Setosphaeria turcica, is one of the most important diseases of maize world-wide, and one of the major reasons behind yield losses in maize crop in Asia. In the present investigation, a high-resolution genome wide association study (GWAS) was conducted for NCLB resistance in three association mapping panels, predominantly consisting of tropical lines adapted to different agro-ecologies. These panels were phenotyped for disease severity across three locations with high disease prevalence in India. High density SNPs from Genotyping-by-sequencing were used in GWAS, after controlling for population structure and kinship matrices, based on single locus mixed linear model (MLM). Twenty-two SNPs were identified, that revealed a significant association with NCLB in the three mapping panels. Haplotype regression analysis revealed association of 17 significant haplotypes at FDR ≤ 0.05, with two common haplotypes across three maize panels. Several of the significantly associated SNPs/haplotypes were found to be co-located in chromosomal bins previously reported for major genes like Ht2, Ht3 and Htn1 and QTL for NCLB resistance and multiple foliar disease resistance. Phenotypic variance explained by these significant SNPs/haplotypes ranged from low to moderate, suggesting a breeding strategy of combining multiple resistance alleles towards resistance for NCLB.Northern Corn Leaf Blight (NCLB) caused by  About 80 per cent of the tropical maize is grown under rainfed conditions in sub-Saharan Africa, South and Southeast Asia, and Latin America, and is particularly vulnerable to an array of abiotic and biotic stresses. Among the biotic stresses, Northern Corn Leaf Blight (NCLB) also known as Turcicum Leaf Blight (TLB), is the most important disease of maize caused by hemi-biotrophic pathogen Setosphaeria turcica anamorph Exserohilum turcicum formerly known as Helminthosporium turcicum [Pass] Leonard and Suggs". The disease has a widespread occurrence throughout the world and shows its presence in Asia, Africa, Europe and America. Low temperature, high humidity, heavy dew and high rainfall are conducive for the proliferation of the pathogen to cause the disease. S. turcica is differentiated into various races, some of the common races being 0, 1, 2, 3, 12, 23, 23 N, 123 N, identified based on their virulence against Ht (Helminthosporium turcicum) genes Ht1, Ht2, Ht3, ht4, HtM, HtP, Htn1, HtNB, and rt in maize plants2. Ht genes are known to confer qualitative resistance which is race specific, inherited by single genes, and mostly dominant in gene action, and were initially identified in different genetic backgrounds. Expression of Ht genes in maize plants and or avirulence genes of S. turcica are altered by environmental conditions like temperature and light intensity, creating unstable and less durable resistance. While most of the Ht genes are mapped on consensus genetic maps (www.maizegdb.org), some of them have been fine mapped and cloned. Chung et al.3 characterized and mapped a region on chromosome bin 8.06 from a maize hybrid DK888, and suggested that QTL NLB8.06DK888 was identical, allelic or closely linked and functionally related to Ht2. Hurni et al.4 cloned Htn1 gene which confers quantitative and partial resistance to NCLB by delaying the onset of lesion formation. Using high resolution map based cloning, a receptor-like kinase gene was identified to be underlying the Htn1 gene.Maize is the world’s leading cereal crop in terms of production, with 1016 million metric tons (MMT) produced on 184 million hectares (M ha) globallyHt genes can easily turn ineffective in case of the occurrence of a virulent strain2. In temperate environments, where pathogen variability is less, pyramiding multiple Ht genes is a good strategy towards NCLB resistance breeding. In tropical environments with high pathogen abundance and variability, Ht genes were found to provide only partial resistance5. Broad-based quantitative resistance to NCLB is preferred in tropical environments, which could be achieved by quantitative disease resistance loci (dQTLs) alone, or in combination with effective Ht genes. dQTLs are loci of small effects and is less likely to be overcome by evolution of new pathogens, and therefore it is practically more useful to breeders6. Inheritance studies of quantitative NCLB resistance using classical methods have revealed predominantly additive gene action controlling the trait8.Qualitative resistance usually leads to a high level of resistance when avirulent races dominate the fungal population, whereas some 9. A number of mapping studies have been undertaken for identifying QTLs for NCLB resistance in varied germplasm and environments  family of R genes. Meta-QTL analysis have also revealed that chromosome 8 possesses a cluster of QTLs and significant real (consensus) QTLs for NCLB, GLS and SLB with confidence interval (CI) lesser than 5 cM10 .Quantitative trait loci (QTL) or linkage mapping is an effective approach for studying complex and polygenic forms of disease resistancets Table . Previouts Table . Meta-QT6. In most of the QTL mapping studies, the mapping populations and breeding populations are unrelated, and hence the translation of the QTLs identified to breeding targets had been very few. GWAS, in assembled mapping panels representing the wide diversity in breeding programs, is another powerful tool to dissect complex traits and complements linkage mapping by improving mapping resolution. GWAS has been used to identify allelic variants that allow improved tolerance to various biotic and abiotic stresses in maize. Resistance to a large number of economically important and complex diseases of maize like Fusarium ear rot16, GLS18, head smut19, NCLB21, SLB22, sugarcane mosaic virus23, Maize streak virus24, Maize lethal necrosis25, sorghum downy mildew26 and tar spot27 have been dissected using GWAS. Several reports on GWAS for NCLB resistance in maize are available, mostly in temperate germplasm and environments. In a GWAS study conducted by Van Inghelandt et al.21, a large association mapping panel of 1487 inbred lines of temperate origin was used to dissect the genetic architecture of NCLB resistance, and reported association of significant SNPs on chromosomes 2, 5, 6 and 7 whereas, some of the SNPs were also identified on chromosomes 7 and 9 after correcting for flowering time variate. In a nested association mapping population of 4630 RILs, 208 SNPs associated with NCLB resistance on all 10 chromosomes of maize were identified, along with 29 QTLs, mostly with multiple loci6. Ding et al.20 studied the CIMMYT tropical maize germplasm that were phenotyped at different locations of Mexico and Africa for NCLB resistance and identified 12 SNPs on chromosome 3, 4, 6, 7, 8 and 10 for AUDPC, 14 SNPs on chromosome 1, 2, 3, 4, 6, 7, 9 and 10 for mean disease rating and 19 SNPs on chromosome 3, 4, 5, 7 and 10 for NCLB rating. In general, QTL mapping and GWAS studies in maize have revealed one important aspect that, NCLB resistance is a polygenic trait, and resistance due to major effects contributed by Ht genes was rare in most of the germplasm and environments studied. These studies suggested that there could be environment-specific and germplasm-specific moderate to large effect genomic regions controlling resistance to NCLB, which could be exploited in incorporating quantitative resistance to this important disease in breeding programs. Association mapping studies for NCLB resistance have been conducted by various research groups using temperate and tropical maize germplasm in American, African and European environments. However genome wide association studies using high density markers from large set of maize lines from Asian region are seldom reported. Therefore, the present research was designed to conduct GWAS for NCLB resistance under Asian conditions using tropical maize germplasm represented in three maize panels. These three panels represented maize germplasm from CIMMYT and several national partners, bred in different geographies across the tropics, and hence potentially selected in the presence of varied races of S. turcica. They represent most of the genetic diversity that is available across different tropical/sub-tropical geographies where CIMMYT breeding programs operate, and hence could be ideal resources for understanding the genetics of NCLB disease in South Asian tropics. In this study, apart from single SNP-based GWAS, we also identified haplotypes for resistance to NCLB within and across association panels representing differing germplasm backgrounds.QTL mapping, though a powerful tool, has its own limitations such as i) limited number of recombination events during population development resulting in low mapping resolution, ii) only two alleles of each mapping population studied and iii) difficult to identify the positional candidate genes or to make strong inference on linkage relationships among other QTL identifiedh2) was moderate to high (0.58–0.70) across individual locations with presence of significant genotypic variance . DTMA panel at Mandya observed a mean of 2.85 with minimum disease score of 1.97 and maximum score of 4.76. Broad sense heritability estimated was 0.53, with highly significant genotypic variance . Similarly, NCLB scores in IMAS panel ranged from 1.5–4.00 at Mandya in the first year and 2.00–5.00 in the second year, with mean rating of 2.55 and 3.44 during the two years, respectively. Overall analysis across the years revealed an average disease score of 3.00 with a maximum score of 4.55 and a minimum score of 1.96 where overall heritability (h2) estimate of 0.54 was observed with 0.48 in season 1 and 0.74 in season 2, respectively. IMAS panel also revealed significant genotypic variance   , Drought Tolerant Maize for Africa (DTMA) and Improved Maize for African Soils (IMAS), consisting of 376, 224 and 324 lines, respectively were evaluated for NCLB resistance across different locations/years