Bemisia tabaci, to benefit their spread, although this may not always be the case. Here, the new begomovirus Ramie mosaic virus (RaMoV) and its vector B. tabaci MED, which is dominant in China and many regions of the world, were used as a model to examine direct and indirect interaction and virus transmission by B. tabaci MED of different sexes. No significant direct or indirect effects of RaMoV were observed in B. tabaci MED females, although RaMoV could shorten the life span of B. tabaci MED females by up to 4 days. A test of RaMoV transmission by different sexes of B. tabaci MED showed that there was higher virus transmission efficiency by females than males. Overall, RaMoV is transmitted by B. tabaci MED in a sex-dependent manner, and further research is needed to uncover the mechanism of the difference in RaMoV transmission by different sexes of B. tabaci.Begomoviruses can modify their transmission vector,  The whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a species complex with a worldwide distribution, and at least 35 cryptic species are widely recognized based on the evidences of molecular phylogenetic data and reciprocity between genetic groups2. Of these cryptic species, two invasive species, Mediterranean (MED) and Middle East-Asia Minor 1 (MEAM1), severely harm plants by direct feeding and by indirect transmitting plant viruses3.Approximately 80% of epidemic plant viruses in the field are transmitted by insect vectors from plant to plant and spread to distantly located regions in this wayTomato yellow leaf curl virus (TYLCV) could directly mediate the behaviours of its vector, whitefly, including settling, probing and feeding, to enhance its transmission efficiency5. In the case of a plant virus indirectly affecting its vector, the fecundity, longevity and population density of whiteflies increased when feeding on Tomato yellow leaf curl virus (TYLCV)-infected tobacco plants9.Plant viruses are not passively transmitted by their vectors; instead, they can produce direct and indirect effects on the vectors to modify their behaviour, life span and fitness to benefit their own transmission. B. tabaci MEAM1, females possessed higher TYLCV transmission efficiency on tomato plants10. A similar sex-based difference in transmission efficiency was also found in B. tabaci MED11.In addition to the direct and indirect mutual relationships of plant viruses and their vectors, plant virus transmission efficiency by whitefly is relatively different depending on sex. Compared with males of 12. Although the characteristics of acquisition, retention and transmission of several plant viruses in the genus Begomovirus by B. tabaci have been well documented15, the detailed characteristics of the begomoviruses transmitted by B. tabaci still require additional study to understand the outbreaks of Begomovirus in the world.The capacity of vectors to acquire, retain, and transmit plant viruses is critical information for inferring plant virus epidemiologyBoehmeria leiophylla17. Plant viruses in the genus Begomovirus are important pathogens in the tropical and sub-tropical regions of the world, are exclusively transmitted by whitefly in a persistent-circulative mode18 and severely damage a wide range of economic crops, such as tomato, pepper and tobacco19. It is rational to deduce that RaMoV would be a potential threat to important crops.Ramie mosaic virus (RaMoV) is a new bipartite begomovirus that was documented from infected B. tabaci MED has progressively become a dominant species in China23; thus, in this study, direct and indirect effects were examined using the RaMoV and B. tabaci MED model. In addition, the acquisition, retention and transmission of RaMoV by the different sexes of B. tabaci MED was compared. The results would scientifically evaluate the potential epidemiology of RaMoV in the field.In recent years, B. tabaci MED, RaMoV-infected tobacco plants were produced. Tobacco plants with 6 true leaves were inoculated with RaMoV in the 3rd leaf by Agrobacterium tumefaciens containing an infectious clone of RaMoV. At 10 dpi, compared with healthy tobacco plants, RaMoV-infected tobacco plants were severely stunted and lacked apical dominance. The new leaves of RaMoV-infected tobacco plants manifested shrivelling and mosaic symptoms , the difference was not significant . Figure F1,80 = 0.533, P = 0.467) in the fecundity of females of B. tabaci MED was associated with RaMoV  and the longevity  of B. tabaci MED females were not significantly different among the three treatments   and decreased after 48 h. During the whole AAP in which B. tabaci acquired RaMoV, the females acquired more virus than the males; the difference was not significant in the 6–48 h AAPs (P > 0.05) and was significant at 72 h AAP (P < 0.05)    and equilibrated from day 9 to day 18 in both sexes of B. tabaci. The retained RaMoV DNA in females was always higher than that in males of B. tabaci, and the difference was significant from day 12 to day 15    after 7 d  and 14 d .The relative amount of RaMoV DNA accumulation in tobacco leaves inoculated by 24. Nevertheless, distinct effects of certain plant viruses on their vectors exist, and such interactions are ambiguous25.It is well known that plant viruses have co-evolved with their transmission vectors and benefited via enhanced virus transmissionB. tabaci MED, as a model, that there is no significant direct or indirect effect of RaMoV on females of B. tabaci MED , which also had no significant effect on the fertility and longevity of its whitefly vector26. However, the begomovirus Tomato yellow leaf curl china virus (TYLCCNV) could significantly decrease the fertility and longevity of females of B. tabaci6. The direct and indirect effects of RaMoV and its vector, females of B. tabaci MED, together with a previous study, reconfirmed the disparate interactions of plant viruses and their vectors.This study revealed, using a new bipartite begomovirus, RaMoV, and its vector, MED Fig. . This reB. tabaci are able to acquire, retain and transmit RaMoV, and the ability of females to acquire and retain the RaMoV DNA was significantly higher than that of males  and cucumber (Cucumis sativus L. cv. ChangChunMiChi) were grown in a potting mix in 1.5 L pots (one plant/pot) at 26 ± 2 °C under a 16 h light/8 h dark day cycle. The tobacco plants at the 6–8 true-leaf stage was inoculated with virus by A. tumefaciens GV3101 containing a RaMoV plasmid, which was constructed and stored in our laboratory.Tobacco (B. tabaci MED population was maintained in whitefly-proof screen cages at 26 ± 2 °C with a 16 h light/8 h dark day cycle. The purity of the MED population was monitored by CAPS (cleavage amplified polymorphic sequence) of mtCOI  with the restriction endonuclease VspI29.The B. tabaci were examined by clip-caging viruliferous or non-viruliferous insects with leaves of healthy cucumber. Viruliferous insects were generated from new-born female adults fed on the leaves of RaMoV-infected tobacco for 48 h by clip-caging and then transferred to leaves of healthy cucumber. Non-viruliferous insects were generated on healthy tobacco of the same growth stage. A set of 40 female B. tabaci individuals in each experimental group was used.The direct effects of RaMoV on the fecundity and longevity of females of B. tabaci MED were examined by clip-caging newly emerged non-viruliferous B. tabaci from healthy cucumber plants with healthy tobacco, RaMoV-infected tobacco or Agrobacterium-infected tobacco. The clip-cages were attached to the third to sixth leaf of each tobacco plant. Each newly emerged non-viruliferous B. tabaci MED adult female was clip-caged to ovipositor on tobacco for five days (one clip-cage per tobacco plant) until death.The indirect impacts of RaMoV on the fecundity and longevity of female The eggs deposited on cucumber/tobacco leaves were counted and marked with an insect needle under a stereomicroscope . The adults and the clip-cages were then moved to new leaves. Every female was checked every 5 days until death, and their longevity was calculated to determine the effects of RaMoV for each replicate.B. tabaci MED, approximately 300 newly emerged (0–24 h) non-viruliferous female or male adult individuals were taken from their respective cultures and transferred to feed on RaMoV-infected tobacco plants enclosed in a clip-cage. To ensure that the insects remained healthy, the transfer process of insects from leaf to leaf was performed gently. Following adult transfer, we randomly collected 20 adults from the leaves of the two plants at the end of 6 designated acquisition access periods . The collected adults were stored at −20 °C and later assayed individually for detectable RaMoV DNA by qPCR.To test the acquisition of RaMoV by different sexes of B. tabaci MED on the basis of sex, approximately 300 newly emerged (0–24 h) non-viruliferous female or male adults were transferred to feed on two RaMoV-infected tobacco plants enclosed in an insect-proof cage for 48 h AAP to obtain 100% viruliferous B. tabaci MED. Then, the viruliferous adult female or male B. tabaci MED were collected and released to feed on a healthy cucumber plant, a non-host of RaMoV. After the initial release, a group of 20 live adults were collected at 0, 3, 6, 9, 12 and 15 d. The insect samples collected were stored at −20 °C, and later, RaMoV DNA was detected by qPCR.To evaluate the retention of RaMoV by B. tabaci MED on the basis of sex, viruliferous adults generated as above were collected and inoculated in groups of 1 and 2 on the second leaf from the bottom of an uninfected tobacco plant at the three-true-leaf stage using a clip-cage for a 48 h inoculation access period. After 7 d and 14 d, the tobacco plants in each of the treatments were examined for virus infection by RaMoV symptoms and conventional PCR, and the RaMoV DNA accumulation in tobacco leaves was quantified by qPCR.To estimate the transmission of RaMoV to tobacco plants by B. tabaci and tobacco was extracted by the CTAB method30. For specific detection of RaMoV in B. tabaci and tobacco, the specific primer  of the partial CP gene fragment of RaMoV (774 bp) (GenBank No. NC_010791.1) was used to conduct conventional PCR. The reaction mixture (20 µL) contained 10 × PCR buffer (no Mg2+) 2 µL, MgCl2 (50 mM) 0.8 µL, dNTPs (10 mM) 0.2 µL, primers (10 mM) 0.2 uL each, enzyme mix (1000 U) 0.5 µL, RNA inhibitor 0.1 µL, ddH2O 16 µL. The following cycling conditions were used: initial denaturation at 94 °C for 2 min; 35 cycles of 94 °C for 30 s, 55 °C for 1.5 min, and 72 °C for 1.5 min; and a final extension step at 72 °C for 10 min. The PCR products were sequenced to ensure the accuracy of the insertion sequence.Total genomic DNA of 13 with minor modifications. The specific primers (qPCR-F: GGTTCTGCGTAAAGTCCG and qPCR-R: TTGACCTCCAGTAACAGTTGAC) for the partial CP gene fragment of RaMoV (200 bp) (GenBank No. NC_010791.1) were used. qPCR reactions were carried out in a 96-well optical plate in an Analytik Jena AG PCR instrument, and the accompanying software was used for qPCR data normalization and quantification. Amplifications for RaMoV were performed with 2 × TransStar Green qPCR Super Mix UDG (Trans Gene Biotech (Beijing), Co., Ltd). For each sample, three replicates were performed for each of the three biologically independent experiments. The relative gene expression of the RaMoV CP gene was calculated using the 2−△△Ct method31. β-actin from B. tabaci13 and EF-1a32 from tobacco were used as reference genes to normalize the gene expression level.For quantification of RaMoV DNA in insects and tobacco, qPCR was conducted as in a previous studyStatistical analyses were performed with SPSS . Means were compared by the least significant difference (LSD) test at P = 0.05.


