Other measures of auditory function of the nonimplanted ear were not significantly correlated with bimodal benefit. The FFR holds potential as an objective tool that may allow data-driven counseling regarding expected benefit from the nonimplanted ear. It is possible that this information may eventually be used for clinical decision-making, particularly in difficult-to-test populations such as young children, regarding effectiveness of bimodal hearing versus bilateral CI candidacy.Multiple studies have shown significant speech recognition benefit when acoustic hearing is combined with a cochlear implant (CI) for a bimodal hearing configuration. However, this benefit varies greatly between individuals. There are few clinical measures correlated with bimodal benefit and those correlations are driven by extreme values prohibiting data-driven, clinical counseling. This study evaluated the relationship between neural representation of fundamental frequency (F0) and temporal fine structure via the frequency following response (FFR) in the nonimplanted ear as well as spectral and temporal resolution of the nonimplanted ear and bimodal benefit for speech recognition in quiet and noise. Participants included 14 unilateral CI users who wore a hearing aid (HA) in the nonimplanted ear. Testing included speech recognition in quiet and in noise with the HA-alone, CI-alone, and in the bimodal condition , measures of spectral and temporal resolution in the nonimplanted ear, and FFR recording for a 170-ms/da/stimulus in the nonimplanted ear. Even after controlling for four-frequency pure-tone average, there was a significant correlation ( Most unilateral cochlear implant (CI) recipients receive significant speech recognition benefit in quiet and in noise when acoustic hearing is added in the nonimplanted ear via a hearing aid (HA), also termed bimodal benefit e.g., . Yet, thA number of studies have investigated this large variability in bimodal benefit by examining auditory characteristics of the nonimplanted ear and bimodal benefit with mixed results . Audiomer = .895) between acoustic spectral modulation detection (SMD) and speech recognition benefit with access to acoustic hearing in the nonimplanted ear . Despite this correlation, it was unclear whether this laboratory-based measure could be clinically translated and whether the correlation would hold with much larger and more diverse populations of bimodal listeners.It is evident from the studies reviewed earlier that we currently lack strong clinically available measures that explain variability in bimodal benefit, rendering clinical decisions regarding ear of implantation, and bilateral versus unilateral implantation not only challenging, but non-evidence-based. Furthermore, the exact cues driving bimodal benefit are unknown, prohibiting development and implementation of a clinical test for the nonimplanted ear. It is, however, understood that both spectral and temporal cues affect bimodal benefit. Proposed explanations for differences in speech recognition for bimodal listeners relate to acoustic processing of spectral cues such as formant frequency (F0) and low-frequency consonant cues  as well as temporal cues including periodicity and temporal fine structure (TFS). General convention holds that the temporal descriptions of sound characteristics are most commonly referencing temporal envelope, which describes the relatively slow changes in amplitude over time . Temporasegregate the signal from the background noise. Although multiple studies have shown the importance of access to F0 information  are also likely used to improve speech recognition performance. As such, there has been recent interest in using the frequency following response (FFR) as an objective tool to quantify F0 and F1 spectrotemporal processing in listeners with hearing loss given the importance of these cues for speech understanding .The human FFR is an auditory-evoked potential that reflects the synchronous neural activity originating in the auditory brainstem. However, it should be noted that recent evidence suggests that the FFR may additionally have a cortical contribution  though tA variety of studies have found a relationship between FFR spectral amplitudes and speech recognition in noise for participants with NH. This has been observed in adults ranging in age from 21 to 30 years  and 60 tResults have been mixed in studies investigating the relationship between FFR and other behavioral measures in participants with NH. F0 place but a measure of combined neural phase locking across all cochlear filters. The systematic impact of various degrees and configurations of hearing loss on FFR F0 energy recorded from the scalp is currently unclear.Much less is known about FFR in populations with sensorineural hearing loss. Extensive research has investigated the auditory steady state response (ASSR), a subcategory of the EFR , in indiResults are mixed regarding whether individuals with hearing loss also appear to have poorer neural synchrony than listeners with NH . Furtherbetter hearing ear during the preoperative evaluation  with a range of 25.0 to 79.4 years of age. Across 14 participants, 6 were implanted with Advanced Bionics (AB), 3 with MED-EL, and 5 with Cochlear Ltd. devices. All but two participants self-identified as full-time HA users prior to completion of this study. Study inclusion criteria included at least 6 months of CI experience as well as audiometric thresholds ≤ 80 dB HL at 250 Hz in the nonimplanted ear. All study procedures were approved by the Vanderbilt University Institutional Review Board (IRB # 171526), and all participants provided written informed consent prior to participation in the study. Participants for this study included 14 adults (11 women and 3 men) with sensorineural hearing loss who were unilaterally implanted with a current generation CI and wore a HA in the nonimplanted ear. At the time of testing, participants had a mean age of 57.8 years (SD = 14.65).All participants received a hearing evaluation to ensure they met study criteria. Audiometric thresholds were measured at octave and interoctave frequencies from 125 to 8000 Hz in the non-CI ear using ER-3A insert earphones and standard audiometry methods. A real-ear-to-coupler difference was measured in the participant’s nonimplanted ear and was used for HA verification in the coupler. The participant’s HA was verified via an Audioscan RM500SL system test box using NAL-NL2 prescriptive targets . ParticiSpeech recognition was measured using guidelines recommended by the minimum speech test battery  for adulready . Performance was scored in percentage of correct words. For AzBio sentences at +5 dB SNR, the speech stimuli and the noise were colocated from the speaker placed at 0 degrees (S0N0). Participants were encouraged to repeat as much of the sentence as they heard and to guess if they were not sure. Performance was scored as the percentage of correct words.Speech recognition was assessed using consonant–nucleus–consonant (CNC) words  in quiet0N45–315). These speech and noise levels were chosen to be consistent with typical levels reported for upscale restaurants which have more favorable listening conditions . The equation for normalized acoustic benefit is as follows:In the scenario that speech recognition decreased with the addition of a HA to the nonimplanted ear , the equation was reworked to divide the acoustic benefit by the amount of possible rau decrement. As before, the maximum amount of possible rau decrement depends on the speech recognition stimulus. For CNC words and AzBio sentences, the minimum rau value is −16.47 and −19.11, respectively. As all CI-alone scores were greater than 0, the equation was able to be reworked as follows:These equations result in a score from −100% to 100% indicating a decrement in performance in the bimodal condition to the minimum rau value and improvement in performance in the bimodal condition to the maximum rau value, respectively. This method of calculation is described further with raw speech recognition scores in the publication by Participants completed two measures of spectral resolution in the nonimplanted ear: (a) quick spectral modulation detection (QSMD) task and (b) a fast method of measuring PTCs.SD = 6). Individual SLs of the QSMD stimulus were measured using each participant’s pure-tone average (PTA) at 500, 1000, and 2000 Hz converted to dB SPL as reference. On average, the SL of the stimulus was 23.8 dB SL (SD = 10.2). Participants selected which interval sounded different than the other two by selecting the corresponding number on a touchscreen. Performance was scored as percent correct at each modulation depth constructing a psychometric function using a general linear model. Specifically, a logit link function was generated for each subject using the MATLAB statistics toolbox function glmfit. This psychometric function was used to extract a threshold (to the nearest dB) for the modulation depth corresponding to 70% correct.The QSMD task  consisteThe fast method for determining PTCs  was origfmin) to 393 Hz (fmax) with a bandwidth of 52 Hz. For the 440-Hz tone, the noise had a center frequency which was swept from 220 Hz (fmin) to 660 Hz (fmax) with a bandwidth of 88 Hz. A run was completed with the noise masker swept from fmin to fmax and another run was completed with the noise swept from fmax to fmin for a total of four runs (two runs per tested frequency). The participant was instructed to press and hold the space bar on a keyboard whenever she or he heard the tone and release the space bar when the tone was no longer audible. When the space bar was pressed, the narrowband noise increased in level at a rate of 2 dB/sec and decreased at the same rate when the space bar was released. At the completion of each test, Q10  and an estimation of the PTC tip frequency were recorded. Q10 values were automatically measured in the SWPTC software using double regression and quadratic function measurements.Throughout the duration of testing (1 run =3 minutes), a narrowband noise masker was presented. For the 262-Hz tone, the noise had a center frequency which was swept from 131 Hz (Amplitude modulation (AM) detection thresholds were measured in the nonimplanted ear using a three-interval forced choice task to acquire a psychophysical estimate of temporal envelope resolution. A flat spectrum noise carrier with a bandwidth of 125 to 5600 Hz was modulated using two different modulation frequencies: 4 Hz and 128 Hz. The stimulus was presented at 90 dB SPL via an ER-3A insert earphone. Using a method of constant stimuli, modulation index, 20log(m), ranged from 0 dB to −22 dB in 2-dB steps for each modulation frequency. Three trials were completed at each of the 12 modulation indices for a total of 36 trials per modulation frequency. Average percent correct across the three trials was calculated at each modulation index and plotted to generate a psychometric function using a general