Bio-based fillers for the polymer composites are still interesting from the scientific and industrial point of view, due to their low cost and renewable nature. In this work partially green composites were obtained by the mixing of thermoplastic poly(ester-urethane) with the unmodified and modified (by acetylation) grinded buckwheat husks. Obtained biocomposites were characterized in the terms of their chemical structure (FTIR), microstructure (SEM), thermal stability (TGA), thermomechanical properties (DMTA), and selected mechanical properties. The results showed that introduction of grinded buckwheat husks (even if the amount is 60 wt%) permit retaining high values of tensile strength (around 8–10 MPa), but the increasing amount of applied filler is connected with the decreasing of elongation at break. It can result from good interaction between the polymer matrix and the bio-based filler . The applied chemical treatment results in changing of mechanical properties of filler and composites. Obtained results confirmed the possibility of using powdered buckwheat husks as filler for thermoplastic polyurethane. By-products from the agricultural industry are generally the source of bio-based fillers, which can be applied to the production of polymer-based composites. The chemical modification of fillers can improve the mechanical performance of polymer composites. Several different chemical modifications were proposed for the bio-based fillers, e.g., mercerization (using sodium hydroxide), acetylation (using acetic anhydride and acetic acid), oxidation (using potassium permanganate), grafting , and others. The procedure of single chemical modification can be different for different types of bio-based fillers and it results from the different chemical composition, size, surface characteristic, and mechanical properties of filler subjected to the process ,2,3.The green thermoplastic polyurethane materials can be obtained using bio-based substrates for the synthesis of thermoplastic polyurethanes  or/and using bio-based fillers. Bio-based TPU can be synthesized using, for example: fatty acids derivatives , lysine Rice and buckwheat husks are examples of agriculture by-products that can be applied as a fillers in the preparation of polymer composites. In the area of polyurethane-based biocomposites special place is occupied by rice husks. Rozman et al.  describeBuckwheat husks, which are considered in this work, are another example of bio-based filler that can be used for the preparation of polymer composites. Andrzejewski et al.  obtainedThe main aim of this work was to obtain thermoplastic polyurethane-based composites filled with powdered buckwheat husks. The bio-based filler was used unmodified and chemically modified by the acetylation. Thermoplastic polyurethane was synthesized by prepolymer method using 4,4′-diphenylmethane diisocyanate, ester-based oligomeric diol, and 1,4-butanediol. Composites, prepared using a Brabender mixer, contained 10, 20, 30, 40, 50, or 60 wt% of bio-based filler. Obtained materials were characterized in the terms of their chemical structure (FTIR), microstructure (SEM), viscoelastic properties (DMTA), thermal stability (TGA), and mechanical properties .−1, hydroxyl number 58 mg KOH g−1 and acid number 0.3 mg KOH g−1. The synthesis of prepolymer was carried out at 80 °C for 2 h (under the vacuum) and resulted polymer was characterized by the amount of unreacted isocyanate groups equal 7.2 wt%. Thermoplastic polyurethane was prepared using two-step method, also called prepolymer method. In the first step , urethanIn the next step  prepolymThe acetylation of grinded buckwheat husks was performed by the soaking of 50 g of filler in solution containing 200 mL of acetic anhydride, 198 mL of acetic acid, and 2 mL of concentrated sulphuric(VI) acid. The soaking time was equal 2 days and after that the filler were washed with distilled water several times until natural pH was obtained. The filler were dried for 48 h at 80 °C and 24 h at 100 °C. The unmodified filler was dried for 24 h at 100 °C. The general scheme of acetylation of cellulose-based filler is presented in The small pieces of TPU and bio-based filler, i.e., unmodified (BWH) and modified (BWH-Ac) grinded buckwheat husks, were mixed using a Brabender mixer at 170 °C, for 10 min with mixing speed equal 100 rpm. After that, the obtained materials were pressed by using a hydraulic press in two steps: (I) 170 °C, 5 MPa, 5 min and (II) 21 °C, 5 MPa, 5 min. The reference sample (TPU-REF) without fillers was also prepared by the same procedure. The sample code indicates the type  and amount (in wt%) of applied filler, for example: sample coded as TPU-BWH-60 is composites filled with unmodified powdered buckwheat husks, which amount in the material is equal 60 wt%.