in India. Disease severity was high in the CAAM panel, with maximum score of 5.00 on a scale of 1.00–5.00 over all the three locations, with minimum disease score of 2.02, 1.50 and 1.46 at Mandya, Arabhavi and Kashmir, respectively. The average disease score across locations was 3.74. Broad-sense heritability  was performed by using the high density Genotyping by Sequencing (GBS) data, filtered for a call rate > 0.9, minor allele frequency > 0.1 and LD pruning at r2) between adjacent pairs of markers versus the distance between adjacent markers in Kb .Chromosome 8 displayed the slowest LD-decay (4.69 Kb at r2 = 0.1 and 1.63 Kb at r2 = 0.2). DTMA panel showed the slowest genome wide LD-decay amongst the three panels with LD decay of 5.03 Kb at r2 = 0.1 and 1.75 Kb at r2 = 0.2. Chromosome wise LD-decay revealed that the fastest decay was in chromosome 6 (3.22 Kb at r2 = 0.1 and 1.11 Kb at r2 = 0.2) while chromosome 8 showed the slowest decay (12.81 Kb at r2 = 0.1 and 4.43 Kb at r2 = 0.2) decay. IMAS panel showed the genome LD-decay of 2.84 Kb at r2 = 0.1 and 0.99 Kb at r2 = 0.2 with chromosome 6 (2.02 Kb at r2 = 0.1 and 0.70 Kb at r2 = 0.2) and chromosome 8 (5.24 Kb at r2 = 0.1 and 1.83 Kb at r2 = 0.2) showed the fastest and slowest LD-decay, respectively (Supplementary Table The genome wide linkage disequilibrium (LD) was plotted as LD , explaining phenotypic variance ranging from 8.45 to 9.65%. A group of 8 SNPs located at close physical co-ordinates near 157 Mb on chromosome 8 were among the 14 SNPs which were identified to be showing most significant association with NCLB in the IMAS panel  were studied to identify the genes based on the SNPs associated with NCLB resistance. Several significant SNP associations in these three GWAS studies were located within genes with functional domains leading to biotic and abiotic stress tolerance, immune response, metabolism, plant development and maturity and responses to abiotic stresses  plots  was carried out with 112 haplotypes on NCLB BLUP estimates of individual maize panels separately. HTR analysis in the CAAM panel identified 21 haplotype blocks with FDR value ≤ 0.05 that explained 1.98–8.46% variance  existing population can be used rather than developing new bi- parental population for mapping. (2) Large number of alleles can be surveyed (3) Higher mapping resolution and (4) Lesser research time32.NCLB is an important foliar disease of maize in almost all temperate and tropical maize growing regions of the world. Resistance for NCLB in maize can be achieved through breeding using qualitative and quantitative resistance, either separately or together. However, resistance provided by qualitative/major genes becomes ineffective in the presence of virulent strains. Tropical environments show high pathogen abundance and high genetic diversity which leads to inflated disease severity, and hence the chances of breakdown of resistance are high. Compared to other grass crops like rice and wheat, majority of disease resistance deployed by maize breeders are quantitative in nature, and not qualitative33. However the CAAM panel lines were bred or/and selected for the Asian environments, and had a history of breeding for resistance to diseases like downy mildews35. The CAAM panel evaluated at three locations observed highest disease score mean at Kashmir, located at higher altitude in the northern boundary of India, which may be due to highly congenial environment for disease development owing to cool and humid weather, and probable presence of more virulent races of the pathogen at that location. For the phenotyping trials, artificial inoculation was conducted at all locations with the pathogen sources collected from respective locations.The three association mapping panels used in this study represent most of the genetic diversity that is available across different geographies where CIMMYT breeding programs operate, and hence could be ideal resources for understanding the genetics of NCLB disease in Asia. The Trial means for NCLB scores of IMAS and DTMA panels were lesser compared to CAAM panel at Mandya, which is a location with high disease severity and where all the three panels were evaluated, indicating that higher levels of resistance is available in the African and Latin American CIMMYT germplasm, as compared to CIMMYT-Asia germplasm. One of the reasons could be that the DTMA and IMAS panel included lines predominantly adapted to Sub Saharan Africa (SSA), where large number of lines were evaluated for foliar diseases like GLS, NCLB and common rust by collaborators through a regional maize disease nursery project (REGNUR)r2 threshold of 0.5 were used. LD-pruning was done to reduce the confounding effects due to large blocks of SNPs that have strong LD with each other36. There was only moderate structure observed in the three panels, with no clear differentiation of major adaptation groups, except in the IMAS panel. The CIMMYT maize germplasm was not found to have strong population structure in various earlier studies37. George et al.38 observed that CIMMYT’s tropical and sub-tropical lines in the Asian region possess significant genetic diversity that did not allow a clear distinction into separate clusters. Warburton et al.39 observed that the CIMMYT pools and populations which served as the germplasm sources for derivation of many breeding lines in the tropical and sub-tropical adaptation groups had a large amount of diversity within, than between source populations. This heterogeneous nature of the CIMMYT populations was suggested to be responsible for the lack of a well-defined population structure in the germplasm. A rapid LD decay was observed in all the panels . Lu et al.40 found that the LD decay distance in temperate maize germplasm (10–100 kb) was 2 to 10 times higher than that of tropical maize germplasm (5–10 kb). Our results were more similar to the finding by Romay et al.41 that LD decays much more rapidly in the tropical germplasm to about 1 kb at r2 = 0.2. The higher LD decay in tropical germplasm suggests the more diverse genetic base that resulted from the historic recombination events and might have more rare alleles than temperate germplasm42. The LD decay was different for the 10 chromosomes in all panels, with the slowest decay observed in chromosome 8 consistently in all the panels studied. This was also observed by Suwarno et al.43 in a Carotenoid association mapping panel comprising of tropical, sub-tropical and a small proportion of temperate lines. Pace et al.44 have also observed this pattern in a sub-set of AMES panel, which is predominantly a temperate maize lines panel. This is an interesting observation and will require further probing to understand the reasons behind the slower LD decay in chromosome 8 and its implications in molecular breeding applicability in terms of traits like resistance to NCLB, with genomic regions conferred by genes/QTL located on Chromosome 8.For principal component and kinship analysis, SNPs fulfilling the criteria of CR ≥ 0.9, MAF ≥ 0.1 and LD pruned at an 45. A Total of 22 SNPs significantly associated with NCLB resistance were identified on chromosomes 1, 6, 7, 8 and 10. The most significant association in the CAAM panel was with SNP S7_165196774 , at 165.19 Mb, in the bin 7.04 , co-located within the physical interval of markers flanking the major gene Ht321. Ht3 has been introgressed from Tripsacum floridanum into maize46and Ht3 gene provides resistance against the S. turcica races 0, 1, 2, N, 12 and 2N47, by inhibiting the extension of chlorotic spots and decreasing the production of spores by the pathogen46. S. turcica races 0, 1 and 2 are prominent in the Asian countries like China and India48, and Ht3 could be effective against these races. Considering the most significant SNP identified could possibly be in LD with Ht3, owing to its physical location, it provides a strong lead to follow up for future NCLB resistance mapping and deployment efforts. A glutathione S. transferase (GST) gene, belonging to a plant-specific clade implicated in defence has also been identified in bin 7.04 that confers multiple disease resistance to NCLB, SLB and GLS49. In the DTMA panel, two highly significant SNPs , closely located at 110 Mb (bin 7.02) on chromosome 7, were found to be associated with NCLB resistance. Van Inghelandt et al.21 identified SNPs on bin 7.02 associated with NCLB resistance in a GWAS of 1487 maize inbred lines representing elite European and North American germplasm. Similarly, another significantly associated SNP identified in the DTMA panel (S7_131034143) was located on chromosomal bin 7.03. Dingerdissen et al.50 identified a QTL in this chromosomal bin for area under disease progression curve (AUDPC) trait in F2:3 lines derived from Mo17 and B52 at Embu, Kitale and Muguga, Kenya against the races 0 and N prevalent in Kenya.The single locus mixed linear model was used after correcting for population structure and familial relationships (kinship), for conducting GWAS in all the panels to reduce the genomic inflation. Highly significant SNPs associated with NCLB resistance were selected based on the significance threshold corrected for multiple testing corrections, taking average extent of genome-wide LD into consideration3. It is considered as an important genomic region for many dQTLs and major genes like Ht2 and Htn1 for NCLB resistance28. Though there are apparent differences in the definition of qualitative and quantitative resistance, sometimes, pure qualitative and quantitative resistance are considered to be two ends of the same continuum and most resistance genes exist between the two extremes51. Chung et al.3 fine mapped a major QTL explaining a large proportion (14–62%) of phenotypic variance in NCLB resistance for the race 0 and 1 on bin 8.06, and it was described as either identical or allelic or closely linked and functionally similar to the major gene Ht2, which is partially dominant and is