linear model. The psychometric function was created using a similar method as the earlier described QSMD analysis. Threshold was determined by the modulation index corresponding to 70% correct.FFR was measured in the nonimplanted ear using a 170-ms/da/stimulus  = 100 Hz, first formant frequency (F1) = 700 Hz) presented using a magnetically shielded Etymotic ER-3C insert earphone in a sound-treated booth at a rate of 4.35 Hz. Stimuli were presented at a fixed intensity of 90 dB SPL. Each measurement included an average of 3,000 repetitions of the stimulus with artifact rejection set to ±31 µV. A high-pass filter was set to 1 Hz and a low-pass filter was set to 5000 Hz to allow for post hoc filtering. The stimulus was presented with alternating polarities to allow for the addition and subtraction of the responses measured in condensation and rarefaction polarities during analysis. Processing the data in this way provides access to neural encoding of specific features such as envelope (added) and TFS  was transformed into the frequency domain using a fast Fourier transform (FFT) with 1-Hz resolution. The envelope of the FFR is enhanced by the addition of opposite polarities, whereas TFS is diminished. As the F0 of the stimulus was 100 Hz, the amplitude of the envelope spectrum at 100 Hz was analyzed for each participant. The alternate stimulus polarities were also subtracted, effectively enhancing the spectral components of the FFR and eliminating the FFR envelope see  for a momore musically sophisticated. Scores over 500 indicate that the respondent has a greater than 50% likelihood of being classified as more musically sophisticated, and scores less than 500 indicate that the respondent has a less than 50% likelihood of being classified as more musically sophisticated and is therefore classified as less musically sophisticated.Given that musical training and years of music experience is related to differences in the FFR , particiResults of the TEN test revealed a present cochlear dead region at one or more of the tested frequencies for six participants. Of these participants, two had a dead region at 750 Hz (Subjects 1 and 8), two at 1000 Hz (Subjects 4 and 8), four at 1500 Hz , and two at 2000 Hz (Subjects 1 and 14).SD = 20.2), 62.0 rau (SD = 27.6), and 72.1 rau (SD = 20.5), respectively. For AzBio sentences at +5 dB SNR (S0N0), average performance in the HA-alone, CI-alone, and bimodal listening conditions was 25.1 rau (SD = 26.6), 24.5 rau (SD = 35.3), and 54.8 rau (SD = 223.5), respectively. Finally, average performance for AzBio sentences at +5 dB SNR (S0N45–315) in the HA-alone, CI-alone, and bimodal listening conditions was 31.7 rau (SD = 25.3), 39.5 rau (SD = 27.4), and 63.4 rau (SD = 21.8), respectively. Individual speech recognition scores in the CI-only and the bimodal listening conditions for CNC words in quiet as well as AzBio sentences in +5 dB SNR (S0N0 and S0N45–315) are shown in F = 4.72, p = .049, 0N0), F = 19.40, p < .0001, 0N45–315), F = 21.10, p < .0001, For CNC words, average performance in HA-alone, CI-alone, and bimodal listening conditions was 39.6 rau (0N0 and S0N45–315). Average acoustic benefit was 10.1-, 30.3-, and 23.8-rau for CNC, AzBio +5 dB SNR (S0N0), and AzBio +5 dB SNR (S0N45–315), respectively. 0N0 and S0N45–315). Average normalized benefit was 17.6%, 29.5%, and 29.8% for CNC, AzBio +5 dB SNR (S0N0), and AzBio +5 dB SNR (S0N45–315), respectively.As detailed in the Methods section, bimodal benefit was measured in two ways: acoustic benefit and normalized acoustic benefit. r = .82, p = .0006), as expected; however, aided SII was included in analysis as it provides a more precise measure of audibility to speech spectrum  as well as age at testing and bimodal benefit for AzBio +5 dB SNR (S0N0) exhibited strong correlation coefficients.Correlation analyses were completed comparing audiometric threshold at 125 Hz, four-frequency PTA , aided SII, and age at testing with both acoustic benefit  and normspectrum . Since wSD = 3.9 dB). A correlation analysis was performed comparing QSMD threshold (in dB) in the nonimplanted ear and both acoustic and normalized acoustic bimodal benefit for CNC words, AzBio sentences at +5 dB SNR (S0N0), and AzBio sentences at +5 dB SNR (S0N45–315). Across all six correlations, there was no statistically significant correlation between QSMD thresholds and any measure of bimodal benefit. Correlations between QSMD threshold and bimodal benefit were as follows: normalized acoustic benefit for CNC words , normalized acoustic benefit for AzBio +5 , normalized acoustic benefit for AzBio +5 , acoustic benefit for CNC words , acoustic benefit for AzBio +5 , and acoustic benefit for AzBio +5 . Although none of the correlations reached statistical significance for an α of .05, according to Cohen’s classification , the double regression Q10 value was used. Furthermore, when a participant’s tuning curve was so broad that a Q10 could not be recorded with a quadratic function or double regression, the participant was assigned a value of 0 for that frequency . Mean Q10 value and center frequency for the 262 Hz stimulus was 1.54 (SD = 0.84) and 270.3 Hz (SD =21.96 Hz), respectively. For the 440 Hz stimulus, the mean Q10 value was 1.5 (SD = 0.75) and the average center frequency was 441.3 Hz (SD = 41.1 Hz).The sharpness  of the auditory tuning curve in the nonimplanted ear at 262 Hz and 440 Hz was measured using the quick method for acquiring the PTC . Q10 was0N0 and S0N45–315) for a total of 12 correlations. There were no statistically significant correlations for any of the correlational analyses (p ≥ .05) with correlation coefficients ranging from .01 to .38. There was still no significant correlation between Q10 values (262 and 440 Hz) and acoustic or normalized acoustic benefit for any speech recognition measure  when participant Q10 data equal to 0 was excluded.Correlational analyses were completed to compare Q10 values at 262 and 440 Hz with acoustic and normalized acoustic benefit for CNC words and AzBio sentences at +5 dB SNR (Sn = 4). Mean AM detection thresholds were 12.4 dB (SD = 4.2 dB) and 6.7 dB (SD = 6.0 dB) for 4 and 128 Hz, respectively.Temporal resolution, via sinusoidal AM detection, was quantified as the modulation index, between 0 and −22 dB, necessary to reach a performance level of 70% correct. Two runs were completed for each participant, one for each modulation frequency . Four participants had sufficiently poor performance with a 128 Hz modulation frequency that their performance did not reach 70% correct even for 100% modulation (m = 1.0). Thresholds for these individuals were assigned as 0 dB (0N0 and S0N45–315). This resulted in a total of 12 correlational analyses. There were no statistically significant correlations between acoustic and normalized acoustic benefit for any speech recognition measure and temporal envelope resolution at either modulation frequency (p ≥ .05) with correlation coefficients ranging from .01 to .47.Correlation analyses were completed for temporal modulation thresholds at both modulation frequencies and bimodal benefit, both acoustic and normalized acoustic benefit, for CNC and AzBio +5 dB SNR (Sp ≥ .05) with correlation coefficients ranging from .01 to .45.Excluding the four participants’ data for whom an AM detection threshold of 0 dB was assigned at the 128 Hz modulation frequency, there was still no significant correlation between temporal resolution and either acoustic or normalized acoustic benefit for any of the speech recognition measures (SEM). The average FFR spectral envelope amplitude at F0  was equal to 0.08 µV (SD = 0.04) and the average FFR spectral fine structure amplitude at F1  was equal to 0.005 µV (SD = 0.004), shown by the arrows in t test assuming unequal variances showed no significant difference between groups of individuals with and without cochlear dead regions related to FFR F0 amplitude, t(10) = 0.3, p = .77, or FFR F1 amplitude, t(10) = 0.2, p = .84.An additional analysis was completed to determine whether the presence or absence of a cochlear dead region as measured by the TEN test impacted FFR amplitude. Of the 12 participants with FFR data, 6 participants had at least one measurable cochlear dead region and 6 participants had no measurable cochlear dead region. An unpaired two-tailed r = .83, p < .001) and normalized acoustic benefit  as shown in p ≥ .05), though this is likely due to the fact that F1 was poorly represented in the individual FFRs as a result of the magnitude of hearing loss at 700 Hz and above  and at the first formant of the stimulus (700 Hz) and acoustic and normalized acoustic benefit for the three speech recognition tasks. For CNC words, F0 amplitude was significantly correlated with acoustic (nd above .0N0), there was no significant relationship between FFR F0 amplitude and acoustic benefit  or normalized acoustic benefit  displayed in p ≥ .05). For AzBio +5 dB SNR (S0N45–315), there was a significant positive correlation between FFR amplitude at F0 and acoustic benefit  but not for normalized acoustic benefit  as shown in p ≥ .05).For AzBio sentences at +5 dB SNR (Sr(10) = .73, p = .01, even when controlling for four-frequency PTA and accounting for multiple comparisons via Bonferroni correction. For speech recognition in noise, the results of partial correlation analysis revealed that FFR F0 amplitude was not significantly correlated with bimodal benefit for AzBio +5 in the colocated condition (S0N0), r(10) = .49, p = .12, after controlling for four-frequency PTA. In the spatially separated condition (S0N45–315), FFR F0 amplitude was correlated with acoustic benefit, r(10) = .63, p = .04, even when controlling for four-frequency PTA; however, after accounting for multiple comparisons via Bonferroni correction, this correlation did not quite meet statistical significance.A partial correlation was then computed between FFR F0 amplitude and bimodal benefit, controlling for four-frequency PTA. If four-frequency PTA was the principle determinant of bimodal benefit, the partial correlation between FFR F0 amplitude and bimodal benefit should not be significant. First considering acoustic benefit, FFR F0 amplitude was correlated with bimodal benefit for CNC words, r(10) = .62, p = .04, even when controlling for four-frequency PTA; however, after accounting for multiple comparisons via Bonferroni correction, this correlation did not meet statistical significance. For speech recognition in noise, the results of partial correlation analysis revealed that FFR F0 amplitude was not significantly correlated with bimodal benefit for AzBio +5 in the colocated condition (S0N0), r(10) = .21, p = .53, or in the spatially separated condition (S0N45–315), r(10) = .06, p = .87, when controlling for four-frequency PTA.Next considering normalized acoustic benefit, FFR F0 amplitude was correlated with bimodal benefit for CNC words, 0N45–315), but not AzBio sentences at +5 dB SNR (S0N0), when benefit was