A RIL population of 242 F₅:F₇-F₈ lines was developed from a cross between two yellow bean genotypes of the Andean gene pool: Ervilha (ADP0512) and PI527538 (ADP0468) (Figure 1; Bassett and Cichy, 2020). The RILs were developed by advancing F₂ seed via single seed descent to the F₅ generation and then bulking seeds from individual plants to form RILs.

Ervilha is a pale yellow Manteca seed type with a gray hilum with a seed weight of 52.8 (g per 100 seeds) in this study (Supplementary Table 1). Ervilha was originally collected at a marketplace in Angola in 2010 (Cichy et al., 2015a). PI527538 is a yellow-green Njano seed type with hints of purple and a black hilum with a seed weight of 48.0 (g per 100 seeds) in this study (Supplementary Table 1). PI527538 was originally collected in Burundi in 1985 (Cichy et al., 2015a). Both genotypes are likely members of race Nueva Granada. These genotypes were selected to develop a RIL population after a screening of 206 lines of the Andean Diversity Panel (ADP) for cooking time, mineral concentration, and iron bioavailability (Cichy et al., 2015b; Katuuramu et al., 2018). Ervilha cooks about 10 min faster than PI527538 when soaked, and this relative difference in cooking time is stable across growing environments, although specific cooking times vary depending on the growing environment and storage conditions (Cichy et al., 2015b; Katuuramu et al., 2020).

The genotypes were grown at the Montcalm Research Farm in Lakeview, MI in 2016 and 2017. The soil type is Eutric Glossoboralfs (coarse-loamy and mixed) and Alfic Fragiorthods (coarse-loamy, mixed, and frigid). Two row plots 4.75 m long with 0.5 m spacing between rows were arranged in a randomized complete block design with two replications per genotype. In 2016, 100 seeds were planted per plot due to limited seed, and in 2017, 160 seeds were planted per plot. Standard agronomic practices were followed as described in the MSU SVREC 2017 Farm Research Report (Kelly et al., 2017). Plants were hand-pulled at maturity and threshed with a Hege 140 plot harvester (Wintersteiger, Salt Lake City, UT, United States). Following harvest, seeds were cleaned by hand to remove field debris, off types, and damaged seed. Seed weights (g per 100 seeds) and seed yield (kg per ha) were recorded for each field replicate.

For both years, images were collected for one field replicate of each genotype using a custom machine vision system as described in Mendoza et al. (2017). For each image, a 60 × 15 mm petri dish was filled with representative seeds cleaned of debris and damaged seeds. The EOS Rebel T3i software settings were consistent across each image as follows: lens aperture f = 5.6, shutter speed 1/125, white balanced, and ISO = 100. Following image collection, each image was cropped to center the petri dish and minimize background. To examine the relationship among color, cooking time, and sensory attributes, CIELAB values were obtained using a custom batch macro in ImageJ developed by Bornowski et al. (2020) that applies a gamma correction of 0.5, excludes background pixels outside the petri dish, and measures each slice of the LAB stack. CIELAB uses three values to describe color: L^∗ for black (0) to white (100), a^∗ for green (−) to red (+), and b^∗ for blue (−) to yellow (+). These values were collected relative to the imaging conditions and reflect average color of seeds without calibration for the purpose of observing differences among lines rather than determining absolute color.

Variability in seed-coat postharvest darkening among genotypes was observed after the first year, so the potential presence of the non-darkening trait in this population was explored. Genotypes grown in 2017 were stored for approximately 2 years in opaque paper bags in a cool, dry barn prior to evaluation for seed-coat postharvest darkening in January 2020. Samples that appeared visibly darkened after this storage period were given a score of 1 and those that remained light were given a score of 0.

For each year, two field replicates of 30 seed per genotype were equilibrated to 10–14% moisture in a 4°C humidity chamber prior to evaluating for cooking time. Each 30 seed sample was soaked for 12 h in distilled water prior to cooking time evaluation using an automated Mattson cooker method (Wang and Daun, 2005). Genotypes were cooked in a random order to minimize seed aging effects. Seed weights after soaking were recorded for each sample to determine water uptake. Mattson cookers loaded with soaked seeds were placed into 4 L stainless steel beakers with 1.8 L of boiling distilled water on Cuisinart CB-30 Countertop Single Burners to cook. The Mattson cookers (Michigan State University Machine Shop, East Lansing, MI, United States) use twenty-five 65 g stainless steel rods with 2 mm diameter pins to pierce beans as they finish cooking in each well. As the pins drop, a custom software reports the cooking time associated with each pin. A low boil was maintained during cooking, and the 80% cooking times were recorded and regarded as the time required to fully cook each sample. Final cooked seed weights were recorded.

