dataesr/scientific-paragraphs-categorization huggingface dataset:
We focus discussion in the primary manuscript on the simulations which assumed h 2 pan = ĥ2 PED as the impact of xAM depends on the total heritability of the traits involved, not just that tagged by common variants, though results assuming h 2 pan = ĥ2 SNP are presented in the supplement. We next compared the projected correlation estimates under xAM alone, ρxAM , to empirical LDSC effect correlation estimates using real (rather than synthetic) phenotype data, which we denote ρemp. To simplify discussion, we define the ratio γ = ρxAM /ρ emp , (3) which measures the strength of the projected LDSC effect correlation estimate due to xAM-induced artifact relative to the empirical LDSC effect correlation estimate for a given pair of phenotypes. When γ is close to zero, empirical effect correlation estimates are far greater than what might be attributable to xAM alone.
Simulation framework We present results derived from two forward-time simulation frameworks: a highly realistic but computationally intensive scheme (hereafter referred to as the large-scale framework) and a simplified, computationally efficient scheme (hereafter referred to as the simplified framework), both of which we describe in detail in the following section. We justify employing the simplified framework by validating its output against that of the realistic framework, concluding that the simplified framework is sufficient for modeling the effects of xAM. Further, comparing these methods allows us to isolate the impacts (or lack thereof) of several phenomena of interest, including pleiotropy and local linkage disequilibrium (LD). We provide the code necessary to repeat or extend these analyses at https://github.
This corresponds to an exchangeable cross-mate correlation structure with all cross-mate correlations r yy , r yz , r zz equal to a single input parameter r mate. Though this method is computationally efficient and thus well-suited to this particular use-case, it is incapable of achieving arbitrary cross-mate correlation structures. Thus, a more flexible approach was required for the simulations presented in Section 2. 4, which were based on empirical estimates of the cross-mate correlation parameters. We achieved arbitrary cross-mate correlation structures, a problem for which we were unable to find a previously published solution, by using an ad hoc approach based on propensity-score matching methods as implemented in the R package MatchIt v4. 2. 0  [47]. Specifically, we randomly split the sample in half and used the nearest Mahalanobis distance matching algorithm on a scalar multiple of each of the two phenotypes, their product, and their difference.
For each trait pair, we produced two sets of results, once substituting pedigree-based heritability estimates ĥ2 PED for the panmictic heritability inputs and again substituting marker-based heritability estimates ĥ2 SNP. Unfortunately, the sample variance-covariances of ĥ2 y , ĥ2 z , ρβ are not reported by the LDSC software and were not available for any of the literature-derived estimates, and were treated as independent by necessity. That is, only the off-diagonal elements of Ω corresponding to the cross-mate correlation parameters, which we estimated via structural equation modeling, were nonzero.
Thus, we estimated both quantities via a single parameter, ρ ;eo , in the constrained structural model         ˆ y;even ˆ y;odd ˆ z;even ˆ z;odd         ∼ MVN         0,         1 φ yy 1 ψ yz ρ ;eo 1 ρ ;eo ψ zy φ zz 1                 , (7) where ρ ;eo is the only free parameter of interest. For the simpler case of single-trait even/odd correlations, we used the correspondingly simple bivariate Gaussian structural model. Again, we estimated structural models using the R package lavaan v0. 6-8  [49]  via maximum likelihood with asymptotic standard errors. Finally, we evaluated the relationships between even/odd chromosome polygenic score correlation estimates and both cross-mate cross-trait correlation estimates and genetic correlation estimates across all pairs of UKB sample phenotypes.
We examined these associations in the context of naïve linear models, which don't account for heteroskedasticity and sampling error in the predictor, but also using a Bayesian measurement error model as implemented in the R package brms v1. 8  [50] , which does. between X i , an arbitrary causal variant for phenotype Y and X j , an arbitrary causal variant for phenotype Z, after t > 0 generations of positive xAM across Y and Z. At every generation, we can factor the covariance E[ Xi Xj ] using the possible patterns of inheritance: E[ Xi Xj ] = 1 4 E[X * i X * j ] + 1 4 E[X * i X * * j ] + 1 4 E[X * * i X * j ] + 1 4 E[X * * i X * * j ] = 1 2 E[X * i X * j ] + 1 2 E[X * i X * * j ]. As by definition E t-1 [ Xi Xj ] = E t [X * i X * j ], this induces the following recurrence relation: E t [X i X j ] = 1 2 E t-1 [X * i X * j ] + 1 2 E t-1 [X * i X * * j ] = 2 -t E 0 [X i X j ] + t-1 l=1 2 t-l E l [X * i X * * j ].
