Heritability of interpack aggression in a wild pedigreed population of North American grey wolves

Aggression is a quantitative trait deeply entwined with individual fitness. Mapping the genomic architecture underlying such traits is complicated by complex inheritance patterns, social structure, pedigree information and gene pleiotropy. Here, we leveraged the pedigree of a reintroduced population of grey wolves (Canis lupus) in Yellowstone National Park, Wyoming, USA, to examine the heritability of and the genetic variation associated with aggression. Since their reintroduction, many ecological and behavioural aspects have been documented, providing unmatched records of aggressive behaviour across multiple generations of a wild population of wolves. Using a linear mixed model, a robust genetic relationship matrix, 12,288 single nucleotide polymorphisms (SNPs) and 111 wolves, we estimated the SNP‐based heritability of aggression to be 37% and an additional 14% of the phenotypic variation explained by shared environmental exposures. We identified 598 SNP genotypes from 425 grey wolves to resolve a consensus pedigree that was included in a heritability analysis of 141 individuals with SNP genotype, metadata and aggression data. The pedigree‐based heritability estimate for aggression is 14%, and an additional 16% of the phenotypic variation was explained by shared environmental exposures. We find strong effects of breeding status and relative pack size on aggression. Through an integrative approach, these results provide a framework for understanding the genetic architecture of a complex trait that influences individual fitness, with linkages to reproduction, in a social carnivore. Along with a few other studies, we show here the incredible utility of a pedigreed natural population for dissecting a complex, fitness‐related behavioural trait.     

Heritability estimates from genomewide relatedness matrices in wild populations: Application to a passerine,using a small sample size

Genomic developments have empowered the investigation of heritability in wild populations directly from genomewide relatedness matrices (GRM). Such GRM‐based approaches can in particular be used to improve or substitute approaches based on social pedigree (PED‐social). However, measuring heritability from GRM in the wild has not been widely applied yet, especially using small samples and in nonmodel species. Here, we estimated heritability for four quantitative traits (tarsus length, wing length, bill length and body mass), using PED‐social, a pedigree corrected by genetic data (PED‐corrected) and a GRM from a small sample (n = 494) of blue tits from natural populations in Corsica genotyped at nearly 50,000 filtered SNPs derived from RAD‐seq. We also measured genetic correlations among traits, and we performed chromosome partitioning. Heritability estimates were slightly higher when using GRM compared to PED‐social, and PED‐corrected yielded intermediate values, suggesting a minor underestimation of heritability in PED‐social due to incorrect pedigree links, including extra‐pair paternity, and to lower information content than the GRM. Genetic correlations among traits were similar between PED‐social and GRM but credible intervals were very large in both cases, suggesting a lack of power for this small data set. Although a positive linear relationship was found between the number of genes per chromosome and the chromosome heritability for tarsus length, chromosome partitioning similarly showed a lack of power for the three other traits. We discuss the usefulness and limitations of the quantitative genetic inferences based on genomic data in small samples from wild populations.     

RAD‐sequencing for estimating genomic relatedness matrix‐based heritability in the wild: A case study in roe deer

Estimating the evolutionary potential of quantitative traits and reliably predicting responses to selection in wild populations are important challenges in evolutionary biology. The genomic revolution has opened up opportunities for measuring relatedness among individuals with precision, enabling pedigree‐free estimation of trait heritabilities in wild populations. However, until now, most quantitative genetic studies based on a genomic relatedness matrix (GRM) have focused on long‐term monitored populations for which traditional pedigrees were also available, and have often had access to knowledge of genome sequence and variability. Here, we investigated the potential of RAD‐sequencing for estimating heritability in a free‐ranging roe deer (Capreolous capreolus) population for which no prior genomic resources were available. We propose a step‐by‐step analytical framework to optimize the quality and quantity of the genomic data and explore the impact of the single nucleotide polymorphism (SNP) calling and filtering processes on the GRM structure and GRM‐based heritability estimates. As expected, our results show that sequence coverage strongly affects the number of recovered loci, the genotyping error rate and the amount of missing data. Ultimately, this had little effect on heritability estimates and their standard errors, provided that the GRM was built from a minimum number of loci (above 7,000). Genomic relatedness matrix‐based heritability estimates thus appear robust to a moderate level of genotyping errors in the SNP data set. We also showed that quality filters, such as the removal of low‐frequency variants, affect the relatedness structure of the GRM, generating lower h² estimates. Our work illustrates the huge potential of RAD‐sequencing for estimating GRM‐based heritability in virtually any natural population.     