expressed as acoustic benefit, in rau. However, when controlling for four-frequency PTA and accounting for multiple comparisons, only the relationship between FFR F0 amplitude and acoustic benefit for CNC words remained significant. When bimodal benefit was expressed as normalized acoustic benefit accounting for CI-alone performance, FFR F0 amplitude was only correlated with CNC normalized acoustic benefit. However, that relationship was no longer significant when accounting for multiple comparisons. FFR F1 amplitude was not correlated with any speech recognition measure used in this study.In summary, FFR F0 amplitude was significantly correlated with bimodal benefit for CNC words in quiet and AzBio sentences at +5 dB SNR (SSD = 157.8). None of the participants scored over 500 , meaning all participants were classified as less musically sophisticated. Correlation analyses were completed comparing OMSI scores and FFR spectrum amplitude at F0 and F1 as well as OMSI and acoustic and normalized acoustic benefit for all three speech recognition measures. There were no significant correlations between OMSI scores and FFR F0 or F1 amplitude (p > .05).Eleven of the 14 participants completed the OMSI. Of those who completed the online music survey, the average score was 178.2 , and temporal envelope resolution with bimodal benefit . SMD thrSD = 18.6, range = 15 to 65 dB HL; see On average, participants in this study demonstrated significant bimodal benefit for all speech measures tested; however, considerable variability was observed consistent with previous studies . In contSpectral resolution measured via QSMD was not significantly correlated with acoustic or normalized acoustic benefit for any of the speech recognition measures. Thus, these results did not agree with our prediction that QSMD performance would correlate with bimodal benefit. However, many of the participants in this study were fairly high performers on the QSMD task, likely due to the relatively better hearing observed in this sample as compared to previous publications investigating bimodal benefit . PerhapsOne difference between this study and Spectral resolution at 262 Hz and 440 Hz via swept PTC was not significantly correlated with acoustic or normalized acoustic bimodal benefit. These data are consistent with previous research  and our Temporal resolution via sinusoidal AM detection was not significantly correlated with acoustic or normalized acoustic benefit for any of the speech recognition measures tested. These results, which match our original hypothesis, replicate findings from 0N45–315) speech recognition tasks even when controlling for four-frequency PTA. The relationship between acoustic benefit for CNC words and FFR F0 amplitude, but not AzBio sentences at +5 dB SNR (S0N45–315), remained significant when accounting for multiple comparisons. As the strength of the neural representation of F0 increased, acoustic benefit also increased. This trend was also seen for AzBio sentences at +5 dB SNR (S0N0); however, the relationship did not reach statistical significance . It should be noted that this relationship was not consistently seen when bimodal benefit was calculated as normalized acoustic benefit. For normalized acoustic benefit, a significant correlation was only observed with CNC words when controlling for four-frequency PTA but was no longer significant when accounting for multiple comparisons.The major finding of this study was the significant positive correlation between FFR F0 amplitude and acoustic benefit for CNC words and AzBio sentences at +5 dB SNR  to be achieved. Depending on how benefit is calculated, it can appear that the participant receives meager or great bimodal benefit. Although the strength of calculating normalized acoustic benefit is its ability to control for CI-alone performance, perhaps the speech recognition performance of this particular sample lends itself to be better represented by acoustic benefit. No participant reached absolute ceiling effects with the CI-alone or in the bimodal listening condition.SD of 17 Hz, close to the F0 observed of the/da/FFR stimulus. AzBio sentences, on the other hand, use two male and two female speakers. Zhang et al. extracted F0 from 80 sentences and found an average F0 of 131 Hz (SD = 35 Hz) and 205 Hz (SD = 45 Hz) for male and female speakers, respectively. It is possible that FFR F0 spectral amplitudes better correlated with bimodal benefit for CNC words because the average F0 of CNC words more closely matched the FFR stimulus F0 than AzBio sentences.It is also important to note that the relationship between FFR F0 spectral amplitude and bimodal benefit was much stronger for CNC words in quiet compared to both speech recognition measures using AzBio sentences in noise. We suspect that the spectral characteristics of the different speech recognition measures may explain the differences in correlation strength. 0N45–315) and FFR F0 spectral amplitudes. This analysis was only completed for the S0N45–315 testing condition as male and female percent correct scores are not calculated in the clinical method of AzBio testing at +5 dB SNR (S0No). If a speaker F0 that more closely matches the FFR stimulus F0 results in a stronger relationship between FFR spectral amplitudes and bimodal benefit, we would expect to see a stronger correlation between bimodal benefit for male spoken AzBio sentences and FFR F0 spectral amplitudes. Of the 12 participants who had useable FFR data, scores for male and female speakers were available for 11 participants. There was a significant relationship between FFR F0 spectral amplitudes and acoustic benefit for male-spoken  and female-spoken . These correlations do not differ greatly from each other and closely approximate correlations of acoustic benefit for gender nonspecific sentences as discussed earlier in the article. There was however a greater difference between male- and female-spoken sentences when looking at normalized acoustic benefit. Although neither reached statistical significance, correlations between FFR F0 spectral amplitudes and normalized acoustic benefit for male-spoken AzBio sentences at +5 dB SNR  were considerably stronger than for female-spoken sentences . Although this does not provide sufficient evidence to explain the differences in correlations between CNC and AzBio bimodal benefit with FFR spectral amplitudes, it raises the notion that spectral characteristics of chosen speech stimuli may impact findings. These results highlight the importance of F0 processing for speech recognition and provide additional support to the theory of segregation as an explanation for bimodal benefit.To test this, further analysis was completed looking at bimodal benefit for male versus female AzBio sentence recognition at +5 dB SNR , regardless of whether participants whose thresholds were assigned a value of 0 were included or excluded. This provides evidence that FFR and AM detection thresholds do not reflect the same type of temporal processing.Finally, given the observed relationship between FFR amplitude at F0 and bimodal benefit, but the lack of relationship between temporal resolution and bimodal benefit, further analyses were completed to determine whether there was a relationship between FFR amplitude and temporal resolution measurements. This was assessed as previous study has shown significant relationships between human EFRs and behavioral measures of temporal resolution, including AM detection thresholds, suggesting EFRs reflect similar temporal processing as noted for behavioral tasks . No relaThe results of this study warrant further investigation to better understand the relationship between FFR amplitude and bimodal benefit. The current findings suggest that the FFR has the potential to be an objective tool that can assess the integrity of the auditory system and help predict bimodal benefit from the nonimplanted ear. It is possible that this information may eventually be used for clinical decision-making, particularly in difficult-to-test populations. Conversely, other measures of auditory function of the nonimplanted ear including audiometric thresholds, spectral resolution, and temporal resolution are not appropriate measures for predicting bimodal benefit. Currently, we lack the tools to provide data-driven counseling regarding the expected amount of bimodal benefit which holds significant clinical implications for optimizing bimodal hearing and determining bilateral CI candidacy—particularly given the poor relationship between audiometric thresholds and bimodal benefit for individuals with moderate-to-severe hearing losses. Without behavioral testing with and without the HA, the decision of whether to pursue a second CI or continue with a bimodal listening configuration is exceedingly difficult. For pediatric patients, by the time such behavioral testing can be completed, it may be too late for optimal speech recognition benefit to be achieved with a second CI if testing shows that the child does not receive benefit from their HA. Further complicating this matter is that some pediatric bimodal listeners exhibit significant asymmetry in neural maturation , 2019 thr = .13, p = .69) or the fine structure spectrum amplitude at F1 . This suggests that age was not mediating the observed relationship between FFR F0 amplitude and bimodal benefit.There are limitations of this study that should be acknowledged. First, age was not controlled. Previous reports have shown a decrease in FFR amplitudes with age in people with NH . It is pAnother limitation was the fixed stimulus level used during FFR testing. By fixing the presentation level, it remains unclear whether differences in stimulus SL impacted the results. A fixed level of 90 dB SPL was chosen for this experiment to approximate amplification provided by a HA using the half-gain rule. However, to tease apart the effects of audibility of the stimulus on FFR strength, future studies should present stimuli at a variety of levels and use the participant’s PTA to determine the SL of the stimulus. This will allow for comparisons across fixed stimulus levels as well as across SLs. In this study, however, acoustic benefit for CNC words was still significantly correlated to FFR F0 amplitude even when controlling for four-frequency PTA. Although differences in audibility of the stimulus do influence the observed relationships between FFR F0 and bimodal benefit, they do not fully account for this relationship.In conclusion, this study demonstrated a strong positive correlation between FFR F0 amplitude in the nonimplanted ear and bimodal benefit. Other measures of auditory function of the nonimplanted ear such as SMD, audiometric thresholds, and temporal modulation detection were not significantly correlated with bimodal benefit. Further study on the impact of age and audibility on FFR strength must be completed to better understand the relationship between this electrophysiological measure and bimodal benefit.


