DOI: 10.5061/dryad.73n5tb33v
title: Data from: Does the number of functional olfactory receptor genes predict olfactory sensitivity and discrimination performance in mammals?

description: The number of functional genes coding for olfactory receptors differs
 markedly between species and has repeatedly been suggested to be
 predictive of a species’ olfactory capabilities. To test this assumption,
 we compiled a database of all published olfactory detection threshold
 values in mammals and used three sets of data on olfactory discrimination
 performance that employed the same structurally related monomolecular odor
 pairs with different mammal species. We extracted the number of functional
 olfactory receptor genes of the 20 mammal species for which we found data
 on olfactory sensitivity and/or olfactory discrimination performance from
 the Chordata Olfactory Receptor Database. We found that the overall
 olfactory detection thresholds significantly correlates with the number of
 functional olfactory receptor genes. Similarly, the overall proportion of
 successfully discriminated monomolecular odor pairs significantly
 correlates with the number of functional olfactory receptor genes. These
 results provide the first statistically robust evidence for the relation
 between olfactory capabilities and their genomics correlates. However,
 when analysed individually, of the 44 monomolecular odorants for which
 data on olfactory sensitivity from at least five mammal species are
 available, only five yielded a significant correlation between olfactory
 detection thresholds and the number of functional olfactory receptors
 genes. Also, for the olfactory discrimination performance, no significant
 correlation was found for any of the 74 relationships between the
 proportion of successfully discriminated monomolecular odor pairs and the
 number of functional olfactory receptor genes. While only a rather limited
 amount of data on olfactory detection thresholds and olfactory
 discrimination scores in a rather limited number of mammal species is
 available so far, we conclude that the number of functional olfactory
 receptor genes may be a predictor of olfactory sensitivity and
 discrimination performance in mammals.
description: <strong>Figure S1</strong>. Monotonic correlations
 between the number of functional olfactory receptor genes and olfactory
 detection threshold values for 1-octanol (A, rho = -0.975 p = 0.005),
 2-octanone (B, rho = -1 p = 0.017), 2-nonanone (C, rho = -1 p =
 0.017), <em>n</em>-butyl acetate (D, rho = 0.883 p = 0.009),
 and iso-butyl acetate (E, rho = -0.886  p = 0.033).
 <strong>Figure S2.</strong> Plot of the 128 monotonic
 correlations performed against the number of functional olfactory receptor
 genes. A: results based on Spearman's rank-correlation test and B,
 results based on PGLS.<strong> </strong>
 <strong>Figure S3.</strong> Distributions of the
 likelihood ratio statistic for two evolutionary models (Brownian motion
 (BM) and white noise) fitted to the residuals of the regressions between
 the number of functional olfactory receptor genes and the threshold
 detection of the n-butanoic acid (A) as well as between the number of
 functional olfactory receptor genes and the success of odor pairs
 discrimination (B, C, D, E). For the success of odor pairs discrimination,
 we considered the enantiomers, level 1 (B), the discrimination of
 aliphatic with different carbon chain length, 1-Alcohols, level 2A (C),
 the Acetic esters C5 vs. C7, level 3A (D), and the Limonene, level 3C (E).
 The vertical line (sometimes too close form the Y-axis to be visible)
 indicates the observed value of likelihood ratio.
 <strong>Figure S4.</strong> Significant pairwise
 differences in the detection threshold of monomolecular odorants across 12
 and 10 mammal species. A negative delta value supports the hypothesis of a
 negative relation between the number of functional olfactory receptor
 genes and olfactory sensitivity, while a positive delta value indicates
 the opposite. Subplot A shows results without Bonferroni correction, and
 subplot B shows results with Bonferroni correction. For
 this plot, we employed a Welch test to compare the series of threshold
 detection common to pairs of species, selecting species with at least four
 monomolecular threshold test comparisons in common. When the cell is
 colored, it indicates a significant difference between the paired species.
 Subsequently, the mean threshold detection of shared monomolecular
 odorants was calculated, and this value was compared between the paired
 species, specifically between the species with the highest number of
 olfactory receptor genes and the other species in the pair. This resulting
 value represents the delta. A negative delta p-value implies that, in the
 pairwise comparison, the species with a higher number of functional
 olfactory receptor genes exhibits a lower olfactory sensitivity threshold.