S1. 3. 1 Identification of unrelated European-ancestry individuals in the UK Biobank From among 488,363 UK Biobank participants, we retained putative "White British" individuals using field f. 22006 (n=409,692). We then filtered out 199 individuals with excess genotype missingness (>0. 05), 312 individuals with a mismatch between self-reported and genetic sex, 999 inviduals with excess heterozygosity (≥5 standard deviations above the mean), and 90 individuals who requested their data be redacted. We then removed 629 individuals related to ten or more individuals (KING coefficient ≥ 2 -9/2 ) as a preprocessing step to the application of the maximal_independent_set algorithm implemented in the NetworkX Python package [54]. This resulted in 342,257 unrelated individuals. In contrast to  [21] , who estimated kinships using ≈92,000 common SNPs with small loadings onto the first few PCs in the full sample (including multiple ancestries; see S3.
Numerical Results In this section, we report several numerical experiments showing the performances of both considered algorithms for joint SISR and IP. All experiments reported in this section are conducted on a workstation Intel i5-6500, 8GB RAM using codes written in MATLAB R2019b and freely available as part of this work. Test problems and evaluation of results. We simulate blurred and noisy LR acquisitions starting from ground-truth (GT) HR images by applying model  (1). Unless otherwise specified, we set as default parameters the standard deviation of the Gaussian blur σ H = 1, the size of the Gaussian kernel equal to 9 × 9 and the standard deviation of the Gaussian noise σ η = 0. 01. We denote by L the super-resolution factor. Following our previous notations, we thus have N h = L 2 • N l.
The operators H, D x and D y can be represented as discrete convolution operators, hence their action on HR images can be efficiently computed as an element-wise product between their Optical Transfer Function (OTF) and the 2D Fourier Transform of the HR image. To do so, we make use of the MATLAB functions psf2otf and fft2 to compute the OTF of H, D x and D y and the 2D Fourier Transform of the HR image, respectively. Concerning the evaluation of the output image, we remark that we will not consider in the following common reconstruction-based Figures Of Merits (FOM), such as PSNR, SSIM since they are useful to compare the quality of the output image with the original one based on a comparison with the ground truth. Given the particular type of smoothing enforced by the 0 regularizer acting on the image gradient and the final IP objective our algorithms are expected not to provide outputs similar to the desired GT, but, rather, to provide a smoothed super-resolved image with simplified image contents.
2b ). We calculated the RTG-induced recombination by comparing the basal level of cells growing on 5-FOA measured in the unsporulated cultures (T0) to the RTG cells obtained after 6 hours (T6) and 14 hours (T14) of sporulation induction (Methods). We detected a 10-fold and threefold increase of cells growing on 5-FOA, indicating an increased LOH rate at T14 in OS1364 LYS2/URA3 and in OS1431 LYS2/URA3 , respectively, whereas the increase was not significant at T6, consistent with the fact that cells did not progress sufficiently through meiosis at this timepoint (Fig. 2b , Supplementary Data 5). The absolute percentage of cells grown at T14, calculated subtracting the T0 background value to the T14 (Methods), was almost twofold higher in OS1431 LYS2/URA3 (0. 10% in OS1364 LYS2/URA3 , 0. 19% OS1431 LYS2/URA3 ), in line with its lower heterozygosity on chromosome II (Fig.
1c ) where pre-existing small LOH regions might represent preferential sites of recombination  29. To further prove that RTG induced the increase in recombination observed at the LYS2/URA3 locus we deleted all three copies of SPO11, a gene essential for inducing DSBs in meiosis, in OS1364 LYS2/URA3 (Supplementary Figure  2b ) and measured recombination. We did not detect any significant increase between the T0 and the respective T14 in the OS1364 LYS2/URA3 spo11Δ strain (Supplementary Figure  2c , Supplementary Data 5), consistent with RTG caused the increased recombination as it relies on the Spo11p induced DSBs in early meiosis. We next performed whole-genome sequencing of the LYS2/ URA3 parental strains, controls (T0) (n = 2 OS1364, n = 2 OS1431, Supplementary Data 1) and RTG-evolved clones (T14) (n = 11 OS1364, n = 11 OS1431, Supplementary Data 1) isolated on 5-FOA plates to evaluate the genome-wide impact of RTG.