Ervilha, PI527538, and the RILs were evaluated in duplicate using a modified Quantitative Descriptive Analysis (QDA) approach (Stone et al., 1974), in which four panelists per session independently evaluated samples using a non-consensus approach to limit group bias. For the purposes of this study, the QDA approach was modified as described by Bassett et al. (2020b) to increase suitability for implementation in public breeding programs with limited resources. In brief, seeds from each field replicate were prepared for sensory evaluation in the same order that they were cooked for cooking time evaluation to minimize seed aging effects. Sensory evaluation sessions were held daily with four panelists per session until each genotype had been evaluated twice for each year. Twelve genotypes were evaluated at each session including Ervilha and PI527538 as controls. Each sample was evaluated using 5-point attribute intensity scales (low → high intensity) for total, beany, vegetative, earthy, starchy, bitter, and sweet flavor intensities as well as seed-coat perception and cotyledon texture. The scale for seed-coat perception ranged from imperceptible (1) to tough and lingering (5). For cotyledon texture, the scale ranged from mushy (1) to very gritty/firm (5) (Bassett et al., 2020b). This sensory evaluation protocol was approved by the Institutional Review Board of Michigan State University (IRB# x16-763e Category: Exempt 6).

Panelists were recruited from the USDA (East Lansing, MI, United States) and Michigan State University dry bean breeding programs due to their familiarity with dry beans and their availability for long-term sensory evaluation projects. Initially, seven panelists were trained using a diverse set of dry bean genotypes selected from the USDA and MSU dry bean programs with the intention of exposing panelists to a wide range of attribute intensities. This initial set included dark red kidney, Jacob’s cattle, white kidney, and yellow beans. A training set of genotypes exhibiting extreme attribute intensities identified in the ADP (Bassett et al., 2020b) was used to train eleven panelists for the second year. This training set was grown at the MSU Montcalm Research Center in Lakeview, MI alongside the RIL population.

Panelists were trained over multiple sessions using a consensus approach. Panelists then practiced using a non-consensus approach to improve their familiarity with the selected scales and their sensory evaluation skills. Panelist performance was assessed via ANOVA with F_Genotype (p-value < 0.05) indicating ability to discriminate and F_rep (p-value > 0.05) indicating consistency (Meilgaard et al., 1999; Armelim et al., 2006). Sensory evaluation commenced after successful training of each panelist. Following screening of the parents and RILs from both years, panel performance was assessed as during training.

Samples were prepared as described in Bassett et al. (2020b). Prior to each session, four seeds per panelist of each genotype scheduled for evaluation were soaked for 12 h in distilled water prior to cooking. Large tea bags filled with the soaked samples were boiled in distilled water for the cooking time determined by the Mattson cooker method, timed so they all finished cooking together. The cooked samples were poured into preheated (105°C) ceramic ramekins, covered with aluminum foil, and placed in a chafing dish to maintain temperature prior to evaluation. Samples were given a random letter code to mask their identity. Panelists were asked to refrain from wearing strong scents or eating during the hour before each session. Samples were served out of the ceramic ramekins with a plastic spoon onto paper plates. Lemon water was made available as a palate cleanser, and panelists were asked to drink water between samples.

PROC MIXED in SAS version 9.4 of the SAS System for Windows (SAS Institute Inc., Cary, NC, United States) was used to conduct analyses of variance (ANOVAs) for all traits. For seed weight, seed yield, water uptake, and cooking time, the fixed effects were genotype, year, and genotype by year with replicate as a random effect. For L^∗, a^∗, and b^∗ color values, the fixed effects were genotype and year with no random effects. For the sensory attribute intensity traits, the fixed effects were genotype, year, and genotype by year with replicate, panelist (year), and session (year) as random effects. Least squares estimates for sensory traits were calculated via the LSMeans statement in PROC MIXED for visualization of trait distributions. Mean separation of parents was determined using pdiff in PROC MIXED. Density plots of traits were generated in R (R Core Team, 2017) using the sm package version 2.2–5.6 (Bowman and Azzalini, 2018).

