Genome‐wide association study of six quality traits reveals the association of the TaRPP13L1 gene with flour colour in Chinese bread wheat

Flour colour, kernel hardness, grain protein content and wet gluten content are important quality properties that determine end use in bread wheat. Here, a wheat 90K genotyping assay was used for a genome‐wide association study (GWAS) of the six quality‐related traits in Chinese wheat cultivars in eight environments over four years. A total of 846 significant single nucleotide polymorphisms (SNPs) were identified, explaining approximately 30% of the phenotypic variation on average, and 103 multienvironment‐significant SNPs were detected in more than four environments. Quantitative trait loci (QTL) mapping in the biparent population confirmed some important SNP loci. Moreover, it was determined that some important genes were associated with the six quality traits, including some known functional genes and annotated unknown functional genes. Of the annotated unknown functional genes, it was verified that TaRPP13L1 was associated with flour colour. Wheat cultivars or lines with TaRPP13L1‐B1a showed extremely significantly higher flour redness and lower yellowness than those with TaRPP13L1‐B1b in the Chinese wheat natural population and the doubled haploid (DH) population. Two tetraploid wheat lines with premature stop codons of the TaRPP13L1 gene mutagenized by ethyl methanesulfonate (EMS) showed extremely significantly higher flour redness and lower yellowness than wild type. Our data suggest that the TaRPP13L1 gene plays an important role in modulating wheat flour colour. This study provides useful information for further dissection of the genetic basis of flour colour and also provides valuable genes or genetic loci for marker‐assisted selection to improve the process of breeding quality wheat in China.     

Genome‐wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China

Bread wheat is a leading cereal crop worldwide. Limited amount of superior allele loci restricted the progress of molecular improvement in wheat breeding. Here, we revealed new allelic variation distribution for 13 yield‐related traits in series of genome‐wide association studies (GWAS) using the wheat 90K genotyping assay, characterized in 163 bread wheat cultivars. Agronomic traits were investigated in 14 environments at three locations over 3 years. After filtering SNP data sets, GWAS using 20 689 high‐quality SNPs associated 1769 significant loci that explained, on average, ~20% of the phenotypic variation, both detected already reported loci and new promising genomic regions. Of these, repetitive and pleiotropic SNPs on chromosomes 6AS, 6AL, 6BS, 5BL and 7AS were significantly linked to thousand kernel weight, for example BS00021705_51 on 6BS and wsnp_Ex_c32624_41252144 on 6AS, with phenotypic variation explained (PVE) of ~24%, consistently identified in 12 and 13 of the 14 environments, respectively. Kernel length‐related SNPs were mainly identified on chromosomes 7BS, 6AS, 5AL and 5BL. Plant height‐related SNPs on chromosomes 4DS, 6DL, 2DS and 1BL were, respectively, identified in more than 11 environments, with averaged PVE of ~55%. Four SNPs were confirmed to be important genetic loci in two RIL populations. Based on repetivity and PVE, a total of 41 SNP loci possibly played the key role in modulating yield‐related traits of the cultivars surveyed. Distribution of superior alleles at the 41 SNP loci indicated that superior alleles were getting popular with time and modern cultivars had integrated many superior alleles, especially for peduncle length‐ and plant height‐related superior alleles. However, there were still 19 SNP loci showing less than percentages of 50% in modern cultivars, suggesting they should be paid more attention to improve yield‐related traits of cultivars in the Yellow and Huai wheat region. This study could provide useful information for dissection of yield‐related traits and valuable genetic loci for marker‐assisted selection in Chinese wheat breeding programme.     

Pleiotropy informed adaptive association test of multiple traits using genome‐wide association study summary data

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large‐scale genome‐wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease‐related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans‐Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the “single trait” association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well‐characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome‐phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual‐level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P‐value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta‐analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta‐analysis. All the proposed methods are implemented in a publicly available R package.     