 Conversely, a positive delta p-value suggests that, in the pairwise
 comparison, the species with the lower number of functional olfactory
 receptor genes has the lower olfactory sensitivity threshold.
 Without Bonferroni correction, out of the 56 tested pairwise
 comparisons, 32 are significant, comprising 20 negative delta values and
 12 positive delta values. With Bonferroni correction, out of the 56 tested
 pairwise comparisons, 14 are significant, comprising 10 negative delta
 values and 4 positive delta values. We express
 skepticism about this approach as it minimally considers the actual
 difference in the number of functional olfactory receptor genes. It solely
 identifies whether one species has a higher number than another, without
 accounting for the magnitude of the difference. Consequently, drawing
 meaningful conclusions from this approach is challenging. We find greater
 confidence in the classical approach, where we plot and test the overall
 raw threshold of detection against continuous values of the number of
 functional olfactory receptor genes. This is exemplified by methods such
 as linear regression and logistic regression (Figure 1 and Table
 S8). <strong>Table S1:</strong> Tables
 including the lists of the references used to extract olfactory
 performance data. <strong>Table
 S2:</strong> Raw data and results of tests of correlation between
 the number of functional olfactory receptor genes and a species’ olfactory
 sensitivity. In this table and all the others, 'NA' indicates
 the value is not available or not applicable. Yellow highlights indicate
 the significance of the test. <strong>Table
 S3:</strong> Raw data and results of tests of correlation between
 the number of functional olfactory receptor genes and the proportion of
 successfully discriminated odor pairs according to the level 1 with data
 set A, B, and C (see Materials and Methods).
 <strong>Table S4:</strong> Raw data and results of
 tests of correlation between the number of functional olfactory receptor
 genes and the proportion of successfully discriminated odor pairs
 according to the level 2 with data set A, and B (see Materials and
 Methods). <strong>Table S5:</strong> Raw
 data and results of tests of correlation between the number of functional
 olfactory receptor genes and the proportion of successfully discriminated
 odor pairs according to the level 3 with data set A (see Materials and
 Methods). <strong>Table S6:</strong> Raw
 data and results of tests of correlation between the number of functional
 olfactory receptor genes and the proportion of successfully discriminated
 odor pairs according to the level 3 with data set B (see Materials and
 Methods). <strong>Table S7:</strong> Raw
 data and results of tests of correlation between the number of functional
 olfactory receptor genes and the proportion of successfully discriminated
 odor pairs according to the level 3 with data set C (see Materials and
 Methods). <strong>Table S8:</strong>
 Results of tests of correlation and model fitting for the unbalanced
 overall data sets and balanced subsets for olfactory detection thresholds
 as well as proportion of successfully discriminated monomolecular odor
 pairs. <strong>Table S9:</strong> Results
 of the phylogenetic inertia tests. <strong>Table
 S10:</strong> Results of the tests presented in the Table S8 but in
 this case, the number of functional olfactory receptor genes was
 transformed into numerical rankings. These rankings range from
 'one,' corresponding to the species with the lowest number of
 functional olfactory receptor genes, to the maximum number.
 <strong>Table S11:</strong> Results of the tests
 presented in the Table S10 but in this case, the data exclude trials
 wherein all species successfully discriminated odor
 pairs.<strong> </strong>
 <strong>Table S12:</strong> Acknowledgments and
 credits for the Creative Commons silhouettes used in the graphical
 abstract from the main research article: Does the number of functional
 olfactory receptor genes predict olfactory sensitivity and discrimination
 performance in mammals? The silhouettes herein referred to are only
 presented in the graphical abstract of the main article and not in the
 Dryad. All the files uploaded in the Dryad are under CC0 license terms
 (without any restriction). <strong>Data S1:
 </strong>CSV files and R scripts. The root folder contains five
 folders for olfactory discrimination tests with three levels (1, 2, 3
 corresponding respectively to large scale, small scale, and fine scale)
 tested and the three datasets (A, B, and C). In these respective folders,
 various subfolders contain R scripts and CSV files used to perform the
 analyses specified in the folder names. These analyses include
 evolutionary model comparison (BM and white noise), PGLS analyses, tests
 of statistical power, Spearman's rank-correlation test, and tests of
 phylogenetic inertia. The root folder also contains similar analyses for
 olfactory sensitivity, the comparison of phylogenetic inertia between a
 dataset of 20 species and a dataset of 396 species, Welch two-tailed
 tests, and the previous analyses transforming the data into numerical
 ranking from 1 to 5 with or without all the success. All these scripts
 were run with R studio version 2023.06.2+561 and the following packages:
 caper; lme4; ggplot2; tidyverse; lmtest; nlme; pmc; ggplot2; tidyr; dplyr;
 pwr; phytools; reshape2.
