-schoenbach).Christian’s immediate colleagues at Nazarbayev University felt his sudden demise most poignantly and stated ‘His rational and strategic thinking, his attention to detail, and his exceptional organizational skills helped shape today’s Department of Biology and the School. Dr Schoenbach will be sorely missed’ (‘Bandhu sakhe praptasya’—A friend is a treasure acquired.We know for sure that Christian has advised and mentored many more students and junior scientists throughout his career. Remembering Christian Schönbach, what stands out most in our memories is his contagious energy, his concern for others, and his tireless contributions to science. His passing is an irreparable loss to all of us who knew him.


In many forensic settings, identity of a DNA sample is sought from poor-quality DNA, for which the typical STR loci tabulated in forensic databases are not possible to reliably genotype. Genome-wide SNPs, however, can potentially be genotyped from such samples via next-generation sequencing, so that queries can in principle compare SNP genotypes from DNA samples of interest to STR genotype profiles that represent proposed matches. We use genetic record-matching to evaluate the possibility of testing SNP profiles obtained from poor-quality DNA samples to identify exact and relatedness matches to STR profiles. Using simulations based on whole-genome sequences, we show that in some settings, similar match accuracies to those seen with full coverage of the genome are obtained by genetic record-matching for SNP data that represent 5–10% genomic coverage. Thus, if even a fraction of random genomic SNPs can be genotyped by next-generation sequencing, then the potential may exist to test the resulting genotype profiles for matches to profiles consisting exclusively of nonoverlapping STR loci. The result has implications in relation to criminal justice, mass disasters, missing-person cases, studies of ancient DNA, and genomic privacy. In forensic genetics, the identity of a DNA profile is often sought from a biological sample with poor DNA quality, for which standard molecular techniques used with high-quality samples are unlikely to successfully produce genotypes. When the sample originates from trace sources such as burned, degraded, or ancient materials, only limited portions of the original genome might remain in the sample.Routine genotyping of short-tandem-repeat loci (STRs) assumes that high-quality DNA samples contain DNA fragments in long sections of sequence. Hence, in a high-quality sample, the polymerase chain reaction can amplify the fragment that contains the entire section of DNA that lies between a specified pair of primer sequences . The amFor degraded DNA samples, however, standard STR genotyping procedures can be unlikely to succeed –4. DNA fWith next-generation sequencing of fragmentary materials, no particular genomic site can be reliably expected to appear in the genotype data. In particular, the STR loci that underlie standard forensic databases –15—and tIs it possible to identify genetic matches between a fragmentary SNP genotype profile from a degraded DNA sample and the genotypes of a nonoverlapping set of STRs? In a technique termed “genetic record-matching,” we have recently shown that, owing to genotypic correlations between STRs and their neighboring SNPs, it is frequently possible to identify matches between pairs of profiles, when one member of the pair is a SNP profile and the other is a forensic STR profile . FurtherOur calculations, however, have made use of genome-wide SNP datasets with high genotyping quality, with high genomic coverage around each forensic STR locus. What if the SNP data were instead fragmentary, in the manner expected for degraded DNA and fragmented genotyping? This problem of record-matching between STR profiles and fragmentary SNP profiles represents any of several possible scenarios: matching the SNP profile of a degraded crime-scene sample to the STR profile of a specific known suspect, querying a degraded crime-scene SNP profile against a database of STR profiles, matching the SNP profile of an ancient DNA sample to specific STR profiles of possible living relatives, matching the SNP profile of a degraded DNA sample in a missing-persons or mass-disasters case to STR genotypes from known missing persons or their relatives, or querying it against an STR database of many potential candidates.Here, we consider genetic record-matching between STRs and fragmentary SNP data. We assess many genomic coverage levels, examining scenarios in which the hypothesis is that a SNP profile and an STR profile originate from the same person, from a parent–offspring pair, or from siblings.Codis STRs in 872 individuals from 52 populations—the 13 original Codis loci and 4 in the expanded set.We examine two datasets containing both SNP and STR genotypes. First, the Human Genome Diversity Panel dataset (HGDP), as studied by  and 17]17], contCodis core loci and all 7 expanded Codis core loci  = {1, 2, …, I}, Mjk = λ is the entry for STR profile Rj from individual j and SNP profile Sk for individual k. From matrix M, we assigned matches by one of four schemes [I true matches. In needle-in-haystack matching, accuracy is defined as the proportion of true matches with greater match scores than the largest score across all non-matching pairs.We constructed an  schemes , 17  in Eq.  in Eq. ), we aga∆true = ∆test, and in Table ∆true ≠ ∆test. For the median, we used the lesser choice when the number of unique values was even.For all three relatedness scenarios, for