QTL‐seq approach identified genomic regions and diagnostic markers for rust and late leaf spot resistance in groundnut (Arachis hypogaea L.)

Rust and late leaf spot (LLS) are the two major foliar fungal diseases in groundnut, and their co‐occurrence leads to significant yield loss in addition to the deterioration of fodder quality. To identify candidate genomic regions controlling resistance to rust and LLS, whole‐genome resequencing (WGRS)‐based approach referred as ‘QTL‐seq’ was deployed. A total of 231.67 Gb raw and 192.10 Gb of clean sequence data were generated through WGRS of resistant parent and the resistant and susceptible bulks for rust and LLS. Sequence analysis of bulks for rust and LLS with reference‐guided resistant parent assembly identified 3136 single‐nucleotide polymorphisms (SNPs) for rust and 66 SNPs for LLS with the read depth of ≥7 in the identified genomic region on pseudomolecule A03. Detailed analysis identified 30 nonsynonymous SNPs affecting 25 candidate genes for rust resistance, while 14 intronic and three synonymous SNPs affecting nine candidate genes for LLS resistance. Subsequently, allele‐specific diagnostic markers were identified for three SNPs for rust resistance and one SNP for LLS resistance. Genotyping of one RIL population (TAG 24 × GPBD 4) with these four diagnostic markers revealed higher phenotypic variation for these two diseases. These results suggest usefulness of QTL‐seq approach in precise and rapid identification of candidate genomic regions and development of diagnostic markers for breeding applications.     

Next‐generation sequencing identified genomic region and diagnostic markers for resistance to bacterial wilt on chromosome B02 in peanut (Arachis hypogaea L.)

Bacterial wilt, caused by Ralstonia solanacearum, is a devastating disease affecting over 350 plant species. A few peanut cultivars were found to possess stable and durable bacterial wilt resistance (BWR). Genomics‐assisted breeding can accelerate the process of developing resistant cultivars by using diagnostic markers. Here, we deployed sequencing‐based trait mapping approach, QTL‐seq, to discover genomic regions, candidate genes and diagnostic markers for BWR in a recombination inbred line population (195 progenies) of peanut. The QTL‐seq analysis identified one candidate genomic region on chromosome B02 significantly associated with BWR. Mapping of newly developed single nucleotide polymorphism (SNP) markers narrowed down the region to 2.07 Mb and confirmed its major effects and stable expressions across three environments. This candidate genomic region had 49 nonsynonymous SNPs affecting 19 putative candidate genes including seven putative resistance genes (R‐genes). Two diagnostic markers were successfully validated in diverse breeding lines and cultivars and could be deployed in genomics‐assisted breeding of varieties with enhanced BWR.     

Whole‐genome resequencing‐based QTL‐seq identified candidate genes and molecular markers for fresh seed dormancy in groundnut

The subspecies fastigiata of cultivated groundnut lost fresh seed dormancy (FSD) during domestication and human‐made selection. Groundnut varieties lacking FSD experience precocious seed germination during harvest imposing severe losses. Development of easy‐to‐use genetic markers enables early‐generation selection in different molecular breeding approaches. In this context, one recombinant inbred lines (RIL) population (ICGV 00350 × ICGV 97045) segregating for FSD was used for deploying QTL‐seq approach for identification of key genomic regions and candidate genes. Whole‐genome sequencing (WGS) data (87.93 Gbp) were generated and analysed for the dormant parent (ICGV 97045) and two DNA pools (dormant and nondormant). After analysis of resequenced data from the pooled samples with dormant parent (reference genome), we calculated delta‐SNP index and identified a total of 10,759 genomewide high‐confidence SNPs. Two candidate genomic regions spanning 2.4 Mb and 0.74 Mb on the B05 and A09 pseudomolecules, respectively, were identified controlling FSD. Two candidate genes—RING‐H2 finger protein and zeaxanthin epoxidase—were identified in these two regions, which significantly express during seed development and control abscisic acid (ABA) accumulation. QTL‐seq study presented here laid out development of a marker, GMFSD1, which was validated on a diverse panel and could be used in molecular breeding to improve dormancy in groundnut.     