−1 at nominal resolution 4 cm−1. Each spectrum is an average from 64 scans.Chemical structure of fillers, thermoplastic polyurethane, and composites was verified by Fourier Transform Infrared Spectroscopy (FTIR) using Nicolet 8700 FTIR Spectrophotometer . Spectra were registered at room temperature for wave numbers between 500 and 4000 cmMicrostructure of cross-sections of prepared composites was studied using a Phenom G2 PRO scanning electron microscope . The micrographs were prepared using desktop scanning electron microscope with accelerating voltage of 5 kV. Unmodified and chemically modified powdered buckwheat husks were analysed using Quanta FEG scanning electron microscope equipped with the Everhart–Thornley detector (ETD) and accelerating voltage of 10 kV.−1. The measurements were performed in three-point bending mode at operating frequency 1 Hz under nitrogen atmosphere. The dimensions of the composites samples subjected to the test were 10 mm (width) × 40 mm (length) × 2 mm (thickness). The variation of storage modulus (E′), loss modulus (E″) and damping factor (tan δ) vs. temperature was determined. Glass transition temperature (Tg) of prepared materials was determined as temperature at which tan δ reaches maximum value.Dynamic mechanical thermal analysis (DMA) was carried out using a DMA Q800 analyzer  in a temperature range from −100 to +100 °C at heating rate of 4 °C min−1 under inert atmosphere (nitrogen).Thermal stability of materials were determined using TGA Pyris 1  analyzer. Weight of the sample was around 5–10 mg. The samples were heated from 50 to 600 °C at a rate of 20 °C minSelected mechanical properties (tensile properties and hardness) were characterized for the prepared thermoplastic polyurethane and polyurethane composites with powdered buckwheat husks. Tensile properties  were determined using universal testing machine Zwick/Roell Z020 and dumbbell specimens, according to ISO 527-2 standard. The tensile speed was 200 mm/min and presented values are averages from five independent tests. Hardness was determined using an electronic Shore type A Durometer (Zwick/Roell) according to ISO 868 standard and presented results are averages from random five independent measurements (different places at the surface of the same sample).−1, and can be assigned to carbonyl stretching vibration, methyl in-plane bending, and C–O stretching, respectively. Ohkoshi [−1 for acetylated wood. Zhou et al. [3), and 1235 cm−1 (vC–O). Ohkoshi  observedu et al.  indicate−1 corresponds to stretching vibrations of hydrogen-bonded amine groups (presented in urethane moieties). The double peak with maxima at 1728 and 1701 cm−1 is related to stretching vibrations of free and hydrogen bonded carbonyl groups (presented in soft segments and urethane moieties), respectively. The absorbance of peaks related to carbonyl stretching vibrations is not affected by the introduction of powdered buckwheat husks.−1) confirm that in the chemical structure of modified filler was replaced by acetyl –O-C(O)–CH3 groups during chemical treatment. In general, the FTIR spectra of TPU and TPU-based composites with BWH and Ac-BWH are very similar.The applied chemical treatment results in decreasing of hydroxyl group content in the chemical structure of bio-based filler . The broad peak visible in FTIR spectra of obtained materials (mainly in composites contained unmodified buckwheat husks) related to stretching vibrations of –OH groups (3200–3600 cmSEM micrographs of unmodified and acetylated buckwheat husks are presented in SEM micrographs of cross-sections of prepared composites with unmodified and acetylated buckwheat husks are presented in g SS) and during the glass transition of soft segments significant decreasing of this parameter is noted as a result of significant increase in the mobility of polymer chain segments during glass transition. At room temperature, where composites are in rubbery state, storage modulus reach relatively low values (47–156 MPa), but obtained results suggest that introduced bio-based filler improves materials stiffness . Decreasing of E′ at room temperature suggest that applied chemical treatment decreasing the stiffness obtained composites with treated filler in comparison to composites with untreated filler. Another decreasing of storage modulus is observed above 60 °C which can be related to the glass transition temperature of hard segments (Tg HS). According to the literature Tg HS of thermoplastic polyurethanes is observed below 100 °C, while the melting occurs above 100 °C [The temperature dependence of storage modulus (E′), loss modulus (E″), and damping factor (tan δ) is presented in e 100 °C . Increase 100 °C . The maxg SS values  and second one to thermal decomposition of cellulose and lignin (350–370 °C). The applied chemical modification results in the decreasing of temperatures at which occur degradation of mentioned constituents probably due to acidic nature of substrates used for the acetylation of buckwheat husks, which may cause degradation (etching) of microstructure of filler and its constituents (dissolving and further removing). The mentioned effects can be also confirmed by third step (400–600 °C) of degradation of observed only for acetylated buckwheat husks powder.Thermal decomposition of synthesized thermoplastic polyurethane can be described as one-step process, which is typical for the polyurethanes without or with low