基于高密度SNP标记估计群体间遗传关联

联合育种的准确性受到群体间遗传关联程度的影响.本研究通过比较基于系谱数据和基因组数据计算的群体遗传关联,探究高密度SNP标记在遗传关联估计中的应用前景.本研究同时使用了模拟数据和真实数据,采用6种不同的遗传关联计算方法,包括PEVD(prediction error variance of differences)、P...     

The influence of nonrandom extra‐pair paternity on heritability estimates derived from wild pedigrees

Quantitative genetic analysis is often fundamental for understanding evolutionary processes in wild populations. Avian populations provide a model system due to the relative ease of inferring relatedness among individuals through observation. However, extra‐pair paternity (EPP) creates erroneous links within the social pedigree. Previous work has suggested this causes minor underestimation of heritability if paternal misassignment is random and hence not influenced by the trait being studied. Nevertheless, much literature suggests numerous traits are associated with EPP and the accuracy of heritability estimates for such traits remains unexplored. We show analytically how nonrandom pedigree errors can influence heritability estimates. Then, combining empirical data from a large great tit (Parus major) pedigree with simulations, we assess how heritability estimates derived from social pedigrees change depending on the mode of the relationship between EPP and the focal trait. We show that the magnitude of the underestimation is typically small (<15%). Hence, our analyses suggest that quantitative genetic inference from pedigrees derived from observations of social relationships is relatively robust; our approach also provides a widely applicable method for assessing the consequences of nonrandom EPP.     

Small‐scale spatial variation in evolvability for life‐history traits in the storm petrel

The evolutionary potential in the timing of recruitment and reproduction may be crucial for the ability of populations to buffer against environmental changes, allowing them to avoid unfavourable breeding conditions. The evolution of a trait in a local population is determined by its heritability and selection. In the present study, we performed pedigree‐based quantitative genetic analyses for two life‐history traits (recruiting age and laying date) using population data of the storm petrel over an 18‐year period in two adjacent breeding colonies (only 150 m apart) that share the same environmental conditions. In both traits, natal colony effect was the main source of the phenotypic variation among individuals, and cohort variance for recruitment age and additive genetic variance for laying date were natal colony‐specific. We found significant heritability only in laying date and, more specifically, only in birds born in one of the colonies. The difference in genetic variance between the colonies was statistically significant. Interestingly, selection on earlier breeding birds was detected only in the colony in which heritable variation in laying date was found. Therefore, local evolvability for a life‐history trait may vary within a unexpectedly small spatial scale, through the diversifying natural selection and insulating gene flow. © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 106 , 439–446.     

Quantitative genetic parameters for wild stream‐living brown trout: heritability and parental effects

Adaptability depends on the presence of additive genetic variance for important traits. Yet few estimates of additive genetic variance and heritability are available for wild populations, particularly so for fishes. Here, we estimate heritability of length‐at‐age for wild‐living brown trout (Salmo trutta), based on long‐term mark‐recapture data and pedigree reconstruction based on large‐scale genotyping at 15 microsatellite loci. We also tested for the presence of maternal and paternal effects using a Bayesian version of the Animal model. Heritability varied between 0.16 and 0.31, with reasonable narrow confidence bands, and the total phenotypic variance increased with age. When introducing dam as an additional random effect (accounting for c. 7% of total phenotypic variance), the level of additive genetic variance and heritability decreased (0.12–0.21). Parental size (both for sires and for dams) positively influenced length‐at‐age for juvenile trout – either through direct parental effects or through genotype‐environment correlations. Length‐at‐age is a complex trait reflecting the effects of a number of physiological, behavioural and ecological processes. Our data show that fitness‐related traits such as length‐at‐age can retain high levels of additive genetic variance even when total phenotypic variance is high.     