ROH ranged from 0.03 in YHC to 0.12 in NDC. For each of three ROH classes with different sizes , the numbers and total lengths of ROH per individual showed considerable differences across breeds. Moreover, we obtained 993 to 3603 ROH hotspots (which were defined where ROH frequency at a SNP within each breed exceeded the 1% threshold) among eight cattle breeds. Our results also revealed several candidate genes embedded with ROH hotspots which may be related to environmental conditions and local adaptation. In conclusion, we generated baselines for homozygosity patterns in diverse Chinese cattle breeds. Our results suggested that selection has, at least partially, played a role with other factors in shaping the genomic patterns of ROH in Chinese local cattle and might provide valuable insights for understanding the genetic basis of economic and adaptive traits.Genome-wide single nucleotide polymorphism (SNP) arrays can be used to explore homozygosity segments, where two haplotypes inherited from the parents are identical. In this study, we identified a total of 27,358 runs of homozygosity (ROH) with an average of 153 ROH events per animal in Chinese local cattle. The sizes of ROH events varied considerably ranging from 0.5 to 66 Mb, with an average length of 1.22 Mb. The highest average proportion of the genome covered by ROH (~11.54% of the cattle genome) was found in Nanda cattle (NDC) from South China, whereas the lowest average proportion (~3.1%) was observed in Yanhuang cattle (YHC). The average estimated F The occurrence of ROH within a population can be influenced by both reduced effective population size in the past and recent inbreeding. In addition, potential effect from genetic drift may also contribute to the formation of ROH. The availability of high-density SNP arrays has promoted the recent studies of regions of autozygosity. Initially, ROH analysis was utilized as a strategy to map recessive diseases genes in human3. Genome-wide analysis of homozygosity and their variation across individuals can offer important insights into genetic diversity and demographic history4. Abundance of ROH and their directional dominance on complex traits have been demonstrated in diverse human population5. Previous studies have investigated both genomic and geographic distribution of ROH and their contribution to population history. In human, recent studies suggested the total number and length of ROH per individual display considerable variation in worldwide human populations6, and the genomic distribution of ROH followed an obvious South to North gradient which was in consistent with European population history7.Runs of homozygosity (ROH) are defined by contiguous homozygous segments where the two haplotypes transmitted from the parents are identical20. In cattle, Purfield et al. assessed the level of ROH in a wide range of daily and beef cattle breeds11, and their findings suggested different distribution patterns of ROH length may imply variations in breed origins and recent management among them. Extensive studies have been performed for evaluation of inbreeding and their negative effects on production and reproductive ability based on ROH in commercial daily cattle18. Also, several studies have been conducted to estimate inbreeding depression and investigate climatic adaptation in local cattle21.ROH has also been widely utilized in farm animals to characterize population structure and demography history. Also, analyses of ROH can help to disclose the genetic relationships among individuals, measure the genome inbreeding level, and identify the region associated with economically important trait22. The autozygosity across genome can help to improve the understanding of selection process and identify potential candidate genes associated with important traits in cattle29. The formation of ROH of various sizes (long vs. short) may reflect different evolutionary events on recent vs. ancient inbreeding12, with long ROH indicate more recent inbreeding, while short ROH suggest more ancient inbreeding. The investigation of these ROH segments can further improve our understanding of selection histories for special traits among diverse populations24.Previous studies suggested the occurrence of ROH is not randomly distributed across the genomes, and ROH hotspots appear to be shared among individuals, which is probably caused by selection pressure32. Despite several studies have conducted on analysis of population structures and admixture in Chinese local cattle33, no comprehensive study has been reported to explore the ROH pattern using high density SNP arrays. The aims of the present study were to (i) investigate genome-wide autozygosity patterns and genomic inbreeding level in Chinese local cattle; (ii) characterize profiles of ROH with different sizes and their gene contents with ROH hotspots; (iii) evaluate potential diverse selection based on the landscape of ROH across genome.Most of Chinese local cattle have been isolated in local feed environments, thus the potential homozygosity regions could be generated among them, which may also be influenced by adaptive selection and other factors in specific environmentHo) with r = 0.71. In addition, we found the numbers and sizes of ROH events per individual varied across populations. NDC had larger average numbers and sizes of ROH events than other breeds  was estimated in NDC. The FGRM values were lower when compared to FROH, which ranged from −0.07 to 0.36, whereas the coefficient for FHOM ranged from −0.15 to 0.33. The highest correlation  was observed between FROH vs. FHOM, and the significant correlations were found for FROH vs. FGRM  and FHOM vs. FGRM .ROH regions were determined by merging ROH identified across all individuals using previously published protocols implemented in BEDTools12. The ROH distributions by total length and number for each group were shown in Fig. To investigate the ROH pattern, we divided them into three size classes: A. Small (500 kb to 1 Mb), B. Medium (1 Mb to 5 Mb), and C. Large (>5 Mb), as described in the previous studyOur result showed two breeds (MGC and CDM) had the largest total lengths of Large ROH , while none of them is strongly correlated with the total length of Large .To assess the genomic distribution patterns of total ROH length, we also conducted pairwise comparisons among the total lengths of three classes of Small, Medium and Large ROH in individual cattle genomes Fig. . We founCTNNA1, LRRTM2, SIL1, SNHG4, MATR3, PAIP2, MZB1, SPATA24, DNAJC18, ECSCR, TMEM173, UBE2D2 were shared across eight breeds. The DAVID annotations for these top annotated genes for each of eight breeds were presented in Supplementary File To investigate effect of selection in diverse cattle, we explored the occurrences of ROH across genome. The frequency of a SNP locus (%) in those ROH was assessed for each breed, and then plotted against the position of the SNP along autosomes Fig. . The thr(ETF1 and HSPA9) at 51.5 Mb on BTA7 and three genes  at 32 Mb on BTA21. In MGC, we detected top three regions on BTA7, BTA11 and BTA23: the top region on BTA7 overlapped with six genes CTNNA1, LRRTM2, SIL1, SNHG4, MATR3 and PAIP2, while no known gene was found within the regions on BTA11 and BTA23. Also, we observed the top three regions on BTA5, BTA13 and BTA14 in CDM. Among them, the top region on BTA5 contained genes YEATS4 and LYZ1. We found 17 genes were included in the region ranging from 54.48 to 54.85 Mb on BTA13, while no known gene was found within the region on BTA14. In the LSC cattle, we observed one significant region on each of BTA5, BTA7 and BTA16, which overlapped with many genes and spanned 0.6 Mb, 7 Mb and 0.1 Mb, respectively. While in the ZTC, only one region surpassed the 50% occurrence of ROH which ranged from 51.6 to 52.7 Mb, containing the important gene like CTNNA1, ISL2, DNAJC18, and SPATA24. Finally, no significant region (>50% occurrence) was detected for the PWC in Southwest groups. For two breeds in South group, we observed that multiple regions on BTA2, BTA5, BTA7, BTA11, BTA16, BTA14 and BTA22 were shared by WSC and NDC within the regions of above 50% occurrences.Among 382 unique genes, a total of 137 were identified in two and more populations. In YHC, two regions were detected on BTA7 and BTA21, including two genes In addition, a total of 247 genes were identified in unique breed based on the occurrence of ROH, of those, 56, 9, 15, 69, 28, 38, 24, 8 genes were uniquely identified in YHC, MGC, CDM, LSC, PWC, ZTC, WSC and NDC, respectively.8. However, the ROH patterns and their distributions in Chinese local cattle remain largely unexplored. To our knowledge, this study is the first attempt to investigate the occurrence and distribution of ROH in Chinese local cattle using high-density SNP arrays. The high density SNP arrays are more sensitive for the determination of small segments, as previously reported35, while the BovineSNP50K array may underestimate the numbers of segments of 1 to 4 Mb in length.Cattle represent important genetic resources that contribute to local economy. Many studies have been explored the ROH pattern and its associated inbreeding depression related to traits at the genomic level in multiple cattle populationROH, FHOM and FGRM. In general, the correlations among FROH, FHOM and FGRM varied dramatically across studies. Our study revealed the relative high correlation (0.78~0.85) among them, and high correlations between FROH and the two other estimates of genomic inbreeding (FGRM and FHOM) can be considered as an accurate estimator of the IBD genomic proportion17. On the other hand, several other studies have found a low to moderate FGRM-FROH correlation for dairy breeds37, while moderate to high correlations were reported in Holstein cattle and Jersey populations38. We suspected that these different results could be related to ROH type, sample populations and SNP array platforms.We estimated inbreeding coefficients within each population using three methods including F11. The British Isles breeds including Hereford, Guernsey, Angus and Jersey cattle displayed the highest sum of ROH per animal compared other breeds, while African breeds displayed high variability in total number of ROH among breeds11.In this study, we found clear differences