Population structure and genetic basis of the agronomic traits of upland cotton in China revealed by a genome‐wide association study using high‐density SNPs

Gossypium hirsutum L. represents the largest source of textile fibre, and China is one of the largest cotton‐producing and cotton‐consuming countries in the world. To investigate the genetic architecture of the agronomic traits of upland cotton in China, a diverse and nationwide population containing 503 G. hirsutum accessions was collected for a genome‐wide association study (GWAS) on 16 agronomic traits. The accessions were planted in four places from 2012 to 2013 for phenotyping. The CottonSNP63K array and a published high‐density map based on this array were used for genotyping. The 503 G. hirsutum accessions were divided into three subpopulations based on 11 975 quantified polymorphic single‐nucleotide polymorphisms (SNPs). By comparing the genetic structure and phenotypic variation among three genetic subpopulations, seven geographic distributions and four breeding periods, we found that geographic distribution and breeding period were not the determinants of genetic structure. In addition, no obvious phenotypic differentiations were found among the three subpopulations, even though they had different genetic backgrounds. A total of 324 SNPs and 160 candidate quantitative trait loci (QTL) regions were identified as significantly associated with the 16 agronomic traits. A network was established for multieffects in QTLs and interassociations among traits. Thirty‐eight associated regions had pleiotropic effects controlling more than one trait. One candidate gene, Gh_D08G2376, was speculated to control the lint percentage (LP). This GWAS is the first report using high‐resolution SNPs in upland cotton in China to comprehensively investigate agronomic traits, and it provides a fundamental resource for cotton genetic research and breeding.     

Genetic dissection of drought and heat‐responsive agronomic traits in wheat

High yield and wide adaptation are principal targets of wheat breeding but are hindered by limited knowledge on genetic basis of agronomic traits and abiotic stress tolerances. In this study, 277 wheat accessions were phenotyped across 30 environments with non‐stress, drought‐stressed, heat‐stressed, and drought‐heat‐stressed treatments and were subjected to genome‐wide association study using 395 681 single nucleotide polymorphisms. We detected 295 associated loci including consistent loci for agronomic traits across different treatments and eurytopic loci for multiple abiotic stress tolerances. A total of 22 loci overlapped with quantitative trait loci identified by biparental quantitative trait loci mapping. Six loci were simultaneously associated with agronomic traits and abiotic stress tolerance, four of which fell within selective sweep regions. Selection in Chinese wheat has increased the frequency of superior marker alleles controlling yield‐related traits in the four loci during past decades, which conversely diminished favourable genetic variation controlling abiotic stress tolerance in the same loci; two promising candidate paralogous genes colocalized with such loci, thereby providing potential targets for studying the molecular mechanism of stress tolerance–productivity trade‐off. These results uncovering promising alleles controlling agronomic traits and/or multiple abiotic stress tolerances, providing insights into heritable covariation between yield and abiotic stress tolerance, will accelerate future efforts for wheat improvement.     

A genome‐wide association study suggests new candidate genes for milk production traits in Chinese Holstein cattle

A genome‐wide association study (GWAS) was conducted on 15 milk production traits in Chinese Holstein. The experimental population consisted of 445 cattle, each genotyped by the GGP (GeneSeek genomic profiling)‐BovineLD V3 SNP chip, which had 26 151 public SNPs in its manifest file. After data cleaning, 20 326 SNPs were retained for the GWAS. The phenotypes were estimated breeding values of traits, provided by a public dairy herd improvement program center that had been collected once a month for 3 years. Two statistical models, a fixed‐effect linear regression model and a mixed‐effect linear model, were used to estimate the association effects of SNPs on each of the phenotypes. Genome‐wide significant and suggestive thresholds were set at 2.46E‐06 and 4.95E‐05 respectively. The two statistical models concurrently identified two genome‐wide significant (P < 0.05) SNPs on milk production traits in this Chinese Holstein population. The positional candidate genes, which were the ones closest to these two identified SNPs, were EEF2K (eukaryotic elongation factor 2 kinase) and KLHL1 (kelch like family member 1). These two genes could serve as new candidate genes for milk yield and lactation persistence, yet their roles need to be verified in further function studies.     