SNP‐based heritability and genetic architecture of tarsal osteochondrosis in North American Standardbred horses

Osteochondrosis is a common developmental orthopedic disease characterized by a failure of endochondral ossification. Standardbred horses are recognized as being predisposed to tarsal osteochondrosis. Prior heritability estimates for tarsal osteochondrosis in European Standardbreds and related trotting breeds have been based on pedigree data and range from 17–29%. Here, we report on genetic architecture and heritability based on high‐density genotyping data in a cohort of North American Standardbreds (n = 479) stringently phenotyped for tarsal osteochondrosis. Whole‐genome array genotyping data were imputed to ~2 million single nucleotide polymorphisms (SNPs). SNP‐based heritability of osteochondrosis in this population was explained by 2326 SNPs. The majority of these SNPs (86.6%) had small effects, whereas fewer SNPs had moderate or large effects (10% and 2.9% respectively), which is consistent with a polygenic/complex disease. Heritability was estimated at 0.24 ± 0.16 using two methods of restricted maximum likelihood analysis, as implemented in gcta (with and without a weighted relatedness matrix) and ldak software. Estimates were validated using bootstrapping. Heritability estimates were within the range previously reported and suggest that osteochondrosis is moderately heritable but that a significant portion of disease risk is due to environmental factors and/or genotype × environment interactions. Future identification of the genes/variants that have the most impact on disease risk may allow early recognition of high‐risk individuals.     

Data depth, data completeness, and their influence on quantitative genetic estimation in two contrasting bird populations

Evolutionary biologists increasingly use pedigree‐based quantitative genetic methods to address questions about the evolutionary dynamics of traits in wild populations. In many cases, phenotypic data may have been collected only for recent parts of the study. How does this influence the performance of the models used to analyse these data? Here we explore how data depth (number of years) and completeness (number of observations) influence estimates of genetic variance and covariance within the context of an existing pedigree. Using long‐term data from the great tit Parus major and the mute swan Cygnus olor, species with different life‐histories, we examined the effect of manipulating the amount of data included on quantitative genetic parameter estimates. Manipulating data depth and completeness had little influence on estimated genetic variances, heritabilities, or genetic correlations, but (as expected) did influence confidence in these estimates. Estimated breeding values in the great tit were not influenced by data depth but were in the mute swan, probably because of differences in pedigree structure. Our analyses suggest the ‘rule of thumb’ that data from 3 years and a minimum of 100 individuals per year are needed to estimate genetic parameters with acceptable confidence, and that using pedigree data is worthwhile, even if phenotypes are only available toward the tips of the pedigree.     

A new genomic tool for walnut (Juglans regia L.): development and validation of the high‐density Axiom™ J. regia 700K SNP genotyping array

Over the last 20 years, global production of Persian walnut (Juglans regia L.) has grown enormously, likely reflecting increased consumption due to its numerous benefits to human health. However, advances in genome‐wide association (GWA) studies and genomic selection (GS) for agronomically important traits in walnut remain limited due to the lack of powerful genomic tools. Here, we present the development and validation of a high‐density 700K single nucleotide polymorphism (SNP) array in Persian walnut. Over 609K high‐quality SNPs have been thoroughly selected from a set of 9.6 m genome‐wide variants, previously identified from the high‐depth re‐sequencing of 27 founders of the Walnut Improvement Program (WIP) of University of California, Davis. To validate the effectiveness of the array, we genotyped a collection of 1284 walnut trees, including 1167 progeny of 48 WIP families and 26 walnut cultivars. More than half of the SNPs (55.7%) fell in the highest quality class of ‘Poly High Resolution’ (PHR) polymorphisms, which were used to assess the WIP pedigree integrity. We identified 151 new parent‐offspring relationships, all confirmed with the Mendelian inheritance test. In addition, we explored the genetic variability among cultivars of different origin, revealing how the varieties from Europe and California were differentiated from Asian accessions. Both the reconstruction of the WIP pedigree and population structure analysis confirmed the effectiveness of the Applied Biosystems? Axiom? J. regia 700K SNP array, which initiates a novel genomic and advanced phase in walnut genetics and breeding.     

High‐density SNP assay development for genetic analysis in maritime pine (Pinus pinaster)

Maritime pine provides essential ecosystem services in the south‐western Mediterranean basin, where it covers around 4 million ha. Its scattered distribution over a range of environmental conditions makes it an ideal forest tree species for studies of local adaptation and evolutionary responses to climatic change. Highly multiplexed single nucleotide polymorphism (SNP) genotyping arrays are increasingly used to study genetic variation in living organisms and for practical applications in plant and animal breeding and genetic resource conservation. We developed a 9k Illumina Infinium SNP array and genotyped maritime pine trees from (i) a three‐generation inbred (F2) pedigree, (ii) the French breeding population and (iii) natural populations from Portugal and the French Atlantic coast. A large proportion of the exploitable SNPs (2052/8410, i.e. 24.4%) segregated in the mapping population and could be mapped, providing the densest ever gene‐based linkage map for this species. Based on 5016 SNPs, natural and breeding populations from the French gene pool exhibited similar level of genetic diversity. Population genetics and structure analyses based on 3981 SNP markers common to the Portuguese and French gene pools revealed high levels of differentiation, leading to the identification of a set of highly differentiated SNPs that could be used for seed provenance certification. Finally, we discuss how the validated SNPs could facilitate the identification of ecologically and economically relevant genes in this species, improving our understanding of the demography and selective forces shaping its natural genetic diversity, and providing support for new breeding strategies.     