effective against the 0, 1, 3 and N races. Htn1 gene is also present in this genomic region, which is known to delay the lesion development up to four weeks after infection, reduce the number of lesion and delay the sporulation and found to be effective against most NCLB races5. Htn1 was cloned and found to be a wall associated receptor-like protein, and confer quantitative and partial resistance against NCLB4. Many other studies have also identified NCLB QTLs in these chromosomal bins. Poland et al.6 identified a large effect QTL at 152.2 Mb on bin 8.06, segregating in multiple NAM families. Similarly Chen et al.52 also identified a QTL in bin 8.06 for lesion width, while studying a RIL population. A major QTL was identified for AUDPC on chromosome 8 between the bins 8.05–8.06 in F2:3 populations studied for NCLB resistance53. Recently, a study conducted on a nested near isogenic line library for resistance to NCLB, also identified NILs with introgressions across centromeric region of chromosome 8 (bin 8.05), which overlaps two major genes ht2 and htn54. The fact that one of our mapping panels also identified a strongly associated set of closely located SNPs in this important chromosomal bin, indicated possible presence of a quantitatively expressed major gene or dQTL in this region present in the genetic background of the maize lines of this particular panel. S. turcica races prevalent in the locations that have been used for phenotyping in the present study are not yet reported, but physiological race 1 of S. turcica of maize was reported in the adjoining areas48. Wang et al.55 identified two minor QTLs on bin 8.03 associated with NCLB resistance in a RIL population. Our study also identified a group of closely linked three SNPs on bin 8.03 of chromosome 8  associated with NCLB resistance in the CAAM panel.In the IMAS panel, eight closely located SNPs, located at 157 Mb on bin 8.06 of chromosome 8, were identified to be the most significant association to NCLB resistance. In the maize genome, chromosome 8 (bin 8.05–8.06) is known to harbour genes for various defence pathways, and could be considered as one of the “complex, important and interesting” genomic region in terms of maize disease resistance, and NCLB resistance in particular56. The size of the haplotype block depends on the degree of LD present in the population57. Haplotype information can be beneficial when identifying marker phenotype associations and can offer advantages for the genetic dissection of loci underlying the complex trait20. Out of the 17 common haplotypes identified to be significant for NCLB resistance across different AM panels, eight haplotypes were shared between CAAM and DTMA panel, six were common between DTMA and IMAS panels and three haplotypes were shared between CAAM and IMAS panels. Haplotype Hap_1.1 was identified on chromosomal bin 1.06 in the CAAM and DTMA panels, and this bin is considered to be an important genomic region controlling resistance to multiple foliar diseases like NCLB, Stewart’s wilt, GLS and SLB60. Jamann et al.60 identified a receptor-like kinase gene, pan1, that underlie a QTL for NCLB in this region. Similarly, the physical co-ordinates of the SNPs forming the haplotype block Hap_9.1 identified in DTMA and IMAS panels fall within the confidence interval of qMdr9.02 reported for multiple disease resistance to NCLB, GLS and SLB61. Another haplotype identified on chromosomal bin 9.03 (Hap_9.2) identified in CAAM and DTMA panels was found to be located in close physical proximity to two closely spaced SNPs at 99.41 Mb identified in the Iodent material in a GWAS study conducted by Van Inghelandt et al.21. QTLs for resistance to NCLB and for multiple disease resistance on chromosomal region 4.05 have been identified in various studies62, and our study also identified two haplotype blocks Hap_4.2 and Hap_4.3 in this chromosomal bin. Overall, it was found that several SNPs/haplotypes identified in this study are in close proximity to previously reported major genes and QTL clusters, but many novel genomic regions were also discovered that could be environment and germplasm-specific.Haplotype regression analysis identified 17 haplotype blocks that are common across at least two panels among the three panels studied, and hence considered to be candidates for further studies towards NCLB resistance in Asian tropics. The use of haplotypes increase the phenotypic variance explained, and thus allows the identification of genomic regions responsible for controlling a large part of variation in the trait of interest63. ABH esterase regulates the response of salicylic acid in plants, which is a key hormone to plant immune responses64. Highly significant SNPs identified in the DTMA panel on chromosome 7 are located within GRMZM2G334165 gene coding for protein kinase superfamily. Protein kinases play a central role in signalling during pathogen recognition and the subsequent activation of plant defence mechanisms. The microbial (pathogen) elicitors, also known as pathogen-associated molecular patterns (PAMPs), are recognized by the membrane-localized pattern recognition receptors (PRRs) of plants65. Transmembrane receptor kinases are one of the PRRs which help in plant defence mechanism. Eight significantly associated SNPs on chromosome 8 in the IMAS panel were found to be located in GRMZM2G319130 gene putatively coding for regulator of chromosome condensation (RCC1) family protein. RCC1 proteins contain plant specific disease resistance, zinc finger, chromosome condensation (DZC) domain66, and RML3 gene implicated in resistance to Leptosphaeria mculans in Arabadopsis was found to have RCC1 domain. It was also found to be effective for broad spectrum resistance against several necrotrophic fungi. Two genes BQ081031 and BQ080005 encoding candidate regulators of RCC1 family protein were found to be down-regulated specifically in the resistant reaction following Phytophthera. sojae infection which causes stem and root rot in soybean67.Some of the SNPs identified in this study were found to be located in annotated genes (B73 RefGen_V2) with functional domains implicated in defence mechanisms in crops like maize, rice, and Arabidopsis. Highly significant SNP S7_165196774, identified in the CAAM panel is located in the gene GRMZM2G116426, having functional domains of alpha/beta-Hydrolases (ABH) superfamily proteins. ABHs support a variety of unique catalytic functions for defence and hormone regulationHt3, Ht2 and Htn1 and many previously reported dQTLs, and multiple foliar disease resistant QTL. These regions will be candidates for further validation studies and possible utilization in the breeding programs in Asia. Considerable differences were observed among different germplasm in terms of resistance to NCLB, and hence it is suggested to bring together diverse sources of resistance alleles to improve resistance to NCLB.From three GWAS panels genotyped at high density, and phenotyped for NCLB disease under artificial disease pressure in multiple environments in India, 22 significant SNP associations were identified. Seventeen haplotypes were identified which were significantly associated with the trait across two or more panels studied. Several SNPs/haplotypes identified in this study were located within or in close proximity to major genes like 68 and resistance for sorghum downy mildew26. The DTMA panel consisted of 285 elite inbred lines which include CIMMYT’s drought tolerant (DT) lines, with reasonable resistance to foliar diseases and insect pests. Apart from drought tolerant lines derived from various selection cycles of the DT populations like DTP1, DTP2 and La Posta Sequia, the panel also included the elite set of lines from CIMMYT breeding programs in Latin America, eastern and southern Africa, and a large set of lines from the multiple borer resistant populations developed at CIMMYT, Mexico. The lines belong to medium to late maturity group with mostly white kernel color. GWAS was previously conducted in the DTMA panel for resistance to various biotic stresses like NCLB, Maize streak virus, Maize lethal necrosis, and Tar spot27. The IMAS panel constituted of 380 inbred lines which included elite CIMMYT Maize Lines (CMLs), lines developed from CIMMYT breeding programs in Kenya, Zimbabwe and Mexico, and lines developed by national partners in Kenya (KALRO) and South Africa (ARC). IMAS panel was earlier used in GWAS analysis of resistance to MLN25.Three association mapping panels CAAM, DTMA and IMAS panels assembled by CIMMYT, Global Maize Program were used to study genome wide association for NCLB resistance. The CAAM panel included 419 tropical/ sub-tropical lines from the different breeding programs of CIMMYT adapted to Asian ecologies. This diverse panel included the lines derived from the different source populations for drought, waterlogging, heat stress, acid soil tolerance and downy mildew resistant lines. The panel has early, medium and late maturing lines with predominantly yellow kernel color. This panel has been earlier studied for GWAS for traits like root traits under droughtCAAM panel of 419 inbred lines were evaluated for NCLB at three high disease prevalence locations for one season at Mandya  Arabhavi  and Khudwani, Kashmir . The sub-set of 285 lines of DTMA panel were evaluated for one season at Mandya and the set of 380 lines of IMAS panel were evaluated at Mandya for two seasons. CAAM and DTMA panels were evaluated as replicated trials with two replications using alpha lattice design. The IMAS panel was evaluated using complete block augmented design in 40 blocks, with block size 12 and the two checks (resistant check-CML451 and susceptible check- CML474), that were replicated in each block. These trials were conducted during the rainy season as the conditions were more congenial for disease development. All entries were planted in 2 m row plot using a spacing of 0.75 m between rows and 0.20 m between plants in