Linkage disequilibrium pattern and genome‐wide association mapping for meat traits in multiple porcine F2 crosses

In the present study, data from four F2 crosses were analysed and used to study the linkage disequilibrium (LD) structure within and across the crosses. Genome‐wide association analyses (GWASes) for conductivity and dressing out meat traits were conducted using single‐marker and Bayesian multi‐marker models using the pooled data from all F2 crosses. Porcine F2 crosses generated from the distantly related founder breeds Wild Boar, Piétrain and Meishan, as well as from a porcine F2 cross from the closely related founder breed Piétrain and an F1 Large White × Landrace cross were pooled. A total of 2572 F2 animals were genotyped using a 62K SNP chip. The positions of the SNPs were based on genome assembly Sscrofa11.1. After post‐alignment and genotype filtering, approximately 50K SNPs were usable for LD studies and GWASes. The main findings of the present study are that the breakdown of LD was faster in crosses from closely related founder breeds compared to crosses from distantly related founders. The fastest breakdown of LD was observed by pooling the data. Based on the single‐marker results and LD structure, clusters and windows were built for 1‐Mb intervals. For conductivity and dressing out, 183 and 191 nominal significant associations respectively and six and five clusters respectively were found. Dominance was important for conductivity, and considering dominance in GWASes improved the mapping signals. Most clear signals were found for conductivity on SSC6, 8 and 15 and for dressing out on SSC2 and 7. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. However, further research is needed to investigate if dominance is also important in crossbreed pig breeding schemes.     

Metabolite‐based genome‐wide association study enables dissection of the flavonoid decoration pathway of wheat kernels

The marriage of metabolomic approaches with genetic design has proven a powerful tool in dissecting diversity in the metabolome and has additionally enhanced our understanding of complex traits. That said, such studies have rarely been carried out in wheat. In this study, we detected 805 metabolites from wheat kernels and profiled their relative contents among 182 wheat accessions, conducting a metabolite‐based genome‐wide association study (mGWAS) utilizing 14 646 previously described polymorphic SNP markers. A total of 1098 mGWAS associations were detected with large effects, within which 26 candidate genes were tentatively designated for 42 loci. Enzymatic assay of two candidates indicated they could catalyse glucosylation and subsequent malonylation of various flavonoids and thereby the major flavonoid decoration pathway of wheat kernel was dissected. Moreover, numerous high‐confidence genes associated with metabolite contents have been provided, as well as more subdivided metabolite networks which are yet to be explored within our data. These combined efforts presented the first step towards realizing metabolomics‐associated breeding of wheat.     

The genetic architecture of genome‐wide recombination rate variation in allopolyploid wheat revealed by nested association mapping

Recombination affects the fate of alleles in populations by imposing constraints on the reshuffling of genetic information. Understanding the genetic basis of these constraints is critical for manipulating the recombination process to improve the resolution of genetic mapping, and reducing the negative effects of linkage drag and deleterious genetic load in breeding. Using sequence‐based genotyping of a wheat nested association mapping (NAM) population of 2,100 recombinant inbred lines created by crossing 29 diverse lines, we mapped QTL affecting the distribution and frequency of 102 000 crossovers (CO). Genome‐wide recombination rate variation was mostly defined by rare alleles with small effects together explaining up to 48.6% of variation. Most QTL were additive and showed predominantly trans‐acting effects. The QTL affecting the proximal COs also acted additively without increasing the frequency of distal COs. We showed that the regions with decreased recombination carry more single nucleotide polymorphisms (SNPs) with possible deleterious effects than the regions with a high recombination rate. Therefore, our study offers insights into the genetic basis of recombination rate variation in wheat and its effect on the distribution of deleterious SNPs across the genome. The identified trans‐acting additive QTL can be utilized to manipulate CO frequency and distribution in the large polyploid wheat genome opening the possibility to improve the efficiency of gene pyramiding and reducing the deleterious genetic load in the low‐recombining pericentromeric regions of chromosomes.     

Pathway analysis of genome‐wide association datasets of personality traits

Although several genome‐wide association (GWA) studies of human personality have been recently published, genetic variants that are highly associated with certain personality traits remain unknown, due to difficulty reproducing results. To further investigate these genetic variants, we assessed biological pathways using GWA datasets. Pathway analysis using GWA data was performed on 1089 Korean women whose personality traits were measured with the Revised NEO Personality Inventory for the 5‐factor model of personality. A total of 1042 pathways containing 8297 genes were included in our study. Of these, 14 pathways were highly enriched with association signals that were validated in 1490 independent samples. These pathways include association of: Neuroticism with axon guidance [L1 cell adhesion molecule (L1CAM) interactions]; Extraversion with neuronal system and voltage‐gated potassium channels; Agreeableness with L1CAM interaction, neurotransmitter receptor binding and downstream transmission in postsynaptic cells; and Conscientiousness with the interferon‐gamma and platelet‐derived growth factor receptor beta polypeptide pathways. Several genes that contribute to top‐ranked pathways in this study were previously identified in GWA studies or by pathway analysis in schizophrenia or other neuropsychiatric disorders. Here we report the first pathway analysis of all five personality traits. Importantly, our analysis identified novel pathways that contribute to understanding the etiology of personality traits.     

