Early sexual debut among adolescent girls may result in teenage pregnancy and sexually transmitted diseases. In sub-Saharan Africa (SSA), pregnancy among adolescent girls may adversely impact the continuation of their education, general health status, and birth outcomes. However, few cross-national studies have examined the role that the social environment plays in adolescent girls' sexual behaviors in SSA. In this study, we explored adolescent girls' social environment and the impact on their sexual behaviors..The country selection was based on availability of Global School-Based Student Health Survey (GSHS) national data (2003–2015). The total analytic sample was 22,067 adolescent girls from 12 countries in SSA. Descriptive statistics were generated to determine the characteristics of adolescent girls and independent samples t-test analysis were performed to determine whether there were differences between the social environment and age of sexual debut and sexual partners. Logistic regression models were used to determine the association between adolescent girls’ social environment and sexual debut.The study results showed variations across the 12 countries. Almost one in five (19.9%) adolescent girls reported to have ever engaged in sexual intercourse. Their mean age of sexual debut was 13.21 (13.04–13.37) years and mean number of sexual partners was 2.19 (2.08–2.29). We found that adolescent girls who reported not being connected with their parents were more likely to debut sex . Parental monitoring was significantly associated with sexual debut but after controlling for the confounding variables , the association was no longer significantly positively associated. Adolescent girls who felt supported by their peers had a significantly higher number of sexual partners than those who did not feel supported by their peers.The social environment of adolescent girls plays a very important role in sexual debut, age of sexual debut, and the number of sexual partners. Sexual health policies targeting adolescent girls are likely to achieve positive impacts if they focus on improving parental connectedness and peer support. Adolescent girls in sub-Saharan Africa are at risk of dropping out of school when they get pregnant. There are also several health risks associated with adolescent pregnancies. This study explored factors in the adolescent girl’s social environment which could impact their sexual behaviors. We examined data from adolescent girls in 12 sub-Saharan African Countries, namely, Kenya, Tanzania, Uganda, Ghana, Mauritania, Senegal, Swaziland, Zambia, Botswana, Namibia, Seychelles and Mozambique. After analyzing the data, we found that adolescent girls who reported not being close with their parents were more likely to debut sex. Adolescent girls who felt supported by their friends had a higher number of sexual partners. Thus, support from parents and peers play a vital role in the sexual behaviors of adolescent girls. A World Health Organization (WHO)  report iAccording to Sawyer et al.  a focus Compared to the rest of the world, young women in sub-Saharan Africa (SSA), face a double burden of unplanned pregnancy and HIV risk . Early iSub-Saharan Africa has the highest level of adolescent fertility in the world, which significantly contributes to the region’s lifetime average of 5.1 births per woman . PregnanAccording to Dimbuene and Defo  one of tA stronger parent–child relationship and higher levels of parental control decreased the risk of premarital intercourse . DiclemeThe study utilizes the conceptual model of the relationship between social networks and social support on health proposed by Heaney & Israel . This moThus, developing positive child-parent relationships characterized by connectedness and monitoring, in addition to perceived peer support could lead to lower risky sexual and reproductive health behaviors. To date, there have been few cross-national studies in sub-Saharan Africa exploring the social environment of adolescent girls and the impact on their sexual health. Consequently, the aim of this study is to examine the impact of parental attitudes and peer influence on the sexual debut of adolescent girls in sub-Saharan Africa. The study objectives are, (a) to estimate the prevalence rate of initiation of sexual relationship among adolescent girls and to determine the mean age of sexual debut, and mean number of sexual partners; (b) to estimate the levels of parental monitoring, parental connectedness and peer support among adolescent girls and (c) examine the association between adolescent girls' social environment and sexual debut, age of sexual debut and number of sexual partners.We hypothesized that adolescent girls who were monitored by and felt connected to their parents and felt supported by their peers would have lower odds of experiencing sexual debut.The Global School-based Student Health Survey (GSHS) was the primary data source for this study. The GSHS is a standardized survey developed by the WHO in collaboration with the United Nations International Children’s Emergency Fund (UNICEF), the United Nations Educational, Scientific and Cultural Organization (UNESCO), and the United Nations Program on HIV/AIDS, with technical assistance provided by the Centers for Disease Control and Prevention (CDC). The GSHS is a collaborative surveillance project designed to help countries measure and assess health behaviors and protective factors among students between the ages of 13 and 17 years. The GSHS has ten key questionnaire modules and two of the modules address protective factors and adolescent sexual behaviors .The country selection was based on publicly available GSHS national data and data availability on adolescent girls’ social environment and sexual behaviors in sub-Saharan Africa from currently available data 2003–2015) . The tot5 . The tThis study utilizes the secondary cross-sectional study design, drawing on primary data collected from the Global School-based Student Health Survey (GSHS).The study participants were 12,067 school going adolescent girls from 12 sub-Saharan African Countries.The study utilized the conceptual model of the relationship between social networks and social support on health proposed by Heaney and Israel . MeasureThe data was analyzed using SPSS version 21 software . The GSHS uses the complex sampling design and this was accounted for in the data analysis. The weighting process was used. The weighting process used adds weights, stratum and primary sampling unit (PSU) to every student record in the GSHS data file to reflect the weighting process and the two-staged sampling design. The weight variable allows generalizability of the GSHS results to the entire students’ population whereas the stratum and PSU account for the two-stage sample design used for the GSHS. Specifically, the stratum reflects the GSHS sampling process at the first level which is done in schools and the PSU reflects the second level of the GSHS sampling process conducted in classrooms .Descriptive statistics were generated to determine the number and percentage of adolescent girls who were monitored and felt connected with their parents as well as girls who reported being supported by their peers. The number and percentage of adolescent girls who had initiated sex and used condoms were also analyzed. The mean age of sexual debut and the mean number of sexual partners were also determined. Results were presented for each of the 12 countries and then for all countries.An independent samples t-test analysis was performed to determine whether there were differences between (i) the mean age of sexual debut based on parental monitoring, parental connectedness, and perceived peer support and (ii) the mean number of sexual partners based on parental monitoring, parental connectedness, and perceived peer support. Data analysis was performed for each country and then for the pooled data set for 12 countries.Logistic regression models were used to measure the association between each of the social environment components  and sexual debut.. Crude odds ratios (OR) of these associations were generated, and then adjusted for age, class grade and drug use for each country. 'We pooled data for 12 countries to determine the association between parental attitudes and perceived peer support on adolescent girls’ sexual behaviors. The crude ORs and adjusted ORs were generated for this analysis.The study results showed that more than 50% of adolescent girls reported being monitored by their parents or guardians, felt connected with their parents, and perceived having supportive peers. Table  AdolesceThe sexual debut prevalence for all adolescent girls was 19.9%. The highest prevalence of sexual debut was observed among adolescent girls in Mozambique (45.8%) and Namibia (43.6%). Adolescent girls in Swaziland had the lowest prevalence (7%) of sexual debut. Among girls who had initiated sex, condom use was more predominant among adolescents’ girls in Botswana (80.8%), Namibia 79.2%) and Mozambique (77.9%). The mean age of sexual debut among all the adolescent girls was 13.21 years and the mean number of sexual partners was 2.19 . In Seychelles and Swaziland parental monitoring and perceived peer support resulted in a significantly lower number of sexual partners respectively.Overall, in all countries, adolescent girls who felt connected with their parents were older when they initiated sex than those who did not feel connected with their parents (13.34 vs 12.99). In all 12 countries, adolescents who perceived support from their peers had a significantly higher number of sexual partners than those who did not feel supported by their peers (Table Nine Countries (75%) recorded a significant association between parental monitoring and sexual debut. In all nine countries, adolescent girls who were not monitored were more likely to initiate sex than adolescent girls who were monitored. The strongest association was observed in Seychelles . After adjusting for age, class grade and drug use, only three countries  observed a significant association between parental monitoring and sexual debut. Overall, in 12 countries, adolescent girls who reported not experiencing monitoring by their parents compared to those who were monitored had 1.29 times the odds to initiate sex. Table .Table 4LSeven out of the twelve countries (58%) observed a significant association between parental connectedness and sexual debut. Adolescent girls in Senegal reported the strongest association between lack of parental connectedness and sexual debut  (OR = 2.72). Of the seven countries that observed a significant association, two countries  remained significant after adjusting for age, class grade and drug use. The results from the pooled data of all 12 countries showed that adolescents who were not connected with their parents were 1.32 times as likely to initiate sex compared to adolescent girls who felt connected to their parents.  observed a significant association between perceived peer support and sexual debut. Adolescent girls who did not feel supported by their peers were more likely to initiate sex in Ghana, Kenya and Senegal. This association remained significant in Senegal  after controlling for age, class grade and drug use Table .The study findings indicate that adolescent girls in sub-Saharan Africa whose parents did not know what they did during their free time (not monitored by their parents) were more likely to initiate sex compared to their counterparts who were monitored by their parents. Likewise, adolescents’ girls who felt their parents or guardians did not understand their problems and worries (not connected with their parents) were also more likely to initiate sex compared to adolescent girls who felt connected with their parents.Our findings are consistent with previous studies conducted in some countries in sub-Saharan Africa. In Ghana, Kumi-Kyereme et al.  found thThe study results showed that adolescent girls in all 12 countries, who felt supported by their peers, had a significantly higher number of sexual partners. Previous studies have also found peer influence on adolescent sexual behaviors , 19, 23.We found that the age of sexual debut differed among adolescents in the countries included in our study. This is consistent with previous studies. For example, Melesse et al.  found thThe study utilized the conceptual model of the relationship between social networks and social support on health proposed by Heaney and Israel . The stuThe study is one of the few studies to explore the association between the social environment of adolescent girls and their sexual behaviors in sub-Saharan Africa. Despite this strength, the study had a few limitations. The GSHS data surveys school going adolescents as such adolescent girls who might have dropped out of school may not have been captured.Secondly, adolescents had to recall their sexual behaviors during the past 30 days. This approach could lead to the potential of recall bias. Also, the GSHS questions were used as proxy for this study, thus the parental influence scale and the perceived peer support scale tailored towards sexual behaviors among adolescents would have been preferred. For example, previous research has shown that self-efficacy, presence of sexual coercion, age of partner, partner-type could influence adolescent sexual behaviors , 36 but The secondary nature of our data analysis limits the exploration of additional relevant variables, and by extent, the measurement protocols utilized by the CDC and the WHO. We do note that these variables are reliable and have been used in other published studies , 38. ThiWe conveniently sampled countries that GSHS national data that was publicly available, and the data years ranged from 2003 to 2015. We acknowledge that they might be some behavioral changes among adolescent girls in the countries included in the study since the GSHS data was collected. Thus, the study warrants caution with interpretation.Lastly, we would have preferred to also examine whether differences exist in adolescent girls’ sexual debut among girls living in single parent homes compared to those living with both parents, but the data was not available in the GSHS survey.Adolescent girls who are not monitored and do not feel connected with their parents are more likely to debut sex early. We conclude that adolescent girls' social environment in sub-Saharan Africa plays a vital role in sexual debut, age of sexual debut, and the number of sexual partners.