To analyze both years combined while minimizing environmental effects, best linear unbiased predictors (BLUPs) were generated for each trait using the lme4 package (Bates et al., 2015) in R version 4.0.3 with genotype, year, genotype by year, and rep nested in year as random effects. For sensory traits, panelist nested in year and session nested in year were also included as random effects. For analysis within individual years, BLUPs were calculated for sensory traits with genotype, rep, panelist, and session included as random effects. The normality of trait distributions was assessed visually using Q–Q plots.

Broad sense heritability (H²) was calculated on a family mean basis for each trait using the following equation:

where σg2 is genotypic variance, σg⁢y2 is genotype year interaction variance, σe2 is environmental variance, y is number of years, and r is number of replications. For seed-coat postharvest non-darkening, the σg⁢y2y component was excluded from the equation as the trait was only assessed in 1 year. Variance components were calculated using PROC VARCOMP in SAS version 9.4 with method = restricted maximum likelihood method (reml) (Holland et al., 2003). Principal component analysis (PCA) among traits was conducted via singular value decomposition of the centered and scaled BLUPs from both years combined using the prcomp function in R.

Genomic DNA was extracted from young trifoliate leaf tissue from three plants each for the 242 RILs and the two parental lines (Ervilha and PI527538) using a Macherey-Nagel NucleoSpin Plant II kit. For each genotype, 10 μL aliquots of DNA at a minimum concentration of 50 ng/μL were loaded into 96-well plates with parental lines prepared in quintuplicate. The plates were genotyped at the USDA Beltsville Agricultural Research Center in Beltsville, MD (BARC) using the BARCBean12K BeadChip, which includes all SNPs from the BARCBean6K_3 (Song et al., 2015) and additional SNPs among a set of Andean accessions. Illumina’s GenomeStudio software was used to confirm variant calling. SNP positions were reported according to Phaseolus vulgaris v2.1 genome (DOE-JGI and USDA-NIFA¹) positions. Raw SNPs (11,292 markers) were filtered to eliminate markers that were heterozygous or monomorphic (9,085 markers), duplicates (1,301 markers), or exhibiting extreme segregation distortion (p-value < 1e^–10; 31 markers).

Linkage mapping was performed using MapDisto version 2.1.7 (Heffelfinger et al., 2017). Markers causing excessive map length and/or exhibiting aberrant segregation distortion patterns were excluded (five markers). A fixed-order genetic map of 439.53 cM was generated using the Kosambi function with the remaining 870 markers. Markers were grouped by chromosome with marker order reflecting physical positions. The Ripple function was used to confirm marker order.

Quantitative trait loci mapping was performed using QTL Cartographer version 2.5 (Wang et al., 2012). The composite interval mapping (CIM) procedure was performed with the parameters set to 10 cM window size and 1 cM walkspeed with forward and backward regression. BLUPs were used for all traits in QTL mapping for both years combined and for sensory traits for individual years, and means were used for analyses of all other traits for individual years. The LOD thresholds for each trait in each year and across years were determined using 1,000 permutations in scanone from rQTL with the extended Haley–Knott method (p-value < 0.05) (Broman et al., 2003; Feenstra et al., 2006). QTL regions were defined including all significant markers for each QTL peak. LOD information by position is available for each trait in the Supplementary Material. The constructed linkage maps with QTL overlaid were visualized using Mapchart 2.32 (Voorrips, 2002). Each QTL was named according to the guidelines for common bean QTL nomenclature (Miklas and Porch, 2010).

Genotype and year significantly affected water uptake, and cooking time (p-value < 0.05) and genotype by year significantly affected cooking time (p-value < 0.05) (Table 1 and Supplementary Table 1). The parental lines, Ervilha and PI527538 had water uptakes of 109.3 and 98.8% and cooking times of 21.0 and 29.7 min, respectively averaged across both years.

Water uptake and cooking time for the RILs exhibited approximately normal distributions (Supplementary Table 2 and Figure 2). Averaged across both years, water uptake ranged 69.2–117.4%, and cooking time ranged 19.1–33.9 min (Table 1). Broad-sense heritability for cooking time was 0.68 and water uptake was 0.25 (Table 1).