in terms of total number and length of ROH in diverse Chinese local cattle, and the relationship between them varied considerably among individuals. This was consistent with previous studies with large-scale datasets in cattle, their findings suggested that ROH are often differentially distributed across breeds, and the patterns of ROH (number and length) may imply differences in breed origins and recent management36, and their study suggested that dairy breeds have longer and larger size ROH than beef and dual-purpose cattle, and inbreeding is more recent in these dairy breeds. In our study, we found some breeds like WSC and NDC in South China displayed larger ROH size, which may reflect the effects of current inbreeding. We suspected that both breeds may be selected in isolated conditions with relative small effective population sizes. This finding also implied that continuing isolation and a reduced population size might play an essential role in the formation of ROH.A recent study revealed that the average total length of ROH ranged from 106 Mb in Piedmontese to 371 Mb in Brown cattle28. A large proportion of individuals with large number and length were identified in NDC and WSC  cattle17, while our study found the average value of ROH coverage per animal were 323, 235 and 231 Mb in NDC, WSC and MGC, respectively. We also observed the cumulative ROH length was dominated by an abundance of Small to Medium ROH in WSC as well as NDC, which implied either of them is an isolated breed based on a source population of substantial size. Abundance of ROH due to genetic isolation has also been reported in European cattle populations13 and Japanese wild boar12. The accumulation of large ROH in individuals of MGC and CDM could be caused by potential selection pressure for special environmental conditions of these populations, which has also been reported in Spanish local goat breeds20. Three ROH class  may be influenced via different processes, including recent inbreeding and recombination on different evolutionary scales6. Clustering ROH into different size classes make it possible to detect and interpret genomic difference among breeds. In addition, previous study reported that long ROH are mostly enriched in regions with deleterious mutations, and inbreeding can elevate the occurrence of rare recessive diseases that represent homozygotes for deleterious mutations39. Therefore, control the increase of inbreeding can effectively promote the implement conservation programs and maintain genetic diversity in local cattle breeds.A recent study assessed the pattern of homozygosity and they found the average coverage of genome with autozygosity was 175 Mb in Gyr . Neither class Small or Medium is strongly correlated with the total length of Large ROH . Our study revealed that most of ROH belongs to the Short to Medium ROH categories. Compared with breeds from Southwest and South, our results suggested that two breeds (MGC and CDM) have been high inbred based on high coverage of Large ROH  Member 9 (HSPA9) in seven Chinese local breed, this gene is immune-related genes, and also shows differentially expressed during bovine muscle development46. Previous studies suggested that HSPA9 abundance in muscle had positive correlation with beef tenderness48. This regulation of HSPA9 on meat tenderness is likely due to its anti-apoptotic effect, which prevents the formation of protein aggregates49. Moreover, we observed several genes like CTNNA1, SIL1, PAIP2 in eight breed, and ZBTB46 and GMEB2 in CDM located within/near the ROH hotspots, which are also involved in selection in goat and sheep52.The occurrence of ROH hotspots in genomic regions that harbor candidate genes may be involved in selection pressure in response to environmental condition. Previous study has investigated homozygosity in Polish Red cattle, which are characterized by high fertility and easy calving, good immunity and health, as well as high milk qualityEEF1A2 in CDM, which was reported to be related to fatty acid and potentially regulate meat tenderness55. Several genes SIRT6, CREB3L3 and B3GNT5 were specifically detected in YHC cattle, which are reported related to energy meat quality, homeostasis and glycan synthesis59. Two genes TCF12 and TXNDC9 uniquely detected in LSC which were related to muscle lipid composition and marbling in beef cattle61. Moreover, we identified breed-specific genes  related to growth traits and immune process in PWC, WSC and ZTC, respectively64. Using the ROH approach, we detected many genes which show potential effects for important traits as reported in many previous studies. Our finding further supported that pattern of ROH can be utilized to explore genomic regions and genes under specific selection, and the shared ROH may potentially contain alleles associated with important traits. Despite many genes were identified in our ROH analysis, functional validation of them is still warranted in the future.To assesses the breed-specific differences in ROH occurrence related to selection, we investigated the genes within or near the ROH islands. In our study, we identified one gene named 21, and found out our settings generally agreed with the most of published studies using the Bovine HD SNP array. For example, Purfield et al. tested a minimum run length of 58 SNPs was needed to produce <5% randomly generated ROH and the maximum gap between two consecutive homozygous SNPs in a run was set at a 100 kb11. We found their options used was like ours, which slides a window of 50 SNPs, in one SNP intervals, across the genome estimating homozygosity. Although a previous simulation study has tested out ROH calling programs and their settings and reported the best program (PLINK) and its optimal settings65, the ROH calling methods and their settings are still challenging35, thus these results including ours should be interpreted by cautions. Also, to avoid bias and overestimation for ROH detection using PLINK, more options should be considered carefully according to the genome size and the number of SNPs genotyped.It was noted that we compared our settings to some other studies which used PLINK under its default settingsIn summary, our study revealed the occurrence of autozygosity varied largely across breeds in Chinese local cattle as different recent and ancient events. We further speculate that selection has partially contributed to landscape of ROH and their functions in the Chinese local cattle.All animal experiments were approved by the Chinese Academy of Agricultural Sciences . All animal procedures were performed in strict accordance with the guidelines proposed by the China Council on Animal Care and the Ministry of Agriculture of People’s Republic of China.32. The full names, associated abbreviation for each breeds and additional information on the locations of the sampling areas was presented in Supplementary File The genome-wide SNP data of 179 samples from eight populations including Yanhuang (YHC), Menggu (MGC), Caidamu(CDM), Liangshan (LSC), Pingwu(PWC), Zhaotong (ZTC), Wenshan (WSC) and Nandan (NDC) were genotyped by Illumina BovineHD SNPs array, which was retrieved from our previous study66. The ROH were defined by a minimum of 0.5 Mb in length to avoid short and common ROH that occur throughout the genome due to LD. The PLINK software detect homozygous segments by scanning along genotype for each individual. The following six criteria were considered to define a ROH: (i) a sliding window of 50 SNPs across the genome; (ii) the proportion of homozygous overlapping windows was 0.05; (iii) the minimum number of consecutive SNPs included in a ROH was 100; (vi) a required minimum density was set at 50 kb/SNP; (v) the minimum length of a ROH was set to 500 kb, the maximum gap between consecutive homozygous SNPs was 100 kb; and (vi) a maximum of two SNPs with missing genotypes and up to one heterozygous genotype were allowed in a ROH.ROH were detected across autosomes for each individual using PLINK v1.07HOM was assessed based on the proportion of homozygotes using PLINK v1.07 software. (2) FROH was calculated as described by McQuillan et al. 67. (3) FGRM was estimated based on the variance of additive genotypes using GCTA 1.19.2 method according to Yang et al.68. Correlations of inbreeding coefficient for three methods were estimated using cor.test function in R v3.2.4.ROH were divided into three classes based on length: 0.5–1 Mb, 1–5 Mb and >5 Mb and were identified as Small, Medium and Large, respectively. Three metrics were used to estimate levels of inbreeding: (1) Fcor.test functions in R v3.4.2. The ROH pattern based on the total number of ROH per individual and the total length of those ROH were measured for three classes. For analysis of distribution of ROH across genome, each chromosome was divided into 20 equal size segments, and their relative distribution over these chromosomal segments were calculated across breeds. All plots in current study were generated with the R programing (v.3.2.4).The correlations between the average proportion of the genome coverage by ROH and average observed heterozygosity (Ho) for each breed were estimated by 70.To investigate genomic regions that were associated with occurrences of ROH within each breed, the fraction of SNPs in ROH was estimated based on the frequency of a SNP in them across individuals. ‘ROH hotspot’ was identified as a region of adjacent SNPs with an ROH frequency per SNP above the threshold of 1%. Genomic coordinates for all identified ROH regions were used to annotate genes using the UMD3.1 genome assembly. The function of these genes and GO terms were further assessed using DAVID Functional Annotation Bioinformatics Microarray Analysis platformSupplementary filesSupplementary File1_Table S1, Supplementary File2_Table S2, Supplementary File3_Table S3, Supplementary File4_Table S4