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.     

A genome‐wide association study of bipolar disorder and comorbid migraine

Both migraine and bipolar affective disorder (BPAD) are complex phenotypes with significant genetic and nongenetic components. Epidemiological and clinical studies have showed a high degree of comorbidity between migraine and BPAD, and overlapping regions of linkage have been shown in numerous genome‐wide linkage studies. To identify susceptibility factors for the BPAD/migraine phenotype, we conducted a genome‐wide association study (GWAS) in 1001 cases with bipolar disorder collected through the NIMH Genetics Initiative for Bipolar Disorder and genotyped at 1 m single‐nucleotide polymorphisms (SNPs) as part of the Genetic Association Information Network (GAIN). We compared BPAD patients without any headache (n = 699) with BPAD patients with doctor diagnosed migraine (n = 56). The strongest evidence for association was found for several SNPs in a 317‐kb region encompassing the uncharacterized geneKIAA0564 {e.g. rs9566845 [OR = 4.98 (95% CI: 2.6–9.48), P = 7.7 × 10^?8] and rs9566867 (P = 8.2 × 10^?8)}. Although the level of signficance was significantly reduced when using the Fisher's exact test (as a result of the low count of cases with migraine), rs9566845 P = 1.4 × 10^?5 and rs9566867 P = 1.5 × 10^?5, this region remained the most prominent finding. Furthermore, marker rs9566845 was genotyped and found associated with migraine in an independent Norwegian sample of adult attention deficit hyperactivity disorder (ADHD) patients with and without comorbid migraine (n = 131 and n = 324, respectively), OR = 2.42 (1.18–4.97), P = 0.013. This is the first GWAS examining patients with bipolar disorder and comorbid migraine. These data suggest that genetic variants in the KIAA0564 gene region may predispose to migraine headaches in subgroups of patients with both BPAD and ADHD.     

A genome‐wide association study identifies multiple loci associated with mathematics ability and disability

Numeracy is as important as literacy and exhibits a similar frequency of disability. Although its etiology is relatively poorly understood, quantitative genetic research has demonstrated mathematical ability to be moderately heritable. In this first genome‐wide association study (GWAS) of mathematical ability and disability, 10 out of 43 single nucleotide polymorphism (SNP) associations nominated from two high‐ vs. low‐ability (n = 600 10‐year‐olds each) scans of pooled DNA were validated (P < 0.05) in an individually genotyped sample of 1 2356 individuals spanning the entire distribution of mathematical ability, as assessed by teacher reports and online tests. Although the effects are of the modest sizes now expected for complex traits and require further replication, interesting candidate genes are implicated such as NRCAM which encodes a neuronal cell adhesion molecule. When combined into a set, the 10 SNPs account for 2.9% (F = 56.85; df = 1 and 1881; P = 7.277e–14) of the phenotypic variance. The association is linear across the distribution consistent with a quantitative trait locus (QTL) hypothesis; the third of children in our sample who harbour 10 or more of the 20 risk alleles identified are nearly twice as likely (OR = 1.96; df = 1; P = 3.696e–07) to be in the lowest performing 15% of the distribution. Our results correspond with those of quantitative genetic research in indicating that mathematical ability and disability are influenced by many genes generating small effects across the entire spectrum of ability, implying that more highly powered studies will be needed to detect and replicate these QTL associations.     

Genome‐wide association study for pigmentation traits in Chinese Holstein population

With the Illumina BovineSNP50K BeadChip, we performed a genome‐wide association study (GWAS) for two pigmentation traits in a Chinese Holstein population: proportion of black (PB) and teat colour (TC). A case–control design was used. Cases were the cows with PB <0.30 (n = 129) and TC <2 points (n = 140); controls were those with PB >0.90 (n = 58) and TC >4 points (n = 281). The RM test of roadtrips (version 1.2) was applied to detect SNPs for the two traits with 42 883 and 42 741 SNPs respectively. A total of nine and 12 genome‐wide significant (P < 0.05) SNPs associated with PB and TC respectively were identified. Of these, two SNPs for PB were located within the KIT and IGFBP7 genes, and the other four SNPs were 23~212 kb away from the PDGFRA gene on BTA6; nine SNPs associated with TC were located within or 21~78.8 kb away from known genes on chromosomes 4, 11, 22, 23 and 24. By combing through our GWAS results and the biological functions of the genes, we suggest that the KIT, IGFBP7, PDGFRA, MITF, ING3 and WNT16 genes are promising candidates for PB and TC in Holstein cattle, providing a basis for further investigation on the genetic mechanism of pigmentation formation.     