Genomic reaction norm models exploiting genotype × environment interaction on sexual precocity indicator traits in Nellore cattle

Brazilian beef cattle are raised predominantly on pasture in a wide range of environments. In this scenario, genotype by environment (G×E) interaction is an important source of phenotypic variation in the reproductive traits. Hence, the evaluation of G×E interactions for heifer’s early pregnancy (HP) and scrotal circumference (SC) traits in Nellore cattle, belonging to three breeding programs, was carried out to determine the animal’s sensitivity to the environmental conditions (EC). The dataset consisted of 85 874 records for HP and 151 553 records for SC, from which 1800 heifers and 3343 young bulls were genotyped with the BovineHD BeadChip. Genotypic information for 826 sires was also used in the analyses. EC levels were based on the contemporary group solutions for yearling body weight. Linear reaction norm models (RNM), using pedigree information (RNM_A) or pedigree and genomic information (RNM_H), were used to infer G×E interactions. Two validation schemes were used to assess the predictive ability, with the following training populations: (a) forward scheme—dataset was split based on year of birth from 2008 for HP and from 2011 for SC; and (b) environment-specific scheme—low EC (−3.0 and −1.5) and high EC (1.5 and 3.0). The inclusion of the H matrix in RNM increased the genetic variance of the intercept and slope by 18.55 and 23.00% on average respectively, and provided genetic parameter estimates that were more accurate than those considering pedigree only. The same trend was observed for heritability estimates, which were 0.28–0.56 for SC and 0.26–0.49 for HP, using RNM_H, and 0.26–0.52 for SC and 0.22–0.45 for HP, using RNM_A. The lowest correlation observed between unfavorable (−3.0) and favorable (3.0) EC levels were 0.30 for HP and −0.12 for SC, indicating the presence of G×E interaction. The G×E interaction effect implied differences in animals’ genetic merit and re-ranking of animals on different environmental conditions. SNP marker–environment interaction was detected for Nellore sexual precocity indicator traits with changes in effect and variance across EC levels. The RNM_H captured G×E interaction effects better than RNM_A and improved the predictive ability by around 14.04% for SC and 21.31% for HP. Using the forward scheme increased the overall predictive ability for SC (20.55%) and HP (11.06%) compared with the environment-specific scheme. The results suggest that the inclusion of genomic information combined with the pedigree to assess the G×E interaction leads to more accurate variance components and genetic parameter estimates.     

Genome‐wide association and epistasis studies unravel the genetic architecture of sudden death syndrome resistance in soybean

Soybean [Glycine max (L.) Merr.] is an economically important crop that is grown worldwide. Sudden death syndrome (SDS), caused by Fusarium virguliforme, is one of the top yield‐limiting diseases in soybean. However, the genetic basis of SDS resistance, especially with respect to epistatic interactions, is still unclear. To better understand the genetic architecture of soybean SDS resistance, genome‐wide association and epistasis studies were performed using a population of 214 germplasm accessions and 31 914 SNPs from the SoySNP50K Illumina Infinium BeadChip. Twelve loci and 12 SNP–SNP interactions associated with SDS resistance were identified at various time points after inoculation. These additive and epistatic loci together explained 24–52% of the phenotypic variance. Disease‐resistant, pathogenesis‐related and chitin‐ and wound‐responsive genes were identified in the proximity of peak SNPs, including stress‐induced receptor‐like kinase gene 1 (SIK1), which is pinpointed by a trait‐associated SNP and encodes a leucine‐rich repeat‐containing protein. We report that the proportion of phenotypic variance explained by identified loci may be considerably improved by taking epistatic effects into account. This study shows the necessity of considering epistatic effects in soybean SDS resistance breeding using marker‐assisted and genomic selection approaches. Based on our findings, we propose a model for soybean root defense against the SDS pathogen. Our results facilitate identification of the molecular mechanism underlying SDS resistance in soybean, and provide a genetic basis for improvement of soybean SDS resistance through breeding strategies based on additive and epistatic effects.     