Eleusine coracana L.) is an important dry-land cereal in Asia and Africa because of its ability to provide assured harvest under extreme dry conditions and excellent nutritional properties. However, the genetic improvement of the crop is lacking in the absence of suitable genomic resources for reliable genotype-phenotype associations. Keeping this in view, a diverse global finger millet germplasm collection of 113 accessions was evaluated for 14 agro-morphological characters in two environments viz. ICAR-Vivekananda Institute of Hill Agriculture, Almora (E1) and Crop Research Centre (CRC), GBPUA&T, Pantnagar (E2), India. Principal component analysis and cluster analysis of phenotypic data separated the Indian and exotic accessions into two separate groups. Previously generated SNPs through genotyping by sequencing (GBS) were used for association mapping to identify reliable marker(s) linked to grain yield and its component traits. The marker trait associations were determined using single locus single trait (SLST), multi-locus mixed model (MLMM) and multi-trait mixed model (MTMM) approaches. SLST led to the identification of 20 marker-trait associations (MTAs)  for 5 traits. While advanced models, MLMM and MTMM resulted in additional 36 and 53 MTAs, respectively. Nine MTAs were common out of total 109 associations in all the three mapping approaches . Among these nine SNPs, five SNP sequences showed homology to candidate genes of Oryza sativa (Rice) and Setaria italica (Foxtail millet), which play an important role in flowering, maturity and grain yield. In addition, 67 and 14 epistatic interactions were identified for 10 and 7 traits at E1 and E2 locations, respectively. Hence, the 109 novel SNPs associated with important agro-morphological traits, reported for the first time in this study could be precisely utilized in finger millet genetic improvement after validation.Finger millet ( Eleusine coracana (L.) Gaertn] is a potential future crop because of its adaptability to environmental fluctuations and high nutritional value. It is a self-pollinating allotetraploid (AABB) with basic chromosome number of 9 (2n = 4x = 36). The crop is thought to be domesticated in Uganda about 5000 years ago from where its cultivation extended to the Ethiopian highlands and India. Among millets, finger millet ranks fourth in importance after sorghum, pearl millet and foxtail millet. The area occupied by the crop at the global level is estimated to be approximately 4.0–4.5 million hectares with a production of 5 million tonnes annually [Finger millet [annually . The higImprovement to a crop generally involves exploiting the existing genetic variability in desired traits, and nature as well as degree of their association among them . Most ofAssociation mapping is considered to be one of the most promising tools for plant breeders. It is based on linkage disequilibrium (LD) and overcomes the drawbacks of classical linkage analysis which is based on biparental mapping populations. Moreover, this approach helps in the detection of a large number of alleles and enhances the mapping resolution ,5. HowevThe availability of SSR markers in finger millet is limited ,10 and oviz. ICAR-Vivekananda Institute of Hill Agriculture, Almora  experimental farm (E1)  and Crop Research Centre (CRC), GBPUA&T, Pantnagar (E2)  in the year 2015. These two locations represent two different ecosystems of finger millet cultivation i.e. hill ecosystem and plain ecosystem.The material comprises 113 diverse finger millet accessions representing world collections from India, Nepal, Africa, Maldives and Germany. Of the 113 accessions, 56 (IE numbers) belong to International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) mini core collection, 23 accessions (GE numbers) belong to All India Coordinated Small Millets Improvement project core collection, 16 accessions (VHC lines) are landraces from Uttarakhand hills and 18 others are improved genotypes including white grain genotypes of Indian National Agricultural Research System (NARS). The distribution of selected finger millet accessions across the world has been discussed by Kumar et al. . The fieThe crop was raised in an alpha lattice experimental design in two replications as described by Barreto et al. during thAnalysis of variance (ANOVA) for alpha lattice design was done in Indian NARS statistical computing portal of ICAR-IASRI, New Delhi using SAS software (SAS 9.3) for each location separately. The adjusted mean values of all agro-morphological traits for both the locations were used for further analyses. JMP 9.0  was used for hierarchical cluster and principal component analyses, while correlation analysis was done using R software . Heritab2). The analysis was conducted with consideration of the admixed genotype identified by STRUCTURE at K = 3. The LD-values between all pairs of loci were plotted as triangle LD plots to estimate the general view of genome-wide LD patterns using TASSEL software.The SNP data generated earlier by Kumar et al.  was usedhttp://www.maizegenetics.net), which involves association of individual markers/locus with each of the 14 agro-morphological traits, employing the two models- General Linear Model (GLM) and Mixed Linear Model (MLM) based on the kinship matrix (K model). The kinship matrix was generated in TASSEL by converting the distance matrix obtained from TASSEL’s cladogram function into similarity matrix. P < 0.01 was set as the significant threshold for the association analysis.Single locus single trait association mapping was performed using TASSEL version 5.2 (https://cynin.gmi.oeaw.ac.at/home/resources/mlmm) [2 > 0.2) were used  . For MTMere used . Three dMarker-trait associations detected through SLST, MTMM and MLMM, were subjected to two dimensional epistatic interaction analyses for individual trait. This analysis was performed using the function interaction “Pval” available in SNPassoc package  of R-sofOryza sativa), wheat (Triticum aestivum), maize (Zea mays), foxtail millet , sorghum (Sorghum bicolor) and purple false brome or Switchgrass (Panicum virgatum) were used for BLAST (nucleotide) search. This was carried out using Phytozome v10.3 , an online web tool [In-silico comparative genomics analysis was conducted for the common marker sequences of identified QTLs which were found to be linked with the agronomic traits in all the three approaches. Due to the unavailability of finger millet reference genome, cross species validation of SNP markers was done to see if there is any sequence similarity using Basic Local Alignment Search Tool (BLAST). The sequences of monocot plants viz., rice  experimental farm (E1) and Crop Research Centre (CRC), GBPUA&T, Pantnagar (E2), resulted in highly significant differences (P<0.01) among accessions for most of the traits, except for PH, FLBW at E2 and WLF at E1. We obsePhenotypic association analysis showed positive significant correlation of DF with DM, EL, EW, FLBW and LLF in E1 . Grain yPrincipal component analysis (PCA) was used to evaluate interrelationships among different traits. The first five PCA components provided a reasonable summary of the data and explained 75 and 65 per cent of the total variation in E1 and E2 locations, respectively. The first principal component (PC1) was the most important and explained 26 and 20 per cent of the total variation in E1 and E2 locations, respectively . PC1 wasTwo-way cluster analysis separated the accessions into two major groups in both the locations . Group A2 ≥0.05) and 2.9% (r2 ≥ 0.1) of the marker pairs based on r2 estimates showed significant LD. Triangle plots for pairwise LD between SNP markers demonstrated significant LD blocks in the genome-wide LD analysis (The linkage disequilibrium (LD) of the studied genotypes was determined using SNP markers. In total 17.9% , resulted in the identification of 499 linked SNPs through GLM, whereas, MLM showed 303 associated SNPs for 14 traits at a significant threshold level  in E1 location. Two hundred and sixty-one (261) marker-trait associations were found common in both GLM and MLM analysis for 14 quantitative traits at E1 location. In E2 location, 433 linked SNP were identified through GLM, while MLM analysis showed 308 SNP-trait associations for 14 traits at a significant threshold level . Both GLM and MLM had 242 SNPs in common for fourteen agro-morphological traits at E2. Association analysis also revealed many multiple SNP-trait associations and single SNP-multiple trait associations in both the locations i.e. in several cases one or more SNPs were significantly associated with more than one trait and vice versa .2 value of 9.5% at E1 and 10.2% at E2. GY was found to be associated with eight SNPs viz., TP1071491, TP1310091, TP1431319, TP1510146, TP28411, TP64954, TP760377 and TP878687, which showed phenotypic variance of 12.4%, 10.1%, 15.2%, 10.7%, 16.8%, 14.3%, 15.0% and 10.7%, respectively at E1 and 11.1%, 11.1%, 16.4%, 10.1%, 16.4%, 14.7%, 13.4% and 10.2%, respectively at E2. The next trait with higher number of trait-SNPs associations was DM, which was associated with seven SNPs viz., TP133182, TP1381676, TP1600637, TP214730, TP414254, TP627932 and TP872087, explaining phenotypic variance of 12.2%, 12.4%, 25.4%, 20.6%, 18.2%, 30.3%, 11.4%, respectively at E1 and 9.1%, 16.8%, 25.7%, 19.8%, 16.5%, 26.8%, 9.45%, respectively at E2. GY and DF were associated with a common SNP, TP1071491. Similarly, DF and DM also showed association with a common SNP, TP1600637. FLBL was associated with a SNP (TP641195) explaining phenotypic variance of 9.9% and 11.8% at E1 and E2, respectively  through GLM and MLM approaches . Out of ectively .Fifty-three marker-trait associations were identified for eight agro-morphological traits  at E1 and E2 using MLMM approach . Ten of viz., TP1071491, TP133182, TP214730, TP872087, TP1310091, TP1431319, TP28411, TP760377 and TP878687 were common among all the three approaches  at E1 location . SimilarZea mays), rice (Oryza sativa), foxtail millet , wheat (Triticum aestivum), sorghum (Sorghum bicolor) and purple false brome or Switchgrass (Panicum virgatum). The SNPs, TP1071491, TP133182, TP214730, TP1310091, TP872087, TP28411, TP760377 and TP1431319 showed multiple hits, while the SNP TP878687 showed no hit in the genomes of monocot species  in both the environments were subjected to local alignment (BLASTx) with the genomes of monocot plants. Orthologues of 8 of the 9 common SNPs were present in the genomes of maize  in both the locations were mostly of Indian origin. Similarly, all low yielding accessions (45) in both the environments were mostly of African origin again. The accessions having grain yield greater than the average grain yield of