Overall, we showed that highly recombined RTG M-D pairs can be selected by exploiting natural colony phenotypic variation and proved that these phenotypes arise as a result of RTGinduced recombination. RTG recombination shapes industrial fitness. Next, we probed the potential of RTG to improve industrial phenotypes. First, we compared the fermentation performances of OS1364 and OS1431 to the commercial diploid strain WLP001 to evaluate the fermentation performances of our parental strains (Supplementary Data 1). We confirmed that our strains are competitive for industrial fermentation, with OS1364 outperforming both WLP001 and OS1431 for fast fermentation (Supplementary Data 10). However, both OS1364 and OS1431 had a substantial decline in cell viability (of ~25% and ~50%, respectively) often observed in chronologically aged polyploid S.
Next, we selected RTG samples derived from OS1364 based on their phenotypic performance (n = 12) and their LOH landscape across a core set of fermentation genes (n genes = 74), and all the M-D RTG pairs from OS1431 (n = 10) (Supplementary Data 7 and 8). We inoculated the selected RTGs in a high-gravity wort (32°Plato), representing extreme fermentative conditions, to further investigate differences in stress resistance, carrying out a flask-scale fermentation for 13 days evaluating mass loss and ethanol produced at regular intervals (Supplementary Data 9). Some RTG samples showed increased fermentation kinetics (Fig. 4d ) as well as a superior alcohol production (Supplementary Figure  8d, e ) compared to the parental strains. We also noticed that in some M-D RTGs derived from OS1431 the fermentation performances were complementary (Supplementary Figure  8e , Supplementary Table  9 ).
Remarkably, one OS1364 RTG had a large increase in post-fermentation cell viability (Fig. 4e , Supplementary Data 11), reaching a level similar to WLP001, showing that RTG can purge detrimental traits. In contrast, OS1431 RTGs were slightly better compared to their parent in fermentation performances (Supplementary Figure  9a , Supplementary Data 10) but without increased post-fermentation viability (Fig. 4e ). Furthermore, we evaluated the aroma profile and the postfermentation residual sugars, two parameters that are highly relevant in the beer industry. The two parental strains produced distinct aroma profiles from that of WLP001 and consumed almost all the sugars present in the wort (Supplementary Data 11). The four selected RTG samples did not show any deleterious trade-off in either phenotypes with the exception of a slight increase of acetaldehyde, in sm408, which is not desirable but its increase was negligible as those sensory thresholds can be individual dependent and affected by the beer style (Sensory threshold = 10 mg/mL, sm408 = 10.
e Post-fermentation viability measured after a 2L-fermentation in high-gravity wort for the two parents, the respective RTG samples and a commercial strain in triplicate. One RTG (sm244) derived from OS1364 showed a huge increase in viability. Each bar plot represent the average of n = 3 independent fermentations and the error bars represent the standard deviation. f Variability in the aroma profile across the four RTGs from the 2 L scale fermentation, the values are expressed as relative change compared to the concentration produced by the parental strain. Empty and closed dots respectively represent compounds with concentrations below or above the sensory threshold. Source data are provided as a Source Data file. The annotated non-reference regions on chromosome XIII of OS1364 were extracted from the fasta file of the collapsed assembly genomes (the haplotypes were not phased) and blasted by using the application "blastn" at https://www.
ncbi. nlm. nih. gov/ against the database of ascomycetes. Sporulation monitoring and spore viability. Yeast strains were patched on YPD plates and incubated at 30 °C for 24 hours. From the patch a streak was done and incubated at 30 °C for 48 hours, then single colonies were isolated and grown overnight at 30 °C in 10 mL of liquid YPD in a shaking incubator at 220 rpm. The following day, single colonies were inoculated in different tubes containing 10 mL of the pre-sporulation medium SPS (1% peptone, 1% potassium acetate, 0. 5% yeast extract, 0. 17% yeast nitrogen base, 0. 5% ammonium sulfate, 1. 02% potassium biphthalate) and kept at 30 °C in a shaking incubator for 24 hours. Tubes were then centrifuged and washed three times with sterile water and cells were resuspended in erlenmeyer flasks containing 25 mL of KAc 2% to reach a final OD of 1, and incubated at 23 °C in a shaking incubator.
8 Zeiss. CNVs detection in parental strains and RTG samples. Short reads Illumina sequencings were performed at the genomic platform of the Institute Curie. Reads were mapped to the S288C reference genome with the bwa-mem algorithm. Optical duplicates of the sequencing were removed using "samtools rmdup". Processed BAM files were then indexed using "samtools index" and coverage was extracted using "samtools depth". Coverage along the chromosomes was plotted using in-house R scripts in which sliding windows of non-overlapping 10 kbp were used to calculate a local average coverage, which was then normalized with the median coverage along the chromosome. Genome-wide coverage profiles were manually inspected to detect artifacts due to smiley pattern  5  or lower coverage affecting only small chromosomes.