Genetics and caging type affect birth weight in captive pigtailed macaques (Macaca nemestrina)

The heritability of birth weight was estimated in 3,562 captive pigtailed macaques using 30 years of breeding and pedigree records. Based on a pedigree of over 12,000 animals, quantitative genetic analyses were performed using statistical variance decomposition methods. The model included additive genetic effects, cytoplasmic genetic effects, birth environment, shared maternal environment, and unmeasured environmental effects. The results demonstrated a strong (h(2) = 0.51) heritable component of birth weight overall, and included significant additive genetic heritability (h(2) = 0.23), and cytoplasmic heritability (h(2) = 0.09). In addition, a significant effect of birth location and cage type was identified, explaining an additional 6% of birth weight variance. The use of a nonhuman primate model for studying the effects of genes on birth weight eliminated many of the problems associated with confounding variables in human studies, and allowed for the quantification of a heritable component of birth weight.     

Pedigree‐free quantitative genetic approach provides evidence for heritability of movement tactics in wild roe deer

Assessing the evolutionary potential of animal populations in the wild is crucial to understanding how they may respond to selection mediated by rapid environmental change (e.g. habitat loss and fragmentation). A growing number of studies have investigated the adaptive role of behaviour, but assessments of its genetic basis in a natural setting remain scarce. We combined intensive biologging technology with genome‐wide data and a pedigree‐free quantitative genetic approach to quantify repeatability, heritability and evolvability for a suite of behaviours related to the risk avoidance‐resource acquisition trade‐off in a wild roe deer (Capreolus capreolus) population inhabiting a heterogeneous, human‐dominated landscape. These traits, linked to the stress response, movement and space‐use behaviour, were all moderately to highly repeatable. Furthermore, the repeatable among‐individual component of variation in these traits was partly due to additive genetic variance, with heritability estimates ranging from 0.21 ± 0.08 to 0.70 ± 0.11 and evolvability ranging from 1.1% to 4.3%. Changes in the trait mean can therefore occur under hypothetical directional selection over just a few generations. To the best of our knowledge, this is the first empirical demonstration of additive genetic variation in space‐use behaviour in a free‐ranging population based on genomic relatedness data. We conclude that wild animal populations may have the potential to adjust their spatial behaviour to human‐driven environmental modifications through microevolutionary change.     

Evaluating seasonal patterns of female aggression: Case study in a cavity‐nesting bird with intense female–female competition

Seasonal plasticity in aggression is likely to be shaped by the contexts in which aggression is beneficial, as well as the constraints inherent in its underlying mechanisms. In males, seasonal plasticity in testosterone (T) secretion is thought to underlie seasonal plasticity in conspecific aggression, but it is less clear how and why female aggression may vary across different breeding stages. Here, we integrate functional and mechanistic perspectives to begin to explore seasonal patterns of conspecific aggression in female tree swallows (Tachycineta bicolor), a songbird with intense female–female competition and T‐mediated aggression. Female tree swallows elevate T levels during early breeding stages, coinciding with competition for nest boxes, after which time T levels are roughly halved. However, females need to defend ownership of their nesting territory throughout the breeding season, suggesting it may be adaptive to maintain aggressive capabilities, despite low T levels. We performed simulated territorial intrusions using 3D‐printed decoys of female tree swallows to determine how their aggressive response to a simulated intrusion changes across the breeding season. First, we found that 3D‐printed decoys produce data comparable to stage‐matched studies using live decoys, providing researchers with a new, more economical method of decoy construction. Further, female aggressiveness remained relatively high through incubation, a period of time when T levels are quite low, suggesting that other mechanisms may regulate conspecific female aggression during parental periods. By showing that seasonal patterns of female aggression do not mirror the established patterns of T levels in this highly competitive bird, our findings provide a unique glimpse into how behavioural mechanisms and functions may interact across breeding stages to regulate plasticity.     

Sex differences in the genetic architecture of aggressiveness in a sexually dimorphic spider

Sex differences in the genetic architecture of behavioral traits can offer critical insight into the processes of sex‐specific selection and sexual conflict dynamics. Here, we assess genetic variances and cross‐sex genetic correlations of two personality traits, aggression and activity, in a sexually size‐dimorphic spider, Nuctenea umbratica. Using a quantitative genetic approach, we show that both traits are heritable. Males have higher heritability estimates for aggressiveness compared to females, whereas the coefficient of additive genetic variation and evolvability did not differ between the sexes. Furthermore, we found sex differences in the coefficient of residual variance in aggressiveness with females exhibiting higher estimates. In contrast, the quantitative genetic estimates for activity suggest no significant differentiation between males and females. We interpret these results with caution as the estimates of additive genetic variances may be inflated by nonadditive genetic effects. The mean cross‐sex genetic correlations for aggression and activity were 0.5 and 0.6, respectively. Nonetheless, credible intervals of both estimates were broad, implying high uncertainty for these estimates. Future work using larger sample sizes would be needed to draw firmer conclusions on how sexual selection shapes sex differences in the genetic architecture of behavioral traits.     