all the accessions were considered to be high yielding and the remaining were considered to be low yielding . The resopulation. This tyCorrelation values for most of the traits were in corroboration to Bharathi , who repOn the basis of projection of the accessions on the first two principal components, two major groups were detected. The red dots represent Indian accessions whereas blue dots indicate exotic accessions in the biplot . There wTwo-way cluster analysis broadly separated the accessions based on trait variation. The first group A contained accessions with high culm thickness, basal tillers, peduncle length and high grain yield. This group mostly contained all accessions of Indian origin, indicating that Indian accessions can be used as donors for these traits. The accessions in group B were late in maturity and had long fingers and ears . Thus, t2, and the probability [2 > 0.05. Appearance of long haplotypic blocks in the present study might be due to the selection pressure imposed by the breeders for desirable traits which resulted in accumulation of favourable genes in most of the finger millet genotypes. Secondly, the recombination frequency in finger millet is less than that of cross pollinated crops, since it is a self pollinated crop.Identification of disequilibrium between markers is highly useful since it is prerequisite before conducting association mapping analysis. Since physical map distances between markers were not available, LD was represented by the disequilibrium matrix visualizing the linear arrangement of LD between polymorphic sites, represented by rbability ,36. In tThe marker-trait association with agro-morphological traits clearly showed the importance of SNP markers in deciphering QTLs for various yield component traits in the present study. Since, SNPs occur in the genomes in much higher frequency than SSRs, they provide high density of markers near a locus of interest, evolutionary stable and do not change significantly from generation to generation. The complex polygenic nature of traits and the polyploid genome of finger millet make the use of association mapping a great challenge compared to other studies conducted on crops with lesser genome complexity.SLST analysis led to the identification of twenty potential and significant SNPs linked to five useful agronomic traits in both the environments through GLM and MLM approaches. An additional covariate of kinship is included in MLM approach for effective regulation of false discovery of associations as compared to GLM, due to better control for population structure and relatedness within genome-wide association studies . Weaker Further, MTMM analysis identified 24 pleiotropic MTAs for both the environments involving QTLs that are associated with eight pairs of correlated traits (out of 15 pairs of correlated traits examined). It can be speculated that the remaining correlations might be due to environmental effect or LD rather than pleiotropy /linkage . These 2Epistasis or interactions between non allelic genes is an important factor that affects phenotypic expression of genes and genetic variation in populations . EpistatIn silico analysis showed multiple hits for eight SNPs in the different grass genomes. Five out of these eight SNP markers were found orthologous to candidate genes of Oryza sativa and Setaria italica, encoding ATP synthase, photosystem I, photosystem II, GRF zinc finger and ribosomal protein, which play an important role in plant growth and grain yield  in the present study could provide great leads in this direction. Genome wide association studies for grain yield and its components would help in the identification of loci associated with each trait and subsequently genomic regions could be highlighted for targeted resequencing for marker discovery in finger millet.Finger millet genotypes used in the study showed wide genetic variation with respect to agro-morphological traits. Indian and exotic finger millet genotypes showed differences in trait values and were clustered separately based on trait values. Marker trait association analysis using three different approaches identified nine common SNPs marker associations for grain yield and its component traits. Five out of nine SNPs were found to have orthologous candidate genes for plant growth and grain yield in related genera through S1 Figa) Cluster analysis of 202 genotypes on the basis of 14 agro-morphological traits at E1. b) Cluster analysis of 202 genotypes on the basis of 14 agro-morphological traits at E2.(TIF)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableTrait-pair showing correlation coefficient value >0.2 were used in multi-trait analysis and are highlighted in bold.(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.S5 Table(DOC)Click here for additional data file.S6 Table(DOC)Click here for additional data file.


Heterosis has been suggested to be caused by dominance effects. We performed a joint genome-wide association analysis (GWAS) using data from multi-breed and crossbred beef cattle to identify single nucleotide polymorphisms (SNPs) with significant dominance effects associated with variation in growth and carcass traits and to understand the mode of action of these associations.Illumina BovineSNP50 genotypes and phenotypes for 11 growth and carcass traits were available for 6796 multi-breed and crossbred beef cattle. After performing quality control, 42,610 SNPs and 6794 animals were used for further analyses. A single-SNP GWAS for the joint association of additive and dominance effects was conducted in purebred, crossbred, and combined datasets using the ASReml software. Genomic breed composition predicted from admixture analyses was included in the mixed effect model to account for possible population stratification and breed effects. A threshold of 10% genome-wide false discovery rate was applied to declare associations as significant. The significant SNPs with dominance association were mapped to their corresponding genes at 100 kb.U6atac, AGBL4, bta-mir-2888-1, REPIN1, ICA1 and NXPH1. These genes have interesting biological functions related to the regulation of gene expression, glucose and lipid metabolism and body fat mass. For most of the identified loci, we observed over-dominance association with the studied traits, such that the heterozygous individuals at any of these loci had greater genotypic values for the trait than either of the homozygous individuals.Seven SNPs with significant dominance associations were detected for birth weight, weaning weight, pre-weaning daily gain, yearling weight and marbling score across the three datasets at a false discovery rate of 10%. These SNPs were located on bovine chromosomes 1, 3, 4, 6 and 21 and mapped to six putative candidate genes: Our results revealed very few regions with significant dominance genetic effects across all the traits studied in the three datasets used. Regarding the SNPs that were detected with dominance associations, further investigation is needed to determine their relevance in crossbreeding programs assuming that dominance effects are the main cause of (or contribute usefully to) heterosis.The online version of this article (10.1186/s12711-018-0405-y) contains supplementary material, which is available to authorized users. Genome-wide association studies (GWAS) offer the opportunity to use available genotypes in the form of single nucleotide polymorphisms (SNPs) such as the Illumina BovineSNP50 BeadChip  to identify genomic regions that are associated with phenotypic variation in economically important traits in cattle , 2. For Few studies have investigated the importance of non-additive gene effects and these concluded that accounting for these effects in animal genetic evaluation models can improve genomic prediction in comparison to additive models –13. In bTherefore, the objective of our study was to identify genomic regions or SNPs with simultaneously additive and non-additive effects that are associated with growth and carcass traits in beef cattle and to understand the mode of action of these associations.http://www.beefbooster.com).Data from 6796 multi-breed and crossbred beef cattle born between 1998 and 2012 were collated from various projects and research herds across Canada including: 3692 from the Phenomic Gap Project based at Lacombe Research Centre; 2350 from the University of Alberta’s Roy Berg Kinsella Research Ranch; and 754 from the University of Guelph’s Elora Beef Cattle Research Station. The population structure, breeds, and animal management were previously described in detail by Lu et al. . BrieflyPhenotypic records including birth weight (BWT), weaning weight (WWT), pre-weaning daily gain (PDG), average daily gain on feedlot (ADG), yearling weight (YWT), hot carcass weight (HCW), back fat thickness (FAT), rib eye area (REA), marbling score (MBS), lean meat yield (LMY) and yield grade (YG) were available. Yield grade was calculated according to the United States Department of Agriculture (USDA) specification . The datp < 0.05) from Hardy–Weinberg equilibrium [All animals with phenotype records were genotyped with the 50K SNP panel at Delta Genomics, Edmonton, AB, Canada. Quality control was performed to remove SNPs with a minor allele frequency (MAF) lower than 0.01, a call rate higher than 0.90 and that deviated significantly  and 0 for the two homozygous genotypes (AA and BB) for each SNP; In an earlier study , assuminsoftware .As noted by Falconer and Mackay , epistasp values for each SNP were used to compare observed distributions of − log  to the expected distribution under the null hypothesis for each trait. The Manhattan plots of p values for each SNP were also used to illustrate significant associations at the level of each chromosome and trait. All plots were completed using the R package qqman [The false discovery rate (FDR)  implemenge qqman .The SNPs with a significant dominance association identified from the GWAS analyses were mapped to their corresponding genes or near to the genes, i.e. at 100 kilo base pairs (kbp) on either side using NGS-SNP , based opulation .AA, AB and BB, i.e. the SNP genotype was fitted as a classification factor. The least square means of each genotypic class was determined and plotted to characterise the mode of