QTL‐seq for rapid identification of candidate genes for 100‐seed weight and root/total plant dry weight ratio under rainfed conditions in chickpea

Terminal drought is a major constraint to chickpea productivity. Two component traits responsible for reduction in yield under drought stress include reduction in seeds size and root length/root density. QTL‐seq approach, therefore, was used to identify candidate genomic regions for 100‐seed weight (100SDW) and total dry root weight to total plant dry weight ratio (RTR) under rainfed conditions. Genomewide SNP profiling of extreme phenotypic bulks from the ICC 4958 × ICC 1882 population identified two significant genomic regions, one on CaLG01 (1.08 Mb) and another on CaLG04 (2.7 Mb) linkage groups for 100SDW. Similarly, one significant genomic region on CaLG04 (1.10 Mb) was identified for RTR. Comprehensive analysis revealed four and five putative candidate genes associated with 100SDW and RTR, respectively. Subsequently, two genes (Ca_04364 and Ca_04607) for 100SDW and one gene (Ca_04586) for RTR were validated using CAPS/dCAPS markers. Identified candidate genomic regions and genes may be useful for molecular breeding for chickpea improvement.     

Next‐generation sequencing for identification of candidate genes for Fusarium wilt and sterility mosaic disease in pigeonpea (Cajanus cajan)

To map resistance genes for Fusarium wilt (FW) and sterility mosaic disease (SMD) in pigeonpea, sequencing‐based bulked segregant analysis (Seq‐BSA) was used. Resistant (R) and susceptible (S) bulks from the extreme recombinant inbred lines of ICPL 20096 × ICPL 332 were sequenced. Subsequently, SNP index was calculated between R‐ and S‐bulks with the help of draft genome sequence and reference‐guided assembly of ICPL 20096 (resistant parent). Seq‐BSA has provided seven candidate SNPs for FW and SMD resistance in pigeonpea. In parallel, four additional genotypes were re‐sequenced and their combined analysis with R‐ and S‐bulks has provided a total of 8362 nonsynonymous (ns) SNPs. Of 8362 nsSNPs, 60 were found within the 2‐Mb flanking regions of seven candidate SNPs identified through Seq‐BSA. Haplotype analysis narrowed down to eight nsSNPs in seven genes. These eight nsSNPs were further validated by re‐sequencing 11 genotypes that are resistant and susceptible to FW and SMD. This analysis revealed association of four candidate nsSNPs in four genes with FW resistance and four candidate nsSNPs in three genes with SMD resistance. Further, In silico protein analysis and expression profiling identified two most promising candidate genes namely C.cajan_01839 for SMD resistance and C.cajan_03203 for FW resistance. Identified candidate genomic regions/SNPs will be useful for genomics‐assisted breeding in pigeonpea.     

QTL-seq for the identification of candidate genes for days to flowering and leaf shape in pigeonpea

To identify genomic segments associated with days to flowering (DF) and leaf shape in pigeonpea, QTL-seq approach has been used in the present study. Genome-wide SNP profiling of extreme phenotypic bulks was conducted for both the traits from the segregating population (F₂) derived from the cross combination- ICP 5529 × ICP 11605. A total of 126.63 million paired-end (PE) whole-genome resequencing data were generated for five samples, including one parent ICP 5529 (obcordate leaf and late-flowering plant), early and late flowering pools (EF and LF) and obcordate and lanceolate leaf shape pools (OLF and LLS). The QTL-seq identified two significant genomic regions, one on CcLG03 (1.58 Mb region spanned from 19.22 to 20.80 Mb interval) for days to flowering (LF and EF pools) and another on CcLG08 (2.19 Mb region spanned from 6.69 to 8.88 Mb interval) for OLF and LLF pools, respectively. Analysis of genomic regions associated SNPs with days to flowering and leaf shape revealed 5 genic SNPs present in the unique regions. The identified genomic regions for days to flowering were also validated with the genotyping-by-sequencing based classical QTL mapping method. A comparative analysis of the identified seven genes associated with days to flowering on 12 Fabaceae genomes, showed synteny with 9 genomes. A total of 153 genes were identified through the synteny analysis ranging from 13 to 36. This study demonstrates the usefulness of QTL-seq approach in precise identification of candidate gene(s) for days to flowering and leaf shape which can be deployed for pigeonpea improvement.Subject terms: Genetic association study, Plant hybridization

QTL-seq approach was utilized for mapping of genomic regions/genes associated with days to flowering and leaf shape in pigeonpea. Analysis of genomic regions and associated SNPs with days to flowering and leaf shape revealed 1 and 4 non-synonymous SNPs, respectively. The study demonstrated sequencing-based trait mapping approach can accelerate trait mapping of the targeted traits.     