Genetic variants and underlying mechanisms influencing variance heterogeneity in maize

Traditional genetic studies focus on identifying genetic variants associated with the mean difference in a quantitative trait. Because genetic variants also influence phenotypic variation via heterogeneity, we conducted a variance‐heterogeneity genome‐wide association study to examine the contribution of variance heterogeneity to oil‐related quantitative traits. We identified 79 unique variance‐controlling single nucleotide polymorphisms (vSNPs) from the sequences of 77 candidate variance‐heterogeneity genes for 21 oil‐related traits using the Levene test (P < 1.0 × 10^?5). About 30% of the candidate genes encode enzymes that work in lipid metabolic pathways, most of which define clear expression variance quantitative trait loci. Of the vSNPs specifically associated with the genetic variance heterogeneity of oil concentration, 89% can be explained by additional linked mean‐effects genetic variants. Furthermore, we demonstrated that gene × gene interactions play important roles in the formation of variance heterogeneity for fatty acid compositional traits. The interaction pattern was validated for one gene pair (GRMZM2G035341 and GRMZM2G152328) using yeast two‐hybrid and bimolecular fluorescent complementation analyses. Our findings have implications for uncovering the genetic basis of hidden additive genetic effects and epistatic interaction effects, and we indicate opportunities to stabilize efficient breeding and selection of high‐oil maize (Zea mays L.).     

Impact of reconstructed pedigrees on progeny-test breeding values in red spruce

Pedigrees reconstructed through DNA marker assigned paternities in polymix (PMX) and open pollinated (OP) progeny tests were analyzed using mixed models to test the effect of unequal male reproductive success and pedigree errors on quantitative genetic parameters. The reconstructed pedigree increased heritabilities in the larger PMX test. Increased heritability resulted from adding the paternities to the pedigree per se, not by correcting the male reproductive bias by specifying the exact pedigree. Removing hypothesized pedigree errors had no effect on quantitative parameters, either because the magnitude of the errors was too small (PMX) or the progeny test was too small to detect variance components reliably (OP). Although there was no advantage in backwards selection, the increased additive variance, heritabilities and accuracy of progeny with assigned paternities in the pedigree, should permit forward selection of offspring with greater genetic gain and complete control of coancestry for future breeding decisions. Some possible breeding population structures with the new genetic information are discussed.     

High‐throughput genomics in sorghum: from whole‐genome resequencing to a SNP screening array

With its small, diploid and completely sequenced genome, sorghum (Sorghum bicolor L. Moench) is highly amenable to genomics‐based breeding approaches. Here, we describe the development and testing of a robust single‐nucleotide polymorphism (SNP) array platform that enables polymorphism screening for genome‐wide and trait‐linked polymorphisms in genetically diverse S. bicolor populations. Whole‐genome sequences with 6× to 12× coverage from five genetically diverse S. bicolor genotypes, including three sweet sorghums and two grain sorghums, were aligned to the sorghum reference genome. From over 1 million high‐quality SNPs, we selected 2124 Infinium Type II SNPs that were informative in all six source genomes, gave an optimal Assay Design Tool (ADT) score, had allele frequencies of 50% in the six genotypes and were evenly spaced throughout the S. bicolor genome. Furthermore, by phenotype‐based pool sequencing, we selected an additional 876 SNPs with a phenotypic association to early‐stage chilling tolerance, a key trait for European sorghum breeding. The 3000 attempted bead types were used to populate half of a dual‐species Illumina iSelect SNP array. The array was tested using 564 Sorghum spp. genotypes, including offspring from four unrelated recombinant inbred line (RIL) and F₂ populations and a genetic diversity collection. A high call rate of over 80% enabled validation of 2620 robust and polymorphic sorghum SNPs, underlining the efficiency of the array development scheme for whole‐genome SNP selection and screening, with diverse applications including genetic mapping, genome‐wide association studies and genomic selection.