action for significant associations with the traits of interest. Analyses were conducted in R statistical software using default package where applicable [To determine the mode of action of SNPs with significant dominance effects, genotype effects were estimated according to Model (3):plicable .p values showed departures from the expected distribution under the null hypothesis of polygenic variation , respectively, were associated with BWT. For WWT and PDG, two SNPs rs29027109 and rs42779004 on BTA3 and 21 showed significant pleiotropic dominance association for both traits. One SNP, rs109808526 on BTA4 was associated with YWT, while two SNPs, rs110361335 and rs110564527 on BTA4 were associated with MBS  being significantly (p < 0.01) higher than those for either of the two homozygotes (AA and BB) for both traits  higher than those of either of the homozygotes across the three traits  and the other SNPs. Similarly, the numbers of significant epistatic interactions between SNPs rs110564527, rs110704582, rs109808526, rs41596755, rs29027109 and rs42779004 with the other SNPs at a FDR of 10% were equal to 111, 19, 11, 6, 2 and 1 for MBS, BWT, YWT, BWT, PDG and PDG, respectively. Unfortunately, none of the identified SNPs showed a significant epistatic interaction for WWT.The numbers of additive × additive epistatic interactions between the seven leading significant dominant SNPs and the rest of the SNPs across the genome were examined for BWT, WWT PDG, YWT and MBS in the three datasets Fig. . At leasMost of the studies on the genetic evaluation of beef cattle traits using genomic information have focused on the discovery or use of additive genetic effects , 31, 32 The ability to identify QTL with large effects on any trait depends partly on the amount of trait variation that can be attributed to the different genetic sources. For example, in beef cattle, growth and carcass traits are moderately to highly heritable , which rMost of the heterotic QTL identified in this study were associated with growth traits including birth weight, weaning weight, pre-weaning daily gain and yearling weight, which are known to express heterosis –41. AlthGrowth traits such as birth weight, weaning weight, pre-weaning daily gain and yearling weight are economically important traits in beef cattle, which are traditionally included in the selection criteria of beef cattle breeding programs  because U6atac, which is a known non-coding RNA involved in mRNA splicing, while SNP rs41596755 was not associated with any candidate gene. For WWT and PDG, two SNPs rs29027109 and rs42779004 on BTA3 and 21, respectively, were found to exhibit a pleiotropic effect on both traits, which is very likely due to the relationship between WWT and computation of PDG. SNP rs29027109 is located within an intron of the AGBL4 gene, which encodes the ATP/GTP binding protein-like 4 [bta-mir-2888-1, which encodes the microRNA 2888-1. This microRNA is involved in post-transcriptional regulation of gene expression in multicellular organisms by affecting both the stability and translation of mRNAs [REPIN1 at about 11 kbp. It encodes the replication initiator 1 protein, a zinc finger protein that plays a role in insulin sensitivity, body fat mass and lipid metabolism by regulating the expression of key genes associated with glucose and lipid metabolism [In this study, we identified two SNPs with significant dominance effects: rs110704582 and rs41596755 for BWT on BTA1 and 6, respectively. SNP rs110704582 is located in the intergenic region near the candidate gene tabolism .As a further step, the modes of action of SNPs with significant dominance associations with growth traits were evaluated across the three datasets. In the purebred data, SNP rs42779004 showed characteristics of over-dominance association with WWT and PDG Fig.  such thalongissimus muscle and is associated with the tenderness, flavor and juiciness of beef [islet cell autoantigen 1 (ICA1) gene, which is known to be associated with glucose regulation and type 1 diabetes in humans [neurexophilin 1 (NXPH1), which has no known association or characterization in cattle.Carcass marbling score (MBS) is an objective assessment of flecks of intramuscular fat evaluated at the 12th and 13th rib interface of the  of beef . The gre of beef , 13. In  of beef , positiv of beef , 52, 53.n humans , while SICA1 gene, which has a role in the regulation of glucose metabolism [Furthermore, several peaks of epistatic interactions were observed between SNPs rs110361335 and rs110564527 and the other SNPs. Because epistatic interactions were only tested for SNPs with significant dominance association, other important epistatic interactions between pairs of SNPs across the studied traits would not have been found. Therefore, interpretation of the extent of epistatic effects should be done with caution. However, the epistatic interaction between SNP rs110361335 and the other SNPs may be biologically plausible given that this SNP is located within the tabolism . Thus, iU6atac, AGBL4, bta-mir-2888-1, REPIN1, ICA1 and NXPH1) need further investigation to determine their relevance for crossbreeding programs and their ability to predict heterosis.In this work, we detected several SNPs with significant dominance (over or under) associations with growth and carcass marbling in multi-breed and crossbred beef cattle. The identified potential candidate genes within the vicinity of these SNPs  and two and more way crosses involving Angus, Hereford, Charolais, Gelbvieh, Simmental, Limousin, and Piedmontese breeds. Table S3. Identity, position, and effects of significantly associated additive SNPs obtained by single-SNP regression mixed model for growth and carcass traits in the combined population of beef cattle.Additional file 2: Figure S1. Quantile–quantile (Q–Q) plot of p values for additive SNP association with growth traits in purebreds. Figure S2. Quantile–quantile (Q–Q) plot of p values for additive SNP association with carcass traits in purebreds. Figure S3. Quantile–quantile (Q–Q) plot of p values for additive SNP association with growth traits in crossbreds. Figure S4. Quantile–quantile (Q–Q) plot of p values for additive SNP association with carcass traits in crossbreds. Figure S5. Quantile–quantile (Q–Q) plot of p values for additive SNP association with growth traits in combined data. Figure S6. Quantile–quantile (Q–Q) plot of p values for additive SNP association with carcass traits in combined data. Figure S7. Quantile–quantile (Q–Q) plot of p values for dominance SNP association with growth traits in purebreds. Figure S8. Quantile–quantile (Q–Q) plot of p values for dominance SNP association with carcass traits in purebreds. Figure S9. Quantile–quantile (Q–Q) plot of p values for dominance SNP association with growth traits in crossbreds. Figure S10. Quantile–quantile (Q–Q) plot of p values for dominance SNP association with carcass traits in crossbreds. Figure S11. Quantile–quantile (Q–Q) plot of p values for dominance SNP association with growth traits in the combined data. Figure S12. Quantile–quantile (Q–Q) plot of p values for dominance SNP association with carcass traits in the combined data. Figure S13. Joint genome-wide association analysis for additive SNP effects on growth traits in purebreds. Figure S14. Joint genome-wide association analysis for additive SNP effects on carcass traits in purebreds. Figure S15. Joint genome-wide association analysis for additive SNP effects on growth traits in crossbreds. Figure S16. Joint genome-wide association analysis for additive SNP effects on carcass traits in crossbreds. Figure S17. Joint genome-wide association analysis for additive SNP effects on growth traits in the combined data. Figure S18. Joint genome-wide association analysis for additive SNP effects on carcass traits in the combined data. Figure S19. Joint genome-wide association analysis for dominance SNP effects on growth traits in purebreds. Figure S20. Joint genome-wide association analysis for dominance SNP effects on carcass traits in purebreds. Figure S21. Joint genome-wide association analysis for dominance SNP effects on growth traits in crossbreds. Figure S22. Joint genome-wide association analysis for dominance SNP effects on carcass traits in crossbreds. Figure S23. Joint genome-wide association analysis for dominance SNP effects on growth traits in the combined data. Figure S24. Joint genome-wide association analysis for dominance SNP effects on carcass traits in the combined data.


Identification of loci for agronomic traits and characterization of their genetic architecture are crucial in marker-assisted selection (MAS). Genome-wide association studies (GWAS) have increasingly been used as potent tools in identifying marker-trait associations (MTAs). The introduction of new adaptive alleles in the diverse genetic backgrounds may help to improve grain yield of old or newly developed varieties of wheat to balance supply and demand throughout the world. Landraces collected from different climate zones can be an invaluable resource for such adaptive alleles.10P >3) MTAs for the original and imputed SNP data sets, respectively. Gene ontology results showed that 27 and 27.5% of MTAs of SNPs in the original set were located in protein-coding regions for well-watered and rain-fed conditions, respectively. While, for the imputed data set 22.6 and 16.6% of MTAs represented in protein-coding genes for the well-watered and rain-fed conditions, respectively.GWAS was performed using a collection of 298 Iranian bread wheat varieties and landraces to explore the genetic basis of agronomic traits during 2016–2018 cropping seasons under normal (well-watered) and stressed (rain-fed) conditions. A high-quality genotyping by sequencing (GBS) dataset was obtained using either all original single nucleotide polymorphism  or with additional imputation  based on W7984 reference genome. The results confirm that the B genome carries the highest number of significant marker pairs in both varieties  and landraces . The strongest linkage disequilibrium (LD) between pairs of markers was observed on chromosome 2D (0.296). LD decay was lower in the D genome, compared to the A and B genomes. Association mapping under two tested environments yielded a total of 313 and 