CW), carcass conformation (CC) and carcass fat (CF) in cattle. The data used comprised carcass data, along with imputed, high-density genotype data on 618,837 single nucleotide polymorphisms (SNPs) from 23,687 cattle; all animal genotypes were phased with respect to parent of origin. Based on the phased genotypes and a series of single-locus linear models, 24, 339, and 316 SNPs demonstrated imprinting associations with CW, CC, and CF, respectively. Regardless of the trait in question, no known imprinted gene was located within 0.5 Mb of the SNPs demonstrating imprinting associations in the present study. Since all imprinting associations detected herein were at novel loci, further investigation of these regions may be warranted. Nonetheless, knowledge of these associations might be useful for improving the accuracy of genomic evaluations for these traits, as well as mate allocations systems to exploit the effects of genomic imprinting.Genomic imprinting is an epigenetic phenomenon defined as the silencing of an allele, at least partially, at a given locus based on the sex of the transmitting parent. The objective of the present study was to detect the presence of SNP-phenotype imprinting associations for carcass weight ( Genetic selection has translated into performance gains in farmed livestock globally . The calOf interest to the present study is the epigenetic phenomenon known as genomic imprinting. Genomic imprinting is where the expression of an allele differs depending on the sex of the transmitting parent . While tCW), carcass conformation (CC) and carcass fat (CF)] using phased , high-density genotype data across the genome. Although SNP) data used herein. Results from the present study should provide further insight into the epigenetic components underlying the carcass merit of cattle. Furthermore, such information could potentially be used to improve the accuracy of genomic evaluations and to inform mate selection programs.For carcass traits in cattle , genomic imprinting has been reported to account for 8–39% of the total additive genetic variance ; while iPreviously imputed high-density genotypes, comprising 734,159 autosomal SNP genotypes from 638,662 cattle were available. An in-depth description of the pipeline used to impute the genotype data to high density is provided by The three carcass phenotypes of interest to the present study were CW, CC, and CF, all of which were measured in accordance with the EUROP grading system. Carcass weight is measured in kg, on average, one hour after slaughter, following the removal of the head, hide, legs, thoracic and abdominal organs, and internal fat. With regard to CC and CF, the former reflects the shape and development of the carcass, particularly on the round, back and shoulders, and the latter reflects the level of fat covering the carcass, as well as within the thoracic cavity of the carcass . Under tOnly genotyped young bulls, heifers and steers slaughtered between the ages of 12 and 36 months, inclusive, with recorded carcass phenotypes, of which there was 93,470 animals, were considered for the present study. Additionally, all young bulls, heifers and steers considered were born to dams with a parity number ≤10 and were not born from embryo transfer. Finally, any cattle with more than three inter-herd movements during their life, or a movement 100 days before slaughter were not considered further. Following all edits, the birth herd of all remaining genotyped animals were categorized as either beef or dairy based on parameters outlined by For each of the 23,687 animals of interest, the parental origin of each allele was inferred by phasing their genotypes using a combination of pedigree and genotype information in FImpute2 . The pedFollowing phasing, four unique genotypes were distinguishable at each locus . For the purpose of the association analyses, the four genotypes were coded to represent additive , dominance  and imprinting effects  .via a genomic relationship matrix. All phenotypes were adjusted in the GenABEL package in R  regions associated with CW, CC, and CF were defined based on the flanking linkage disequilibrium patterns around the SNPs with significant or suggestive associations. To estimate the start and end positions of the QTL regions, all SNPs within a 0.5 Mb window that were in linkage disequilibrium (r2 of ≥0.5) with significantly or suggestively associated SNPs were considered part of a single QTL region. In the case where QTL regions overlapped, these regions were merged together and considered a single region. Additionally, the presence of candidate genes within a 0.5 Mb window of the QTL regions, as well as genes located within the QTL regions were investigated using ENSEMBL (https://www.ensembl.org/) on the ARS-UCD 1.2 genome build, alongside the biomaRt package in R  were inspected to identify known imprinted genes within a 0.5 Mb window, either upstream or downstream, of the SNPs of interest.Quantitative trait loci (age in R . FurtherBos taurus (BTA) chromosome 2, demonstrated imprinting associations with CW. In addition, a further five SNPs located on BTA 2 (three SNPs), BTA 19 (one SNP) and BTA 23 (one SNP) demonstrated suggestive imprinting associations (0.01 < q ≤ 0.05) with CW. Based on the 24 SNPs demonstrating an imprinting association with CW, two distinct QTL regions were identified. These QTL regions stretched from 2.60 to 2.65 Mb on BTA 2 and from 3.43 to 3.71 Mb on BTA 2. Both lead SNPs  in the QTL regions were intergenic variants, with no currently known imprinted gene residing within a 0.5 Mb window of the QTL regions.The associations between CW and the additive, dominance and imprinting genotype codes of each SNP are shown in Manhattan plots with the additive, dominance, and imprinting associations between each of the 618,837 SNPs of interest and CC are presented in The additive, dominance and imprinting associations of each SNP of interest and CF are shown in The term epigenetics or, in other words, the manifestation of divergent phenotypes from the same genotype, can be attributed to Conrad Waddington . An examBos taurus genome (Mus musculus genome is as high as 600 (www.geneimprint.com), novel loci demonstrating imprinting associations were detected in the present study. Nonetheless, the imprinting effects  associated with the novel loci detected in the present study were biologically small. For example, the largest imprinting effect estimated for the different lead SNPs associated with CW, CC, and CF in the present study were equivalent to 5.0%, 7.1%, and 8.9% of the respective genetic standard deviations estimated for the traits of interest by The presence of imprinting genes in cattle has been previously documented, with 20–30 imprinted genes currently validated in the s genome . Furthers genome , althougs genome , although as 600 . Wolf etAs per Bos taurus genome; these included BTA 3, 4, 6, 13, 18, 21, and 25. Of the SNPs demonstrating imprinting associations located on these chromosomes, the nearest SNP demonstrating an imprinting association to a known imprinted gene  was 2.7 Mb downstream from ZNF597 on BTA 25.The presence of imprinting associations with the carcass traits of interest were detected on 19 different chromosomes in the present study. Of these, the vast majority of the SNPs detected to have imprinting associations were located on BTA 2; this is despite the fact that no imprinted genes have, to the best of our knowledge, yet been validated on BTA 2. Nonetheless, the presence of novel imprinting regions for carcass traits, namely CW and kidney, pelvic and heart fat, on BTA 2 was previously documented in cattle by p values ≥0.628), ≥0.56 for CC , and ≥0.59 for CF ; extending the window to 0.5 Mb, the q values associated with the imprinting effects estimated for SNPs within the larger windows were all ≥0.86 for CW , ≥0.56 for CC , and ≥0.16 for CF . This signifies that, based on the analysis conducted herein, even if a less conservative genome-wide significance threshold was used, no currently known imprinted genes demonstrated imprinting associations with the carcass traits evaluated. The relatively large dataset of animals employed for this analysis should ensure that the analysis conducted had the necessary power to detect any imprinting associations for the carcass traits, should they exist. Similarly, While not all known imprinted cattle genes had intronic SNPs included in the present study , all known imprinted genes did have at least one SNP within a 50 Kb window of their location included in the analyses. Of the SNPs located within these 50 Kb windows, the average and standard deviation of their minor allele frequencies were 0.30 and 0.13, respectively. All q values associated with the imprinting effects estimated for SNPs within a 50 Kb window of a known imprinted gene were ≥0.99 for CW , with Of the 618,837 SNPs included in the association analyses, 24 SNPs, 339 SNPs, and 316 SNPs, spread across 19 different chromosomes, demonstrated some form of imprinting association  with CW, CC, and CF, respectively. The fact that all imprinting associations detected for the carcass traits of interest were at novel loci warrants further investigation in these regions, particularly, BTA 2 where the vast majority of the imprinting associations were detected. Nonetheless, the data used to detect these imprinting associations comprised high-density genotype data from a relatively large group of cattle, which should ensure the analyses conducted had the power to detect imprinting associations, should they exist. Knowledge of loci at which imprinting associations are present for the carcass traits of interest could be incorporated into genomic evaluations in an attempt to improve the accuracy of such evaluations for these traits. Furthermore, this knowledge could be incorporated into mate allocation programs to exploit (or avoid) the effects of genomic imprinting in potential offspring.