description: # Data from: Does the number of functional olfactory receptor genes
 predict olfactory sensitivity and discrimination performance in mammals?
 ## Description of the data
 and file structure The data consist of four figures, twelve tables, and
 one folder containing all the R scripts. **Figure S1**. Monotonic
 correlations between the number of functional olfactory receptor genes and
 olfactory detection threshold values for 1-octanol (A, rho = -0.975 p =
 0.005), 2-octanone (B, rho = -1 p = 0.017), 2-nonanone (C, rho = -1 p =
 0.017), *n*-butyl acetate (D, rho = 0.883 p = 0.009), and iso-butyl
 acetate (E, rho = -0.886  p = 0.033). **Figure S2.** Plot of the 128
 monotonic correlations performed against the number of functional
 olfactory receptor genes. A: results based on Spearman's
 rank-correlation test and B, results based on PGLS.** ** **Figure S3.**
 Distributions of the likelihood ratio statistic for two evolutionary
 models (Brownian motion (BM) and white noise) fitted to the residuals of
 the regressions between the number of functional olfactory receptor genes
 and the threshold detection of the n-butanoic acid (A) as well as between
 the number of functional olfactory receptor genes and the success of odor
 pairs discrimination (B, C, D, E). For the success of odor pairs
 discrimination, we considered the enantiomers, level 1 (B), the
 discrimination of aliphatic with different carbon chain length,
 1-Alcohols, level 2A (C), the Acetic esters C5 vs. C7, level 3A (D), and
 the Limonene, level 3C (E). The vertical line (sometimes too close form
 the Y-axis to be visible) indicates the observed value of likelihood
 ratio. **Figure S4.** Significant pairwise differences in the detection
 threshold of monomolecular odorants across 12 and 10 mammal species. A
 negative delta value supports the hypothesis of a negative relation
 between the number of functional olfactory receptor genes and olfactory
 sensitivity, while a positive delta value indicates the opposite. Subplot
 A shows results without Bonferroni correction, and subplot B shows results
 with Bonferroni correction. For this plot, we employed a Welch test to
 compare the series of threshold detection common to pairs of species,
 selecting species with at least four monomolecular threshold test
 comparisons in common. When the cell is colored, it indicates a
 significant difference between the paired species. Subsequently, the mean
 threshold detection of shared monomolecular odorants was calculated, and
 this value was compared between the paired species, specifically between
 the species with the highest number of olfactory receptor genes and the
 other species in the pair. This resulting value represents the delta. A
 negative delta p-value implies that, in the pairwise comparison, the
 species with a higher number of functional olfactory receptor genes
 exhibits a lower olfactory sensitivity threshold. Conversely, a positive
 delta p-value suggests that, in the pairwise comparison, the species with
 the lower number of functional olfactory receptor genes has the lower
 olfactory sensitivity threshold. Without Bonferroni correction, out of the
 56 tested pairwise comparisons, 32 are significant, comprising 20 negative
 delta values and 12 positive delta values. With Bonferroni correction, out
 of the 56 tested pairwise comparisons, 14 are significant, comprising 10
 negative delta values and 4 positive delta values. We express skepticism
 about this approach as it minimally considers the actual difference in the
 number of functional olfactory receptor genes. It solely identifies
 whether one species has a higher number than another, without accounting
 for the magnitude of the difference. Consequently, drawing meaningful
 conclusions from this approach is challenging. We find greater confidence
 in the classical approach, where we plot and test the overall raw
 threshold of detection against continuous values of the number of
 functional olfactory receptor genes. This is exemplified by methods such
 as linear regression and logistic regression (Figure 1 and Table S8).