Next, we used an in-house R script to compare the frequencies of the alternative and reference alleles of each marker in the evolved samples and in the parental ones. We identified putative recombined markers like the ones showing an AF shift comparing the parental and the RTG AF, independently of the direction of the AF shift. Heterozygous markers present in the parental strain but not genotyped as heterozygous in the RTG samples were considered to have reached a homozygous reference genotype if the position was sequenced. Moreover, the allele of the marker tagged as having an AF shift was compared with the known allele of the marker present in the ancestral strain, and those not matching the expected allele were filtered out. Given the complexity of polyploids analysis, we decided to use a stringent threshold and we annotated regions of AF shift only if they contained at least nine consecutive markers with AF shift regardless of the directionality of the allele that is gained or lost due to recombination.
In this way, we avoided calling false-positive AF shift regions at the cost of decreasing our resolution. This approach may decrease the number of gene-conversion events detected as they are generally small (< 2 kbp) and thus may fall below our threshold. Moreover, our resolution in mapping long recombination events was limited by the local heterozygosity between the haplotypes as we did not have information on the phased haplotypes for our strains. Regions of local homozygosity between the two recombining haplotypes may be intervaling regions of heterozygosity on which we identified an AF shift thus breaking the AF shift region and resulting in the fragmented pattern observed. For this reason, we refrained to give numbers of events but only referring to AF shift regions. Among those LOHs/AF shift regions, the ones overlapping for >80% of their length in at least ≥ 70% of the samples considering each datasets separately (wild-type RTG, LYS2/URA3 selected RTG, and ndt80Δ RTG) were filtered out because were considered to be unlikely generated by RTG, but rather representing pre-meiotic events.
Growth curves for the microcultures were modeled based on the OD 600 values over time using the 'GrowthCurver'-package for R  48. Flask-scale very high-gravity wort fermentations. 50 mL-scale fermentations were carried out in 100 mL Schott bottles capped with glycerol-filled airlocks. Yeast strains were grown overnight in 25 mL YPM medium at 25 °C. The pre-cultured yeast was then inoculated into 50 mL of 32 °P wort made from malt extract (Senson Oy, Finland) at a rate of 7. 5 g fresh yeast L -1. Fermentations were carried out in duplicate at 20 °C for 13 days. 2L scale high-gravity wort fermentations. Strains were characterized in fermentations performed in a 20 °P high-gravity wort at 20 °C. Cultures were propagated essentially with the use of a "generation 0" fermentation prior to the actual experimental fermentations  49.
Acknowledgements S. M. and G. L. :  Agence Nationale de la Recherche  ( ANR-13-BSV6-0006-01 ,  ANR-18-CE12-0004 ,  ANR-15-IDEX-01 ,  ANR-20-CE12-0020 ),  Fondation pour la Recherche Médicale  ( EQU202003010413 ),  UCA AAP Start-up Deep tech, CEFIPRA ,  Association pour la Recherche sur le Cancer  ( ARCPJA32020070002320 ). G. L. , S. M. , and A. N. : convention CIFRE 2016/0582 between Meiogenix and  ANRT. K. K. and B. G. : no relevant funding. We thank  D'Angiolo M. and  Adekunle D. for their critical reading of the manuscript.
The kidney-specific gene UMOD encodes for uromodulin, the most abundant protein excreted in normal urine. Rare large-effect variants in UMOD cause autosomal dominant tubulointerstitial kidney disease (ADTKD), while common low-impact variants strongly associate with kidney function and the risk of chronic kidney disease (CKD) in the general population. It is unknown whether intermediate-effect variants in UMOD contribute to CKD. Here, candidate intermediate-effect UMOD variants were identified using large-population and ADTKD cohorts. Biological and phenotypical effects were investigated using cell models, in silico simulations, patient samples, and international databases and biobanks. Eight UMOD missense variants reported in ADTKD are present in the Genome Aggregation Database (gnomAD), with minor allele frequency (MAF) ranging from 10 25 to 10 23.
one of the most common monogenic kidney diseases  (12). UMOD is thus implicated at both extremes of the genetic disease spectrum: ultrarare variants with large-effect size (ADTKD-UMOD) and common (MAF > 0. 05) low-impact variants associated with reduced eGFR and risk of CKD in the general population  (13). The availability of large genomic datasets suggests that the dichotomy of rare high-effect variants causing Mendelian disorders and frequent low-effect variants involved in complex diseases must be completed by variants with intermediate-effect sizes. These intermediate-effect variants lie on a spectrum based on allelic frequency (AF), biological consequence, and phenotypical readout. They can manifest as a nonfully penetrant Mendelian disease or an oligo-/polygenic model modifying disease expressivity  (14, 15).