Pinus sylvestris (Scots pine) is the most widespread coniferous tree in the boreal forests of Eurasia, with major economic and ecological importance. However, its large and repetitive genome presents a challenge for conducting genome‐wide analyses such as association studies, genetic mapping and genomic selection. We present a new 50K single‐nucleotide polymorphism (SNP) genotyping array for Scots pine research, breeding and other applications. To select the SNP set, we first genotyped 480 Scots pine samples on a 407 540 SNP screening array and identified 47 712 high‐quality SNPs for the final array . Here, we provide details of the design and testing, as well as allele frequency estimates from the discovery panel, functional annotation, tissue‐specific expression patterns and expression level information for the SNPs or corresponding genes, when available. We validated the performance of the PiSy50k array using samples from Finland and Scotland. Overall, 39 678 (83.2%) SNPs showed low error rates (mean = 0.9%). Relatedness estimates based on array genotypes were consistent with the expected pedigrees, and the level of Mendelian error was negligible. In addition, array genotypes successfully discriminate between Scots pine populations of Finnish and Scottish origins. The PiSy50k SNP array will be a valuable tool for a wide variety of future genetic studies and forestry applications. In the genomic era, the gigantic size of conifer genomes still hampers advances in a wide range of fields of applied and fundamental sciences. To facilitate Scots pine genetic research and modern breeding methods, we developed a 50K SNP genotyping array and provide information that links the array markers to genetic diversity and gene expression levels across tissues. To understand the genotype–phenotype link in plants, tools connecting phenotype, genotype, gene expression and environment are needed. Many forest trees and especially gymnosperms have a long history of studies on the phenotypic variation across geographical regions and environmental conditions  is one of the world’s most widely distributed conifers , a new SNP genotyping array for Scots pine. We describe the SNP sources, discovery panels and selection processes used during the array design. The final array combines a set of high‐performing SNPs from a previously developed Axiom_PineGAP trans‐specific SNP array of Pinus , the occurrence of spurious SNPs is a common problem. We relied on the patterns of genetic diversity at each locus to remove dubious markers. This was achieved by identifying genotypes from seed megagametophyte tissue (haploid), where observed heterozygosity indicates false SNPs, and in samples genotyped from needle or seed tissues (diploid) by excluding loci that deviated from the Hardy–Weinberg (HW) equilibrium and/or that had an excess of observed heterozygosity. We further filtered the data based on sequencing depth and Mendelian inheritance errors, depending on the data set. Of an initial set of 3.7 million SNPs, we submitted sequences of up to 71 nt for 1.3 million SNPs to ThermoFisher Scientific for probe design and in silico evaluation. This evaluation consisted in predicting the performances of the probes based on the entire set of sequences, accounting for the base composition of the sequence and the potential to map to multiple locations of the references: the reference genome (Pita v1.01) and transcriptome ; no minor homozygote ; mono high resolution ; call rate below threshold (CRBT); off‐target variant ; and others. When choosing SNPs for the PiSy50k SNP array based on the screening array, we considered conversion types PHR, NMH and MHR as successful. Of 407 540 SNPs in the screening array, 245 149 (60.2%) were converted successfully and 157 325 (38.6%) were polymorphic  compared with the distribution expected based on the standard neutral model (SNM) Figure . This isIn addition, ascertainment bias influenced the estimates of genetic structure among samples. Principal component (PC) analyses of the screening array genotypes of UOULU RNA‐seq and UOULU exomeFEB2019 SNPs clearly set trees included in the discovery panel separately from the rest of the samples Figure . The ascr² > 0.9), keeping the SNPs with the higher MAFs from each pair. From the remaining loci, we first retained all SNPs from high‐priority sources and favored SNPs with higher MAFs in the remaining set. SNPs in a highly outcrossing wind‐pollinated natural population of Scots pine are expected to be in HW equilibrium, hence we used deviation from HW (P < 0.001) to identify and filter out potentially paralogous and other error‐prone SNPs. As expected, the markers selected for the PiSy50k SNP array deviated less from the HW expectations and showed less extreme heterozygosity, compared with all screening array markers before selection . Of the eight data sources, markers from RNA‐seq origin were the most numerous  and diploid sources (75) were located on one of the 4226 scaffolds mapped on the 4 and dipWe evaluated the performance of the PiSy50k SNP array by genotyping 2688 samples from Finland , Scotland (496), Australia (3) and Estonia (11). Of these, 2308 samples had call rates above 97% (85.9% of samples), the recommended threshold for Axiom SNP genotyping arrays. In total, 40 405 (84.7%) markers were successfully converted, 39 678 of which were polymorphic Table .Of the 21 control samples, three needle and six megagametophyte samples passed the 97% call rate (CR) threshold Table . Of the n = 916) were not shared among controls. This suggests that the error probably occurred during the genotype call for a single sample only, as opposed to the marker itself being unreliable. There are 14 markers for which errors were observed among both megagametophyte and needle controls, and these are provided in Data N = 7592) using the needle control present on both arrays also showed low error rates , the majority , we assessed the heterozygosity levels of the megagametophyte samples. The two megagametophyte replicates have very low heterozygosity levels (mean 0.9%) compared with the needle replicates (mean 29.3%), suggesting a low level of errors as a result of paralogy. Of the 40 405 converted markers, 38 906 were homozygous in both replicates, 1060 were ‘no call’ in at least one replicate, 165 were heterozygous in both replicates and 274 were homozygous in one replicate and heterozygous in the other. The SNPs that were heterozygous in the megagametophyte samples are indicated in Data S2.To evaluate the potential of the PiSy50k SNP array for pedigree reconstruction and assess the proportion of Mendelian errors in the SNP array, we analyzed the pairwise relatedness of the full‐sib progeny and their parents in a subset of 135 trios across 10 families of our sample. By plotting the kinship coefficient  within each family. We examined all 40 405 SNPs in 135 trios and identified 16 040 errors across 5837 loci  using a subset of 122 samples from different localities in Scotland and Finland Figure . The firTo assess the effects of ascertainment bias on the MAF distribution in the PiSy50k SNP array, we compared the frequency distributions obtained from the array with a previously published exome capture data set , in south‐east Finland: a cross between maternal tree 463 and paternal tree 485 . The material sampled consisted of needles of both parental trees, one megagametophyte of the paternal tree, two megagametophytes of the maternal tree from open‐pollinated seeds and, from two seeds of the cross progeny, two embryos and a megagametophyte. We excluded positions with depths of <4 per genotype. We removed twenty‐five base pairs, both upstream and downstream, from each heterozygous site found in haploid megagametophyte as potential areas with paralog or mapping issues.https://www.ncbi.nlm.nih.gov/nuccore/GILO00000000.1). From this initial set, we first excluded markers identified in contigs associated with potential contaminants . Second, we removed heterozygous SNPs in haploid samples. Finally, we compared the genotypes called in megagametophyte, embryo and diploid tissues collected from the same tree to identify and exclude loci with Mendelian errors. In total, we retained 736 827 SNPs.The UOULU RNA‐seq set refers to markers derived from RNA‐seq data . Additionally, for a subset of those markers, we collected allele frequency estimates from two genotyping assay experiments on 426 Scots pine trees . These SNPs, referred to as UOULU candidate VIP in the metadata, were given higher priority during the SNP array manufacture, in both the screening and PiSy50k SNP arrays, by increasing their probe‐set counts and, in this way, improving their call rates during the genotyping.The UOULU candidate set contains SNPs reported in multiple publications and genetic databases on various candidate genes of Scots pine. This set includes the SNP markers used by Kujala et al. , and addhttps://www.pacb.com) . We used the long PacBio sequences as a reference to map short reads from exome captures of megagametophyte samples of Scots pine collected across Europe . In