degree of phase separation (commonly observed for cross-linked and some thermoplastic ones). The temperature at which degradation rate reaches maximum value was around 405 °C, and that value is in the rage 380–420 °C, which was observed by Datta et al.  for polyGenerally increasing amount of filler in polymer matrix results in decreasing of thermal stability of TPU-based composites, as a result of presence of bio-based filler characterized by lower thermal stability. It can be stated that thermal decomposition of prepared composites occurs in two stages, where first one is related to the degradation of powdered buckwheat husks, while the second one is connected to the thermal decomposition of polymer matrix. In the case of composites containing at least 40 wt% of filler another step is also visible at the beginning of thermal decomposition, probably due to the fact the filler becomes predominant constituent in the microstructure of the composite.The selected mechanical properties of synthesized TPU and TPU composites are presented in Hardness of composites increasing with increasing amount of introduced filler, which is resulted from higher hardness and stiffness of applied filler in comparison to polymer matrix.c is the tensile strength of the prepared composites, σm is the tensile strength of the thermoplastic polyurethane, B is the parameter related to the matrix–filler interactions (which is related to size of interface and properties of the interphase), Vf is the filler volume fraction. Vf is calculated using following equation:c is the density of the prepared composites, ρf is the density of the filler, wf is weight fraction of the filler in the composite.The effectiveness of stress transfer between the polyurethane matrix and buckwheat husk filler can be improved by the interaction of filler with polymer matrix. The relationship between the tensile strength of polymer composites and interfacial interactions was described by Pukanszky  and can f. The slope of a straight line is interaction parameter B.Equation (1) should be rewritten in the following way:The densities of the samples, filler volume fractions and matrix–filler interaction parameters  for the prepared TPU-based composites filled with unmodified and modified are presented in The obtained results confirmed that powdered buckwheat husks can be used as a filler for thermoplastic polyurethanes. High values of tensile strength (around 8–11 MPa) can be achieved even when the amount of bio-based filler is equal 50–60 wt%, which results from good interaction between polymer matrix and filler confirmed by high value of B parameter (B = 3.89) determined using Pukánszky model related to tensile strength of polymer composites. Unfortunately increasing content of the filler in the composite results in decreasing of elongation at break, which commonly observed trend in polymer biocomposites. The applied chemical treatment  decreasing hydrophilicity of the filler, which weakening the interaction between polymer matrix and carbohydrate-based filler (B = 3.67) which is connected with decreasing of tensile strength and increasing of elongation at break. The stiffness  of the materials increasing with increasing amount of filler and higher values were observed for unmodified buckwheat husk powder. Thermal stability of obtained materials is negligible lower in comparison to unfilled TPU, which is resulted from lower thermal stability of bio-based filler (which degradation begins at around 200 °C).TPUs filled with bio-based fillers (including particulate fillers and fibres) can be used for several applications in construction, automotive, and sports industries. Prepared partially green composites extend the group of materials that can be used in listed above areas, but additional tests are required to confirm suitable performance for specific applications.


In mammals, the pigment molecule pheomelanin confers red and yellow color to hair, and the intensity of this coloration is caused by variation in the amount of pheomelanin. Domestic dogs exhibit a wide range of pheomelanin intensity, ranging from the white coat of the Samoyed to the deep red coat of the Irish Setter. While several genetic variants have been associated with specific coat intensity phenotypes in certain dog breeds, they do not explain the majority of phenotypic variation across breeds. In order to gain further insight into the extent of multigenicity and epistatic interactions underlying coat pheomelanin intensity in dogs, we leveraged a large dataset obtained via a direct-to-consumer canine genetic testing service. This consisted of genome-wide single nucleotide polymorphism (SNP) genotype data and owner-provided photos for 3,057 pheomelanic mixed breed and purebred dogs from 63 breeds and varieties spanning the full range of canine coat pheomelanin intensity. We first performed a genome-wide association study (GWAS) on 2,149 of these dogs to search for additional genetic variants that underlie intensity variation. GWAS identified five loci significantly associated with intensity, of which two (CFA15 29.8 Mb and CFA20 55.8 