Indel‐seq: a fast‐forward genetics approach for identification of trait‐associated putative candidate genomic regions and its application in pigeonpea (Cajanus cajan)

Identification of candidate genomic regions associated with target traits using conventional mapping methods is challenging and time‐consuming. In recent years, a number of single nucleotide polymorphism (SNP)‐based mapping approaches have been developed and used for identification of candidate/putative genomic regions. However, in the majority of these studies, insertion–deletion (Indel) were largely ignored. For efficient use of Indels in mapping target traits, we propose Indel‐seq approach, which is a combination of whole‐genome resequencing (WGRS) and bulked segregant analysis (BSA) and relies on the Indel frequencies in extreme bulks. Deployment of Indel‐seq approach for identification of candidate genomic regions associated with fusarium wilt (FW) and sterility mosaic disease (SMD) resistance in pigeonpea has identified 16 Indels affecting 26 putative candidate genes. Of these 26 affected putative candidate genes, 24 genes showed effect in the upstream/downstream of the genic region and two genes showed effect in the genes. Validation of these 16 candidate Indels in other FW‐ and SMD‐resistant and FW‐ and SMD‐susceptible genotypes revealed a significant association of five Indels (three for FW and two for SMD resistance). Comparative analysis of Indel‐seq with other genetic mapping approaches highlighted the importance of the approach in identification of significant genomic regions associated with target traits. Therefore, the Indel‐seq approach can be used for quick and precise identification of candidate genomic regions for any target traits in any crop species.     

RNA‐Seq bulked segregant analysis enables the identification of high‐resolution genetic markers for breeding in hexaploid wheat

The identification of genetic markers linked to genes of agronomic importance is a major aim of crop research and breeding programmes. Here, we identify markers for Yr15, a major disease resistance gene for wheat yellow rust, using a segregating F₂ population. After phenotyping, we implemented RNA sequencing (RNA‐Seq) of bulked pools to identify single‐nucleotide polymorphisms (SNP) associated with Yr15. Over 27 000 genes with SNPs were identified between the parents, and then classified based on the results from the sequenced bulks. We calculated the bulk frequency ratio (BFR) of SNPs between resistant and susceptible bulks, selecting those showing sixfold enrichment/depletion in the corresponding bulks (BFR > 6). Using additional filtering criteria, we reduced the number of genes with a putative SNP to 175. The 35 SNPs with the highest BFR values were converted into genome‐specific KASP assays using an automated bioinformatics pipeline (PolyMarker) which circumvents the limitations associated with the polyploid wheat genome. Twenty‐eight assays were polymorphic of which 22 (63%) mapped in the same linkage group as Yr15. Using these markers, we mapped Yr15 to a 0.77‐cM interval. The three most closely linked SNPs were tested across varieties and breeding lines representing UK elite germplasm. Two flanking markers were diagnostic in over 99% of lines tested, thus providing a reliable haplotype for marker‐assisted selection in these breeding programmes. Our results demonstrate that the proposed methodology can be applied in polyploid F₂ populations to generate high‐resolution genetic maps across target intervals.     

Confirmation and fine‐mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle

A genome‐wide association study of 2098 progeny‐tested Nordic Holstein bulls genotyped for 36 387 SNPs on 29 autosomes was conducted to confirm and fine‐map quantitative trait loci (QTL) for mastitis traits identified earlier using linkage analysis with sparse microsatellite markers in the same population. We used linear mixed model analysis where a polygenic genetic effect was fitted as a random effect and single SNPs were successively included as fixed effects in the model. We detected 143 SNP‐by‐trait significant associations (P < 0.0001) on 20 chromosomes affecting mastitis‐related traits. Among them, 21 SNP‐by‐trait combinations exceeded the genome‐wide significant threshold. For 12 chromosomes, both the present association study and the previous linkage study detected QTL, and of these, six were in the same chromosomal locations. Strong associations of SNPs with mastitis traits were observed on bovine autosomes 6, 13, 14 and 20. Possible candidate genes for these QTL were identified. Identification of SNPs in linkage disequilibrium with QTL will enable marker‐based selection for mastitis resistance. The candidate genes identified should be further studied to detect candidate polymorphisms underlying these QTL.     