addition, some SNPs were identified and included solely as being polymorphic within the reference individual.The LUKE candidate set comprises SNPs extracted from candidate genes related to phenology  published by Wachowiak et al. (We used SNPs collected during the Axiom_PineGAP (ThermoFisher Scientific) array design  or were highly similar to other probes. Each marker was given a classification: ‘recommended’, ‘neutral’, ‘not recommended’ or ‘not possible’.For each retained site, we built 71‐mer probes by extracting up to 35 bp up‐ and downstream from the source references. We submitted 1 317 798 probes to the Microarray Research Services Laboratory (ThermoFisher Scientific) for scoring Table . During Based on the evaluation from ThermoFisher Scientific and the available metadata on each data source, we established the following priority groups (in order of priority): (i) the 20 795 high‐quality SNPs from the Axiom_PineGAP array; (ii) all recommended or neutral markers identified by ThermoFisher Scientific; (iii) UOULU candidate markers; (iv) LUKE candidate markers; (v) markers from the ‘not recommended’ set in the ProCoGen haploid set, including SNPs of high interest identified by Tyrmi et al. The screening set of 407 540 SNPs was used to confirm the normal segregation of polymorphism in a larger sample from a natural population, to identify potential deviations from HW equilibrium, indications of paralog mapping, such as heterozygote sites in haploid samples, deviations from Mendelian segregation and the identification of loci in strong LD with each other. To this end, we used the screening array to genotype 480 samples of Scots pine from the Punkaharju ISS population, including: 470 diploid needle samples from adult trees, six haploid megagametophytes and four diploid embryos. Two families, ‘463 × 485’ and ‘320 × 251’, with two parents and two offspring (embryos) from each, were used to estimate the Mendelian error rate.® SP Plant DNA Kit . Genotyping and array manufacturing for the screening set was performed by ThermoFisher Scientific. Genotype calling was performed by ThermoFisher Scientific (Applied BiosystemsTM AxiomTM Genotyping Services) following the Axiom Best Practices Workflow . In short, genotype clusters were defined using samples with a quality control call rate (QCCR) ≥ 0.97 and dish quality control rate (dQC) ≥ 0.82. The markers were classified into five conversion categories: PolyHighResolution (PHR), NoMinorHom (NMH), MonoHighResolution (MHR), CallRateBelowThreshold (CRBT), Off‐Target Variant (OTV) and other. We visually inspected a random set of 200 genotype clusters from each conversion category to check that the genotype clusters were clearly separated. We retained markers only from classes PHR and NMH with CR ≥ 0.97 in the subsequent analyses of the screening array and for inclusion on the PiSy50k SNP array.DNA was extracted from dry needles and fresh megagametophytes using the E.Z.N.A.During the design of both screening and PiSy50k SNP arrays, identical SNPs discovered independently across different sources were identified and merged. To keep track of as much information as possible for those markers, we recorded their common presence and IDs in different sources but eventually assigned a single authoritative origin.plink 1.9  and markers with more than one Mendelian error. Markers from the candidate gene sources (LUKE candidate and UOULU candidate) were selected using a lenient inclusion threshold of MAF ≥ 0.01 and marker CR > 0.90, which also included markers from the ThermoFisher Scientific conversion type ‘call rate below threshold’. We filtered SNPs from the Axiom_PineGAP array first to include markers with MAF ≥ 0.05. To increase the number of well‐performing markers, we also included markers with MAF ≥ 0.05 in previously genotyped European samples , for which we allowed at most one, erroneous, heterozygous call per marker. We further granted 358 markers of high interest from sources UOULU candidate (335) and UOULU RNA‐seq (23) a higher probe‐set count in the SNP array to increase their call rate. Finally, to remove the excess from the retained set, we excluded markers from the low‐priority sources with lowest MAFs (MAF ≥ 0.08 after filtering). The final number of markers for PiSy50k was 47 712  plot before and after SNP selection. We compared the observed P values of 10 000 random loci against 100 samples drawn from the null distribution using the hardyweinberg package . We also illustrated the distribution of genotypes with respect to HW expectations in ternary plots showing genotypes before and after the PiSy50k SNP choice.To inspect how SNP selection for the PiSy50k SNP array affected HW deviation compared with the screening array on average, we plotted the observed r package pcadapt  against the expected MAF assuming a standard neutral model Tajima, . Second,P. taeda by Westbrook et al. . When a given SNP was outside the aligned segment of the reference contig, we used the closest position effectively aligned on the genetic map from the same contig as a reference point to infer the position of the focal SNP on the map, assuming the physical distances covered by single contigs from the Pita v1.01 reference genome to be negligible compared with the size of each individual LG.To assess whether markers from the PiSy50k SNP array are homogeneously distributed across all chromosomes, we positioned them on a genetic map produced for k et al. , comprisWe tested the PiSy50k SNP array performance by genotyping 2688 samples (across seven plates). The 2688 samples consisted of 317 Finnish plus trees, 1847 full‐sib offspring from the Finnish breeding population, 489 Scottish samples, three Australia samples, 11 Estonian samples and 21 controls. The needle control was a single tree from Scotland, UK, and was included on each genotyping plate; this sample had also been genotyped on the Axiom_PineGAP array. In addition, seven haploid megagametophyte samples were genotyped twice, such that each sample was genotyped on two different random plates. Other samples were randomized over the plates such that the different geographic locations and sample categories (plus trees and offspring) were spread on all plates to avoid plate effects that could bias the genotyping results of a specific sample category.n = 2 674, including seven controls) were dried and stored in bags with silica gel. For megagametophyte samples (seven control samples included twice each), germination was initiated by placing the seeds on moist filter paper inside a Petri dish for 24 h at room temperature (~21°C). Seeds were then dissected under a microscope to separate megagametophyte from the embryo tissue. The DNA from Finnish and Estonian samples was extracted using E.Z.N.A.® SP Plant DNA Kit (Omega Bio‐tek). The DNA of Scottish needles was extracted using a Qiagen DNeasy Plant kit (https://www.qiagen.com) and checked visually on a 1% agarose gel. DNA was quantified with a Qubit spectrophotometer (ThermoFisher Scientific).The SNP arrays were manufactured by ThermoFisher Scientific and genotyping was conducted by University of Bristol Genomics Facility . Needle samples . We also measured the heterozygosity in megagametophyte samples to assess probe specificity and identify putative paralogous markers in the PiSy50k SNP array.king 2.2.5 (options ‐‐related ‐‐degree 2) .We used a subset of 153 samples from 10 crosses, including 18 parents and their 135 offspring, to estimate the coefficients of kinship (K) and the proportion of sites where individuals share no allele (IBS0) between all pairs using converted SNPs (40 405) with prcomp  population (n = 466) and 56 693 SNPs without missing data in the screening array.Figure S2. Principal component analysis on the screening array data, illustrating ascertainment bias on the observed genetic structure.Figure S3. Hardy–Weinberg equilibrium (HW) test results for the screening array data before filtering and for the selected set for PiSy50k.Figure S4. Mendelian errors (MEs) of the PiSy50k identified in 40 405 SNPs genotyped in 135 trios (10 crosses).Table S1. Conversion type for markers from each data set in the screening array based on individuals with call rates of 97% or above.Table S2. Number and proportions of markers from each source at different steps of the PiSy50k SNP array design.Table S3. Distribution of PiSy50k markers on Pinus taeda linkage groups .Table S4. Conversion type for markers from each data set in the PiSy50k SNP array based on individuals with call rates of 97% or above.Table S5. Evaluation of the PiSy50k SNP array for control samples with call rates of 97% or above.Appendix S1. Additional steps/details in selecting markers from screening array to PiSy50k SNP array.Click here for additional data file.Data S1. The metadata for markers included on the PiSy50k SNP array.Data S2. Shared errors across controls identified in the error evaluation of the PiSy50k, see the main text.Data S3. Genotypes of 268 samples used in this study including 10 progenies and trees from Finland and Scotland.Click here for additional data file.