Himantopus novaezelandiae) and kākāriki karaka/orange‐fronted parakeet . Our findings indicate that SNPs provide more precise estimates of relatedness than microsatellites when assessing empirical parent–offspring and full sibling relationships. Further, our results show that relatedness estimates and subsequent pairing recommendations using PMx are most similar between pedigree‐ and SNP‐based approaches. These combined results indicate that in lieu of robust pedigrees, SNPs are an effective tool for informing pairing decisions, which has important implications for many poorly pedigreed conservation breeding programmes worldwide.Conservation management strategies for many highly threatened species include conservation breeding to prevent extinction and enhance recovery. Pairing decisions for these conservation breeding programmes can be informed by pedigree data to minimize relatedness between individuals in an effort to avoid inbreeding, maximize diversity and maintain evolutionary potential. However, conservation breeding programmes struggle to use this approach when pedigrees are shallow or incomplete. While genetic data  can be used to estimate relatedness to inform pairing decisions, emerging evidence indicates this approach may lack precision in genetically depauperate species, and more effective estimates will likely be obtained from genomic data . Here, we compare relatedness estimates and subsequent pairing decisions using pedigrees, microsatellites and SNPs from whole‐genome resequencing approaches in two critically endangered birds endemic to New Zealand: kakī/black stilt ( Gymnogyps californianus; Walters et al., In order to recover the world's rarest species, a multifaceted approach is needed to address the factors that cause species decline and those that promote species resilience  and to reduce the risk associated with adaptation to captivity  for parents and offspring, as well as for full siblings, is 0.5 when inbreeding is not present, indicating each pair shares 50% of their genomic information. While parents do contribute 50% of their autosomal genomic information to their gametes, the combined effects of recombination, independent assortment and random fertilization can lead to a larger range of realized relatedness between full siblings  and kākāriki karaka/orange‐fronted parakeet —as proof of concept. Kakī and kākāriki karaka are excellent candidates for this research as both have active conservation breeding programmes, as well as multigenerational pedigrees (this study), microsatellite panels  along with braided river habitat loss and degradation  and habitat loss  forests in three North Canterbury Valleys  and to Oruawairua/Blumine Island in the Marlborough Sounds . Eggs from these pairs are harvested, brought into captivity and fostered under surrogate birds, with hatchlings incorporated into the conservation breeding programme. Kakī belong to the Order Charadriiformes and are relatively long‐lived braided river specialists that breed predictably within the bounds of a spring and summer season . Captive kakī and kākāriki karaka are managed by DOC at two facilities in Aotearoa: the DOC Kakī Management Centre in Twizel and the Isaac Conservation and Wildlife Trust in Christchurch. Kakī used in this study are 36 individuals sampled between 2014 and 2017, including 24 individuals from six captive family groups and 12 individuals from wild parents that represent diverse lineages based on the pedigree. Kākāriki karaka sampled in this study are 36 individuals sampled between 2015 and 2019, including individuals from eight captive family groups and one wild individual from the Poulter Valley of North Canterbury . Family groups were further verified by clustering genome‐wide SNP relatedness values calculated using the KGD method .Familial relationships are known for all samples collected, as they were sampled from birds of known provenance in captive conditions. However, to verify that no sample was mislabelled during genetic and genomic processing, parent–offspring relationships were verified through an allele mismatch exclusion analysis  and treating all unknown individuals in the pedigree as wild founders. In order to produce direct comparisons of pairwise relatedness coefficients (R) between pedigree, genetic and genomic data, R was calculated from pedigree kinship data using R(xy) = 2 * f(xy)/√{(1 + Fx)(1 + Fy)}. In this formula, f(xy) is the kinship between two individuals (x and y) and Fx and Fy are the inbreeding coefficients of individuals x and y  into the programme PopLink v. 2.5.1  previously described for kakī were amplified using PCR protocols by Steeves et al. (n = 17) designed for kākāriki karaka and one locus (Cfor0809) for Forbe's parakeet (C. forbesi) that cross‐amplified in kākāriki karaka were amplified using PCR protocols by Andrews et al. (HO) and observed heterozygosity (HE)—were produced using GenAlEx v. 6.5  have their own error corrections embedded in their respective pipelines. Kmer analyses were performed using Jellyfish v. 2.2.10  for an aligned study, and all others were sequenced at a lower coverage depth (approximately 10×). Kākāriki karaka libraries were prepared at IKMB using the Nextera™ DNA Flex Library Prep Kit according to the manufacturer's specifications and sequenced across one lane of an Illumina NovaSeq™ 6,000 at IKMB at a coverage depth of approximately 10×, with one individual sequenced at a depth of approximately 70×, which was additionally used for the reference genome (see above).‐‐very‐sensitive. Resulting SAM files were converted to BAM and were sorted using Samtools v. 1.9  violates the assumptions of random mating. Per site missingness, depth and diversity—including proportion of observed and expected heterozygous SNP sites per individual, nucleotide diversity and SNP density per kb—were evaluated in the final sets using VCFtools v. 1.9 ). This scaling was performed to simplify downstream Mantel tests by creating a standardized diagonal value. This scaling was maintained throughout all analyses, as the scaled approach better approximated parent–offspring relationships, while demonstrating minimal bias to downstream analyses (see Supporting Information for details).To produce estimates of xyr method  and kākāriki karaka .We evaluated the scaled KGD approach with other maker‐based relatedness estimators, including the triadic likelihood approach Wang, , the KIN2.4ape  was additionally calculated to provide an additional measure of concordance between approaches. While our relatedness data sets are nonparametric, Pearson's was used over nonparametric tests, such as rank correlations, as our pedigree and microsatellite data sets have an excess of tied values.Mantel tests using the R package 2.5R. First, we used mate suitability index (MSI), which scores how valuable offspring of a potential pair would be by taking into account four factors: deltaGD , the difference of mean kinship values of the pair, the inbreeding coefficients of resulting offspring and the extent of unknown ancestry  were selected for analysis. PMx settings were set to default, with the exception of treating all unknown individuals in the pedigree as wild  to minimize bias from unknown pedigree assignments. Pedigree‐based MSI scores and MK ranks were produced using pedigree‐based kinship, while pairing recommendations using microsatellites and SNPs were produced using coefficients of relatedness. These genetic and genomic estimates of relatedness were uploaded to PMx, which divides these values by two to create an empirical metric of kinship. These empirical values were weighted to 1 to produce MSI scores and MK ranks that relied only on empirical data.For MSI score and MK rank analyses, only the individuals used in this study (r) was used to evaluate whether pairwise MSI scores and MK ranks between approaches were concordant. To test whether the distribution of MSI scores was statistically different from one another, we used a nonparametric Kruskal–Wallis test with Bonferroni correction and a Tukey honest significant difference test.Pearson's correlation , with averaged full sibling R of 0.52 ± SD 0.02 . The number of founders and founder genome equivalents in the kakī pedigree  is high relative to the kākāriki karaka pedigree , and the % known ancestry is lower  and nine were removed for being monomorphic (Table SD 0.93), with overall fewer alleles found here than reported in previous studies  heterozygosity and expected (HE) heterozygosity for kakī  were higher than kākāriki karaka  was below the minimum expected relatedness value of 0.5, with a larger standard deviation of R values compared to pedigree‐based estimates. Averaged microsatellite‐based full sibling R (0.41 ± SD 0.20) also had a larger deviation around the mean compared to the parent–offspring estimates  was below the minimum expected R value of 0.5, with a larger standard deviation of R values compared to pedigree‐based estimates. Averaged full sibling R (0.49 ± SD 0.21) also had a larger deviation around the mean compared to microsatellite‐based parent–offspring estimates . In addition to the individual used for the reference assembly, 3.64 billion total reads (average = 103.92 ± SD 29.76 million reads) were produced for the additional 35 kākāriki karaka in this study. More SNPs were discovered during initial SNP discovery using kākāriki karaka than kakī, and more remained postfiltering . Averaged full sibling R also had a larger deviation around the mean (0.52 ± SD 0.05) than parent–offspring relationships . Averaged full sibling relatedness also had a larger deviation around the mean (0.52 ± SD 0.05) compared to the pedigree‐based estimates  were lower on average than microsatellite‐based scores (4.73 ± SD 1.63), but not significantly different from each other . SNP‐based MSI scores for kakī (average = 5.67 ± SD 1.39) were significantly higher than pedigree‐ and microsatellite‐based scores , with SNP‐based scores providing the highest frequency of category 7  pairings . Similar to correlations between R estimates, correlation coefficients between pedigree‐ and SNP‐based MSI scores were highest  were significantly higher than pedigree (5.20 ± SD 1.71) and microsatellite (5.04 ± SD 1.61) scores , while pedigree‐ and microsatellite‐based scores did not significantly differ . SNP‐based scores provided the highest frequency of category 7  pairings , with the highest correlation coefficients seen between pedigree‐ and SNP‐based MSI scores  and microsatellite‐ and SNP‐based approaches , which underscores the lack of precision in this approach and how it could inadvertently lead to poorly informed pairing recommendations. These large ranges of relatedness values using microsatellites can be explained because genetic‐based relatedness values between parent–offspring and full siblings are based on allele frequencies, and relatedness between individuals that share common alleles will be lower than individuals that share rare alleles  capture more variation between siblings than parents and offspring Figure . A broadCompared to microsatellite‐based relatedness, SNP‐based relatedness showed a relatively small range with parent–offspring and full sibling relatedness estimates approximating 0.5, and full siblings showing a wider range of values than parent–offspring relationships Figure . Not onlR between these individuals may be downwardly biased by the assumption that no variance in relatedness exists among founders, missing information and/or low pedigree depth , SNPs should be incorporated to provide a precise indicator of relatedness to genetically inform pairing decisions. The microsatellite panels used here have shown low precision in estimating relatedness, with demonstrated downstream effects for pairing recommendations compared to pedigree‐ and SNP‐based approaches. More microsatellites could be developed to mitigate this shortcoming; however, other studies indicate that a larger number microsatellites  may not equate to higher precision for relatedness estimates and inbreeding coefficients, especially in threatened species with low allelic diversity (Nietlisbach et al., Strigops habroptilus, Elliott, Merton, & Jansen, Oryx dammah, Wildt et al., We anticipate SNPs will be particularly applicable in circumstances when pedigrees are the least reliable. For instance, when the founders of a conservation breeding population have no ancestry data available and are likely to be related, SNP‐based relatedness estimates between individuals can be used to avoid highly related matings (Hogg et al., While we expect SNPs will be important for pairing recommendations moving forward, we do not expect they will eclipse well‐established pedigrees, as both approaches have advantages for conservation breeding. Instead, we envision a combined approach where realized relatedness from SNPs can be used to augment data‐rich pedigrees. With that said, there are relatively few studies to date that effectively combine existing pedigree data with genomic estimates of relatedness to inform pairing recommendations (but see Hogg et al., 4.4This study has produced pedigrees and whole‐genome sequences for two critically endangered species. Beyond estimating relatedness, these tools provide an exciting opportunity to explore other questions relevant to conservation, such as characterizing the genomic basis of fitness traits, including those associated with inbreeding depression (Kardos, Taylor, Ellegren, Luikart, & Allendorf, Given that SNPs have been successfully used to estimate relatedness for different purposes across a wide diversity of taxonomic groups outside of this study (as reviewed in Attard et al., None declared. Click here for additional data file.