SNP-based pool genotyping and haplotype analysis accelerate fine-mapping of the wheat genomic region containing stripe rust resistance gene <Emphasis Type="Italic">Yr26</Emphasis>

Key message

NGS-assisted super pooling emerging as powerful tool to accelerate gene mapping and haplotype association analysis within target region uncovering specific linkage SNPs or alleles for marker-assisted gene pyramiding.

Abstract

Conventional gene mapping methods to identify genes associated with important agronomic traits require significant amounts of financial support and time. Here, a single nucleotide polymorphism (SNP)-based mapping approach, RNA-Seq and SNP array assisted super pooling analysis, was used for rapid mining of a candidate genomic region for stripe rust resistance gene Yr26 that has been widely used in wheat breeding programs in China. Large DNA and RNA super-pools were genotyped by Wheat SNP Array and sequenced by Illumina HiSeq, respectively. Hundreds of thousands of SNPs were identified and then filtered by multiple filtering criteria. Among selected SNPs, over 900 were found within an overlapping interval of less than 30 Mb as the Yr26 candidate genomic region in the centromeric region of chromosome arm 1BL. The 235 chromosome-specific SNPs were converted into KASP assays to validate the Yr26 interval in different genetic populations. Using a high-resolution mapping population (>?30,000 gametes), we confined Yr26 to a 0.003-cM interval. The Yr26 target region was anchored to the common wheat IWGSC RefSeq v1.0 and wild emmer WEWSeq v.1.0 sequences, from which 488 and 454 kb fragments were obtained. Several candidate genes were identified in the target genomic region, but there was no typical resistance gene in either genome region. Haplotype analysis identified specific SNPs linked to Yr26 and developed robust and breeder-friendly KASP markers. This integration strategy can be applied to accelerate generating many markers closely linked to target genes/QTL for a trait of interest in wheat and other polyploid species.

     

Reducing the interval of a growth QTL on chromosome 4 in laying hens

In our previous research, we identified a QTL with an interval of 3.4 Mb for growth on chicken chromosome (GGA) 4 in an advanced intercross population of an initial cross between the New Hampshire inbred line (NHI) and the White Leghorn inbred line (WL77). In the current study, an association analysis was performed in a population of purebred white layers (WLA) with White Leghorn origin. Genotypic data of 130 SNPs within the previously identified 3.4‐Mb region were obtained using a 60K SNP chip. In total, 24 significant SNPs (LOD ≥ 4.44) on GGA4 were detected for daily weigh gain from 8 to 14 weeks and two SNPs (LOD ≥ 4.80) for body weight at 14 weeks. The QTL interval was reduced by 1.9 Mb to an interval of 1.5 Mb (74.6–76.1 Mb) that harbors 15 genes. Furthermore, to identify additional loci for chicken growth, a genome‐wide association study (GWAS) was carried out in a WLA population. The GWAS identified an additional QTL on GGA6 for body weight at six weeks (19.8–21.2 Mb). Our findings showed that by using a WLA population we were able to further reduce the QTL confidence interval previously detected using a NHI × WL77 advanced intercross population.     

Genome‐wide association study of body morphological traits in Sudanese goats

Long‐term selection of goats for a certain production system and/or different environmental conditions will be reflected in the body morphology of the animals under selection. To investigate the variation contributing to different morphological traits and to identify genomic regions that are associated with body morphological traits in Sudanese goats, we genotyped 96 females belonging to four Sudanese goat breeds with the SNP52 BeadChip. After quality control of the data, the genome‐wide association study was performed using 95 goats and 24 027 informative single nucleotide polymorphisms (SNPs). Bicoastal diameter was significantly associated (LOD = 6.32) with snp10185‐scaffold1365‐620922 on chromosome 2. The minor allele has an additive effect, increasing the bicoastal diameter by 2.6 cm. A second significant association was found between body length and snp56482‐scaffold89‐467312 on chromosome 3 (LOD = 5.65). The minor allele is associated with increased body length. Additionally, five regions were suggestive for cannon bone, head width, rump length and withers height (LOD > 5). Only one gene (CNTNAP5) is located within the 1‐Mb region surrounding the significant SNP for bicoastal diameter on chromosome 2. The body length QTL on chromosome 3 harbors 49 genes. Further research is required to validate the observed associations and to prioritize candidate genes.     