each row.S. turcica strains were isolated from previous year’s diseased maize leaves. Infected leaves were cut into 5–10 mm small pieces, washed with 0.6% sodium hypochlorite for 1 min and rinsed with sterile distilled water for 3–4 times under aseptic conditions. Excess water was blot dried on sterile tissue paper and infected leaf pieces were placed on Petri plates carrying pure culture Potato Dextrose Agar (PDA). The plates werse incubated at 28 °C for 3–5 days, the growing hyphal tips were transferred to PDA allowed to grow for 8–10 days at 28 °C, conidia were isolated using single spore isolation method. Pure culture of S. turcica were maintained on PDA for further use.S. turcica earlier grown on PDA. Flasks were incubated at 28 °C for 15–20 days until the grains were uniformly covered with fungal growth. The cultured grains were dried and ground into powder and stored in paper bags until use. Trials were inoculated by putting 1 g of ground sorghum powder into the whorl of 30 days old maize crop and the process repeated at 40 days to avoid any escapes. Soon after the inoculations, plain water was sprinkled by manual sprayer of 15 L capacity on all fungus inoculated plants. This increased the humidity and leaf wetness necessary for disease development, and thus better and more reliable phenotyping data.For artificial inoculation in the field experiments, mass multiplication of fungal culture was done on sterile sorghum grains. Approximately 200–250 g of sorghum grains were autoclaved in 500 ml conical flask, and on attaining the normal room temperature, the grains were inoculated with pure culture of 69; Score 1 = highly resistant (HR) where no infection or slight infection with few lesions scattered on lower two leaves 2 = resistant (R), Light infection with moderate number of lesions scattered on lower four leaves, 3 = moderately resistant (MR) moderate to heavy infection abundant number of lesions scattered on lower leaves and few lesions on the middle leaves below the cob 4 = Susceptible (S) heavy infection, abundant number of lesions scattered on lower and middle leaves and lesions spread up to the flag leaf and 5 = highly susceptible (HS) very heavy infection, lesions scattered on almost all the leaves, plant prematurely dried.NCLB symptoms started developing after a week of artificial inoculation, however symptoms became distinguishable after reproductive growth of the plants. Disease rating in trials was recorded two times, first score was taken at 65–70 days of crop, and the second or final scoring was taken on 75th–80th day. NCLB rating was recorded using 1–5 scaleYijko is phenotypic performance of the ith genotype at the jth environment in the kth replication of the oth incomplete block, μ was an intercept term, gi was the genetic effect of the ith genotype, lj was the effect of the jth environment, rkj was the effect of the kth replication at the jth environment, bojk was the effect of the oth incomplete block in the kth replication at the jth environment, and eijko was the residual. For the CAAM panel and DTMA panel, best linear unbiased predictions (BLUPs) were estimated using Meta-R version 4.170 using anthesis date (AD) parameter as covariate because NCLB scores were significantly correlated to AD. In augmented design trials, BLUPs were estimated across years using linear model for repeated entries and linear model for entries in SAS. Linear model for repeated entry o = 1, 2, …, q (repeated entries), j = 1, 2, …, b (blocks), l = 1, 2, …, l (locations) where βj(l): is the effect of the jth block nested in lth location, γl: is the effect of the lth location, τk0: is the effect of the kth repeated entry, (τγ)k0l: is the effect of the interaction between the k0th entry and the lth location and the linear model for entries βj(l): is the effect of the jth block nested in lth location, γl: is the effect of the lth location, τi: is the effect of the ith entry, (τγ)il: is the effect of the interaction between the ith entry and the lth location. Broad- sense heritability (H2) of multi-location trials was estimated as H2 = σ2g/( σ2g + σ2ge/e + σ2e/er), where σ2g, σ2ge and σ2e are the genotypic, genotype-by-environment interaction and error variance components, respectively, and e and r are the number of environments and number of replicates within each environment included in the analysis, respectively. Meta-R version 4.1 was also used in generating descriptive statistics and genetic correlations between the NCLB scores and anthesis date.A Mixed linear model was used for analysis of phenotypic data from alpha-lattice design where genotypes, environments, interaction between genotype with environment and interaction with replication and environment were considered as random effects.71 (CIMMYT 2005). Panels were genotyped at Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA for Single nucleotide polymorphism (SNPs) using genotyping by sequencing method (GBS). The GBS libraries were constructed following the method of Elshire et al.ss72, and SNP calling was performed using TASSEL GBS pipeline73. Physical co-ordinates of all SNPs were derived from the maize reference genome version B73 AGPV2. The original partially imputed GBS SNP data had 955,690 genotypic data points (SNPs) across all the chromosomes of approximately 22,000 maize lines publicly available through Panzea database (www.panzea.org). For GWAS, filtration criteria of call rate (CR) ≥ 0.7 and minor allele frequency (MAF) ≥ 0.05 were used in all panels, yielding 293,606, 297,437 and 309,608 SNPs for CAAM, DTMA and IMAS panels, respectively. For estimating PCA and kinship matrix, high quality SNPs with filtering criteria of CR ≥ 0.9, MAF ≥ 0.1, and pruned at an r2 threshold of ≤ 0.5 were used for selecting 64,344 SNPs for CAAM, 69,254 for DTMA and 69,286 for IMAS panel.DNA of all maize lines constituting association mapping panels was isolated from leaf samples of 3–4 weeks old seedlings using the standardised procedure followed by CIMMYT.74 was conducted in all panels using SNP & Variation Suite (SVS) Version_8.6.0 . The first three principal components were used to project the possible population stratification among the samples using 3D plot. A kinship matrix was computed from identity-by-state (IBS) distance matrix75 as executed in SVS Version_8.6.0. r2 values . For estimation of LD decay across the genome, r2 values between SNPs were plotted against the physical distances between the SNPs76. LD decay plot using non- linear model was plotted in R using ‘nlin’ function77. Average pairwise distances in which LD decayed at r2 = 0.2 and r2 = 0.1 were then estimated based on the model given by Hill & Weir78.The PCA method described by Price et al79 as executed in SVS Version 8.6.0. The mixed association mapping model used was Y = SNP*β + PC*α + K *μ + ε, where Y = response of the dependent variable (NCLB Score), SNP = SNP marker (fixed effects), PC = principal component coordinate from the PCA (fixed effects), K = kinship matrix (random effects), α is the vector of PC, β and μ are the vectors of SNP and K, respectively, and ε is the error. Manhattan plots were plotted using the − log 10 P values of all SNPs used in analysis; Q–Q plots were plotted of the observed − log 10 P values and the expected − log 10 P values to study the genomic inflation. Considering the genome-wide LD between SNPs, the effective number of independent markers was used to obtain the P value thresholds. The number of SNPs in linkage equilibrium with each other were estimated at an r2 threshold of 0.1. A Bonferroni corrected P value threshold at α = 1 was used to compute the significant P value thresholds45 for each panel.GWAS was carried out on AD adjusted BLUPs for NCLB resistance employing three methodologies: uncorrected genotypic data only (G-test or naïve model), genotypic data corrected for structure (Q) using 10 principle components ) and genotypic data corrected for both structure and kinship (K) ). G-test and GLM used association test with additive model and MLM used mixed model single locus (EMMAX)80, EM convergence tolerance of 0.0001 and a frequency threshold of 0.01. To minimise the historical recombination, haplotype blocks were detected based on the block defining algorithm81. Regression analysis was carried out with the haplotypes detected, based on step-wise regression of the NCLB BLUP estimates in all three panels separately with forward elimination at FDR-value cut off of 0.05.SNPs within the bottom 0.1 percentile of the distribution in GWAS in each study panel were selected for haplotype detection and trait regression in all the three panels. Haplotype frequency estimation was done using the Expectation Maximisation (EM) algorithm with 50 EM iterationsSupplementary Tables.