394 significant (−logOur finding suggests that Iranian bread wheat landraces harbor valuable alleles that are adaptive under drought stress conditions. MTAs located within coding genes can be utilized in genome-based breeding of new wheat varieties. Although imputation of missing data increased the number of MTAs, the fraction of these MTAs located in coding genes were decreased across the different sub-genomes. Triticum aestivum L.) is a staple crop both in developing and developed countries and there is a constant need to balance supply and demand [Bread wheat  are now a viable alternative to QTL mapping for dissecting the genetic architecture of quantitative traits . In compGenotyping by sequencing (GBS) can provide access to a large number of SNPs in a cost-efficient manner but it is often plagued by a high fraction of missing data that can limit the accuracy of any genome-wide association study. One approach to deal with missing data is through imputation and this has successfully been implemented in many studies on human and plant genomes –26. ImpuP< 0.01) among varieties and landraces for all studied traits under both environments for two years, except thousand kernel weight under rain-fed conditions , whereas phenology traits and canopy temperature were negatively correlated with grain yield . After de-duplication and alignment, 133,039 SNPs were called for which 10,938 had < 10% missing data, heterozygosity < 10% and a minor allele frequency (MAF) >1%. These SNPs were selected for further analysis. Among the 10,938 SNPs identified, the highest  and lowest  number of markers were observed for MAFs in the range of 0.01–0.1, and 0.45–0.50, respectively  had significant linkage at P< 0.01  with average squared allele frequency correlations or Performing a similar analysis on landraces identified a total of 405,738 MPs with an average LD of 0.097 which is considerably lower than in varieties Table . HoweverWe evaluated population structure using the variance-covariance matrix of individuals (Kinship matrix) obtained from both original and imputed SNPs. For both datasets, the analyses identified three main groups with varying degrees of admixture. For the original dataset, the first two principal components explained 17.2% of the genetic variance  materials with twenty-four varieties in advanced and segregating lines, originating from this center. At least, forty varieties were obtained through a cross-breeding program where one of the CIMMYT advanced lines was one of the parents , seed number , thousand kernel weight (5B), and grain filling period (6B). Using the imputed SNPs, we observed QTLs for grain yield and spike weight on chromosomes 1A and 4B, for leaf greenness on chromosome 1A, three QTLs on 4B and 5B for spike length and one QTL on 5A for days to emergence. These results are in line with previously detected QTLs for spike length [Among 313 and 394 identified MTAs for the original and imputed SNPs, respectively, 86 and 76 falls within coding genes with e length , grain ye length , seed nue length  and thoue length .For rain-fed conditions, we identified QTLs on chromosomes 1B, 2B for spike weight, 6B and 7A for grain yield, 1B for spike length, 2B and 3B for thousand kernel weight, 3B for peduncle length, 5B for plant height, 5B for leaf greenness, 5B for seed number per spike, 6B and 7B for grain filling period and 6B for days to emergence. These results are in line with findings by Ain et al.  for planAlthough imputation of missing data significantly increased the potential number of MTAs, mainly on the A and B genomes, the fraction of SNPs presents within coding genes declined, from about 27% in the original SNP data set to 19.6% for the imputed dataset. This suggests that most SNPs with missing data are located in noncoding DNA regions where read mapping and SNP calling are known to be more problematic in most plant genomes. Dissecting strongly associated chromosomal regions through, for instance, positional cloning to identify putative causal genes is the next logical step following association mapping studies. Apart from using comparative genomics approaches to identify the function of associated genes, independent functional validation is also required to guarantee the success of either positional cloning or transgenic experiments , 49–51. − 3 and 10− 4. It seems that complex inheritance of such quantitative traits and high number of controlling genes exclude greater association. However, a major part of found MTAs explained more than 20% of total phenotypic variation for relevant traits. Although, further studies are required to validate the detected markers in this study using other populations and environments. Gene ontology of identified markers in original and imputed SNPs showed approximately 27% of these markers represent within coding genes, thereby have potential to be used in genome-based breeding of new varieties. Although imputation of missing data could increase the number of associated markers, the percentage of MTAs located in coding regions was decreased across different sub-genomes. The identified markers in this study could provide useful genetic resources to initiate marker-assisted selection, fine mapping and cloning of the underlying genes and QTLs.In the present study, GWAS was performed for important agronomic traits of bread wheat in a diverse panel of 298 varieties and landraces of Iran collection. The highest number of marker pairs in both varieties and landraces was observed on B genome. In total, 313 and 394 MTAs were identified for 14 phenological, agronomic and physiological traits using original and imputed SNPs, respectively. The identified association between markers and traits generally lied in a range of 10A set of 320 Iranian wheat accession, including 102 varieties released between 1942 and 2014, and 218 landraces collected between 1931 and 1968  days to emergence (Zadoks 12), ii) days to heading (Zadoks 50), iii) days to anthesis (Zadoks 65), iv) days to physiological maturity (Zadoks 91), and v) grain filling period when half of each plot had reached to corresponding stages. The Soil Plant Analysis Development  and LIHERO Infrared thermometer were used to measure leaf greenness and canopy temperature at Zadoks 60, respectively. Grain yield and related traits including spike weight, spike length, seed number per spike and thousand kernel weight were measured after harvesting for both years.The development and sequencing of a GBS library for the Iranian wheat have previously been described by Alipour et al. . Brieflyhttps://sites.google.com/site/cmzmasek/home/software/archaeopteryx).Population structure in the sample was estimated using STRUCTURE v.2.3.4  with an t-tests. In general, GAPIT [P-value generated from the F-test for testing H0. Here, both heat map and Manhattan plots were obtained from an enhanced comparison scenario using the GAPIT package [We used both general linear model (GLM) and mixed linear model (MLM) to obtain the unbiased estimation of marker effects. The MLM approach resulted in the most accurate association of marker-traits and different versions of the MLM model, including Q, K or Q + K, were used to control both effects of population structure (Q) and more diffused relationships (K) among accessions using TASSEL v.5 . The GAPl, GAPIT  providedl, GAPIT . In the  package .http://www.gramene.org/) by aligning them to the IWGSC RefSeq v1.0 annotation (https://wheat-urgi.versailles.inra.fr/Seq-Repository/Annotations). The function of putative genes was explored by investigating the pathways which the encoded enzymes were involved in. After aligning SNPs sequences to the reference genome, overlapping genes with the highest identity percentage and blast score were selected for further processing. The gene ontology of each selected gene, including molecular function and biological process, were extracted from the ensemble-gramene database (http://ensembl.gramene.org).Sequences surrounding all significantly associated SNPs were obtained from the wheat 90 K SNP database  used forai) was estimated using the following formula:xij is the phenotypic value of the jth individual for the ith allele, ni is the number of individuals carrying the jth allele, NK is the nth individual phenotypic value for all entries, and nK is the number of individuals. Positive and negative effects of all alleles are represented by ai >0, and ai <0, respectively.Phenotypic data were analyzed using SAS v.9.4  separateAdditional file 1 Table S1. The information of Iranian wheat accessions including varieties released between 1942 and 2014, and landraces collected between 1931 and 1968Additional file 2 Table S2. Analysis of variance for agronomic traits of Iranian wheat accessions exposed to the well-watered conditions over two years. Table S3. Analysis of variance for agronomic traits of Iranian wheat accessions exposed to the rain-fed conditions over two years. Table S4. Person’s coefficient of correlation between agronomic traits of Iranian wheat accessions over two years under well-watered conditions. Table S5. Pearson’s confidents of correlation between agronomic traits of Iranian bread wheat accessions over two years under rain-fed conditions.Additional file 3 Fig. S1. Climate condition include precipitation and temperature of field trail site during the 2016–17 and 2017–18 cropping seasons. Fig. S2. The pattern of LD decay in different chromosomes of genome A in T. aestivum based on original SNPs. Fig. S3. The pattern of LD decay in different chromosomes of genome B in T. aestivum based on original SNPs. Fig. S4. The pattern of LD decay in different chromosomes of genome D in T. aestivum based on original SNPs.Additional file 4 Table S6. Description of expected MTAs using original SNPs for agronomic traits of Iranian wheat accessions exposed to the well-watered condition. Table S7. Description of expected MTAs using original SNPs for agronomic traits of Iranian wheat accessions exposed to the rain-fed condition. Table S8. Description of expected MTAs using imputed SNPs for agronomic traits of Iranian wheat accessions exposed to the well-watered condition. Table S9. Description of expected MTAs using imputed SNPs for agronomic traits of Iranian wheat accessions exposed to the rain-fed condition.