A genome-wide association study for feed efficiency-related traits in a crossbred pig population

Feed efficiency (FE) is one of the most important traits in pig production. However, it is difficult and costly to measure it, limiting the collection of large amount of data for an accurate selection for better FE. Therefore, the identification of single-nucleotide polymorphisms (SNPs) associated with FE-related traits to be used in the genetic evaluation is of great interest of pig breeding programs for increasing the prediction accuracy and the genetic progress of these traits. The objective of this study was to identify SNPs significantly associated with FE-related traits: average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). We also aimed to identify potential candidate genes for these traits. Phenotypic information recorded on a population of 2386 three-way crossbreed pigs that were genotyped for 51 468 SNPs was used. We identified three loci of quantitative trait (QTL) regions associated with ADG and three QTL regions associated with ADFI; however, no significant association was found for FCR. A false discovery rate (FDR) ≤ 0.005 was used as the threshold for declaring an association as significant. The QTL regions associated with ADG on Sus scrofa chromosome (SSC) 1 were located between 177.01 and 185.47 Mb, which overlaps with the QTL regions for ADFI on SSC1 (173.26 and 185.47 Mb). The other QTL region for ADG was located on SSC12 (2.87 and 3.22 Mb). The most significant SNPs in these QTL regions explained up to 3.26% of the phenotypic variance of these traits. The non-identification of genomic regions associated with FCR can be explained by the complexity of this trait, which is a ratio between ADG and ADFI. Finally, the genes CDH19, CDH7, RNF152, MC4R, PMAIP1, FEM1B and GAA were the candidate genes found in the 1 Mb window around the QTL regions identified in this study. Among them, the MC4R gene (SSC1) has a well-known function related to ADG and ADFI. In this study, we identified three QTL regions for ADG (SSC1 and SSC12) and three for ADFI (SSC1). These regions were previously described in purebred pig populations; however, to our knowledge, this is the first study to confirm the relevance of these QTL regions in a crossbred pig population. The potential use of the SNPs and genes identified in this study in prediction models that combine genomic selection and marker-assisted selection should be evaluated for increasing the prediction accuracy of these traits in this population.     

A genome‐wide association analysis for carcass traits in a commercial Duroc pig population

We performed a genome‐wide association study to map the genetic determinants of carcass traits in 350 Duroc pigs typed with the Porcine SNP60 BeadChip. Association analyses were carried out using the gemma software. The proportion of phenotypic variance explained by the SNPs ranged between negligible to moderate (= 0.01–0.30) depending on the trait under consideration. At the genome‐wide level, we detected one significant association between backfat thickness between the 3^rd and 4^th ribs and six SNPs mapping to SSC12 (37–40 Mb). We also identified several chromosome‐wide significant associations for ham weight (SSC11: 51–53 Mb, three SNPs; 67–68 Mb, two SNPs), carcass weight (SSC11: 66–68 Mb, two SNPs), backfat thickness between the 3^rd and 4^th ribs (SSC12: 21 Mb, one SNP; 33–40 Mb, 17 SNPs; 51–58 Mb, two SNPs), backfat thickness in the last rib (SSC12: 37 Mb, one SNP; 40–41 Mb, nine SNPs) and lean meat content (SSC13: 34 Mb, three SNPs and SSC16: 45.1 Mb, one SNP; 62–63 Mb, 10 SNPs; 71–75 Mb, nine SNPs). The ham weight trait‐associated region on SSC11 contains two genes (UCHL3 and LMO7) related to muscle development. In addition, the ACACA gene, which encodes an enzyme for the catalysis of fatty acid synthesis, maps to the SSC12 (37–41 Mb) region harbouring trait‐associated regions for backfat thickness traits. Sequencing of these candidate genes may help to uncover the causal mutations responsible for the associations found in the present study.     

Identification of genetic variants associated with maize flowering time using an extremely large multi‐genetic background population

Flowering time is one of the major adaptive traits in domestication of maize and an important selection criterion in breeding. To detect more maize flowering time variants we evaluated flowering time traits using an extremely large multi‐ genetic background population that contained more than 8000 lines under multiple Sino‐United States environments. The population included two nested association mapping (NAM) panels and a natural association panel. Nearly 1 million single‐nucleotide polymorphisms (SNPs) were used in the analyses. Through the parallel linkage analysis of the two NAM panels, both common and unique flowering time regions were detected. Genome wide, a total of 90 flowering time regions were identified. One‐third of these regions were connected to traits associated with the environmental sensitivity of maize flowering time. The genome‐wide association study of the three panels identified nearly 1000 flowering time‐associated SNPs, mainly distributed around 220 candidate genes (within a distance of 1 Mb). Interestingly, two types of regions were significantly enriched for these associated SNPs – one was the candidate gene regions and the other was the approximately 5 kb regions away from the candidate genes. Moreover, the associated SNPs exhibited high accuracy for predicting flowering time.     