Genotype significantly affected all sensory attributes (p-value < 0.05) (Table 1). Year did not significantly affect any sensory attributes, and genotype by year only significantly affected cotyledon texture (p-value < 0.05). Rep effects were insignificant for all sensory attributes, which indicates panelists were consistent across reps, although significant panelist and session effects were observed (Supplementary Table 3). For the parents Ervilha and PI527538, respectively with least squares estimates averaged across both years, the total flavor intensities were 3.1 and 3.2; beany intensities were 2.2 and 3.3; vegetative intensities were 2.7 and 2.5; earthy intensities were 2.0 and 2.2; starchy intensities were 3.6 and 3.0; sweet intensities were 2.3 and 1.8; bitter intensities were 1.4 and 1.9; seed-coat perceptions were 2.8 and 3.4; and cotyledon textures were 2.4 and 2.0 (Table 1).

Least squares estimates for all sensory attribute intensities varied minimally across years and exhibited approximately normal distributions (Supplementary Table 2 and Figure 3). Across both years, least squares estimates ranged 2.2–4.1 for total flavor intensity, 1.5–3.9 for beany intensity, 1.7–3.4 for vegetative intensity, 1.5–3.1 for earthy intensity, 2.5–3.9 for starchy intensity, 1.3–3.2 for sweet intensity, 1.1–2.3 for bitter intensity, 2.4–3.9 for seed-coat perception, and 1.4–3.0 for cotyledon texture (Table 1). While panelists were able to differentiate among genotypes using 5-point scales, sensory attribute ranges did not exceed 2.4, suggesting panelists did not make full use of the scales. This could reflect the limited differences in sensory attribute intensities observed between the parents.

Broad-sense heritability for sensory attribute intensities were low, ranging from 0.05 to 0.33 (Table 1). Beany intensity and total flavor intensity exhibited the highest broad-sense heritability (0.33 and 0.27), while vegetative intensity, earthy intensity, and cotyledon texture exhibited the lowest (0.05, 0.06, and 0.06).

Genotype significantly affected L^∗, a^∗, b^∗, and seed-coat postharvest non-darkening (p-value < 0.05) (Table 1). Year significantly affected L^∗, a^∗, and b^∗ (p-value < 0.05). For the parents Ervilha and PI527538, respectively averaged across both years, L^∗ values were 64.8 and 54.1; a^∗ values were −0.7 and 3.5; b^∗ values were 22.3 and 14.6; and seed-coat postharvest non-darkening values were 0 (non-darkening) and 1 (darkening).

The L^∗, a^∗, and b^∗ for the RILs varied minimally across years and exhibited approximately normal distributions (Supplementary Table 2 and Figure 4). Averaged across both years, L^∗ ranged from 40.3–67.3; a^∗ ranged from −3.2 to 5.9; and b^∗ ranged from 8.5 to 34.4 (Table 1). Seed-coat postharvest darkening was only determined for seeds from 1 year (2017), and progeny exhibiting both non-darkening and darkening were observed. Broad-sense heritability was high for L^∗ (0.86), a^∗ (0.86), b^∗ (0.78), and seed-coat postharvest non-darkening (1.00).

Genotype, year, and genotype by year significantly affected seed weight and seed yield (p-value < 0.05) (Supplementary Table 1). For the parents Ervilha and PI527538, respectively averaged across both years, the seed weights were 52.8 and 48.0 g per 100 seeds. Seed yield data for Ervilha is not available for 2016 (Supplementary Table 2), and fewer seeds were planted per plot in 2016, making averages across years misleading. In 2017, the seed yields for Ervilha and PI527538, respectively, were 1,731.4 and 2,384.4 kg/ha.

The seed weight for the RILs exhibited approximately normal distributions (Supplementary Table 2 and Supplementary Figure 1). Seed yield for the RILs varied substantially across years due to reduced seeds planted per plot in 2016 but exhibited approximately normal distributions. Averaged across both years, seed weight ranged 39.1–68.4 g per 100 seeds and seed yield ranged 751.0–3,283.9 kg per ha (Supplementary Table 2).

Broad-sense heritability for seed weight (0.89) was high and for seed yield was moderate (0.57) (Supplementary Table 1).