Sturnus vulgaris, to identify parallel and divergent evolutionary change between contemporary native and invasive range samples and their common ancestral population. To do this, we use reduced‐representation sequencing of native samples collected recently in northwestern Europe and invasive samples from Australia, together with museum specimens sampled in the UK during the mid‐19th century. We found evidence of parallel selection on both continents, possibly resulting from common global selective forces such as exposure to pollutants. We also identified divergent selection in these populations, which might be related to adaptive changes in response to the novel environment encountered in the introduced Australian range. Interestingly, signatures of selection are equally as common within both invasive and native range contemporary samples. Our results demonstrate the value of including historical samples in genetic studies of invasion and highlight the ongoing and occasionally parallel role of adaptation in both native and invasive ranges.During the Anthropocene, Earth has experienced unprecedented habitat loss, native species decline and global climate change. Concurrently, greater globalization is facilitating species movement, increasing the likelihood of alien species establishment and propagation. There is a great need to understand what influences a species’ ability to persist or perish within a new or changing environment. Examining genes that may be associated with a species’ invasion success or persistence informs invasive species management, assists with native species preservation and sheds light on important evolutionary mechanisms that occur in novel environments. This approach can be aided by coupling spatial and temporal investigations of evolutionary processes. Here we use the common starling,  Increased intercontinental travel and trade is giving rise to new or reinforced invasion pathways  is a highly invasive pest, introduced and successfully establishing on every other continent except Antarctica  and native range  starlings to historical UK samples collected during the period when the Australian founders were collected. Moreover, this project explores proximate drivers of invasive species’ evolution in the face of novel selection provided by new environments. Specifically, we compare population structure of native and invasive contemporary starling samples, and we explore genomic divergence between contemporary and historical samples and assess the putatively adaptive capacity of these genomic changes. Finally, we use historical samples as a basis of comparison to determine genomic regions of parallel change in both the contemporary native and invasive populations to better understand global shifts in selective forces.22.1N = 15). Historical samples were selected on the basis of sampling location  from the Natural History Museum (NHM) in Tring, UK (3 dried tissue in 1 ml of 0.5 M EDTA for 2 h and extracted DNA using a Qiagen DNeasy Tissue Kit (Qiagen) as per the manufacturer's instructions. DNA was eluted twice with 40 µl of EB buffer (+0.05% Tween‐20) for a final elution volume of 80 µl.DNA extractions of historical samples were conducted in a specialist ancient DNA laboratory at the Australian Centre for Ancient DNA, University of Adelaide. We rehydrated 3–4 mm2.2N = 15, blood, ~100 km from London, where starlings were thought to be sourced for relocation to colonies such as Australia) and rural sites around the city of Newcastle upon Tyne , and one Belgian (BE) location in Antwerp   and Orange in New South Wales   Figure . We sour) Figure : McLaren) Figure . Contemp2.3PstI–SphI. The sequencing was conducted on a Hiseq 2500, producing 312,907,523 single‐end reads of raw data across the 90 samples , removing reads with uncalled bases (‐c) and rescuing barcodes and radtags (‐r). We used the Burrows–Wheeler aligner (bwa) version 0.7.15  during alignment. The reads were then processed through bwasamse and samtools version 1.10  and then populations (parameter information below).We used the stackspopulations with no parameter thresholds specified. We used this data set to produce the unfiltered loci and site counts . We used the dartr version 1.1.11 , a minimum populations per‐site of 2 (‐p 2) and a minimum loci log likelihood value of −15 (‐‐lnl_lim ‐15), with one random SNP per tag retained (‐‐write_random_snp). We used vcftools version 0.1.16 . We chose a high threshold for missingness to not bias the population genetics analysis against the historical samples, which had much higher levels of missingness than the contemporary samples. MAF filtering helps remove misreads, and HWE filtering removed highly non‐neutral loci, both of which are important for capturing neutral population substructure. After filtering, we calculated individual relatedness, and closely related individuals were removed so that there was only one representative from each cluster in the final data  and then used vcftools to filter out only SNPs present in at least 50% of the historical individuals , with additional quality filtering (‐‐minGQ 15 ‐‐minDP 2), resulting in 12,219 SNP sites. Only these sites were then retained to filter the original populations variant file, along with an MAF minimum of 2.5% to remove possible sequencing errors. This produced a data set that retained only SNPs sequenced in at least half the historical individuals, which would be necessary for selection analysis. We filtered the selection variant file to form five pairwise population SNP data comparisons: UK‐HS ; AUeast‐HS ; AUsouth‐HS ; UK‐AUeast ; and UK‐AUsouth . While the native range population may contain a mix of resident and migratory individuals, because we see minimal population structure in the native range and very small FST values (0.003–0.008) we decided to include all contemporary native range samples in this analysis. Conversely, population genetics data from this paper (see Section east and AUsouth). Within each of these five variant file subsets, we retained SNP sites present in at least five contemporary individuals, because the file was already filtered for loci present in at least 50% (5/10) of historical individuals. This relatively lenient filtering was necessitated by the smaller number of genomic sample sites produced by the degraded DNA, and ensured minimal loss of the historical information that was present. This resulted in five pairwise population files used in the Section e‐HS , AUs‐HS , UK‐AUe  and UK‐AUs .We generated a “selection” variant file by using 2.4snprelatesnpgdsPCA function to create a principal components analysis (PCA) of the loci. We used admixture version 1.3.0  1–8 by averaging over 25 runs, and admixture proportion (Q) profiles were generated by clumpak .We analysed the population genetics variant file in several ways to examine the population structure and differentiation across the native and invasive ranges, and between contemporary and historical native ranges. We used R version 3.5.3 , and a false discovery rate (FDR) of 0.05 . We chose this window size primarily based on the ratio of variants to genome size, with the chosen windows putatively spanning three to four variants, with small step sizes then allowing shifts in FST patterns to be pinpointed more exactly. We selected site windows that reported a weighted FST value in the top 99th percentile for each pairwise population selection analysis, and analysed the FST of SNPs within these windows in rank order. As putative outlier SNPs, we retained any SNP within these outlier windows that lay above an FST threshold relevant for each pairwise data set  were designated as sites under parallel selection. The remaining outlier SNPs we identified only in one data set: UK‐HS (putative UK selection), AU‐HS (putative AU selection) or UK‐AU (putative UK‐AU divergence). Classification of SNPs was conducted separately on the AUvariant effect predictor  terms. We used revigo , we considered the sex of samples in our analysis. Because some individuals in this study were not sexed morphologically, we bioinformatically sexed individuals by assessing the inbreeding coefficient on the autosomes and Z sex chromosome, and conducted an analysis of variance (ANOVA) test to see whether major allele frequency was significantly associated with an interaction between SNP type (selection vs. nonselection) and SNP location (sex chromosome vs. autosome) using the aov function in R. We constructed this analysis as many outlier identification methods employ allele frequencies for statistical comparisons between sample groups, and an interaction between SNP location and its categorization as under selection or not would be cause for concern about bias due to data artefacts.Lastly, to test if there was an overrepresentation of SNPs located on the macrochromosomes  done for each pairwise comparison, yielding a total of 31, 30, 32, 35 and 40 unique SNPs across identification methods for UK‐HS, AUe‐HS, AUs‐HS, UK‐AUe and UK‐AUs respectively  and the reverse trend in comparisons involving AUsouth  contained the lowest proportion of intergenic variants . Only one gene was found to be divergent within the native range , while four were found to be divergent within the invasive Australian range: Glutamate ionotropic receptor kainate type subunit 2 (GRIK2), Neurexin‐3 (NRXN3), Voltage‐dependent calcium channel subunit alpha‐2/delta‐3 (CACNA2D3), Natterin‐4 (NATT4) and DNA methyltransferase 1 (DNMT1) (Table E3 ubiquitin/ISG15 ligase TRIM25 (TRIM25), Myb‐binding protein 1A‐like protein (MYBBP1A), CWC27 spliceosome associated cyclophilin (CWC27), Poly(A) RNA polymerase GLD2 (TENT2) and Tripartite motif containing 28 (TRIM28)  Table . A range8) Table . There w8) Table .χ26 = 33.23, p < .001, N = 5068). There was an overabundance of micro‐ and Z sex chromosome SNPs for all groups of SNPs under selection . Males and females were equally represented in our samples , and a more conservative mapping and variant calling approach is suitable for population and selection analyses .Of the three variant calling pipelines Table , we founngth Li, . bwa‐alnWe assessed the unfiltered data, and the base substitution plots per population revealed that though the historical samples reported lower SNP counts, base substitution frequencies were similar across the three population groupings Figure a–c. When4Sturnus vulgaris has not only undergone divergent selection within the invasive Australian range, but that the native and invasive ranges are undergoing parallel selection, possibly in response to global environmental changes. We note that contemporary native range populations, when considering comparative numbers of divergent SNPs, have undergone a similar amount of genetic change when compared to invasive range populations, despite the latter presumably being exposed to radically different and novel selection regimes. Moreover, we identified several genes related to immune function and pollution that appear to be under parallel selection in the contemporary native and invasive range samples, which may reflect global environmental changes over the last century and a half. The genes reported as divergent between the populations capture differing selection regimes driving evolution within the two contemporary populations. We also identified a bias for selection on the Z chromosome in comparison to the autosomes.This study demonstrates that S. vulgaris within the native and Australian invasive ranges, demonstrating the utility of museum collections in aiding evolutionary studies. While the success rate and quality of the historical specimen sequencing reads was less than with their contemporary counterparts, the method nevertheless yielded sufficient SNP data to enable examination of population structure and description of temporal patterns of genomic change in starling populations. Museum resources remain largely untapped in genomic studies across a variety of biological systems, but may serve as an invaluable source of information that will significantly extend knowledge about evolutionary processes and change over time.Importantly, this study has successfully used reduced representation sequencing of historical and contemporary specimens to examine selection in 4.1S. vulgaris, and hence our study provides much needed insight into the population structure and genetic variation of the northwestern region of the native range. We identified low levels of genetic differentiation across the native range localities sampled. Some native range starlings are migratory . The genetic differentiation between the two invasive Australian populations concurs with the two previously described Australian genetic subclusters  for the same allelic variant Table . This maAcross all SNPs under selection, only a few resided in coding regions, with only a couple of these residing in protein coding regions. Other than intergenic variants, intron variants made up the highest proportion of SNPs. Despite not being transcribed gene regions, introns may function as gene regulatory regions, so polymorphisms may still elicit functional changes Shaul, , and eveDrosophila suzukii  in terms of SNPs that were categorized as being under parallel selection . It is possible that this may have arisen due to biases in aspects of the data . However, we found no differences in major allele frequency across SNPs categorized as either under selection or not, across sex chromosomes and autosomes. The conclusion does align with theory that suggests that sex chromosomes are capable of playing a disproportionate role in evolutionary divergence due to their haploid nature in one sex , which is involved in pathogen recognition and host defence pathways in numerous avian species , or in linkage with the identified SNPs .This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at (Supplementary MaterialClick here for additional data file.