Hypothyroidism is a common complex endocrinopathy that typically has an autoimmune etiology, and it affects both humans and dogs. Genetic and environmental factors are both known to play important roles in the disease development. In this study, we sought to identify the genetic risk factors potentially involved in the susceptibility to the disease in the high-risk Giant Schnauzer dog breed.p-value = 5.7 × 10− 6), integrated with whole-genome resequencing and copy number variation analysis, we detected a ~ 8.9 kbp deletion strongly associated  with protection against development of hypothyroidism. The deletion is located between two predicted Interferon alpha (IFNA) genes and it may eliminate functional elements potentially involved in the transcriptional regulation of these genes. Remarkably, type I IFNs have been extensively associated to human autoimmune hypothyroidism and general autoimmunity. Nonetheless, the extreme genomic complexity of the associated region on CFA11 warrants further long-read sequencing and annotation efforts in order to ascribe functions to the identified deletion and to characterize the canine IFNA gene cluster in more detail.By employing genome-wide association followed by fine-mapping (top variant Our results expand the current knowledge on genetic determinants of canine hypothyroidism by revealing a significant link with the human counterpart disease, potentially translating into better diagnostic tools across species, and may contribute to improved canine breeding strategies. The domestic dog has proven to be an effective animal model to identify genetic risk factors underlying phenotypic traits and diseases shared with humans, as demonstrated by several successful studies in recent years –4. Dogs Excluding rare congenital hypothyroidism and other sporadic thyroid-related disorders, autoimmune hypothyroidism accounts for most cases in which this gland fails to produce sufficient amount of its specific hormones, i.e. thyroxine (T4) and triiodothyronine (T3) , 12. In The Beagle, Boxer, Dobermann Pinscher, English Setter, Gordon Setter, Giant Schnauzer, Hovawart, Old English Sheepdog and the Rhodesian Ridgeback are among the dog breeds showing increased risk of developing hypothyroidism , 18–22. Determining the genetic etiology of hypothyroidism is of major interest due to the high prevalence and the impact of the disease in both humans and dogs. In previous genetic studies aimed at mapping this disease in high-risk dog breeds, Kennedy and colleagues , as wellHere we sought to identify additional genetic loci potentially involved in disease susceptibility in dogs. To tackle this challenge, we employed genome-wide association (GWA) analysis followed by a fine-mapping approach using breed-specific variants detected by whole-genome resequencing, as well as copy number variation (CNV) analysis in a high-risk Giant Schnauzer breed. In this study we expand the current knowledge about canine hypothyroidism and its genetic determinants by describing a novel locus associated with the development of this disease. The identification of this locus implicates a noteworthy link with the human counterpart of the disease, thus confirming the validity of employing the domestic dog as a disease animal model.ncases = 73, ncontrols = 42) using ~ 170,000 markers (Illumina 170 K CanineHD BeadChip), and subsequently performed a GWA analysis of hypothyroidism using the markers and individuals passing through the data quality control (QC) and filtering steps . The multidimensional scaling (MDS) plot generated in the individual-based QC step highlighted the presence of six outlier individuals, which were subsequently discarded .We genotyped 115 Giant Schnauzer dogs , we found a suggestive genetic association to a region on CFA11. The top SNP was located at CanFam3.1 genomic coordinate CFA11: 40,777,312  = 0.15, CIOR = 0.05–0.39) (2 ≥ 0.8) of the top SNP to the rest of the SNPs on CFA11. Conditional analysis confirmed the independence of the association signal  . The minn signal . The assWe selected two case and one control samples for high coverage (HC) Illumina short-read whole-genome resequencing (WGS). For these samples we generated an average of 46X genome coverage (SD = 2.9). Moreover, the genomes of 10 case and 10 control samples were sequenced at low coverage (LC) with the same technology, generating an average of 6.8X genome coverage per individual (SD = 1.0). More than 92% (SD = 0.4) of the reads aligned to the dog reference genome in both groups of resequenced samples .n = 740) was genotyped using Sequenom MassARRAY in 96 dogs out of the initial GWA analysis cohort of 107 individuals, leaving out 11 individuals due to poor quality or lack of DNA specimen. Genotyping success rate was 95.5% , leaving 707 polymorphisms for further analyses  on CFA11. In the first pruning step we removed 5264 variants with identical genotypes between the two HC cases and the HC control. The remaining 13,206 SNPs were screened in order to identify a subset of SNPs with functional potential covering the whole region of association. The selection of SNPs with functional potential  that likely represented false variant calls in the HC individuals. The remaining 638 SNPs were subsequently combined with the SNPchip variants covering the extended region of association, while discarding one of the duplicated control SNPs (i.e. those included in both the Ilumina SNPChip and the Sequenom MassARRAY experiments)  based onp-value distribution   (ELAVL2 gene and within a predicted long non-coding RNA according to the Broad Improved Canine Annotation v1 [2 ≥ 0.8) of the fine-mapping top SNP to SNPs on CFA11. The previously identified 8.9 Mbp region was narrowed down to a 4.18 Mbp region  . Despite showing a slight degree of deflation, the inclusion of additional SNPs to the original genotypic data significantly improved the ratio between the observed and the theoretical SNPs ribution . The sta× 10− 6) . We coul01–0.28) . The MAFation v1 . The new90) Fig. . The newIn the samples that were resequenced at high coverage, CNVnator  predictep-value = 1.8 × 10− 7) difference in coverage between the LC case and control groups was overlapping with a CNV detected by CNVnator (Del3)  . Figure In order to confirm the predicted CNVs, LC cases and controls were screened for differences in read depth (RD) in a total of 74,216 windows on CFA11, whereas 634 windows were subsequently removed due to the low coverage. A Bonferroni corrected statistically significant  annotated in the canine genome  genes. The deletion breakpoints were located inside two neighboring predicted IFNA genes (5′ breakpoint in an unannotated IFNA and 3′ breakpoint in the RefSeq annotated IFNA7) with high sequence similarity, creating a fusion gene encoding a protein identical to the one encoded by the IFNA7 located around the deletion end . However, the deletion does not appear to perfectly co-segregate with either the fine-mapping top SNP  or the GWAS top SNP , as confirmed by its less significant p-value (n = 2), Estonia (n = 2), Croatia (n = 1) and USA (n = 3)) and representatives for 17 additional dog breeds (n = 76)   for the p-value = 9.9 × 10− 6) spanning 8.9 Mbp and conferring protection against the disease. The small sample size of our GWA analysis is likely to have hampered the statistical power of our study, as reflected by the magnitude of the association. However, the stringent phenotypic inclusion criteria used in this study may counterbalance the small sample size, the resulting level of association and its reliability, similarly to previous studies mapping canine complex traits [In this study we identified a locus on CFA11 associated with protection against development of canine hypothyroidism in a Swedish cohort of Giant Schnauzer dogs. After performing a GWA analysis using a mixed model approach, we detected an associated locus  [We previously described a susceptibility locus on CFA12 associated with canine hypothyroidism in three different high-risk dog breeds, the Gordon Setter, Hovawart and the Rhodesian Ridgeback [on SNPs) , 36. It Since the protective locus on CFA11 identified in this study could hide the effect of this causative risk allele, we calculated the tagging variant MAF after removing all the dogs with the protective allele on CFA11 (14 controls and 3 cases). The MAF of the tagging risk variant did not significantly change in either cases or in controls, suggesting that this locus does not contribute to hypothyroidism susceptibility in the Giant Schnauzer, thus implicating alternative disease risk loci yet to be discovered. Hence, we strengthen the hypothesis of a complex etiology underlying this disease in the existing domestic dog population, whereas there may be a few loci contributing to disease susceptibility within each breed.By genotyping a large number of selected SNPs detected by WGS we narrowed down the associated protective candidate locus to a ~ 4 Mbp region. The prioritized variants were breed-specific and potentially causative, thus being instrumental in our fine-mapping approach, as neither the Giant Schnauzer nor any other Schnauzer breed was included in the design of the 170 K Illumina SNPChip. Such high-throughput regenotyping of selected relevant variants has previously been proven as an efficient approach for fine-mapping of genome-wide loci of association , 37, eveThe CNV analysis detected a putative structural event  segregating with the genotype of the fine-mapping top SNP CFA11: 42,382,440) in all the resequenced samples. After conclusively refining its genomic location , this deletion was found to be co-segregating with the fine-mapping top SNP in 95% (102 of 107) of the Giant Schnauzers used for the association study (94% in cases (67 of 71) and 97% in controls (35 of 36)). Moreover, it was detected in the Leonberger breed, which is prone to develop hypothyroidism. Therefore, although we could not a priori exclude the possibility of the fine-mapping top SNP being the putative causative mutation, the hypothesis of the identified CNV as the causative variant would certainly be plausible, given that conditioning for its genotype eliminates the association on CFA11 due to the strong LD. Moreover, the hypothesis of a CNV as the causative variant would be very attractive, especially considering the structural variants’ ability to reshape the gene/genomic landscape, as well as to potentially modulate gene expression. Furthermore, it is well established that CNVs contribute to phenotypic variation and disease susceptibility both in domestic animals –42 and h,382,440 IFNA7 gene, coding for the IFNA7 cytokine. However, we could identify the breakpoints of the deletion as located in two different IFNA genes, namely an unannotated IFNA (IFNA?) and the RefSeq annotated IFNA7. The deletion creates an IFNA?