Genomic variation at the tips of the adaptive radiation of Darwin's finches

Adaptive radiation unfolds as selection acts on the genetic variation underlying functional traits. The nature of this variation can be revealed by studying the tips of an ongoing adaptive radiation. We studied genomic variation at the tips of the Darwin's finch radiation; specifically focusing on polymorphism within, and variation among, three sympatric species of the genus Geospiza. Using restriction site‐associated DNA (RAD‐seq), we characterized 32 569 single‐nucleotide polymorphisms (SNPs), from which 11 outlier SNPs for beak and body size were uncovered by a genomewide association study (GWAS). Principal component analysis revealed that these 11 SNPs formed four statistically linked groups. Stepwise regression then revealed that the first PC score, which included 6 of the 11 top SNPs, explained over 80% of the variation in beak size, suggesting that selection on these traits influences multiple correlated loci. The two SNPs most strongly associated with beak size were near genes associated with beak morphology across deeper branches of the radiation: delta‐like 1 homologue (DLK1) and high‐mobility group AT‐hook 2 (HMGA2). Our results suggest that (i) key adaptive traits are associated with a small fraction of the genome (11 of 32 569 SNPs), (ii) SNPs linked to the candidate genes are dispersed throughout the genome (on several chromosomes), and (iii) micro‐ and macro‐evolutionary variation (roots and tips of the radiation) involve some shared and some unique genomic regions.     

Exploiting whole genome sequence data to fine map and characterize candidate genes within a quantitative trait loci region affecting androstenone on porcine chromosome 5

Male piglets are routinely castrated to eliminate boar taint. However, this treatment is undesirable, and alternative approaches, including genetic strategies to reduce boar taint, are demanded. Androstenone is one of the causative agents of boar taint, and a QTL region affecting this pheromone has previously been reported on SSC5: 22.6–24.8 Mb in Duroc. The QTL region is one of the few reported for androstenone that does not simultaneously affect levels of other sex steroids. The main objective of this study was to fine map this QTL. Whole genome sequence data from 23 Norwegian Duroc boars were analyzed to detect new polymorphisms within the QTL region. A subset of 161 SNPs was genotyped in 834 Duroc sires and analyzed for association with androstenone in adipose tissue and testosterone, estrone sulphate and 17β‐estradiol in blood plasma. Our results revealed 100 SNPs significantly associated with androstenone levels in fat (P < 0.001) with 94 of the SNPs being in strong linkage disequilibrium in the region 23.03–24.27 Mb. This haplotype block contains at least four positional candidate genes (HSD17B6, SDR9C7, RDH16 and STAT6) involved in androstenone biosynthesis. No significant associations were found between any of the SNPs and levels of testosterone and estrogens, confirming previous findings. The amount of phenotypic variance explained by single SNPs within the haplotype block was as high as 5.4%. As the SNPs in this region significantly affect levels of androstenone without affecting levels of other sex steroids, they are especially interesting as genetic markers for selection against boar taint.     

Validating genome‐wide associated signals for clinical mastitis in German Holstein cattle

A validation study for six genomic regions previously identified by a genome‐wide association study for somatic cell score was conducted with data of clinical mastitis in German Holstein cattle. Out of 10 tested SNPs, five on chromosomes 6, 13 and 19 were significantly associated with clinical mastitis (P < 0.05). Three SNPs on chromosomes 6 and 19 had the same direction of effect as those previously reported in the initial genome‐wide association study for somatic cell score. The other two SNPs on chromosome 13 had opposite effects. As well as validating associations within known QTL from previous studies, e.g. chromosomes 6 and 19, novel loci on chromosome 13 were confirmed. Promising candidate genes are, for example: deoxycytidine kinase, immunoglobulin J chain, vitamin D binding protein, forkhead box K2, sodium/hydrogen exchanger 8 and cytoplasmic nuclear factor of activated T‐cells 2. Our confirmation study provides additional evidence for the functional role of the linked genomic regions to immune response. This information can be used as a basis for further functional studies for those potential genes.