A PCA for the seed quality trait relationship was conducted and the first two principal components (PCs) explained approximately 52% of the variance (Figure 5). The first PC separates the genotypes approximately by beany, earthy, and bitter intensities as well as L^∗, a^∗, b^∗, and seed-coat postharvest non-darkening and represents over a third of the variation (38.5%). The second PC separates the genotypes approximately by cooking time; total flavor, vegetative, starchy, and sweet intensities; and cotyledon texture and seed-coat perception. The second PC represents over an eighth of the variance (13.0%). The remaining PCs accounted for 11.1, 7.4, 6.0, 5.4, 4.2, 3.2, 2.9, 2.5, 2.2, 1.6, 1.1, and 0.9% of the variance, respectively (data not shown).

The PCA biplot highlights distinct groupings of traits that tend to be observed together. Loadings that group together highlight strong positive relationships within each group, and groups of loadings opposite of each other highlight strong negative relationships between groups. Loadings for starchy intensity, sweet intensity, and cotyledon texture are positioned close to each other and opposite cooking time and seed-coat perception. Loadings for beany intensity and bitter intensity also group together and are somewhat opposite starchy intensity, sweet intensity, and cotyledon texture. The loadings for total flavor intensity earthy intensity, a^∗, and seed-coat postharvest non-darkening group together, opposite of loadings for L^∗ and b^∗. The loading for vegetative intensity does not appear to group with or opposite of other loadings but lies in between loadings for total flavor intensity and sweet intensity. The genotypes are spread across the biplot, with Ervilha and PI527538 positioned opposite each other.

A linkage map was developed with 870 SNPs spread across eleven chromosomes for a total map length of 439.53 cM with a marker density of one SNP per 0.51 cM (Table 2). Significant QTL were identified using BLUPs from both years combined for water uptake, cooking time, total flavor intensity, beany intensity, vegetative intensity, earthy intensity, starchy intensity, sweet intensity, bitter intensity, seed-coat perception, cotyledon texture, L^∗, a^∗, b^∗, seed-coat postharvest non-darkening, seed weight, and seed yield (Tables 3–5, Figures 6–8, and Supplementary Figure 2). Additional QTL were also identified in individual years for these traits (Supplementary Tables 4–7).

Several QTL were identified for water uptake and cooking time in both years combined. For water uptake, three QTL were identified: WU4.1, WU9.1, and WU10.1 (Table 3 and Figure 6). The total proportion of variance explained by the three QTL was 19.0%. For cooking time, six QTL were identified: CT2.2, CT2.3, CT5.3, CT8.1, CT8.2, and CT10.2 (Table 3 and Figure 6). The total proportion of variance explained by the six QTL was 50.9%. CT8.2 and CT10.2 were the most significant cooking time QTL identified. RILs with both fast-cooking alleles for these QTL cooked 5 min faster on average than RILs without fast-cooking alleles (Figure 9).

Many QTL were identified across all sensory characteristics in both years combined. For total flavor intensity, four QTL were identified: TFI2.1, TFI3.1, TFI7.1, and TFI10.1 (Table 4 and Figure 6). The proportion of variance explained by the four QTL was 39.3%. For beany intensity, two QTL were identified: BFI3.1 and BFI10.1 (Table 4 and Figure 7). The proportion of the variance explained by the two QTL was 38.2%. For vegetative intensity, one QTL was identified: VFI7.1 (Table 4 and Figure 7). The proportion of variance explained by VFI7.1 was 9.3%. For earthy intensity, one QTL was identified: EFI3.2 (Table 4 and Figure 7). The proportion of variance explained by EFI3.2 was 5.9%. For starchy intensity, four QTL were identified: STI1.1, STI3.1, STI6.1, and STI10.1 (Table 4 and Figure 7). The proportion of variance explained by the four QTL was 29.2%. For sweet intensity, six QTL were identified: SWI1.1, SWI2.1, SWI3.1, SWI7.1, SWI8.1, and SWI8.2 (Table 4 and Figure 7). The proportion of variance explained by the six QTL was 32.5%. For bitter intensity, three QTL were identified: BI2.1, BI3.1, and BI10.1 (Table 4 and Figure 7). The proportion of variance explained by the three QTL was 24.3%. For seed-coat perception, three QTL were identified: SPE1.1, SPE3.1, and SPE10.1 (Table 4 and Figure 7). The proportion of variance explained by the three QTL was 29.8%. For cotyledon texture, four QTL were identified: CTX3.1, CTX4.1, CTX7.1, and CTX10.1 (Table 4 and Figure 7). The proportion of variance explained by the four QTL was 24.4%.