Mb) replicate previous findings and three  have not previously been reported. In order to assess the combined predictive power of these loci across dog breeds, we used our GWAS data set to fit a linear model, which explained over 70% of variation in coat pheomelanin intensity in an independent validation dataset of 908 dogs. These results introduce three novel pheomelanin intensity loci, and further demonstrate the multigenic nature of coat pheomelanin intensity determination in domestic dogs. For thousands of years, humans have selectively bred domestic dogs for desired physical and behavioral phenotypes, including a wide variety of coat colors and patterns , 2. For MC1R, known as the “E locus”) and agouti signaling protein  [MC1R gene inhibit the synthesis of eumelanin in hair follicle melanocytes, resulting in a solid “recessive red” coat containing only pheomelanin [ASIP variant (Ay), which produces “sable” coats with varying amounts of black/brown hairs concentrated around the dorsal midline, and pheomelanic hairs across the rest of the body [All canine coat colors and patterns result from varied expression of two pigment molecules: eumelanin, which is black or brown, and pheomelanin which is reddish-yellow. Most canids have coats containing a mixture of hairs expressing eumelanin, pheomelanin, or both, but many domestic dogs have coats in which only pheomelanin is expressed. These “pheomelanic” coats result from mutations in and around one of two genes that regulate switching between eumelanin and pheomelanin synthesis in hair follicle melanocytes: melanocortin 1 receptor ( locus”) . At leasomelanin , 17, 18.the body .The intensity of pheomelanic coloration varies widely across and within breeds that are fixed for recessive red or sable coats. For example, Irish Setters have consistently deep red coats, while Soft-coated Wheaten Terriers have coats that vary from cream to tan. Additionally, many breeds with solid white or cream coats have been shown to be recessive red, including Bichon Frisé, Samoyed, West Highland White terrier, and White German Shepherd , 19. OveMC1R gene that are highly predictive of extreme pheomelanin dilution in recessive red Siberian Huskies and Australian Cattle Dogs [MFSD12) that is associated with extreme pheomelanin dilution in a wide variety of breeds [KITLG) that was predictive of red intensity in Nova Scotia Duck Tolling Retriever and Poodle [Three recent studies have identified several genetic variants that are able to explain some coat pheomelanin intensity variation in certain breeds. The first study identified two variants in and upstream of the tle Dogs , but didf breeds . Howeverd Poodle , but notd Poodle ) and pheembarkvet.com/privacy-policy/).Participating dogs were part of the Embark Veterinary, Inc. customer base. Owners provided informed consent to use their dogs’ data in scientific research by agreeing the following statement: “I want this dog’s data to contribute to medical and scientific research”. Ethical approval was not required as non-invasive methods for genotype or phenotype collection were used . Dogs were never handled directly by researchers. Owners were given the opportunity to opt-out of the study at any time during data collection. The discovery and validation cohorts were selected from data available collected between October 2018 and June 2020. All published data have been de-identified of all Personal Information as detailed in Embark’s privacy policy  that encompass the range of coat pheomelanin intensity phenotypes in domestic dogs and obtained their hexadecimal values . We then used the Matplotlib  LinearSeTo assign coat color phenotypes to dogs, a single scientist visually evaluated owner-provided photos and assigned each dog to one of the six levels in the coat color scale or excluded it from further analysis. To account for red countershading—meaning darker red hair along the back, ears, and the tip of the tail in some breeds —all dogsAt this point, our sample contained an excess of purebred dogs from breeds that are fixed for cream coats compared to breeds that are fixed for red coats. In order to achieve a better balance between these two extremes, we used concordant owner-reported and genetically-determined breed assignments to identify an additional 197 genetically pheomelanic, purebred dogs with no owner-provided photo that belonged to breeds that are fixed for red coats . These dogs were assigned the most common six-point phenotype value in their breed across the rest of the sample. The dogs phenotyped in this manner consisted of 21 Brittanys, 2 Ibizan Hounds, 4 Irish Setters, 5 Irish Red and White Setters, 8 Redbone Coonhounds, 138 Rhodesian Ridgebacks, 16 Vizslas, and 3 Welsh Springer Spaniels  using the pandas DataFrame.sample method  and re-pPLINK 1.9  was usedWe grouped the 3,057 dogs according to their breed, subset each breed by six point phenotype value, and split each phenotype group randomly 70:30 into the discovery and validation datasets using the pandas DataFrame.sample method . As a reTo identify genomic regions associated with pheomelanin intensity