Unexpectedly, a few phenotypically masculinised fish are regularly observed in all-female farmed trout stocks. To better understand the genetic determinism underlying spontaneous maleness in XX-rainbow trout, we recorded the phenotypic sex of 20,210 XX-rainbow trout from a French farm population at 10 and 15 months post-hatching. The overall masculinisation rate was 1.45%. We performed two genome-wide association studies (GWAS) on a subsample of 1139 individuals classified as females, intersex or males using either medium-throughput genotyping  or whole-genome sequencing . The genomic heritability of maleness ranged between 0.48 and 0.62 depending on the method and the number of SNPs used for the estimation. At the 31K SNPs level, we detected four QTL on three chromosomes . Using WGS information, we narrowed down the positions of the two QTL detected on Omy1 to 96 kb and 347 kb respectively, with the second QTL explaining up to 14% of the total genetic variance of maleness. Within this QTL, we detected three putative candidate genes, fgfa8, cyp17a1 and an uncharacterised protein (LOC110527930), which might be involved in spontaneous maleness of XX-female rainbow trout.Rainbow trout has a male heterogametic (XY) sex determination system controlled by a major sex-determining gene,  Among teleost fish, salmonids are known as strict gonochoric species with the individual sex determined at fertilization and remaining the same throughout their live1. In rainbow trout (Oncorhynchus mykiss), the genetic determinism of sex has first been described as a male heterogametic system  based on the observations of sex-ratios in progenies of hormonally sex-reversed individuals5 and on the absence of males in gynogenetic offspring6. The master gene controlling sex determination in salmonids8 is an unusual sex determining gene called sdY  that evolved from the duplication of the irf9 (interferon regulatory factor 9) immune related gene. sdY is expressed during early gonad differentiation in the testis where it interacts with the conserved female differentiation factor Foxl29 to prevent cyp19a1a up-regulation and the subsequent oestrogen production needed for ovarian differentiation10. However, despite a strict linkage between the phenotypic sex and the sdY locus in many salmonid species8, spontaneous masculinisation of XX females have been reported in rainbow trout, first in experimental groups11, but also in farmed populations (unpublished data). These spontaneous masculinisation and their transmission across generations have been characterized in gynogenetic families, where the role of minor genetic factors acting in addition to the major sdY sex determination system has been suspected12. Later, Guyomard et al.13 (2014) detected QTL associated to masculinisation in two doubled haploid gynogenetic trout families. In addition to this genetic control, a few studies have also shown that high temperature treatments applied before the sexual differentiation and at the period of thermosensitivity can modulate sex differentiation and enhance the frequency of maleness in rainbow trout15, as shown in some other species including for instance channel catfish , Nile tilapia (Oreochromis niloticus) and seabass (Dicentrarchus labrax)17. Interestingly, in most of the studies in trout, intermediate phenotypes  were recorded.The discovery of factors underlying sex determination in fish is a challenge for fundamental biological and evolutionary perspectives and for aquaculture purposes as in some species, managing the sex ratios of farmed stocks is essential for the production efficiency. Unlike birds and mammals which have highly conserved and simple heterogametic genetic sex determination systems, teleost fish species exhibit an amazing diversity of sex determination systems, with different master sex determining genes as well as many minor genetic determinants interacting with environmental effects (in particular temperature) and epigenetic mechanisms18. The administration of hormones to obtain sex-reversed males is carried out under strict veterinary control according to the European directive , and treated animals are euthanized after reproduction and discarded from the food chain.In rainbow trout farming, the production of all-female populations is advantageous due to their late sexual maturation  compared to males (about 1 year of age). Indeed, maturing males exhibit a reduced growth and flesh quality (reduction of muscle lipid content and discoloration) and are more sensitive to fungal saprolegnosis. All-female stocks are produced by mating standard XX-females with sex-reversed XX-males. Sex reversal is obtained by feeding young trout fry with a diet containing a masculinising hormone, the 17α-methyltestosterone before the sexual differentiation periodTherefore, there is a dual interest in investigating factors that govern the spontaneous maleness in trout. Depending on the nature of these factors, they could be exploited either to reduce the occurrence of undesirable males observed in all-females populations or to produce sex-reversed males without using hormones, an asset for more sustainable aquaculture.To further investigate the genetic architecture of the spontaneous maleness, we performed a Genome-Wide Association Study (GWAS) in a French commercial rainbow trout population, in which spontaneous maleness has been repeatedly reported. Genotype information from a medium-density trout genotyping array and whole genome sequencing (WGS) was combined to accurately map QTL- and identify candidate genes responsible for spontaneous maleness.Fish were reared at the farm « Les Fils de Charles Murgat »  under conditions complying with the Directive 98/58/CE on the protection of animals kept for farming purposes. The fish were euthanized following the approved method used in the farm for animals intended for marketing (electro-narcosis).excludes the killing of animals solely for the use of their organs or tissues” from the procedures covered by the Directive. As such, the experiment was not subject to oversight by an institutional ethic committee.Article 3.1 of the EU Directive 2010/63/ EU on the protection of animals used for scientific purposes “post-mortem, with the authorisation of an official veterinarian.Tissue sampling and necropsy to observe the gonads were carried out 12. It was then decreased by 1 °C/day during 6 days to the same temperature as in the control group (12 °C) and both groups were then reared between 12 and 14 °C. At 84 dpf, 13,000 fish from each temperature group were randomly sampled and retained for the experiment. In order to prevent too much heterogeneity in size within the groups and any risk of biased sampling at time of phenotyping, each group was temporarily split (at 214 dpf) into two subgroups according to the fish size and kept separated for 37 days, allowing the smaller fish to catch up in size with the bigger fish. Fish were then grouped again until sex was recorded. During the whole experiment, fish were fed a commercial diet according standard recommendations.In June 2017, gametes were collected from 2.5 years-old breeders that had been previously isolated in light-proofed tanks and exposed to an artificial photoperiod to induce maturation  lighting, water temperature varied from 11 to 13.5 °C). In total, 50 dams  and 50 XX sex-reversed sires (average body weight of 3.28 ± 0.60 kg) were mated according to 5 full-factorial mating designs  to produce all XX-eggs. Fertilized eggs were separated into two batches corresponding to two temperature treatments  and incubated in McDonald-type jars with a bottom-to-top water flow maintained at 10.5 °C until hatching. Hatching rate was 64% and 58% in the two batches, which is in the usual range when using progenitors whose spawning has been shifted by photo-period. At the end of yolk resorption , larvae of batch 1 were kept at 12 °C (control group) while in batch 2, the water temperature was increased by 1 °C/day until it reached 18 °C. The water temperature was then maintained constant for 1,134 degree-days covering the expected window of gonad differentiationIn order to determine the phenotypic sex of the XX-offspring, 13,241 fish from control and 18 °C groups  were sexed at 10 month-post fertilisation (mpf) and 6969 more fish were sexed 5 months later . The overall mean body weight of fish was respectively 234 g at 10 mpf and around 860 g at 15 mpf. Fish were euthanized by electro-narcosis, according to standard procedures for commercial trout farming and both gonads were visually examined to determine sex. When necessary, visual observation was completed by observation of the gonads under a binocular magnifier (1079 fish) and, in some cases, by a histological control (23 fish).Female, for fish with two ovaries and no visible sign of testis area  (n = 132). The presence of ovarian lamellae was the criterion used to declare a female area in a gonad, whether oocytes were present or not  and no visible sign of any ovary area (n = 162)  as followed:Fish with undifferentiated gonads that could not be sexed after binocular or histological observation were classified as undetermined and removed from the analysis (n = 84).All intersex individuals (n = 132), all males (n = 162) as well as 858 females with well-developed ovaries were kept for QTL detection . Sex was recorded as a categorical variable with three levels: sex = 1 for females, sex = 2 for intersex, sex = 3 for males.19 that contains 57,501 SNPs.Pieces of caudal fin sampled from those 1152 fish were sent to Gentyane genotyping platform  for DNA extraction using the DNAdavance kit from Beckman Coulter following manufacturer instructions. Genotyping was performed with the Axiom Trout Genotyping Array from Thermofisher20, in particular to remove SNPs with probe polymorphism and multiple locations on the genome. In addition, only the 31,811 SNPs with a call rate higher than 0.97, a test of deviation from Hardy–Weinberg equilibrium with a p-value > 0.0001 and a minor allele frequency (MAF) higher than 0.05 were kept for further analyses. Individuals with a call rate lower than 0.90 were removed from the genotype dataset (n = 5). All missing genotypes were imputed using the FImpute software21 (version 2.2) in order to get the full 31,811 SNPs genotypes for all the animals considered in the analyses.Quality controls of genotyped SNPs were performed as described in D’Ambrosio et al.https://get.genotoul.fr/en/). The DNA was prepared according to Illumina protocols using the Illumina TruSeq Nano DNA HT Library Prep Kit. Briefly, DNA was fragmented by sonication, size selection of fragments was performed using SPB beads (kit beads) and adaptors were ligated for latter identification of individuals and fragment were pooled to be sequenced. Library quality was assessed using Advanced Analytical Fragment Analyser