Many QTL were identified for color traits across both years combined. For L^∗, four QTL were identified: SL^∗3.1, SL^∗6.1, SL^∗8.1, and SL^∗10.1 (Table 5 and Figure 8). The total proportion of variance explained by four QTL was 70.4%. For a^∗, four QTL were identified: Sa^∗1.1, Sa^∗3.1, Sa^∗3.2, and Sa^∗10.1 (Table 5 and Figure 8). The total proportion of variance explained by the four QTL was 61.2%. For b^∗, five QTL were identified: Sb^∗3.1, Sb^∗4.1, Sb^∗4.2, Sb^∗5.1, and Sb^∗10.1 (Table 5 and Figure 8). The total proportion of variance explained by the five QTL was 39.3%. For seed-coat postharvest non-darkening, one QTL was identified: ND.10.1 (Table 5 and Figure 8). Seed-coat postharvest non-darkening was only evaluated for 2017 seeds. The proportion of variance explained by ND10.1 was 87.5%, and Ervilha contributed an allele conferring a negative effect, reflecting its lack of darkening over time (Tables 1, 5).

While seed weight and seed yield were not central to this study, several QTL were identified for these traits as well. Additional information is available in the supplementary material (Supplementary Figure 2 and Supplementary Table 7).

Several QTL co-localized on Pv01, Pv03, Pv04, Pv07, Pv08, and Pv10. On Pv01, QTL for starchy intensity (STI1.1), sweet intensity (SWI1.1), and seed-coat perception (SPE1.1) co-localized. Alleles from Ervilha conferred positive effects for STI1.1 and SWI1.1 and a negative effect for SPE1.1. On Pv03, QTL for total flavor intensity (TFI3.1), beany intensity (BFI3.1), earthy intensity (EFI3.1 and EFI3.2), starchy intensity (STI3.1), sweet intensity (SWI3.1), bitter intensity (BI3.1), seed-coat perception (SPE3.1), cotyledon texture (CTX3.1), L^∗ (SL^∗3.1), a^∗ (Sa^∗3.1), and b^∗ (Sb^∗3.1) co-localized. Alleles from Ervilha conferred positive effects for STI3.1, SWI3.1, CTX3.1, SL^∗3.1, and Sb^∗3.1 and negative effects for TFI3.1, BFI3.1, EFI3.1, EFI3.2, BI3.1, SPE3.1, and Sa^∗3.1. On Pv04, QTL for water uptake (WU4.1 and WU4.2) and b^∗ (Sb^∗4.1 and Sb^∗4.2) co-localized. Alleles from Ervilha conferred positive effects for WU4.2, Sb^∗4.1, and Sb^∗4.2 and a negative effect for WU4.1. On Pv07, QTL for total flavor intensity (TFI7.1), vegetative intensity (VFI7.1), sweet intensity (SWI7.1), and cotyledon texture (CTX7.1 and CTX7.2) co-localized. Alleles from Ervilha conferred positive effects for TFI7.1, VFI7.1, SWI7.1, CTX7.1, and CTX7.2. On Pv08, QTL for cooking time (CT8.1), starchy intensity (STI8.1), sweet intensity (SWI8.1 and SWI8.2), and L^∗ (SL^∗8.1) co-localized. Alleles from Ervilha conferred positive effects for STI8.1, SWI8.1, SWI8.2, and SL^∗8.1 and a negative effect for CT8.1. On Pv10, QTL for water uptake (WU10.1), cooking time (CT10.2), total flavor intensity (TFI10.1), beany intensity (BFI10.1), starchy intensity (STI10.1), bitter intensity (BI10.1), seed-coat perception (SPE10.1), cotyledon texture (CTX10.1), L^∗ (SL^∗10.1), a^∗ (Sa^∗10.1), b^∗ (Sb^∗10.1), and seed-coat postharvest non-darkening (ND10.1) co-localized. Alleles from Ervilha conferred positive effects for WU10.1, STI10.1, CTX10.1, SL^∗10.1, and Sb^∗10.1 and negative effects for CT10.2, TFI10.1, BFI10.1, BI10.1, SPE10.1, Sa^∗10.1, and ND10.1.