variation, we encoded coat color as both a case-control (cream versus red) and quantitative trait  and applied a multivariate linear mixed model implemented in GEMMA v.0.98  to our dMC1R and RSPO2 genes on canine chromosome (CFA) 5 and CFA13, respectively (MC1R “e” [MC1R and/or have a high frequency of the “furnished” (“F”) allele at RSPO2 , and tiMC1R “e” , which rat RSPO2 , S1 Fileat RSPO2 . We discDue to the difficulty of obtaining appropriate hair samples for the thousands of dogs in our sample from individual owners, we were not able to experimentally measure the amount of pheomelanin in dogs’ hair coats (as done in ). BecausRaw whole genome paired-end short read sequencing datasets were downloaded as fastq files from the Sequence Read Archive  and aligTo determine which allele at each top GWAS marker was most likely the ancestral allele, we obtained genotypes at these markers across 54 publicly available wild canid whole genome sequencing datasets  from a previously published dataset available in the NCBI Sequence Read Archive (SRA) , 45. GenWe used the PLINK 1.9 —epistasid for each SNP as the difference between the observed and expected mean phenotype values in the heterozygote class. A positive value of d is consistent with the red-associated allele being at least partially dominant, and a negative value of d is consistent with the red-associated allele being at least partially recessive. We considered d to be statistically significant if the 95% confidence interval of the observed heterozygote mean phenotype did not include the additive heterozygote midpoint phenotype. All data used in this analysis are available in To evaluate the dominance relationship between the alleles at each of the top GWAS SNPs, we estimated predicted heterozygote phenotype values under complete additivity as the midpoint of the standardized six point phenotype values in the two homozygote classes . We thenUsing the linear_model module in the Python scikit-learn package version 0.21.3 , we fit 10 scale)  . The mos0 scale) , Table 12 between genotypes at each top GWAS variant and neighboring variants (i.e. linkage disequilibrium), are shown in S3 Fig in The locations of these markers relative to annotated canFam3.1 functional elements in the Ensembl Genes (v95) database , as wellTo the authors’ knowledge, the CFA2, 18, and 21 associations with coat pheomelanin intensity have not been previously reported. The top CFA2 variant, BICF2P1302896, falls within the second exon of the long intergenic non-coding RNA (lincRNA) ENSCAFG00000042716 at CFA2: 74,744,598–74,747,735 bp    that was previously associated with variation in coat pheomelanin intensity in Nova Scotia Duck Tolling Retrievers and Poodles  downstream of a 6 kb copy number variant (CNV) near the KIT ligand gene (MFSD12) at CFA20: 55,856,000 bp  of at least one marker . Howeverd as the difference between the observed and expected mean standardized six point phenotype value for the heterozygote class (Methods) (For each of the top GWAS SNPs (which we refer to by their chromosome number in the remainder of this manuscript), we estimated the dominance effect Methods) .We found that the heterozygote mean phenotypes expected under additivity at the top CFA2 and 15 SNPs fell within the 95% confidence intervals of the observed heterozygote mean phenotypes, suggesting that these loci behave in a mostly additive manner. At the top CFA18, 20, and 21 SNPs, the mean heterozygote phenotypes were significantly higher than the additive expectations, suggesting that the red-associated alleles at these loci are at least partially dominant to the cream-associated alleles.When pairwise tests for epistatic interaction were applied to the top five GWAS variants, seven pairs of variants showed statistically significant interactions: CFA15 x CFA20, CFA18 x CFA20, CFA2 x CFA15, CFA18 x CFA21, CFA2 x CFA18, CFA2 x CFA21, and CFA15 x CFA21 .Two locus genotype and phenotype combinations for these variant pairs are shown in In agricultural, livestock, and canine genetics –53, a coFirst, we fit a model on normalized six point phenotype values that split the genotypes at all five loci into two variables each indicating whether or not they were heterozygous (“_1”), and whether or not they were homozygous for the red-associated allele (“_2”). The ratios of the model coefficients (β) for the _1 and _2 variables at each locus provided an additional evaluation of the dominance relationship between the two alleles: loci for which the _1 β was approximately half of the _2 β fit the assumption of additivity, whereas loci for which the _1 β was approximately zero were more consistent with the red-associated allele being recessive to the other allele, and loci for which the _1 and _2 βs were similar were more consistent with the red-associated allele being dominant to the other allele. Based on the β values for this model , we concNext, we fit five models with six point phenotype values as a function of genotype at each locus using its best dominance encoding in order to estimate the predictive power of each locus individually. This showed that the CFA2 and CFA20 loci each explained over 50% of the variance in six point phenotypes, while the CFA15, 18, and 21 loci each explained less than 10% of the variance .To quantitatively determine the best combination of dominance encodings in a multilocus model, we fit 31 models with each possible combination of the additive and most likely dominance encoding at all five loci. A model treating all five loci as completely additive was able to explain 73% of variation in the six point phenotype (adjusted R-squared = 0.730) . The dom−2) and was significantly more predictive than either the purely additive model (−9) or the model with the best fit dominance encoding and no epistasis (−6). We applied the reduced best fit predictive model to the 908 dogs in the validation sample and found that it was able to explain 72% (adjusted R-squared = 0.7211) of variation in coat pheomelanin intensity across all dogs , located approximately 82 kb downstream of ENSCAFG00000042716 at CFA2: 74,829,960–74,856,947. RUNX3 encodes a transcription factor that shows reduced expression in hair follicles in human premature hair greying and appears to regulate expression of several other genes that also show reduced expression in premature greying samples [RUNX3 is also known to be a regulator of hair shape determination during murine embryonic development [cis-regulatory module consisting of ENSCAFG00000042716, RUNX3, and possibly other unknown genic variants or functional genomic elements. Identifying the causal mutations underlying this association will require fine mapping of the locus, as well as molecular experiments to directly assess the functional impacts of any candidate mutations.The top CFA2 variant falls within a long intergenic non-coding RNA (lincRNA) with unknown functional significance in domestic dog. Many mammalian (including dog) lincRNAs are known to modulate the expression of nearby protein-coding genes via chanisms –57. The  samples . RUNX3 ielopment . We therTYR gene. This gene encodes the enzyme tyrosinase, which catalyzes the oxidation of l-dihydroxy-phenylalanine (DOPA) to DOPA quinone, a precursor of both eumelanin and pheomelanin. Mutations in and around TYR produce varying degrees of pheomelanin dilution in several mammalian species by decreasing the amount of pheomelanin produced in hair shaft melanosomes [TYR mutations might also produce pheomelanin dilution in dogs [TYR variants can modulate coat pheomelanin intensity in dogs was finally supported when a recent study identified a missense mutation in the TYR gene as causal for a unique temperature-dependent pigment dilution phenotype (acromelanism) in a single dog [TYR variants and non temperature-dependent coat pheomelanin intensity variation, although fine mapping and functional validation will be required to definitively identify a causal variant. In multiple species, some of the genes located nearby TYR on CFA21  areSLC26A4 gene has no clear connection to hair coat pigmentation in mammals, it does play a role in a variety of hearing impairment phenotypes in human and inner ear abnormalities in mouse, including hyperpigmentation in the stria vascularis [MITF) [PMEL  [KITLG cause hearing loss in humans [The connection between coat pheomelanin intensity and the gene tagged by the top CFA18 association is less apparent. The A to G substitution at this variant results in an amino acid substitution from isoleucine to methionine in the solute carrier family 26 member 4 (SLC26A4) protein. Based on computational modeling (Sorting Intolerant from Tolerant (SIFT) score = 0.03), this substitution is predicted to be somewhat deleterious . Howeverscularis  and degescularis . There is [MITF)  and PMELas SILV)  genes, was SILV) , 68. Addn humans . Due to KITLG gene on CFA15 encodes a ligand that binds to the c-Kit protein on the surface of melanocytes, triggering the Ras/MAPK signaling pathway and stimulating melanocyte proliferation and melanogenesis [KITLG coding sequence, indicating that its likely affecting pheomelanin intensity by modulating KITLG expression. As noted in the study that first reported this association [KITLG have been associated with both pheomelanin and eumelanin dilution in several mammalian species [SLC26A4 gene on CFA18 encodes a transmembrane ion transporter that is highly expressed on the apical surfaces of epithelial cells in the inner ear [SLC26A4 have been associated with abnormal melanin deposition and hair cell degeneration in the inner ear. Unfortunately, little is known about the role that SLC26A4 plays in these phenotypes. It is also possible that our GWAS signal on CFA18 is actually driven by some other nearby gene that happens to be in high linkage disequilibrium with our top CFA18 variant in this study sample. The MFSD12 gene on CFA20 encodes a transmembrane solute transporter that localizes to melanocyte lysosomes and/or late endosomes in mice [MFSD12 influences hair pigmentation is still not well understood, but it has been suggested that it might regulate melanosome autophagy [MFSD12 cream-associated variant masks the effect of the KITLG red-associated variant by