Meta‐analysis of genome‐wide association from genomic prediction models

Genome‐wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta‐analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal‐centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population‐level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.     

A genome‐wide association study reveals candidate genes for the supernumerary nipple phenotype in sheep (Ovis aries)

Genome‐wide association studies (GWASs) have been widely applied in livestock to identify genes associated with traits of economic interest. Here, we conducted the first GWAS of the supernumerary nipple phenotype in Wadi sheep, a native Chinese sheep breed, based on Ovine Infinium HD SNP BeadChip genotypes in a total of 144 ewes (75 cases with four teats, including two normal and two supernumerary teats, and 69 control cases with two teats). We detected 63 significant SNPs at the chromosome‐wise threshold. Additionally, one candidate region (chr1: 170.723–170.734 Mb) was identified by haplotype‐based association tests, with one SNP (rs413490006) surrounding functional genes BBX and CD47 on chromosome 1 being commonly identified as significant by the two mentioned analyses. Moreover, Gene Ontology enrichment for the significant SNPs identified by the GWAS analysis was functionally clustered into the categories of receptor activity and synaptic membrane. In addition, pathway mapping revealed four promising pathways (Wnt, oxytocin, MAPK and axon guidance) involved in the development of the supernumerary nipple phenotype. Our results provide novel and important insights into the genetic mechanisms underlying the phenotype of supernumerary nipples in mammals, including humans. These findings may be useful for future breeding and genetics in sheep and other livestock.     

Identification of genetic loci affecting amylose content and agronomic traits on chromosome 4A of wheat

 Chromosome 4A of wheat carries the Wx-B1 gene encoding the granule-bound starch synthase involved in amylose synthesis in the endosperm. To determine the pleiotropic effects of this locus and effects of independent QTLs on agronomic traits, genetical analysis of chromosome 4A was conducted using 98 single-chromosome recombinant substitution lines derived from a cross of Chinese Spring and Chinese Spring (Kanto107 4A) with a low amylose content due to the null Wx-B1b allele. For amylose content, most of the genetic variation was explained by the allelic difference at the Wx-B1 locus. An additional QTL of minor effect was mapped in the 6.2-cM Xbcd1738/Xcdo1387 interval on the short arm, where the allele from Kanto107 led to an increase in amylose content. Field trials over two seasons revealed a pleiotropic effect of Wx-B1, or else the effect of a closely linked QTL, on ear emergence time. A QTL linked to Wx-B1 was detected for plant height. For plant yield and its components, there was no evidence for significant main effects associated with Wx-B1 or adjacent regions. One plant-yield QTL was identified by RFLP markers on the short arm and this was identical to QTLs controlling spikelet number/ear and grain weight/ear. At these QTLs for agronomic traits, alleles from Kanto107 contributed to an earlier emergence time, a height reduction and an yield increase. Received: 10 August 1998 / Accepted: 3 November 1998     

A genome‐wide association study using selective DNA pooling identifies candidate markers for fertility in Holstein cattle

The decline in the reproductive efficiency of dairy cows, especially those with high producing potential, has become a challenging problem. In this study, a selective DNA pooling approach was applied to a cow population whose oocytes were fertilized and cultured to obtain phenotypic records of fertilization rate and blastocyst rate. Using a stringent 5% genome‐wide significance level, 22 and five single nucleotide polymorphisms (SNPs) were found to be associated with fertilization rate and blastocyst rate, respectively. SNPs that showed significant association in selective DNA pooling were further evaluated by individual genotyping. Interestingly, the majority of the SNP associations were confirmed by individual genotyping, testifying to the effectiveness of selective DNA pooling using a high‐density SNP genotyping array. This study is the first application of the selective DNA pooling approach using the BovineSNP50 array in cattle.