and libraries were quantified by QPCR using Kapa Library Quantification Kit. DNA sequencing was performed on an Illumina NovaSeq6000 using a paired-end read length of 2 × 150 bp with the Illumina NovaSeq6000 S4 Reagent Kits.Samples of caudal fin were also collected from a set of 60 females (the 50 dams + 10 relatives) for DNA extraction and whole genome sequencing. DNA was extracted with a Promega Relia Prep dDNA tissue miniprep system (A2051) following manufacturer instructions. DNA sequencing was performed at the GeT-PlaGe core facility , 34,064,394 variants were obtained using a homemade pipeline  based on three variant calling tools . Quality controls and filtering were performed using vcftools24 (version 1.15). First, indels and SNPs on un-located contigs or mitochondrial DNA were removed. Only the 21,904,314 bi-allelic SNPs located on identified chromosomes were considered for further analyses. Then, a filtering was performed on variant coverage and variants with either less than 10X reads or more than 50X reads (considered as putative duplicated regions) were removed. Variants with more than 58 individuals being homozygous for either the reference or the alternative allele were removed from the analysis. In other words, 14,478,077 variants with at least two individuals different from the 58 others  remained. Finally, only the 8,784,147 variants with a MAF equal or above 10% were kept for imputation and genome wide association studies (GWAS).After sequence mapping on the reference genome assembly21 (version 3.0) based on the reference population constituted by the 60 sequenced females. After imputation, 8,765,613 SNPs with a MAF higher than 1% were kept for the WGS analysis. In order to reduce the variant dataset to produce a genomic relationship matrix, SNPs in linkage disequilibrium were filtered out with the indep-pairwise option of the PLINK software25 (version 1.09). The filtering was performed first on 50 bp sliding windows. In every window, the r2 between each pair of SNPs was calculated and one SNP of the pair was removed if r2 was higher than 0.7. A second round of filtering was performed using 100 bp sliding windows and r2 higher than 0.3. The reduced dataset obtained was composed of 275,283 SNP.Imputation of the 31K SNPs genotypes of the progeny to whole-genome sequence (WGS) was performed chromosome by chromosome using the latest version of FImpute softwareGWAS were performed at the genotyping level on the 31K SNPs dataset and at the WGS level on the 8.7 million SNPs dataset, using two different approaches, a marker-by-marker analysis and a Bayesian Stochastic Search Variable Selection approach.26 that performs a marker-by-marker analysis under a mixed linear model with a correction for data structure based on a genomic relationship matrix27 (GRM).The first GWAS analysis was performed with the GCTA softwareth individual (sex in 3 levels), 2e the residual variance. The random vector of polygenic effects follows a normal distribution G a GRM constructed using only SNPs information. We first performed a GWAS on the 31K SNPs level with a GRM constructed using all 31K SNPs included in the model, this analysis will be referred to as GCTA-chip (see Table 27. This GWAS will be referred to as GCTA-seq (Table The model used in this first GWAS is described by the Eq. :1\docume28 implemented in the BESSiE software29 (version 1.0). In this BayesCπ approach, at each iteration, a proportion π of SNP is assumed to have a non-zero-effect on the trait, thus at each iteration the number of SNP effects to be estimated is lower than the number of phenotypic records. The statistical model used is defined according to Eq.  and a probability 1 − π that δjk is equal to 0.At each cycle k, the decision to include the SNP j in the model depends on the indicator variable δAs for the marker-by-marker approach, a first GWAS using the BayesCπ model was performed on the 31K SNPs level with parameters determined in order to have 1% of the markers included in the model at each cycle. This analysis will be referred to as BCπ-chip  implemented in GCTA softwared in Eq. . The twoth SNP and th SNP.The values obtained with the 31K SNPs GRM were used as prior for both BayesCπ models and the genetic variance 2g) was estimated as Genomic heritability (h10(α/n) and chromosome-wide threshold = − log10(α/[n/30]), with n the number of SNPs in the analysis . Only SNPs with − log10 over the chromosome-wide threshold for GCTA-chip and over the genome-wide threshold for GCTA-seq were considered. For each QTL with a peak SNP value over the significance threshold, approximate confidence intervals (CIs) were estimated using a drop-off limit30 of 1.5 unit of − log10  and a maximum distance of 1 Mb for the GCTA-chip and 200 kb for the GCTA-seq between two successive SNPs, with − log10 over the drop-off limit starting from the position of the peak SNP.For the GCTA-chip and GCTA-seq analysis, we determined chromosome-wide suggestive and genome-wide significance thresholds using a Bonferroni correction with α = 1%, i.e. genome-wide threshold = − logIn the GCTA-seq, when two successive QTLs were detected at a distance lower than 50 bp and with less than 250 kb between their peak SNPs, the CIs were cumulated in a credibility interval (see Supplementary Table 31 (BF):32. In order to define QTL with the BayesCπ approaches, we considered two categories to classify the strength of the evidence in favour of a QTL33: strong evidence for 8 ≤ logBF < 10 and very strong evidence for logBF ≥ 10. Because the BF is not a statistic test, true confidence intervals cannot be derived, but credibility intervals can be built as defined in Michenet et al.34. Credibility intervals were determined using the threshold logBF ≥ 8 for defining a peak SNP showing evidence for a QTL in either a BCπ-chip or a BCπ-seq analysis. For the BCπ-chip approach, the credibility interval included all SNPs with a logBF > 3 within a 1 Mb sliding window from the peak SNP. For the BCπ-seq approach, it included all SNPs with a logBF > 5 within a 200 kb sliding window from the peak SNP.For the Bayesian approaches, the degree of association between a SNP and the phenotype was assessed using the Bayes FactorThe proportion of the total genetic variance explained by each SNP was derived from the Bayesian analyses and calculated according to the ratio Oncorhynchus mykiss Annotation Release 100 (GCF_002163495.1).Candidate genes located within a reduced interval determined as the intersection of the confidence and the credibility intervals of the main QTLs were listed from the NCBI 35 (version 4.3) with the NCBI Oncorhynchus mykiss Annotation Release 100 (GCA_002163495.1)23 as a reference. SNPs were then filtered according to their estimated putative impact, all SNPs with a modifier putative impact were filtered out and only SNP with a low, moderate, or high putative impact were conserved.Annotation of SNPs within those intervals was performed using the SNPEff software25 (version 1.9) based on information from the 31K SNPs. The percentage of masculinised fish in the genotyped offspring of each dam was calculated (see Supplementary Table Genomic pedigree information was recovered using identity by descent (IBD) estimates from PLINK software version .9 based Les Fils de Charles Murgat ». Among them 98.1% were females (Table −12) higher maleness  was observed in the group that was reared at 12° (2.0%) than in the group reared at 18° (0.9%) . The average percentage of masculinised fish in the genotyped offspring sample was 20.7%, ranging from 0% for eight dams to 71.4% for the AA-dam see Fig. . The 11 Table Based on the BCπ-chip model, we estimated that the genetic variance  = 11.1) and had a very strong evidence under BCπ-chip analysis (logBF = 11.3)  with the GCTA and the BayesCπ analyses Table . With th3) Table . Even if10 = 4.2).The two other QTLs located on Omy12 and Omy20 were detected using the BCπ-chip approach only Table . The QTLUsing the WGS data and GCTA-seq approach, significant QTLs were detected only on Omy1, in a restricted region (in-between 62 and 66 Mb). In total six peak SNPs corresponding to six putative QTLs  approximated by a drop-off approach were all distant from less than 3 kb. Therefore these four CIs were fused into a single credibility interval of 342 kb. This second QTL was also detected with the BCπ-seq analyses  between 12.2 and 13.6 and among them five SNPs with logBF > 9). Interestingly, for this 15 SNPs-haplotype, eight of the 11 dams with almost no masculinised offspring carried two copies of the reference genome haplotype (the three remaining dams being heterozygous at the15 SNPs). Among the 11 dams with a high rate of masculinised offspring, only three were homozygous for the reference haplotype, six were heterozygous and the last two  that have a significant effect on masculinisation , and the peak SNP from both BCπ-seq runs (6.542 Mb) was located within the ninl gene . Among the 669 annotated SNPs spanning the QTL region, only 23 SNPs were indicated with either a low (16 SNPs) or a moderate (7 SNPs) potential effect on gene expressions when annotated with the SNPEff software  = 9.6) and annotated with a potential moderate effect was located at 63 543 061 bp within the ninl gene and annotated as a missense variant  and an uncharacterized protein , hells, and LOC110527930 were also significant  > 10) based on GCTA-seq analysis  = 10.2) was annotated as a missense variant within the cyp17a1 gene  = 12.8 and 11.7) were annotated for a low potential effect on the hells gene expression as they corresponded respectively to a variant within a splice region or an intron of the gene for the first SNP and to a synonymous mutation for the second SNP  = 13.6 and logBF > 11).Within the second QTL region spanning from 64.360 to 64.633 Mb, 13 genes were detected  or after a high temperature treatment during early stages..15 (2014) who observed a masculinising effect of early hot temperature treatment (18 °C vs 12 °C). However, in their experiment, the temperature treatment was applied a little bit earlier during development , so the temperature treatment applied in our study might have not completely overlap the critical thermosensitive period of sex differenciation37, inducing a repression instead of an induction of masculinisation. This hypothesis deserves further investigation to be confirmed. Different genetic backgrounds could also contribute to the observed differences in response to thermal treatment, as the families tested in Validivia et al.15 (2014) belonged to the INRA XX-mal lineage, an experimental line different from the commercial population used in this study. The hypothesis is further supported by the fact that distinct maleness QTLs were detected in INRA XX-mal families13 and in this study (see below), suggesting that masculinising factors may differ according to populations, resulting in differences in susceptibility to temperature. Indeed, a range of sex ratios in response to temperature treatments have been reported in rainbow trout populations with various genetic backgrounds38. In other fish species such as Atlantic silverside39 or Nile tilapia40, sex differentiation is also known to depend both on the water temperature and on the genetic background of the population.The overall masculinisation rate was 1.45%, which is in the range of values reported by several French farmers (unpublished data). Unexpectedly, the masculinisation rate was significantly higher in the group reared at the 12 °C than for the group exposed to the high temperature treatment (18 °C). This result is in contradiction with the results reported by Valdivia et al41 and confirmed by Valdivia et al. 15, in families originating from the INRA XX-mal carrying line. Under standard rearing conditions, the left–right asymmetry (LR) is almost undetectable, as intersex fish remain rare and poorly described. However, the observation of such an asymmetry in another rainbow trout population suggests that this a general feature in rainbow trout and that gonads develop in an asymmetrical LR manner in this species, as it has been reported in mammals, birds, amphibians and reptiles42 and in some fish45.Among masculinised individuals, the extent of the gonad masculinisation was variable, with about 40 and 61% of intersex fish in the 12 °C and 18 °C groups, respectively. Within those intersex individuals, whatever the rearing temperature at early stages, the right gonad was very often more masculinised than the left gonad. This particularity was first described by Quillet et al. (2004)In this study, we reported high genomic heritability estimates for spontaneous maleness (from 0.48 up to 0.62). The QTL we detected explained only a small proportion (up to 14%) of the estimated genetic variance. Using the BCπ-chip method, we estimated that the pan-genomic 31 K SNPs explained 58% of the genetic variance of maleness. The remaining 42% of the genetic variance were probably not captured by the SNPs due to their heterogeneous density distribution across the genome as well as to lack of accuracy their effects (regressed towards 0) with only 1139 phenotyped and genotyped individuals.mal line, Guyomard et al.13 (2014) detected four QTLs associated with spontaneous maleness on four different chromosomes and that one of them was located on Omy20 (linkage group RT17) too. However, the QTL had a wide confidence interval, covering the entire chromosome, so it is not possible to say if the QTL we detected in the present study on Omy20 is the same. As we did not detect QTL on the three other chromosomes reported in Guyomard et al.13 (2014), maleness in rainbow trout with different genetic backgrounds is likely to be controlled by a diversity of underlying genetic mechanisms.Concerning the identification of the genetic determinants of maleness, we detected four QTLs on three different chromosomes, two QTLs on Omy1, one on Omy12 and the last one on Omy20. Both QTLs on Omy12 and Omy20 were only detected in the 31K SNPs analyses and were not confirmed with WGS analyses. The QTL on Omy20 might be the result of a spurious association as it was defined by a single SNP in both BCπ-chip and GCTA-chip analyses. It is worth noting that in their study in gynogenetic families from INRA XX-The two main QTLs associated with masculinisation in our population were both detected on Omy1, which has never been reported as associated with neither sex determinism nor maleness in rainbow trout. Using WGS information, we were able to reduce the confidence interval of the first QTL from 806 kb  asymmetrical gonad development observed in the intersex fish. This gene is part of the fibroblast growth factor family which plays a major role in vertebrate development and has been reported to be fundamental for proper LR asymmetric development of multiples organs  in zebrafish46. The second and biologically more convincing candidate gene is cyp17a1 as it is a major enzyme of the steroidogenic pathway and it is involved in the synthesis of biologically active gonadal androgens and oestrogens. Indeed, cyp17a1 has been identified as potentially involved in spontaneous maleness in common carp (Cyprinus carpio)47 and was recently characterized as being involved in the gonad differentiation in zebrafish with complete masculinisation phenotype in cyp17a1 deficient zebrafish48. However, in our analysis, the only SNP with a putative missense effect for this gene was significant in the GCTA-seq analysis only  = 10.2) and had very low logBF values in both BCπ-seq approach (0 or 2.4). However, even if the cyp17a1 gene was not within the most significant region of the QTL (located in-between the hells gene and LOC110527930) in terms of statistical significance/evidence tests, we cannot not exclude its implication, for instance through long distance regulation of its expression. Based on the GWAS results as well as the SNP annotation, the uncharacterized LOC110527930 protein was the most relevant candidate found in our analysis. Within this gene, we identified a haplotype block of 15 consecutive SNPs (from Omy1-64632011 to Omy1-64632756) that were all significant with the GCTA-seq analysis  > 12) and presented alternative genotypes for four dams with opposite proportions of masculinised progeny (2 dams with highly masculinised offspring and 2 dams with low masculinised offspring). In addition, seven of those 15 SNPs were annotated with a putative missense effect on the LOC110527930 protein. However, there is no published evidence that this uncharacterized protein could play a role in gonadal sex differentiation and more work is still needed to characterise the protein and its potential role in gonadal differentiation.Within this main QTL region, three candidate genes possibly involved in spontaneous masculinisation were identified. First, the of true females, we tested various combinations of SNPs located within the two QTLs identified on Omy1. Within the main QTL (from 64.360 to 64.707 Mb), all significant SNPs would give similar yields, any SNP homozygous for the alternative allele would allow the detection of 44.7 to 51.5% of masculinised fish in our sample, whereas only 6.8 to 13.7% of the homozygous fish for the alternative allele would be females. In particular, using the alternative allele of any SNP from the haplotype block of 15 SNPs described previously  would allow detecting 51.5% of masculinised fish in our sample. Among those 15 SNPs, the SNP Affx-88950822 is present on the commercial 57K chip and could be of immediate use, provided its effect is confirmed in other trout populations. Every combination of two or more SNPs within this main QTL would yield in a smaller proportion of females discarded but also in a slightly smaller proportion of males identified. Always considering our sample, pairing the SNP Affx-88950822 (or any other SNP from the associated 15-SNPs haploblock) with SNPs from the first QTL on Omy1  would slightly increase the test sensitivity (identifying more masculinised fish) without eliminating too many true females. Before applying the same test in other populations, the effect of those SNPs should be confirmed in a large set of populations with diverse genetic backgrounds, or at least, in the target population. Our preliminary results regarding maleness QTLs in either the XX-mal families from the INRA experimental population and the population used in this study suggest that this might not be the case.The high heritability (up to 0.62) of spontaneous maleness estimated in this study opens up opportunities to manage maleness in all-female trout populations. If the two highly significant QTLs detected on Omy1 in the present population were confirmed to play a role in spontaneous maleness in other rainbow trout populations with diverse genetic origins, the identified SNPs could then be used in a cost-efficient genotyping test to identify female broodstock with a higher propensity to transmit male or intersex phenotype in their progeny and help breeders willing to limit the occurrence of undesirable masculinised individuals in their commercial all-female stocks to discard those breeders from reproduction. In order to optimize the efficiency of such a test, i.e. maximising the detection of masculinised fish while keeping low the number Sparolegnia fungus) encountered in commercial stocks with XY-males.From a breeder perspective, additional research is needed to improve knowledge about the genetic basis and the environmental factors determining spontaneous maleness in all-female stocks before any industrial application. In addition to the confirmation that QTLs are present in other rainbow trout populations, the expected genetic and phenotypic responses under different thermal regimes and either pedigree-based or marker-assisted selection should be quantified to assess the efficiency of a strategy aiming at limiting the rate of spontaneous males in all-female stocks. Further investigation is also needed with regard to the production of XX sex-reversed male breeders based on the use of spontaneous sexual inversion to prevent the use of hormones. Indeed, the high heritability of maleness suggests that the use of spontaneously masculinised individuals as progenitors of all-female populations would increase the frequency of undesirable masculinised progeny. Combining masculinising genetic factors together with an environmental (temperature) control of gonad masculinisation according to the destination of the fish  and/or the rearing environment might offer a solution to manage the trade-off. However, because of the overall low masculinisation rates recorded in this study, we were not able to detect any potential interaction between rearing temperature and genotype. We have no suspicion of the existence of such an interaction, as a GWAS with GCTA-chip, carried out with only the individuals from the 12 °C group, detected the same QTL as for the overall population (results not shown). In the current state of knowledge on the effect of temperature on sexual differentiation, it is too early to propose an efficient management of the rearing environment to either enhance or limit spontaneous maleness in rainbow trout. Although the prospect of using hormone-free XX-neomales is very attractive, a potential important drawback may be to fall back into the flesh quality and animal welfare issues  was the most relevant candidate and more work would be needed to characterise the potential role of this protein on gonadal differentiation.In conclusion, in this study we detected minor genetic factors involved in maleness in rainbow trout in the absence of the master gene Supplementary Legends.Supplementary Information 1.Supplementary Information 2.