/IFNA7 intact in-frame fusion gene with the coding sequence identical to the RefSeq annotated IFNA7, but the potential regulatory elements acquired from the IFNA?.According to RefSeq annotation , the mosIFNA, thus possibly causing the protected phenotype.IFN-α proteins belong to type I IFNs, which are cytokines playing a major role in protecting the body from viral infections and in regulating the activity of effector immune cells . In humaIFNA genes are organized in a cluster of paralogous genes and characterized by high levels of sequence identity, which notably complicates their assembly and annotation. The intronless nature of these genes has further contributed to overlapping and non-conclusive assignment of canine IFNA orthologous genes according to RefSeq annotation, and potential reference genome assembly issues. Moreover, the recent improved canine genome annotation (CanFam3.1) failed to annotate the IFNA gene cluster and left many gaps open in the associated locus [http://genome.ucsc.edu, public track hub: Broad Improved Canine Annotation v1). In our Illumina short-read resequencing data, many reads with a reduced or zero mapping quality aligned to the genomic region surrounding IFNA7, thus suggesting ambiguous alignments and confirming the repetitive nature of this gene cluster. Moreover, we could not find paired-reads alignments with insert size greater than expected (indicative of a deletion), suggesting additional rearrangements or reference assembly complications.Type I ed locus  (http://IFNA) and presence of numerous gaps in the reference sequence, we were unable to determine the exact annotation of the IFNA genes located at the deletion breakpoints. As a consequence, we were not able to perform in silico comparative analyses of the regulatory elements presumably abolished by the deletion. Exploring valuable publicly available data  for the corresponding associated region in the human genome would have likely provided us with potential mechanistic interpretations of our study results [IFNA gene cluster are unable to resolve their genomic complexities. Thus, the use of alternative long-read sequencing approaches is advisable, especially in conjunction with the improvement of the assembly and the annotation of the corresponding reference genome. Resequencing bacterial artificial chromosome (BAC) clones, WGS using long-read technology, as well as genome optical mapping would thus be desirable follow-up studies to resolve these issues [Due to high genomic complexity in the region, high sequence similarity between single exon genes .Nevertheless, the knowledge gained in this study might contribute to the development of breeding strategies, via the adoption of a marker-assisted selection eventually increasing the frequency of the candidate protective allele(s) in the population. Furthermore, our results corroborate the important role of type I IFN genes as candidates in autoimmunity and present the dog as a suitable animal model for the corresponding human diseases.Blood and serum samples from privately owned Giant Schnauzer dogs were collected using EDTA  and serum vacutainer tubes in collaboration with licensed veterinarians throughout Sweden after obtaining owners’ written approval. Samples were collected according to local ethical standards . Genomic DNA (gDNA) was extracted and serum obtained as described previously [For all samples used in this study we determined serological concentrations of thyroid stimulating hormone (TSH) and free thyroxine (fT4), as well as the autoantibody against thyroglobulin (TgAA). TSH and fT4 concentrations were detected using Siemens IMMULITE Immunoassay System , 25, wheDogs were classified as cases or controls based on predetermined diagnostic criteria Table  in accorp-values (< 1 × 10− 3) and false discovery rate for Hardy-Weinberg equilibrium (HWE) (< 0.2 only in controls). Furthermore, the dataset was checked for correlation between disease status and sex distribution [A sample set comprising of 115 individuals (73 cases and 42 controls) was genotyped using the Illumina 170 K CanineHD BeadChip . Chromosomal positions of SNPs are based on the dog CanFam3.1 genome assembly . R v3.0.ribution , 67.We performed a GWA analysis on the pruned dataset resulting from the individual- and marker-based QC procedures using R v3.0.2  and GenA95) or an empirical genome-wide significance threshold calculated after 1000 permutations [The statistical significance of the obtained results from the GWA analysis was evaluated as described previously . Brieflyutations .2 ≥ 0.8) of the most significantly associated SNP (top SNP) to the rest of the SNPs on the chromosome. The independence of the association signal was tested by a conditional analysis in which the genotype of the top SNP was included as a covariate in the statistical model. Conditional analysis determines the independence of additional association signals from the leading top SNP and the variants in LD with it compared to the remaining variants in the candidate locus.A QQ plot was constructed using R v3.0.2  and a MaWe performed WGS for individuals representing the key haplotypes as detected by the GWA analysis. One control individual homozygous for the protective allele (control) and two cases homozygous for the non-protective allele (case1 and case2) were sequenced at high coverage (> 40X). Additionally, 10 controls heterozygous for the protective allele and 10 cases homozygous for the non-protective allele were sequenced at low coverage (< 10X). Sequencing libraries were sequenced as paired-end reads (2 × 101 bp) with HiSeq2500  by using the services of the National Genomics Infrastructure at Science for Life Laboratory, Stockholm, Sweden.http://broadinstitute.github.io/picard). Base quality recalibration and local realignment were performed using the Genome Analysis Toolkit (GATK) v1.5–11-g5c5d8e7 [The resulting reads were mapped to the dog genome assembly CanFam 3.1 using the Burrows-Wheeler aligner (BWA) v0.6.2-r126 . The sofg5c5d8e7 . Variantg5c5d8e7 .http://www.ensembl.org/vep). In order to target potential causative mutations and to restrict the number of SNPs suitable for subsequent genotyping and fine-mapping, we sought to categorize the SNPs according to different criteria. We prioritized SNPs based on the determined annotation and the predicted effect, as well as their overlap with either conserved elements according to 29 mammals conservation scores [http://genome.ucsc.edu) [For the following analyses, only SNPs with differing genotypes between the HC control and the HC cases were included. The SNPs were annotated using SnpEFF  and thein scores  or regiocsc.edu) . Moreovecsc.edu)  to confihttp://www.sequenom.com/iplex) was employed to regenotype the selected subset of SNPs in the majority of samples previously included in the GWA analysis (n = 96). The regenotyped SNPs were subsequently phased and imputed in the few missing samples  using Beagle v3.0 [2) value lower than 0.75 were discarded. Secondly, genotypes with imputation probability values lower than 0.8 were labeled as missing. The filtered imputed data were merged with the Illumina SNPChip and regenotyping data produced in previous steps, in order to obtain a comprehensive dataset including all the study samples and all genotyped SNPs. We then performed fine-mapping of the determined genome-wide associated region using GenABEL [Sequenom MassARRAY technology  and the length of the CNV (> 1000 bp). Moreover, we only retained CNV calls without any reads with zero mapping quality (q0 filter). Finally, we discarded all CNV calls overlapping with gaps in the reference genome. The CNVs predicted in the samples sequenced at high coverage were examined in the 10 LC cases and 10 LC controls.In order to detect additional potentially causative variants, we employed the software CNVnator (ver 0.3)  to scan p-value < 6.8 × 10− 7, corrected for the number of tests) was used to define statistically significant coverage differences between the two groups of samples. In addition, we computed M-values as following:It has previously been shown that, despite yielding overall accurate results, CNVnator might fail to detect CNVs in low coverage samples . For thiM-values represent the fold-coverage differences between the LC case and control groups. M-values were calculated in each sample for every window overlapping with the CNV regions shared between all resequenced samples. RD and M-values were plotted and visualized by using R v3.0.2, and the corresponding deviating genomic regions examined in IGV  and UCSCThe detected CNV (deletion) with functional potential was further defined by alignment of HC cases and HC control to the wolf genome , using tFor amplification across the region of interest, long-range PCR primers  were desn = 101), genotyping of the deletion was performed using a three-primer approach (n = 6), the deletion genotype was imputed with the same method as described earlier  different between the Giant Schnauzer case and control dogs.In the individuals with available DNA  MDS plot showing the sample set before quality control (QC). The red circle highlights the outlier samples (n = 6). The black arrow indicates the outlier sample (n = 1) with the standard coat color. (b) MDS plot showing the sample set after quality control (QC).Additional file 2: Figure S2. (a) QQ plot showing the observed versus expected SNPs p-value distribution. After the mixed model approach, the inflation factor λ is equal to 0.93. The QQ plot also shows the empirical genome-wide significance threshold  and empirical 95% confidence intervals (CI95) (indicated by solid grey lines). (b) Manhattan plot showing a peak of association on CFA11 .Additional file 3: Figure S3. Manhattan plot after conditioning the GWA analysis for the top SNP genotype.Additional file 4: Figure S4. (a) QQ plot showing the observed versus expected SNPs p-value distribution of the final complete dataset including both GWA and fine-mapping SNPs. After the mixed model approach, the inflation factor λ is equal to 0.97. The QQ plot also shows the empirical genome-wide significance threshold, p-value = 5.4 × 10− 6 , and empirical 95% confidence intervals (CI95) (indicated by solid grey lines). (b) Manhattan plot confirming the detection of a peak of association on CFA11  during