Seed size is an important yield and quality-determining trait in higher plants and is also crucial to their evolutionary fitness. In African yam bean (AYB), seed size varies widely among different accessions. However, the genetic basis of such variation has not been adequately documented. A genome-wide marker-trait association study was conducted to identify genomic regions associated with four seed size traits  in a panel of 195 AYB accessions. A total of 5416 SNP markers were generated from the diversity array technology sequence (DArTseq) genotype-by-sequencing (GBS)- approach, in which 2491 SNPs were retained after SNP quality control and used for marker-trait association analysis. Significant phenotypic variation was observed for the traits. Broad-sense heritability ranged from 50.0% (seed width) to 66.4% (seed length). The relationships among the traits were positive and significant. Genome-wide association study (GWAS) using the general linear model (GLM) and the mixed linear model (MLM) approaches identified 12 SNP markers significantly associated with seed size traits across the six test environments. The 12 makers explained 6.5–10.8% of the phenotypic variation. Two markers (29420334|F|0-52:C>G-52:C>G and 29420736|F|0-57:G>T-57:G>T) with pleiotropic effects associated with seed width and seed thickness were found. A candidate gene search identified five significant markers  located close to 43 putative genes whose encoding protein products are known to regulate seed size traits. This study revealed significant makers not previously reported for seed size in AYB and could provide useful information for genomic-assisted breeding in AYB. Meeting global food and nutritional demands under limited growing space and changing climatic conditions is now the major target in crop breeding programs. Recently, the focus has shifted to relatively unknown indigenous legumes such as African yam bean (AYB) due to its dietary protein and mineral content and adaptive nature to wide climatic and soil conditions ,2. AfricYield is a complex trait, difficult to improve directly. It is an expression of several component traits that are highly subject to environmental influences . In cropRecent advances in high-throughput genomic platforms have created the opportunity for the genome-wide-level understanding of the genetic basis of variation in complex traits at a finer resolution. Association mapping (AM), originally developed for use in mapping human disease genes , is now The germplasm consisted of 196 AYB accessions obtained from the existing collection of landraces at the Genetic Resource Center, International Institute of Tropical Agriculture, Ibadan, Nigeria. The passport data of the accessions can be found in Olomitutu et al. [The accessions were phenotyped under optimal field conditions across three IITA research farms in Nigeria—Ibadan, Kano, and Ubiaja—during the 2018 and 2019 cropping seasons. The experimental design was a 14 × 14 α lattice with three replications. Each experimental unit consisted of 4 m single-row plots, with an inter-row spacing of 0.75 m and intrarow spacing of 0.5 m. Phosphorus fertilizer application in the form of triple superphosphate at a rate of 50 kg P/ha and staking were performed three weeks after planting. Manual weeding was carried out when necessary to keep the field clean. Details of the phenotyping methodology are described in Olomitutu et al. .https://ordering.seqart.net/files/DArT_DNA_isolation.pdf accessed on 20 January 2019). The isolated gDNA were qualified on 1% agarose gel electrophoresis and quantified using a Nanodrop 2000 spectrophotometer  following the manufacturer’s protocol. The high-quality DNA (100 ng/µL) samples were shipped to DArT Pty Ltd., Canberra, Australia, for genotyping using the whole genome profiling service of DArTseq technology [Leaf samples were collected from three-week-old seedlings of each 196 AYB accession and stored at −80 °C. Genomic DNA (gDNA) was extracted using the diversity array technology (DArT) DNA extraction protocol ).A raw dataset of 5416 DArTseq SNPs was generated. The DArTseq SNPs were filtered using call rate ≥ 70%, average reproducibility ≥ 95%, minor allele frequency (MAF) ≥ 0.01, and 20% missing SNP data to remove poor-quality SNPs. After SNP quality control, a total of 2491 SNPs from 195 accessions were retained and used for the genome-wide association study (GWAS). Accession TSs-442 was filtered out due to low-quality SNPs. In the absence of the AYB genome, trimmed sequences of filtered SNPs were aligned on the common bean reference genome v1.1 (available at 2) was estimated from the phenotypic (σ2p) and genotypic (σ2g) variances [Based on plot means across test locations, a combined analysis of variance (ANOVA) was performed using PROC GLM in SAS , with thariances  and cateariances . Correlaariances . Distribariances . The calp-values for traits, marker-trait associations were declared significant at p-values of ≥10−4 [The 2491 filtered SNPs from 195 accessions were used for the assessment of population structure and GWAS using TASSEL 5 . The popof ≥10−4 ,54. Manhhttps://www.legumeinfo.org accessed on 4 April 2020). Since AYB currently lacks a reference genome, which is a limitation to candidate gene mapping, the genome of a related legume, the common bean (Phaseolus vulgaris), was used. A blast search was performed for trimmed nucleotide sequence (60–80 bps) of significant AYB SNPs on the Phaseolus vulgaris genome database (Phaseolus vulgaris G19833 genome v2.0) in the legume information system. Synteny of related legumes  was also included in the search. After marking the annotated position in the Phaseolus vulgaris genome database, the scroll was zoomed to 1 Mb (500 Kbp up and downstream from the annotated position of the AYB SNP tag in the Phaseolus vulgaris genome database) to check for the surrounding candidate genes and their encoding protein products and know if they regulate the traits of interest. Identified putative candidate genes in the Phaseolus vulgaris genome database were also subsequently further researched in the previous crop studies literature for verification.The SNP markers that were significantly associated with seed size traits through GWAS in the combined location analysis were annotated for candidate gene identification in the LIS-legume information systems  was obtained in seed length, while seed thickness (57.8%), 100-seed weight (51.6%), and seed width (50.0%) had moderate heritability estimates .The distribution of the seed size traits evaluated in the six environments and the correlation coefficients between the traits are presented in A diverse set of 196 AYB accessions were genotyped using a high-depth DArTseq SNP approach and generated a total of 5416 SNPs. After SNP quality control  , a totalPrincipal component analysis revealed that the first three principal components (PCs) respectively accounted for 5.9, 4.8, and 3.7% of the variation among the AYB accessions. No clear clustering (population structure) could be deduced among the accessions based on these two PCs . The coep-values of ≥10−4 in each and combined locations analyses  in the legume information system. Forty-three candidate genes were identified. These genes were located near (less than 500 kbp) five SNP markers  associated with the four AYB seed traits  of significant AYB SNPs in the combined location analysis on the d traits , Table 3The significant differences observed among the accessions for the seed size traits revealed the existence of adequate genetic variability among them. Seeds of AYB are known to harbor vast genetic variability in color, shape, and size ,19,20,87p-values in the Q–Q plots for all the measured traits indicated that spurious associations as a result of population structure and familial relatedness were largely corrected. Using two alternative GWAS models, we found that the MLM model had a stricter decrease in the number of significant markers than the GLM model. This is because GLM is considered a naive model with a high rate of false positives because it does not take population structure into account [At the genetic level, differences in the extent of relatedness among individuals in a population used for association mapping can lead to the formation of a population structure that can cause spurious associations between genotypes and the traits of interest . Princip account , whereas account ,93. Thou account ,94. The  account . MarkersPhaseolus vulgaris genome at a location close to genes whose encoding proteins had been reported in other crops to regulate the same traits as those with which they are associated in AYB can also be considered candidate makers. For example, markers (29420365|F|0-55:C>G-55:C>G and 29420736|F|0-57:G>T-57:G>T) associated with seed length, seed width, and seed thickness were found located close to genes (Phvul.001G153000 and Phvul.007G269900) having ovate family protein 13 as their encoding protein. Ovate family protein 13 is known to regulate fruit shape and seed size in tomato and rice, respectively [In this study, the common bean genome was used to align AYB SNPs, and 17% of the filtered 2491 SNPs aligned widely on common bean chr01 to chr11, including six SNPs on scaffolds. These suggest that the two crops have a syntenic relationship, which might be due to their close evolutionary relationship. Similar results have been reported for another African indigenous legume (Bambara groundnut) having syntenic relationships with other legumes  ,97. The ectively . Likewisectively . Though Phaseolus vulgaris genome should be regarded as candidate markers. It is, therefore, recommended that efforts should be directed toward the validation of the identified significant makers using several mapping populations before they can be targeted for use in marker-assisted selection for seed size traits in AYB.To the best of our knowledge, this study is the first attempt at dissecting the genetic basis of seed size traits in AYB using genome-wide association mapping. Several SNPs were significantly associated with seed size traits in AYB. The five significant SNP markers  found on the