causing abnormal degradation of melanosomes downstream of pro-melanogenic signaling by KITLG.We also found significant evidence for epistatic interactions between the CFA20 locus and both the CFA15 and CFA18 loci. In fact, based on the PRE values in our linear regression analysis, the effect of the CFA15 x CFA20 interaction is greater than the effect of the top CFA15 variant . Based oogenesis –72. The ociation , this as species , 73–77. nner ear , thyroidnner ear , and kidnner ear  in human in mice . The molutophagy . If thisMC1R gene, which underlie cream coats in Siberian Huskies and Australian Cattle Dogs [A multigenic predictive model using genotypes at the most strongly associated single-nucleotide genetic markers on CFA2, 15, 18, 20, and 21, plus two interaction terms, was able to explain over 70% of the phenotypic variation across both the GWAS cohort and an independent validation cohort containing individuals from over 60 breeds as well as mixed breed dogs. This represents a gain of approximately 20% variance explained compared to a model using only the two previously discovered loci . BecauseTaken together, these results demonstrate that coat pheomelanin intensity in the domestic dog is a multigenic trait both across and within breeds, and that some loci controlling this trait likely interact via unknown biological pathways. Further fine mapping and experimental investigation will be required to validate the three novel associations, to characterize the roles these and other genetic loci play in pigmentation in dogs and other species, and to determine whether any mutations associated with coat pheomelanin intensity variation also exhibit pleiotropic effects on canine health, such as deafness.S1 Appendix(PDF)Click here for additional data file.S1 Table(TSV)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(TSV)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 File(TXT)Click here for additional data file.


SPI1 mRNA expression in myeloid cells and lowers risk for AUD and DPW. Our analysis also identifies MAPT as a candidate causal gene specifically associated with DPW. Genes prioritized in this study show overlap with causal genes associated with neurodegenerative disorders. Multi-omics integration analyses highlight, genetic similarities and differences between alcohol intake and disordered drinking, suggesting molecular heterogeneity that might inform future targeted functional and cross-species studies.Identification of causal variants and genes underlying genome-wide association study (GWAS) loci is essential to understand the biology of alcohol use disorder (AUD) and drinks per week (DPW). Multi-omics integration approaches have shown potential for fine mapping complex loci to obtain biological insights to disease mechanisms. In this study, we use multi-omics approaches, to fine-map AUD and DPW associations at single SNP resolution to demonstrate that rs56030824 on chromosome 11 significantly reduces  Alcohol use disorder and drinks per week both have been studied genetically and have different correlations with psychiatric diseases. Here the authors integrate multi-omics data to identify unique and shared variants, genes and pathways for alcohol use disorder and drinks per week. An AUD diagnosis includes aspects of physiological dependence, loss of control over drinking, as well as persistent alcohol intake despite physiological, psychological, and interpersonal consequences7. In contrast, typical alcohol intake, as assessed using measures such as drinks per week (DPW), represents the distribution of alcohol use from casual or social drinking to excessive drinking demarcating risk for AUD9. While heritable, measures such as DPW are more likely to be influenced by environmental and socio-cultural factors and have complex and variable associations with morbidity and mortality9.Alcohol use disorders (AUDs) are complex, moderately heritable (50–60%)N = 435,563) which meta-analyzed AUD with a GWAS of the problem-subscale of the Alcohol Use Disorders Identification Test (AUDIT-P) reported genome-wide associations at 29 loci encompassing 66 genes, the largest tranche of signals for any addictive disorder to date10. In comparison to Zhou and colleagues PAU GWAS, other comparatively large GWAS of alcoholism concentrated on the consumption aspect of addictive disorders. For example, the largest GWAS of typical alcohol intake  identified more than 200 independent genome-wide significant variants within or near more than 150 genes at 81 independent loci11. Despite a genetic correlation (SNP-rg) of 0.77 between PAU and DPW , genetic correlations between these aspects of alcohol involvement and other anthropometric, cardio-metabolic, and psychiatric disorders revealed marked distinctions14. For instance, while AUD and PAU appear to be consistently associated with increased genetic liability for other psychiatric disorders and positively with liability to educational achievement, DPW is genetically uncorrelated with most psychiatric disorders (except ADHD and tobacco use disorder) but correlated negatively with educational achievement and