the fine-mapping experiment.Additional file 5: Figure S5. Manhattan plot after conditioning the GWA analysis for the fine-mapping top SNP genotype.Additional file 6: Figure S6. The 3-primer design for deletion genotyping. Primers NF11 and NR12 give a PCR product (906 bp) from only the allele without the deletion. Primers NF3 and NR12 give PCR products from both alleles without the deletion  and with the deletion (1038 bp). However, using the PCR elongation optimal for amplifying up to 1 kbp produced only the two shorter fragments (906 and 1038 bp), enabling the genotype determination during the subsequent separation on the agarose gel.Additional file 7: Table S1. Table showing the genotypes of the GWA analysis top SNP , average coverage and proportions of reads mapping to Canfam3.1 for the sequenced HC and LC case (highlighted in dark and light red) and HC and LC control (highlighted in dark and light blue) samples. HC: high coverage; LC: low coverage.Additional file 8: Table S2. Number of SNPs that were successfully pooled for high-throughput re-genotyping in all the samples (Pooled Variants), number of SNPs that were subsequently genotyped with success (Successfully genotyped variants), and criteria for variant selection : Conserved elements , VEP , Antisense/Protein coding transcripts (SNPs overlapping predicted antisense and protein coding transcripts), Promoter (SNPs overlapping predicted promoters of genes located in the associated genomic region), AF difference , SNPChip (control SNPs included in the Illumina SNPChip for the genotype concordance check between the two experiments), Fill the gaps (SNPs located in the regions of low coverage in the extended region of association).Additional file 9 Table S3. Table showing all CNVs detected in the fine-mapped region of association by CNVnator. For every CNV the following information is displayed: sample showing the CNV (Sample); type of CNV (Event); genomic coordinates (Chr:start-stop); length of the CNV (Event Length); CNV normalized read depth ; p-value of the mean normalized read depth value difference from genomic average ; p-value from probability of read depth values within the region to be in the tails of Gaussian distribution describing frequencies of read depth values in bins ; same as p-val1, but for the middle of the CNV ; same as p-val2, but for the middle of the CNV ; fraction of reads mapped with mapping quality equal to zero (q0).Additional file 10: Table S4. Table showing the genotypes of the GWA study top SNP , the predicted CNV (Del3) inferred genotypes  and genotypes of the fine-mapping top SNP  of the sequenced HC and LC case (highlighted in dark and light red) and HC and LC control (highlighted in dark and light blue) samples. HC: high coverage; LC: low coverage.Additional file 11: Table S5. Association P-value, number of imputed genotypes, number of detected protective and risk alleles both in cases (n = 71) and controls (n = 36) for the GWAS top SNP, fine mapping top SNP and the deletion associated with protection to hypothyroidism (DELETION).Additional file 12: Table S6. Overview of all individuals genotyped for the deletion.Additional file 13: Table S7. Primer sequences, used annealing temperatures and times and predicted PCR product lengths used in the study.


Bulked segregant analysis (BSA), coupled with next-generation sequencing, allows the rapid identification of both qualitative and quantitative trait loci (QTL), and this technique is referred to as BSA-Seq here. The current SNP index method and G-statistic method for BSA-Seq data analysis require relatively high sequencing coverage to detect significant single nucleotide polymorphism (SNP)-trait associations, which leads to high sequencing cost.We developed a simple and effective algorithm for BSA-Seq data analysis and implemented it in Python; the program was named PyBSASeq. Using PyBSASeq, the significant SNPs (sSNPs), SNPs likely associated with the trait, were identified via Fisher’s exact test, and then the ratio of the sSNPs to total SNPs in a chromosomal interval was used to detect the genomic regions that condition the trait of interest. The results obtained this way are similar to those generated via the current methods, but with more than five times higher sensitivity. This approach was termed the significant SNP method here.The significant SNP method allows the detection of SNP-trait associations at much lower sequencing coverage than the current methods, leading to ~ 80% lower sequencing cost and making BSA-Seq more accessible to the research community and more applicable to the species with a large genome. A while the other bulk has more allele a. BSA was primarily used to develop genetic markers for detecting gene-trait association at its early stage [Bulked segregant analysis (BSA) has been widely utilized in the rapid identification of trait-associated genetic markers for a few decades , 2. The ly stage , 2. The ly stage –20. Thisly stage , 6, 21; The widely used approach in analyzing BSA-Seq data is the SNP index method . For eacIn BSA studies, bulking enriches the trait-associated alleles in either bulk. The more a gene contributes to the phenotype, the more its alleles are enriched, and so are the SNPs within the gene (one bulk contains more REF read while the other bulk contains more ALT read). The SNPs flanking this gene should be enriched as well due to linkage disequilibrium, the closer the SNP to the gene, the more enrichment is achieved. Such SNPs are termed trait-associated SNPs. Based on the above reasoning, we developed a novel, simple, and effective algorithm for analysis of the BSA-Seq data via quantifying the enrichment of likely trait-associated SNPs in a chromosomal interval. The algorithm was implemented in Python and the script was named PyBSASeq. The sequence data of Yang et al.  was usedhttps://github.com/dblhlx/PyBSASeq. The Python implementation of the SNP index method and the G-statistic method can be accessed on https://github.com/dblhlx/. The input file for these scripts are generated via SNP calling, see the Method section for details. The workflow of the scripts is as follows:Read the .tsv input file generated via SNP calling into a Pandas DataFrame;Perform SNP filtering on the Pandas DataFrame;REF1 and ADALT1 of bulk 1 and ADREF2 and ADALT2 of bulk 2) of each SNP in the filtered Pandas DataFrame;Identify the significant SNPs via Fisher’s exact test (the significant SNP method), calculate the Δ(SNP index) values (the SNP index method), or calculate the G-statistic values (the G-statistic method) using the four allele depth (AD) values  values, or the G-statistic values against their genomic positions;Calculate the threshold of the sSNP/totalSNP ratio, the Δ(SNP index), or the G-statistic via simulation. The thresholds were used to identify the significant peaks in the plots generated in step 4.The significant SNP method was implemented in Python, and the code and its detailed usage are available on the website Two files, PyBSASeq.pdf and BSASeq.csv, will be generated after the successful completion of the PyBSASeq script. PyBSASeq.pdf contains plots with the chromosomal distributions of sSNPs, total SNPs, and sSNP/totalSNP ratios, while BSASeq.csv contains information for all the potential significant peaks including the sliding window-specific thresholds of these peaks.The GATK4-identified SNPs are filtered using the following parameters in order: 1) the unmapped SNPs or SNPs mapped to the mitochondrial or chloroplast genome; 2) the SNPs with an ‘NA’ value in any column of the DataFrame; 3) the SNPs with more than one ALT bases; 4) the SNPs with GQ score less than 20.https://github.com/brentp/fishers_exact_test) or scipy.stats.fisher_exact is used for Fisher’s exact test. The former can take four one-dimensional numpy arrays as input and hence is much faster when dealing with a large dataset. Whereas the latter can only take a numpy array or a Python list  as input. When performing Fisher’s exact test on the real SNP dataset, a SNP with its p-value less than 0.01 is defined as a significant SNP.The Python module fisher (The Δ(SNP index) of each SNP in the SNP dataset is calculated as below:O is the observed AD , E is the expected AD under the null hypothesis and is calculated as in the original G-statistic method [ln denotes the natural logarithm.The formula below is used for calculating the G-statistic of each SNP, where c method , and ln The sliding window algorithm is utilized to aid the visualization (plotting) in BSA-Seq data analysis. The window size is 2 Mb, and the incremental step is 10,000 bp. Most of the sliding windows contain hundreds or thousands of SNPs, and some of them could be significant SNPs. For the significant SNP method, the sSNP/totalSNP ratio of a sliding window is the ratio of the number of sSNPs to the total number of SNPs in the sliding windows. A sliding window containing a trait-controlling gene or with such a gene nearby would have a high sSNP/totalSNP ratio because of phenotypic selection via bulking; the more the gene contributes to the trait, the higher the sSNP/totalSNP ratio. For the SNP index method and the G-statistic method, the Δ(SNP index) or G-statistic of a sliding window is the average values of all the SNPs in the sliding window. A sliding window containing a trait-controlling gene or with such a gene nearby would have a high absolute Δ(SNP index) or G-statistic as well.Empty windows would be encountered if the amount of SNPs is too low or the SNP distribution is severely skewed. If a sliding window has zero SNP, its sSNP/totalSNP ratio, G-statistic value, or Δ(SNP index) will be replaced with the value of the previous sliding window. If the first sliding window of a chromosome is empty, the string ‘empty’ will be assigned to this sliding window as a placeholder that will be replaced with a non-empty value of the nearest window later.REF (smADREF) and simulated ADALT (smADALT) of a SNP in a bulk. DP is