The absence of ultrarare and predicted pathogenic variants in the majority of CKD patients  (16)  and the missing heritability when studying polymorphisms  (17)  suggest that intermediate-effect variants, not captured by GWAS but amenable to sequencing approaches, may be part of a continuum underlying CKD. Because UMOD is involved at both ends of the genetic spectrum, it is a plausible candidate gene for intermediate-effect variants modulating the risk of CKD. Here, we identified and characterized intermediate-effect variants in UMOD contributing to CKD by crossing general population datasets with curated variants reported in ADTKD, analyzing biological and phenotypical effect sizes using in silico modeling, cell systems, databases, and biobanks; and validating the impact on kidney failure in the 100,000 Genomes Project and the UK Biobank (SI Appendix, Fig.
Results Identification of Candidate Intermediate-Effect Variants in UMOD. The UMOD gene is implicated at both extremes of a CKD risk continuum (Fig. 1A ); however, bona fide high-effect variants cannot be more prevalent than the disease they cause. We first assessed the missense variants in UMOD reported in the Genome Aggregation Database (gnomAD; "controls")  (18)  as well as in the International ADTKD Cohort  (11)  and the Human Gene Mutation Database (HGMD; "cases"). Shared UMOD variants should be either benign (falsely associated with disease) or intermediate-effect variants associated with nonfully penetrant or milder late-onset disease (Fig. 1B   The majority of UMOD variants listed in gnomAD (360/1,000; 36%) were missense variants (SI Appendix, Fig. S2 ), with an AF ranging from 2. 0 × 10 À2 to 3.
3A , Bottom), further supporting a damaging effect of proline at this position. To analyze the effect of the substitution, we constructed a model of the first EGF of UMOD (amino acids 28 to 65) using the software Phyre2 and introduced the threonine to proline change in PyMOL. The presence of structural clashes is detected when proline is inserted (Fig. 3B ). In contrast, nonproline substitutions at Thr62 in UMOD appear to be tolerated (SI Appendix, Figs. S4C  and S5 ). These results were confirmed by using the prediction programs Missense3D  (25)  and SDM (SI Appendix, Fig. S4C )  (26) , which identified proline as the only substitution affecting protein structure and as the most destabilizing. Furthermore, only Thr62Pro, but not Thr62Ala, Thr62Gly, or Thr62Ser, led to a trafficking defect in HEK293 cells (Fig.
ns, not significant. comparable risk for kidney failure associated with the presence of two risk alleles at APOL1 (29) compared with the wild type (OR = 4. 3 [2. 6 to 7. 1]) or with one risk allele (OR = 4. 0 [2. 3 to 6. 8]) (SI Appendix, Table  S10 ). Similar results were observed in ancestry-matched individuals from the UK Biobank, where UMOD p. Thr62Pro was enriched in probands with CKD stage 5 (OR = 4. 12 [1. 3 to 12. 9], P = 0. 038) and probands with kidney transplantation (OR = 6. 84 [2. 2 to 21. 3], P = 0. 010) (Fig. 5B ). In both databases, no significant enrichment for p. Thr62Pro was detected for milder stages of CKD. UMOD variants p. Leu180Val and p. Thr469Met were not significantly enriched in probands with kidney disease (SI Appendix, Figs. S13  and S14 ), which together with the negative in vitro and in silico data, makes their contribution to human disease unlikely.
Finally, we analyzed clinical indications for 3,122 unrelated patient whole-exome datasets obtained in Western Europe (Eurofins Biomnis). UMOD p. Thr62Pro was detected in 2 (FR1 and IRL1) of 508 exomes (0. 39%) performed for cystic kidney disease, tubulointerstitial kidney disease, or CKD of unknown etiology, but it was absent from the remaining 2,614 exomes performed for glomerular or vascular kidney disease or nonnephrological indications-in line with enrichment of p. Thr62Pro in ADTKD-like disease (SI Appendix, Table  S11 ). Altogether, we detected 27 individuals with kidney disease and UMOD p. Thr62Pro in the UK Biobank, the 100,000 Genomes Project, and the Eurofins exomes (SI Appendix, Table  S11 ).