 **Table S1:** Tables including the lists of the references used to extract
 olfactory performance data. **Table S2:** Raw data and results of tests of
 correlation between the number of functional olfactory receptor genes and
 a species’ olfactory sensitivity. In this table and all the others,
 'NA' indicates the value is not available or not applicable.
 Yellow highlights indicate the significance of the test. **Table S3:** Raw
 data and results of tests of correlation between the number of functional
 olfactory receptor genes and the proportion of successfully discriminated
 odor pairs according to the level 1 with data set A, B, and C (see
 Materials and Methods). **Table S4:** Raw data and results of tests of
 correlation between the number of functional olfactory receptor genes and
 the proportion of successfully discriminated odor pairs according to the
 level 2 with data set A, and B (see Materials and Methods). **Table S5:**
 Raw data and results of tests of correlation between the number of
 functional olfactory receptor genes and the proportion of successfully
 discriminated odor pairs according to the level 3 with data set A (see
 Materials and Methods). **Table S6:** Raw data and results of tests of
 correlation between the number of functional olfactory receptor genes and
 the proportion of successfully discriminated odor pairs according to the
 level 3 with data set B (see Materials and Methods). **Table S7:** Raw
 data and results of tests of correlation between the number of functional
 olfactory receptor genes and the proportion of successfully discriminated
 odor pairs according to the level 3 with data set C (see Materials and
 Methods). **Table S8:** Results of tests of correlation and model fitting
 for the unbalanced overall data sets and balanced subsets for olfactory
 detection thresholds as well as proportion of successfully discriminated
 monomolecular odor pairs. **Table S9:** Results of the phylogenetic
 inertia tests. **Table S10:** Results of the tests presented in the Table
 S8 but in this case, the number of functional olfactory receptor genes was
 transformed into numerical rankings. These rankings range from
 'one,' corresponding to the species with the lowest number of
 functional olfactory receptor genes, to the maximum number. **Table S11:**
 Results of the tests presented in the Table S10 but in this case, the data
 exclude trials wherein all species successfully discriminated odor
 pairs.** ** **Table S12:** Acknowledgments and credits for the Creative
 Commons silhouettes used in the graphical abstract from the main research
 article: Does the number of functional olfactory receptor genes predict
 olfactory sensitivity and discrimination performance in mammals? The
 silhouettes herein referred to are only presented in the graphical
 abstract of the main article and not in the Dryad. All the files uploaded
 in the Dryad are under CC0 license terms (without any restriction).
 **Data_S1_04_12_23:** CSV files and R scripts. The root folder contains
 five folders for olfactory discrimination tests with three levels (1, 2, 3
 corresponding respectively to large scale, small scale, and fine scale)
 tested and the three datasets (A, B, and C). In these respective folders,
 various subfolders contain R scripts and CSV files used to perform the
 analyses specified in the folder names. These analyses include
 evolutionary model comparison (BM and white noise), PGLS analyses, tests
 of statistical power, Spearman's rank-correlation test, and tests of
 phylogenetic inertia. The root folder also contains similar analyses for
 olfactory sensitivity, the comparison of phylogenetic inertia between a
 dataset of 20 species and a dataset of 396 species, Welch two-tailed
 tests, and the previous analyses transforming the data into numerical
 ranking from 1 to 5 with or without all the success. All these scripts
 were run with R studio version 2023.06.2+561 and the following packages:
 caper; lme4; ggplot2; tidyverse; lmtest; nlme; pmc; ggplot2; tidyr; dplyr;
 pwr; phytools; reshape2. ## Sharing/Access information All the data and
 information are available in this data repository, and it may be reused in
 accordance with Dryad's CC0 license policy. ## Code/Software The
 folder 'Data S1' contains all the CSV files, Nexus files, and
 the R scripts used to perform the analyses presented in the original
 research article.