each of the 100 partitions, we constructed the match-score matrix from the test set and assessed record-matching accuracies for the four matching schemes (Section “Match assignment”). Median, minimum, and maximum record-matching accuracies of the 100 replicates using the HGDP dataset appear in Table ∆true = ∆test  distribution. For each test individual i in a simulated fragmentary dataset with X SNPs sequenced in the L windows, we sampled a vector X, p) distribution, p = . Here, ℓth Codis locus in fragmentary SNP data of individual i. For each 1-Mb window around the ℓth Codis locus, we sampled Dℓ SNPs in the full-coverage dataset. Figure We used a simple model in which distinct SNPs have independent random variables for presence or absence of data. At coverage entclass1pt{minimaWe applied the record-matching pipeline of Section “Genetic record-matching” to each simulated fragmentary SNP dataset of the test-set individuals. As noted in Section “Simulation of fragmentary genomic SNP data,” we fixed the training set at the median-accuracy partition generated in Section “Record-matching with HGDP and 1000 Genomes”; it contained the full-coverage SNP–STR haplotypes of the training-set individuals. For each relatedness scenario, we used a same shared training set across all 100 simulated fragmentary SNP datasets.We used the training set as a reference in imputing test-set STR profiles from fragmentary SNP profiles according to Eq. . We alsoc, we computed match scores of all pairs—one with a SNP profile and the other with an STR profile—and obtained a 626 × 626 match-score matrix. We then computed match accuracies under four matching schemes described in Section “Match assignment.” We applied similar procedures for the parent–offspring and sib-pair scenarios (Section S1.6).For the same-individual scenario, the training set contained 1878 individuals and the test set had 626. For each of 100 simulated fragmentary SNP datasets at a given genomic coverage ∆true = ∆test. Under three relatedness scenarios, we examine the effect of the SNP coverage c. This analysis follows the procedures in Section “Record-matching of STR profiles with fragmentary genomic SNP data.” Numerical summaries appear in Table ∆true ≠ ∆test also appear in the supplement , it drops below 1, though it remains high at much lower coverage. The HGDP median accuracies  are achieved at c ≈ 0.05.For one-to-many matching with a SNP query Fig.  or STR qc ≈ 0.3. The HGDP median accuracy for this scheme (0.532) is achieved at c ≈ 0.05.For the needle-in-haystack scheme Fig. , the medλ required to achieve a desired ratio of posterior to prior odds, the likelihood ratio. For example, to obtain posterior odds of a match equal to 104 with prior odds 10−9, λ , then 67% of true matches achieve posterior odds above 1 (ratio 1010), and 9% achieve posterior odds above 107 (ratio 1017). When the prior is uninformative, with prior odds of 1, 99% of true matches exceed the match score required for attaining posterior odds of 1 (ratio 100), and 85% of true match pairs have posterior odds above 107 (ratio 107).For each relatedness scenario and a ratio of posterior and prior odds, we computed the fraction of true matches with match score above the minimum Figure  requiredual Fig. , if the We have examined genetic SNP–STR record-matching on fragmentary SNP datasets. For the sequenced genomes of the 1000 Genomes, record-matching accuracies exceed those seen previously in HGDP genotyping arrays Fig. . Accurac9. Here, even with 15-locus profiles, 67% of pairs would be detected at a more stringent 1010 threshold  and searching for same-individual matches, particularly for the needle-in-haystack scheme, record-matching accuracy increases with sample size  reference panel; imputation accuracies are high in some 1000 Genomes samples in which direct genotypes are available , Supplem. Howeverc, SNPs amplify independently, that amplification patterns are independent across individuals, and that genotypes are accurately obtained. With actual degraded DNA, fragmentary datasets likely possess spatial correlation across the genome, containing multiple neighboring SNPs genotyped on the same DNA fragments .The results have applications in settings in which an investigator would have liked to test STRs for matches against an STR database, but in which STR genotyping was impossible. If samples are degraded so that only SNP genotypes can be obtained—as might occur for older criminal-justice samples, mass disasters, burned material, or ancient DNA—then our approach could be used to test the resulting SNP profile against an STR database. In such cases, genetic record-matching is used simply to overcome the technical challenge of genotyping STRs in degraded material—in existing investigative settings, not by introducing new ones.“Contrary to the view thatCodisgenotypes expose no phenotypes, aCodisprofile on a person together with a SNP database—if the person is in the database—in principle may contain all of the phenotypic information that can be reliably predicted from the SNP record. Conversely, participants in biomedical research or personal genomics who have consented to share their SNP genotypes may be subject to a previously unappreciated risk: identification in a forensic STR database.”Genetic record-matching can also produce new information linkages if investigators or others possess access to both SNP and STR databases , 17. ProThe increased record-matching accuracy that we have detected in a larger, denser dataset than that used by Edge et al.  and Kim Supplemental Material