the real depth per sample value of the SNP in the bulk, and alleleFreq is the frequency of the ALT base in the bulk under the null hypothesis that the SNP is not associated with the trait. alleleFreq is 0.5 in an F2 population or 0.75/0.25 in a backcross population, its value in the bulk is obtained via simulation (see the smAlleleFreq function of the Python script for details), which should be very close to 0.5 or 0.75/0.25. The module returns the smADALT, and the smADREF can be calculated as below:The python module numpy.random.binomial  is used to calculate the simulated ADIt takes around two minutes to calculate the threshold of a single sliding window via simulation on a relatively powerful desktop computer (Intel Core I7–6700 3.40 Ghz Processor and 32 Gb ram), and calculating a threshold for every sliding window of the SNP dataset via simulation would take a very long time. To overcome this obstacle, we first calculate a genome-wide threshold via resampling, peak sliding windows above this threshold are identified as potential significant peaks, then sliding window-specific thresholds are calculated via simulation to verify if the sSNP/totalSNP ratios of these peak sliding windows are really significant.REF1/smADALT1 of bulk 1 and smADREF2/smADALT2 of bulk 2 are obtained via simulation. These simulated AD values of both bulks are used to perform Fisher’s exact test. A SNP with its p-value less than 0.10 is considered an sSNP. The sSNP/totalSNP ratio of this sample is calculated and recorded. This process is repeated 10,000 times, and the 99.5th percentile of these 10,000 simulated sSNP/totalSNP ratios is used as the significant threshold for the detection of potential significant peaks. A higher cut-off p-value  is used here, resulting in the identification of more significant SNPs from the simulated SNP sub-dataset, hence a higher threshold and less false positives.The amount of SNPs that are the same as the average number of SNPs per sliding window are randomly selected from the entire SNP dataset. For each SNP in this sample, smADREF1/smADALT1 of bulk 1 and smADREF2/smADALT2 of bulk 2 are obtained via simulation, and Fisher’s exact test, identification of significant SNPs, and sSNP/totalSNP calculation are performed in the same way as above. This process is repeated 10,000 times, and again the 99.5th of these 10,000 simulated sSNP/totalSNP ratios is used as the threshold for this sliding window.For each SNP in a sliding window, smADREF1/smADALT1 of bulk 1 and smADREF2/smADALT2 of bulk 2 are obtained via simulation. Using these AD values, the Δ(SNP index) or the G-statistic of each SNP is calculated as above . This process is repeated 10,000 times, the 99% confidence interval of the 10,000 Δ(SNP index) values is used as a significant threshold for the SNP index method, and the 99.5th percentile of the 10,000 G-statistic values is used as a significant threshold for the G-statistic method. Please note that the threshold of the Δ(SNP index) or the G-statistic is at the SNP level while the threshold of the sSNP/totalSNP ratio is at the sliding window level.For each SNP in the SNP dataset, smADp-value calculated via either G-test or chi-square test is less than that calculated via Fisher’s exact test, even for sample sizes in the hundreds. To decrease the chance of false positives, Fisher’s exact test was used to identify the likely trait-associated SNPs here, as did by many others [p-value of the Fisher’s exact test suggests that the ALT proportion difference of a SNP between bulks is more likely caused by bulking, and an SNP with its p-value less than 0.01 was considered more likely associated with the trait and was termed significant SNP (sSNP) here. 240,351 sSNPs were identified among total 1,303,084 filtered SNPs (see the Implementation section for the filter criteria), and the chromosomal distribution of SNPs was summarized in Table In BSA-Seq studies, if a SNP is not associated with the trait, its REF/ALT reads would be randomly segregated in both bulks, and the ALT (or REF) read proportions in two bulks should be similar; however, if a SNP is associated with the trait, its REF/ALT reads would be enriched in either bulk due to phenotypic selection via bulking, and the ALT (or REF) read proportions should be significantly different between the bulks. Fisher’s exact test, G-test, or chi-square test can be used to identify such trait-associated SNPs from the SNP dataset, but Fisher’s exact test is more accurate when the sample size is small. For the same set of 2 × 2 contingency table, the y others , 20. A sThe sSNPs should cluster around the genes controlling the trait phenotype on the chromosomes due to linkage disequilibrium. Using the sliding window technique, the number of sSNPs was plotted across all the chromosomes to test if this was the case. We found the sSNP plot approximately matched with the major peaks in plots produced by the SNP index method and the G-statistic method , 22 Figa. HoweveAs stated in the Implementation section, calculating the sSNP/totalSNP ratio threshold of a sliding window via simulation takes around two minutes, and the entire SNP dataset contained 34,919 sliding windows. Calculation of the thresholds of all the sliding windows would take more than a month, thus resampling was utilized to obtain a genome-wide threshold (see the Implementation section) to identify potentially significant peaks in Fig. The genome-wide threshold was acquired using the sample size equal to the average number of SNPs of the sliding windows. We tried different sample sizes for the genome-wide threshold calculation, and the results demonstrated that increasing the sample size decreased the threshold (not shown here). The number of SNPs in sliding windows varied drastically across the genome Fig. a, hence https://github.com/lh3/seqtk) with different random seeds. Random seeds were used here just to ensure that paired sequences in the same bulk were selected when sampling. The sSNPs were identified from these sequence subsets and the ratios of sSNP/totalSNP were plotted along all the chromosomes as above. The results revealed that the plotting patterns were very similar at different sequencing coverage levels  representing the major QTL on chromosome 10 was barely beyond the threshold. With decreasing sequencing coverage, the Δ(SNP index) did not change much, but the thresholds increased dramatically, the QTLs on chromosomes 2, 5, and 10 were missed at 40% of the original sequencing coverage and all the QTL were missed at 30% or lower of the original sequencing coverage  were quite wide Fig. b. An extThe major difference between the significant SNP method and the current methods is how the SNP-trait associations are identified. Both the SNP index method and the G-statistic method use SNP-level thresholds to identify significant sliding windows; whereas the significant SNP method uses sliding window-level thresholds to identify significant sliding windows. The average number of SNPs was 6984 in the sliding windows, much higher than the average sequencing coverage in either bulk (84× in the first bulk and 103× in the second bulk), which could be why the significant SNP method has much higher statistical power and is more sensitive in the detection of SNP-trait associations. GATK is widely used for SNP and small InDel calling, and the new version of GATK4 is also capable of copy number and structural variant calling. PyBSASeq is designed to analyze the GATK-generated variant calling data, though it has only been tested for analysis of the SNP and small InDel calling data, it should be able to handle the GATK4-generated copy number variant and structural variant data as well.The high sensitivity of the significant SNP method allows the detection of SNP-trait associations at reduced sequencing coverage, leading to reduced sequencing costs. Thus, BSA-Seq can be more practically applied to species with a large genome.Oryza sativa subsp. japonica rice cultivar Nipponbare was used as one of the parents in generating the F3 population of the BSA-Seq experiment, and its genome sequence was used as the reference sequence for SNP calling in our study. The size of the F3 population was 10,800 (plants), and the extremely cold-sensitive bulk (ES) contained 430 plants while the extremely cold-tolerant bulk (ET) contained 385 plants. The bulked DNA libraries were sequenced using the Illumina Hiseq 2000 sequencing platform, ~ 360 million 101 bp pair-end reads were obtained from the ES bulk and ~ 440 million 101 bp pair-end reads were obtained from the ET bulk [The sequencing data used in this study were generated by Yang et al. . Using thttps://github.com/ncbi/sra-tools). Quality control, adapter trimming, quality filtering, per-read quality pruning of the downloaded sequences are performed using fastp at the default setting [The raw sequences (SRR834927 and SRR834931) for BSA-Seq analysis are downloaded from NCBI using fasterq-dump ( setting .https://plants.ensembl.org/Oryza_sativa/Info/Index) using BWA [https://software.broadinstitute.org/gatk/documentation/tooldocs/current/. The GATK4-generated .vcf file usually contains the information for two bulks, which are termed the first bulk (fb) and the second bulk (sb), respectively. Using the GATK4 tool, a .tsv  file is generated using the relevant columns  of this .vcf file; Table The preprocessed sequences are aligned to the ‘Nipponbare’ reference genome sequence  of an SNP can be either greater or less than the sum of the ADs in a bulk, here the DP of an SNP in a bulk is defined as below for all the SNPs:The number of REF/ALT reads of a SNP is defined as allele depth (AD) in GATK4. Here they are represented as ADAdditional file 1:Table S1. Verification of potential significant peaks.Additional file 2:Table S2. Chromosomal distribution of SNPs at different sequencing coverage levels.Additional file 3:Figure S1. Replication of the SNP index method and the G-statistic method in Python.