New technologies, such as next-generation sequencing, have advanced the ability to diagnose diseases and improve prognosis but require the identification of thousands of variants in each report based on several databases scattered across places. Curating an integrated interpretation database is time-consuming, costly, and needs regular update. On the other hand, the automatic curation of knowledge sources always results in overloaded information. In this study, an automated pipeline was proposed to create an integrated visual single-nucleotide polymorphism (SNP) interpretation tool called SNPMap. SNPMap pipelines periodically obtained SNP-related information from LitVar, PubTator, and GWAS Catalog API tools and presented it to the user after extraction, integration, and visualization. Keywords and their semantic relations to each SNP are rendered into two graphs, with their significance represented by the size/width of circles/lines. Moreover, the most related SNPs for each keyword that appeared in SNPMap were calculated and sorted. SNPMap retains the advantage of an automatic process while assisting users in accessing more lucid and detailed information through visualization and integration with other materials. Precision medicine is a novel medical approach that customizes healthcare delivery by performing diagnostic tests, especially genetic sequencing, and analyzing results to select compatible therapies and treatment plans, rather than performing a general treatment solution on a large number of patients with varying conditions . In receOn the other hand, the application of whole-exome sequencing (WES) and whole-genome sequencing (WGS) implies that each sequencing could see 200,000–400,000 WES variants or 3,900,000 WGS variants recognized on each subject , and hunResearchers have worked hard over the last few years to develop accurate, rapid, and cost-effective technologies or protocols for variant analysis and interpretation, yielding several distinct approaches. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) have already published recommendations for standards and guidelines in sequence variant interpretation, with detailed rules on evidence, direction, and strength classification . For exaAutomatically curated databases and tools are developed to compensate for these shortcomings. LitVar  is a powAmong these approaches, integrated visualization of information in an automatically updated and curated database is considered a missing piece of the whole process. Furthermore, additional valuable data could be further generated from integrated curated data. Therefore, SNPMap is developed to facilitate better understanding and comprehension of single-nucleotide polymorphisms (SNPs), the most common type of genetic variations.https://lemminflect.readthedocs.io).The primary reference SNPs were obtained from the ClinVar using application programming interfaces (API). These SNPs were selected for their possible relevance to large amounts of biomedical literature, ensuring the ability to build a preprocessed database with sufficient information. When a user initiates a search with SNP that is not pre-selected in SNPMap, an online workflow  will quehttps://echarts.apache.org/). Other calculations and analysis modules used are implemented via Python.The significance of the association between a keyword and SNP depends on how often the keyword appears in all relevant literature, and this significance was assessed by the frequency of the two keywords appearing together in the same document. In the visualization of an SNP, keywords were used to generate network nodes, where the node size reflects the significance of the association between keywords and SNPs, and the thickness of the connecting line between nodes reflects the significance of the association between the two keywords. Since a keyword can be associated with multiple SNPs in different works of literature, through the quantitative assessment of the significance of keyword and SNP associations, we also obtained the ranking information of the SNPs and other keywords corresponding to a keyword, which provides a basis for retrieving SNPs and other keywords by keyword. The database of SNPMap is designed to update monthly. Node.js was used to develop the website, and the visualization was based on Apache ECharts (http://snp.nbscn.org) for users to explore these millions of associations. First, users can query SNP-related data and visualize all the association information by entering the dbSNP Reference SNP identifier (RSID or RefSNP ID) or the HGVS notation of the SNP in the search field. Visualization of an SNP (rs2234693) is taken as an example  but can provide more association information, which comprises some associations with insufficient evidence or inconsistent prevailing results, which has more implications for clinical discovery and identification of novel variant-disease associations.As shown in In many cases, SNPMap outperforms ClinVar regarding coverage of the concepts associated with each SNP. As shown in Because both LitVar and SNPMap’s data get their data from PubTator, there are some similarities between LitVar and SNPMap’s main keywords. When comparing SNPMap results to LitVar, the advantage of SNPMap’s intuitive visualization could be established. As shown in http://snp.nbscn.org/word/breast%20cancer), related SNPs are highlighted by SNPMap with many of them having high beta and p values in previous GWAS studies related to breast cancer. It allows users to quickly navigate to the page that corresponds to the corresponding keyword.In addition, SNPMap offers a reverse search—using concepts as keywords to find SNPs and other concepts that are prevalent in related biomedical literature, a feature that is not available in dbSNP, LitVar, or any other tools. Using the concept “breast cancer” as an example (While SNPMap has been a comprehensive tool for providing variants and concepts with background information, the information it delivers should be considered under the condition that maximum information sources come from biomedical literature. Thus, the results should be interpreted as a general representation of the variant or concept in biomedical literature. The standard of different biomedical literature varies, and the quality and quantity of related biomedical literature will impact the contents of the results. While relations mentioned in more literature are more likely to be valid, no relation could be considered 100% certain. Besides, any possible relations that are not mentioned in previous biomedical literature will not be displayed, so the results displayed should be viewed as retrospective, and even though novel connection discoveries could be promptly added to the SNPMap database, the tool is questionable to be used for finding brand new relations.Another drawback of SNPMap is its limited scale. The study only calculated 598,219 SNPs included in ClinVar, against more than a billion SNPs on dbSNP. The limited scale of SNPs that include enough biomedical literature information to render a keyword graph further cut the size to 46,747 SNPs that could reach the threshold we installed to generate graphs, although less biomedical literature reflects that less research has been performed on the SNPs, implying that researchers are less likely to have the interest to request information into those SNPs. To compensate for the disadvantages brought by the limited scale of the database, we calculate immediate SNP data demands made by our users, which composed of are SNPs that are not yet stored inside our database, in real-time. Users will be notified that the calculation will be completed from a few seconds to a few minutes and will be able to read the data instantly after completion of the calculation.As SNPMap currently stands, the web application has become a useful tool with vast potentials for researchers and clinical practitioners alike. For researchers, SNPMap could be useful in obtaining a thorough picture about how a variant was researched in the past, leading the directions of future research topics on the variant. With the additional resources of concept connections, researchers would also be able to identify significant variants related to diseases, organs, cells etc., thus finding variants for their own research projects. SNPMap could even potentially help researchers dig out new information between SNPs and concepts that were buried in large amounts of biomedical literatures. For practitioners, the tool could be helpful in facilitating quick interpretation and filtration of huge variant datasets, helping practitioners to distinguish variants with significance efficiently, and saving precious time in preparing clinical genetic reports.A user-friendly, visualized and automatically curated SNP interpretation tool called SNPMap was proposed and developed in this study which has applications in several scenarios, including interpretation of clinical testing results and scientific research outcomes, especially under disease-variant research environments. For researchers, it would be a valuable tool in acquiring information on past variant studies, exploring potential variants for further research, and for clinical practitioners, it could be extremely useful in interpreting and reporting genetic testing results with large amounts of variant information. The database will be regularly updated with new SNP/variant information since new biomedical literature works are being published incessantly.


Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.Southern root-knot nematode [SRKN,  Glycine max (L.) Merr.] represents one of the most essential crops to the world’s economy and food security due to its unique seed composition. As a versatile crop with unprecedented seed composition, soybean is extensively used in the food, feed, and many other industries exploring oil and protein-based products  are considered the most economically important and widely distributed species of plant-parasitic nematode, of which southern root-knot nematode  has the most scientific and economic importance  on chromosomes (Chrs.) 10 and 18 in plant introduction (PI) 96354 . The comp-values are then used to rank the SNPs and to select those with a p-value smaller than a pre-set significance level threshold   identify genomic regions associated with a trait or phenotype of interest from a large group of single nucleotide polymorphisms (SNPs) by linear or logistic regression analysis which is performed separately for each SNP. The resulting  of 3.0) . The con of 3.0) . Althoug of 3.0) , linear  of 3.0) . Recentl of 3.0) . ML-basePopular ML models, such as Random Forest (RF) and Support Vector Machine (SVM) have been involved in GWAS for feature (SNPs) selection , performA total of 717 breeding lines derived from 330 unique bi-parental populations and developed by the University of Missouri – Fisher Delta Research Center (MU-FDRC) soybean breeding program was used in this study. The MU-FDRC soybean breeding program has historically advanced the field of nematode resistance in soybeans and developed and released multiple soybean lines with enhanced levels of SRKN resistance by combining multiple sources of resistance . The linBreeding lines were phenotyped for the development of galls in the root system  in a greenhouse of the University of Georgia from 2017 to 2021 using a well-established protocol as previously described . The resR   model waR  using “p package  and the  package .r) of one SNP with the others were calculated, and those with high correlations (| r| > 0.5) were removed from the list. The list was updated immediately and the correlations between the following SNP and the others were calculated. The loop ended when the last SNP correlations were calculated.The SNPs with high VIP scores (>2.0) were kept to be included in the ML-based GWAS and sorted descendingly based on the VIP scores. Starting from the top of the selected SNP list, the Pearson correlations  was calculated using Eq. 5. The SNP with the highest accuracy in the previous loop was kept in the later loop and evaluated with an additional SNP from the list of significant SNPs. The loop ended when no gain in the classification accuracy was observed and output the best combination of SNPs. To assess the effect of potential overfitting on the predictive accuracy of both SVM and RF models, the loop was extended to all selected predictors and accuracy metrics were calculated for each model.where, TP, True Positive; TN, True Negative; FP, False Positive; FN, False Negative.Two models were used for the multi-class problem, namely SVM and RF. The two models were selected due to their high effectiveness in high dimensional cases where the number of predictors is greater than the number of samples, as well as a good balance between the variance-bias trade-off . RF is arandomForest” function in the “randomForest” package (svm” function in the “e1071” (tune” function. The model was turned by going through a grid search for cost (the margin softness parameter) from 0.01, 0.1, 1, 10, 100, and 1,000 and gamma (the variance-bias tradeoff parameter) from 0.0001, 0.001, 0.01, 0.5, and 1. In addition, to compare the efficacy of the proposed methodology in detecting significant SNPs, GWAS was conducted using the package GAPIT  groups individuals based on kinship replacing the genetic effects of individuals in the regular mixed linear model (MLM) with the genetic effects of the corresponding groups. In ECMLM, additional algorithms are provided to cluster individuals into groups including the average and Ward methods. The detailed methodology can be found in De). Since the average of squared VIP scores equals one, a score greater than 1.0 is generally used as a threshold for selecting features that contribute the most toward the aggregate index (r|) higher than 0.5 were eliminated, maintaining the SNP with higher VIP scores. A total of 29 non-correlated SNPs with VIPs higher than 2.0 (range 2.0–8.8) were identified across Chrs. 2, 3, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, and 19, and selected to be included in the analysis , 105 as moderate (average score of 3.0), and 426 as susceptible (average score of 4.9). The distribution was unbalanced as the susceptible (59.4%) lines largely outnumbered the resistant (25.9%) and moderate (14.6%) lines. The average VIP scores across the 4,974 SNPs was 0.89, of which 2,167 SNPs showed VIP scores above the standard threshold of 1.0 . The PLSte index . Alternate index . In thisanalysis .Gm10-1232205, Gm10-2240113, Gm10-214458, Gm10-1586434, and Gm11-63293. Gm10-1232205 was the SNP with the highest VIP score (8.83) and yielded a classification accuracy of 0.74 when used as the only predictor. The addition of Gm10-2240113, Gm10-214458, Gm10-1586434, and Gm11-63293 to Gm10-1232205 improved the model’s ability to classify resistant, moderate, and susceptible genotypes, with an overall increment in prediction accuracy of 5%. A substantial gain in accuracy was observed in the moderate class, increasing from 0.50 to 0.59 (18%). The precision, which measures the ability of the model to classify a true positive prediction based on the total number of positive predictions, increased for all classes with the addition of the four SNPs, however, a drastic increase in precision was observed in the moderate class (0.00–0.63). In addition, specificity, which represents the proportion of true negative predictions by the total number of negative predictions, increased proportionally for the resistant and susceptible classes . Interestingly, a substantial decrease in overall prediction accuracy was observed with the further addition of predictors, which can be attributed to the overfitting of the training set and consequently poor reproducibility in the testing set  using five SNPs as predictors, including ting set .Gm10-1232205, Gm10-2240113, Gm10-214458, Gm11-63293, Gm10-1586434, Gm10-4670275, Gm10-3465857, Gm15-13014539, Gm19-44761515, Gm13-35032818, Gm06-9668798, Gm16-6423098, Gm12-4883 456, Gm18-57126096, Gm16-31397286, Gm03-1718435, Gm11 -1620921, Gm06-3608127, Gm02-3774471, Gm10-39937578, Gm14-48703687, and Gm11-16996443. Like the SVM model, Gm10-1232205, Gm10-2240113, Gm10-214458, Gm10-1586434, and Gm11-63293 were among the most significant SNPs and the RF model using these five SNPs yielded an overall accuracy of 0.78. A total gain in overall classification accuracy of 11% was observed with the addition of 20 SNPs to the model using only Gm10-1232205   was obtained using 21 SNPs as predictors, including ctively) . Similarctively) .Although RF is well-known for sustaining predictive performance under high dimensional data with multicollinearity, excessive noise among predictors, and unbalance between the number of predictors and the number of samples , a substGm10-1232205 and Gm10-1586434 were detected in BLINK, FarmCPU, and ECMLM, as well as in SVM and RF  previously reported in the literature to be significantly associated with resistance to SRKN  and Glyma.10g013900 (Carbohydrate Metabolic Process) were identified as possible candidate genes associated with SRKN resistance. Universal Stress Proteins (USP) are involved in multiple cellular responses to biotic and abiotic stressors, ranging from ion scavenging, hypoxia responses, cellular mobility, and regulation of cell growth and development  overlaps with reports from Glyma10g02090, Glyma10g02100, and Glyma10g02140 (Glyma10g02150 and Glyma10g02160 and encode a pectin methylesterase inhibitor (PMEI) and PMEI-pectin methylesterase, respectively  that has been well reported on the literature , two newbase.org  revealed to SRKN . Studies(Mi gene . AlthougMi gene (Although the major QTL on Chr. 10 can explain most of the phenotypic variance associated with SRKN resistance in soybean, additional minor effect marker-trait associations on Chrs. 10 and 11 were identified to improve the prediction accuracy of both SVM and RF models. The addition of minor effect SNPs enhanced the models’ predictive accuracy in classifying genotype response to SRKN, which could improve the ability of plant breeding programs to identify resistant genotypes through marker-assisted selection and/or genomic prediction early in the breeding pipeline. Interestingly, a decrease in classification accuracy was observed for the ML models as the number of SNPs included in the analysis increased, which reinforces the importance of limiting the unbalance between the number of predictors and the number of samples resulting in overfitting and poor reproducibility of the results. Minimal diversity and evolution are expected since SRKN are parthenogenic nematodes. However, resistance breakdown has been observed in tomatoes against the Mi gene . ResistaMi gene . ExpandiThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.PC and GS contributed to conception, design, and funding resources of the study. AH and ZL contributed to the phenotyping of soybean lines used in this study. CC, MU, TV, DL, and HN contributed to the genotyping of the soybean lines used in this study. CC, JZ, and JFZ contributed to the statistical analysis of this study. CC and JZ wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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