MTNR1A gene polymorphisms on the reproductive performance in Awassi sheep, which is an important and widespread breed in developing Mediterranean countries. A total of 31 SNPs was detected, 5 of which caused amino acid changes. Two of the found SNPs were found to be totally linked and associated with an advanced reproductive recovery in ewes carrying the C allele. The obtained results could be useful for improving reproductive management in developing Mediterranean areas.The purpose of the study was to explore the influence of MTNR1A is the gene coding for Melatonin receptor 1 (MT1), and it is considered to be involved in the reproductive activity in sheep. The aims of this study were: (1) identifying the polymorphisms from the entire MTNR1A coding region and promoter in Lebanese Awassi sheep flocks, and (2) investigating the association between the found polymorphisms and the reproductive performance, assessed as lambing rate, litter size, and days to lambing (DTL). The study was conducted in two districts of Lebanon, where 165 lactating ewes, aged 5.2 ± 1.5 years, with body condition score (BCS) 3.3 ± 0.4, were chosen and exposed to adult and fertile rams. From 150 to 220 days after ram introduction, lambing dates and litter sizes were registered. This study provided the entire coding region of the MTNR1A receptor gene in the Awassi sheep breed. Thirty-one single nucleotide polymorphisms (SNPs) were detected, five of which were missense mutations. The H2, H3, and H4 haplotypes were associated with lower DTL (p < 0.05), as well as the SNPs rs430181568 and rs40738822721, named from now on SNP20 and SNP21, respectively. These SNPs were totally linked and can be considered as a single marker. The ewes carrying the C allele at both these polymorphic sites advanced their reproductive recovery (p < 0.05). These results are essential for improving reproductive management and obtaining advanced lambing in Awassi ewes.The economy in Mediterranean areas is tightly linked to the evolution of the sheep-farming system; therefore, improvement in ewe’s reproductive performance is essential in the developing countries of this area.  Small ruminants residing in temperate latitudes rely upon the photoperiod as a time signal to synchronize their reproductive activity with the external environment . ChangesMTNR1A) gene is located on Chromosome 26 in sheep. It extends from the 17,354,820 to 17,377,973 position of the Oar_rambouillet_v1.0 genome assembly and consists of two exons, separated by an intron nearly 22 kilo-bases (kb) in length. The exons I and II of the MTNR1A gene are approximately 260 and 970 baise-pair (bp) in length, respectively. The MTNR1A locus exhibits several polymorphic sites, which are associated with seasonal reproductive activity in ewes [MTNR1A gene on reproductive seasonality studied so far are related to three specific mutations found in the exon II, at positions 606, 612, and 706 [MTNR1A gene in these two breeds have provided confirmation of the influence of this gene in the control of reproductive seasonality and new knowledge useful for improving the reproductive management of sheep farms. Considering that the economy of many Mediterranean areas is greatly linked to the evolution of the sheep-farming system, it would be beneficial to characterize the MTNR1A gene sequence in the Awassi sheep, which is a very widespread breed in the Mediterranean area [MTNR1A coding region and promoter in Lebanese Awassi sheep flocks. The second aim of this study was to investigate the association between the found polymorphisms and the reproductive performance of the sheep flocks.There are three melatonin receptor subtypes, which belong to the superfamily of G protein-coupled receptors ; these r in ewes ,11, goat in ewes ,13, buff in ewes , and wil in ewes . Whilst  and 706 ,17, the  and 706 . These sean area ,20. The Animals used in this research were managed and treated by the farm’s veterinary service in accordance with the European Commission’s recommendations. An explicit ethical agreement was not necessary as blood samples were taken by the National Health Veterinary Service during routine flock health control procedures. Sheep were included in this trial with the farmers’ permission.The study was conducted in two districts of Lebanon—Bekaa and Jezzine—located in the northeast and south of Lebanon respectively. In each district, a sheep farm was selected, each raising approximately 200 Awassi ewes that had been exposed to natural photoperiods since birth.The Bekaa Plain stretches from Mount Lebanon to Anti-Lebanon, the altitude of which ranges from 600 to 1100 m. The plain is divided into two climatic regions: (1) the north with a sub-desert climate where the average annual temperature is 17 °C, with low rainfall ranging from 200 to 400 mm and (2) the south where the average annual temperature is 15 °C, with higher rainfall ranging from 900 to 1000 mm. Alternatively, in the Jezzine Coastal Plain the average annual temperature is 19 °C and the rainfall ranges from 700 to 1000 mm per year. The rainfall in Lebanon is notably high during the winter, and concentrated from November to February. In contrast, the summer is very dry, particularly in the north of the Bekaa. In both districts, the farming was of the semi-extensive type. Animals usually remain in and around the farm with low use of rangelands. Pastures are usually located near to the farm, and the flocks have short daily movement that allows them to return each evening to their farm. They depend on crop residue plots when available. In order to compensate for low forage quality and/or availability, and the negative effects induced by climatic conditions (summer drought from June to October and winter cold from December to January), ewes were provided with a daily cereal-based supplement. The most common food supplement for flocks in these farms included barley, yellow corn, and wheat, which were fed either whole or cracked. On 20 June 2017, 165 lactating ewes (89 in Bekaa district and 76 in Jezzine) aged 3–8 years (5.2 ± 1.5 years), with body condition score (BCS) 2.5–4.0 (3.3 ± 0.4), were chosen. Their BCS on a 1 to 5 scale  were assFrom 1 July 2017 to 30 August 2017, the 89 ewes in Bekaa and the 76 ewes in Jezzine districts were, respectively, exposed to five and four adult rams. The health of the rams was confirmed by a veterinarian, and they were proven to be fertile as they had produced offspring in previous breeding seasons. The timing of the exposure ensured that lambing occurred between late November and the end of January. From 150 to 220 days after ram introduction, lambing dates and litter size were registered. From the recorded data, lambing rate (number of lambed ewes per ewe exposed to the ram), litter size (number of new-born lambs per lambed ewes), and days to lambing  were calculated.From the jugular vein of each ewe, 10 mL of blood was collected using sterile vacuum tubes  with ethylenediamine tetraacetic acid (EDTA) as an anticoagulant. DNA extraction protocols and all the used primers for the amplifications of the promoter genomic region, exon I, exon II, and partial 3′ UTR were those reported by Luridiana et al. . The polwww.ncbi.nlm.nih.gov/blast/, accessed on 19 January 2021) for the alignment with the latest sheep genome assembly (Oar_rambouillet_v1.0—GenBank assembly accession number: GCF_002742125.1). The found sequences were subjected to Nucleotide Blast  was used to align nucleotides.Bioedit Sequence Alignment Editor software  or haplotype , Ak is the age of each animal , Dl is the random effect of the farming district , GjAk is the interaction between genotype or haplotype and age, and ejkl is the random residual effect of each observation. To compare number of ewes lambed each 10 days, we used a χ2 test. Statistical significance was set at p < 0.05 using the Bonferroni method at α = 0.05.Allele frequency was obtained by direct counting of the found genotypes. The chi-squared test was used to determine the Hardy–Weinberg equilibrium of the variations (Genepop 4.2). R statistical software (Version 4.0.0)  was usedp-values of the Hardy–Weinberg equilibrium are shown in The obtained amplified and sequenced fragments corresponded to the exons I and II coding regions, to the promoter, to part of intron I, and the 3′ UTR region. From the sequence analysis, 31 single nucleotide polymorphisms (SNPs) were detected, and accordingly named SNP1–SNP31 . The allAll of the SNPs causing an amino acid change were predicted as tolerated by variant effect predictor (VEP); their sorting intolerant from tolerant (SIFT) scores (to predict the effect of an amino acid substitution on protein function) were as follows: SNP17 = 0.27; SNP21 = 0.37; SNP22 = 0.22; SNP24 = 0.12; and SNP25 = 1. The SNP20 (rs430181568) and SNP21 (rs407388227) were totally linked (D’ = 1 and r2 = 1), so that they can be considered as a single marker.All the SNPs were analyzed to evaluate their relationship with lambing rate, litter size, and DTL. The overall mean and the standard deviation (SD) for DTL and litter size in the enrolled ewes were 168.6 ± 19.4 and 1.1 ± 0.15, respectively; the lambing rate of the studied population was 0.87. A graphical representation about the distribution of each phenotypic trait based on age of enrolled ewes is shown in the p < 0.05)  and SNP21 (rs407388227) were associated with a shorter DTL, whilst litter size and lambing rate were not associated with the genotypes . None of < 0.05) . Litter The Awassi sheep is the most widespread, medium-sized breed of the Middle East, raised for milk and meat production. Its importance derives mainly from the easy adaptation to the hard environmental conditions; they endure well in the high temperatures, dietary fluctuation, and parasites infestation. Under extensive conditions, the mating period occurs in early summer and first lambing after 20 months of age. The ewes enrolled in this study were 3 to 8 years old, so that they were at least at their second lambing.MTNR1A gene sequences in the Awassi ewes is near to that found in the Sarda breed, however, there are some differences from the Raza Aragonesa breed [The genetic variability emerging from the sa breed ,22. Thirsa breed  detectedsa breed ,25. Thissa breed . The SNPsa breed . Missenssa breed ,29. Furtsa breed , thus suHowever, it should be considered that the sample size may have influenced the effect of SNPs on the analyzed phenotypic traits . In particular, a larger sample size could evidence associations between other SNPs and the recorded reproductive traits, although in a previous study on another sheep breed using a higher number of ewes, no other associations were found . MoreoveMTNR1A, as the silencing of this receptor significantly reduces progesterone production [Progesterone is essential for a regular estrus cycle and for the maintenance of pregnancy after ovulation . Furtheroduction ,41. TherThe data analysis additionally highlighted that the presence of at least one C allele was enough to determine a positive effect on reproductive recovery. These findings are coherent with previous research involving different breeds of sheep ,43. The MTNR1A gene, although we expected at least SNP21 (rs407388227) would influence the fertility rate as registered in other breeds [The fertility rate was not affected by polymorphisms of the r breeds ,26. Thisr breeds  in the IMTNR1A receptor gene in the Awassi sheep breed. Thirty-one polymorphic sites were identified, five of which caused amino acid changes. The genotype and haplotype analysis highlighted that the presence of only one C allele in SNP20 (rs430181568) and SNP21 (rs407388227) is enough to advance reproductive recovery in this breed of sheep. Moreover, the above SNPs were totally linked and might be considered as markers for practical application in the selection of this trait. These results are therefore a valuable tool for improving reproductive management and obtaining advanced lambing in Awassi ewes. In order to better understand the effect of melatonin on the ovary, it would be useful to investigate whether the ewes carrying at least one C allele at SNP20 (rs430181568) and SNP 21 (rs407388227) show changes in the expression of genes promoting follicular development and the antioxidant effect at the ovarian level.This study provided the entire coding region of the