Macaca mulatta) is the most important nonhuman primate model in biomedical research. We present the first genomic survey of wild RMs, sequencing 81 geo-referenced individuals of five subspecies from 17 locations in China, a large fraction of the species’ natural distribution.The rhesus macaque , possessing the largest body size of all Chinese RMs, and the southernmost lineage (M. m. brevicaudus), with the smallest body size of all Chinese RMs, feature positively selected genes responsible for skeletal development. Further, two candidate selected genes  found in M. m. tcheliensis are involved in gluconeogenesis, potentially maintaining stable blood glucose levels during starvation when food resources are scarce in winter. The tropical subspecies M. m. brevicaudus showed positively selected genes related to cardiovascular function and response to temperature stimuli, potentially involved in tropical adaptation. We found 118 single-nucleotide polymorphisms matching human disease-causing variants with 82 being subspecies specific.Populations were structured into five genetic lineages on the mainland and Hainan Island, recapitulating current subspecies designations. These subspecies are estimated to have diverged 125.8 to 51.3 thousand years ago, but feature recent gene flow. Consistent with the expectation of a larger body size in colder climates and smaller body size in warmer climates (Bergman's rule), the northernmost RM lineage (These data provide a resource for selection of RMs in biomedical experiments. The demographic history of Chinese RMs and their history of local adaption offer new insights into their evolution and provide valuable baseline information for biomedical investigation. Macaca mulatta) are, after humans, the world's most widely distributed primates ., 13.M. m maximum , 34. IntM. m. mulatta and M. m. lasiotis) inhabited the western montane region in China, while the pan-eastern population  entered the eastern coastal region. These five subspecies further diverged from each other during the bottleneck caused by the LG. Additionally, barriers such as the Yellow, Yangtze, and Pearl rivers and open sea  to the each of the other subspecies using the fixation index (FST) and genetic diversity (θπ), calculated on 50-kb-long sliding windows  in each pairwise comparison were considered to be potentially under positive selection. For each target subspecies, we identified potential selective-sweep regions as the intersection between the top 5% outliers in all pairwise comparisons (four pairwise comparisons in each case)  . We usedch case) .M. m. tcheliensis from the Taihang Mountains area is the northernmost population of the species. The Taihang Mountains are characterized by a continental monsoon climate, and conditions for RMs are harsh during winter and early spring, with extreme cold temperatures of –14°C , 45M. m.elopment . Among t mammals . Mice la animals . Furtherox6 Fig. , 3d playin human , while e in mice . These gM. m. brevicaudus inhabits the tropical island of Hainan where it copes with a mean annual temperature of 24°C. M. m. brevicaudus has the smallest body size and the smallest body mass among RM subspecies [M. m. brevicaudus   .M. m. brevicaudus , we focused on SNPs in the gene regions of above-described candidate genes. A total of 5,817 SNPs were found with significant differences at the 5% level in the distributions of genotypes between these two subspecies, and 10 SNPs were nonsynonymous variants , Ext2 (I363M), Fto (N10S), and Rpgrip1l (R1281Q) , Axin1 , Hspa4 (I782V), and Ctnna3  were revealed for M. m. brevicaudus  , implyinse genes , indicatvicaudus .M. m. tcheliensis, the 176 identified candidate genes are enriched in GO term “synapse” , known to cause juvenile idiopathic arthritis in humans [M. m. tcheliensis, M. m. brevicaudus, and M. m. littoralis but absent in M. m. lasiotis and M. m. mulatta. Another pathogenic variant  in the Btd gene is involved in biotinidase deficiency [M. m. lasiotis and M. m. mulatta. In addition, we also identified 16 nonsynonymous SNPs in the Noca3 gene, which encodes a protein that modulates the replication and transcriptional reactivation of human immunodeficiency virus type 1 (HIV-1) during virus latency [Given the large evolutionary similarity between macaques and humans, human diseases are better modeled in RMs than in many other animals. Thus, variants in RMs that match to orthologous human variants annotated as “pathogenic” are of particular interest. We examined presumed homologous Chinese RM SNPs in the human genome. A total of 34,850,330 RM SNPs analyzed in this study were successfully identified in the human genome (hg19). Among these SNPs, 118  variantsn humans , has an ficiency . Its hom latency  . All experimental procedures and animal collection were conducted under the supervision of the Committee for Animal Experiments of the Institute of Zoology (CAS).Samples from 79 individuals with information about geographic origin were collected from 17 local wildlife rescue centers, which covered most of the species’ range in China. Muscle samples were collected from deceased individuals, and the blood samples were taken during routine physical examinations. Total genomic DNA was extracted from blood or tissue samples using standard phenol/chloroform methods. For each individual, ∼3 μg DNA was sheared into fragments of 500 bp with the Covaris system. DNA fragments were then processed and sequenced using the Illumina HiSeq 2000 and 2500 platforms. Furthermore, published genomic data for two individuals were downloaded from NCBI  and filtRRID:SCR_010910) [RRID:SCR_002105) [RRID:SCR_006525) [RRID:SCR_001876) on autosomal sites only [RRID:SCR_012821) [High-quality sequence reads were mapped to the macaque reference genome, Mmul_8.0.1 , using t_010910) . Sequenc_002105)  for sort_006525)  for remotes only . We got _012821)   from 2–9 (RRID:SCR_004965) [An NJ tree was constructed for the 81 individuals based on the autosomal genome data using the software TreeBeST. The bootstrap was set to 1,000 times to assess branch support, with the genome information of Indian RMs and FigTree FigTree  . To dete_004965) , with thμ) of 1.0 × 10−8 .RRID:SCR_014931) [fastsimcoal2 [We used PAUP* 4.0a142   to run Ssimcoal2  was perfsimcoal2 . During FST values [θπ ratios for each pairwise comparison among five RM subspecies. We calculated θπ for each population and the FST between the two populations in each comparison using VCFtools  [FST values were Z-transformed, and the log value of θπ ratio (θπ2/θπ1) was estimated. Candidate regions under positive selection were extracted based on the top 5% of log-odds ratios for both Z (FST) and log (θπ –ratio). Finally, for each subspecies, we used the intersection of putatively selected regions generated by all the pairwise comparisons with other subspecies as the candidate regions under positive selection . Genes located in these regions are expected to represent targets of selection. Functional classification and enrichment analysis of GO categories and KEGG pathways for these candidate genes were performed using DAVID v6.8  [P value cutoff was 0.05. Chi-square and P values for the allele frequencies in M. m. tcheliensis vs M. m. brevicaudus for the resequenced SNPs from the candidate genes were assessed with the Haploview program [To identify genomic regions that may have been subject to selection for each subspecies in different habitats, we scanned the genome using one-to-one pairwise comparisons between all five subspecies. We calculated the genome-wide distribution of T values  and θπ r_001235)  with a g_001881) . The mod program .A total of 118 of 52,534,348 RM SNPs analyzed in this study were successfully mapped to human reference sequence version hg19 (GRCh37) using liftOver  and wereGigaScience GigaDB repository [All data generated from this study have been submitted to the NCBI Sequence Read Archive under BioProject PRJNA345528. The datasets supporting the results of this article are available in the pository .Supplementary Fig. S1. Variant number for the 81 individuals in this study.Supplementary Fig. S2. Private and shared SNPs per Chinese RM subspecies.Supplementary Fig. S3. Heterozygosity per base-pair for five subspecies of Chinese RMs.Supplementary Fig. S4. Neighbor-joining tree derived from 1000 bootstrap replicates.Supplementary Fig. S5. Plots of ΔK generated from STRUCTURE results.Supplementary Fig. S6. The species tree based on SVDquartets+PAUP*.Supplementary Fig. S7. Confidence Intervals from 100 parametric bootstraps for inferred demographic parameters.Supplementary Fig. S8. Positive selection flow chart for each subspecies.Supplementary Fig. S9. Distribution of Z(STF) and θπ log ratio of 50kb windows with 25kb sliding window for M. m. tcheliensis.Supplementary Fig. S10. Distribution of Z(STF) and θπ log ratio of 50kb windows with 25kb sliding window for M. m. brevicaudus.Supplementary Fig. S11. Distribution of Z(STF) and θπ log ratio of 50kb windows with 25kb sliding window for M. m. littoralis.Supplementary Fig. S12. Distribution of Z(STF) and θπ log ratio of 50kb windows with 25kb sliding window for M. m. lasiotis.Supplementary Fig. S13. Distribution of Z(STF) and θπ log ratio of 50kb windows with 25kb sliding window for M. m. mulatta.Supplementary Fig. S14. Non-synonymous variants in putatively selected genes.Supplementary Fig. S15. Linkage disequilibrium pattern of the five Chinese RM subspecies.Supplementary Table S1. Overview of sample information and sequencing statistics.Supplementary Table S2. Distribution of autosomal SNPs within various genomic regions of RM.Supplementary Table S3. Identified SNPs and heterozygosity for 81 individuals from 17 sampling locations.Supplementary Table S4. Tracy-Widom (TW) statistics and P values for the ten first eigenvalues in PCA.Supplementary Table S5. Inferred demographic parameters with 95% confidence intervals for fastsimcoal2 model.Supplementary Table S6. List of positively selected genes in the five Chinese RM subspecies.Supplementary Table S7. Enrichment of genes under selective sweep in M. m. tcheliensis.Supplementary Table S8. Morphological differences between the five investigated Chinese RM subspecies.Supplementary Table S9. Enrichment of the genes under selective sweep in M. m. brevicaudus.Supplementary Table S10. Distribution of SNPs in the selected genes described in the part of “Signatures of selection and local adaptation”.Supplementary Table S11. Non-synonymous SNPs with significant differences at the 5% level in the distributions of genotypes between M. m. tcheliensis and M. m. brevicaudus.Supplementary Table S12. List of RM variants scored by HGMD and ClinVar as “disease causing” or “pathogenic”.Supplementary Table S13. Population study for Ncoa3 reveals multiple genotypes.BMP: bone morphogenetic protein CI: confidence interval GO: Gene Ontology HIV-1: human immunodeficiency virus type 1 I-SMAD: inhibitory small mothers against decapentaplegic KEGG: Kyoto Encyclopedia of Genes and Genomes kya: thousand years ago LG: Last Glaciation M: million NCBI: National Center for Biotechnology Information NJ: neighbor-joining PCA: principal component analysis PSMC: pairwise sequential Markovian coalescent RM: rhesus macaque SAM: Sequence Alignment/Map SFS: site frequency spectrum SNP: single-nucleotide polymorphism ssSNP: subspecies-specific single-nucleotide polymorphismThis project was sponsored by grants to the following: M.L. ; Z.L. .The authors declare that they have no competing interests.M.L., Z.L., and M.B. conceived the study and designed the project. Z.L., X.T., P.O., X.Z., L.Z., and S.T. managed the project, performed the analyses, and wrote the manuscript. Z.L., B.S., and H.X. prepared samples. Z.L., X.T., and P.O. performed genetic analyses. Z.L., X.T., P.O., B.R., L.Z., G.L., Z.Y., Z.P., Z.X., C.R., M.B., and M.L. discussed the data. Z.L. and X.T. wrote the manuscript with contributions from P.O., B.W., H.X., W.Z., C.R., M.B., and M.L.. All authors contributed to data interpretation.GIGA-D-17-00291_Original_Submission.pdfClick here for additional data file.GIGA-D-17-00291_Revision_1.pdfClick here for additional data file.GIGA-D-17-00291_Revision_2.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision_1.pdfClick here for additional data file.Reviewer_1_Report_ -- Jeffrey Rogers12/27/2017 ReviewedClick here for additional data file.Reviewer_1_Report_(Revision_1) -- Jeffrey Rogers04/19/2018 ReviewedClick here for additional data file.Reviewer_2_Report_ -- Alexander Nater01/04/2018 ReviewedClick here for additional data file.Reviewer_2_Report_(Revision_1) -- Alexander Nater04/15/2018 ReviewedClick here for additional data file.Reviewer_2_Report_(Revision_2) -- Alexander Nater07/12/2018 ReviewedClick here for additional data file.Supplement FilesClick here for additional data file.