cardio-metabolic disease (which remains uncorrelated with PAU or AUD)14. These findings strongly hint at some common pathological underpinnings to AUD, PAU, and other mental illnesses while genetic liability to DPW appears to be confounded with socio-economic correlates of alcohol use14.Genome-wide association studies (GWASs) of AUD and DPW have identified multiple risk loci. The largest GWAS of problematic alcohol use 17. Indeed, most variants identified by GWAS reside within and affect the activity of regulatory elements  that regulate the expression of target causal genes in specific cell types; the affected genes are often located at quite a distance from the risk variant/ regulatory element18. Several recent studies have integrated GWAS data with expression QTLs (eQTLs) using co-localization or integration methods to identify causal variants and genes associated with schizophrenia, Alzheimer’s disease, and many other complex disorders18. While similar efforts have been targeted at AUD and DPW, they have predominantly relied on bulk mRNA expression data from the small number of brain tissue samples in GTEx14.Few studies have examined the intersection between the loci and genes associated with AUD and DPW, especially with respect to their functional and regulatory significance. As observed in other large GWAS, most genome-wide significant variants associated with AUD and DPW are intergenic and thus not directly mappable to a specific geneN = 138) to validate the differential expression of genes prioritized in the GWAS integration analyses. To the best of our knowledge, this is the largest systematic multi-omics integration analysis to identify the functional impact of variants and genes associated with two correlated but etiologically distinct aspects of alcohol involvement.Here we present a multi-omics systems approach to identify causal variants and genes associated with AUD and DPW. Using Mendelian Randomization-based methods on the largest available transcriptomic and epigenomic data for brain tissues  from the Million Veterans Program (MVP)19, the Psychiatric Genetics consortium (PGC-SUD)12 and the Collaborative Studies on Genetics of Alcoholism (COGA)20 identified 1157 SNPs (31 independent lead SNPs) within or near 79 genes at 10 independent loci associated with AUD  SNPs. A total of 360 SNPs associated with AUD and DPW were in common   data22, we observed a significant enrichment of promoter-specific epigenetic markers (H3K4me1/me3) in the fetal and the adult  brain (P < 5 × 10−8)  regressionmarkers HK4me1/me3PeQTL = 5 × 10−8; P-SMR FDR < 20%; Heidi >0.05)  and 11p11.2 [SPI1] loci.We used the promoter , enhancer (ATAC-Seq), and promoter–enhancer interactome (PLAC-Seq) data for four specific cell types of brain  to elucidate the functional significance of co-localized SNPs28. We also genotyped the brain samples using the UK Biobank Axiom array. More than 97% of subjects (N = 133) included in this study belonged to European ethnicity. All NSWBB samples were imputed to 1000 Genomes using the cosmopolitan reference panel  using SHAPEIT then Impute258 within each array. Only variants with non‐A/T or C/G alleles, missing rates <5%, MAF > 5%, and HWE P ‐values > .0001 were used for imputation. Imputed variants with R2 < .30 were excluded, and genotype probabilities were converted to genotypes if probabilities ≥ .90. All genotyped and imputed variants  with missing rates <10%, MAF ≥ 5% and HWE P ‐values >1 × 10−6 were included in the downstream analyses using MatrixQTL. The gene expression was corrected for the batch, age, sex, RNA integrity number (RIN), Post-mortem Interval (PMI), and alcohol status using the “removeBatchEffect” option from the limma package. The genetically derived PC1 was also added as additional covariate in the eQTL analysis. The eQTL summary statistics from PsychEncode49, ROSMAP50, and COGA-INIA datasets were processed and munged together at single bp and allele level to remove ambiguity due to dbSNP rsids. The gene labels in all three datasets were also matched to Ensembl ids. The summary statistics were saved in binary format files (BESD) using the SMR (https://cnsgenomics.com/software/smr/#DataManagement). The meta-analysis for eQTLs was performed using the conventional inverse-variance-weightedmeta-analysis assuming all cohorts are independent. SMR “–meta” option was used to perform the meta-analysis in all three datasets.RNA Sequencing data on the DLFPC region of the brain for 138 samples was generated as part of COGA-INIA collaboration21. We specifically followed the default parameters to perform the LDSC analyses as listed in the tutorial for partition heritability analysis (https://github.com/bulik/ldsc/wiki/Partitioned-Heritability).We performed the partition heritability analyses for functional annotation using the LDSC program. We obtained the weights for the multi-cell and tissue chromatin marks and performed the LDSC partition heritability analyses on munged summary statistics of AUD and DPW GWAS59 on a set of mQTLs and eQTLs (Top SNP P < 5 × 10−8) from fetal and adult brains. SMR is a Mendelian randomization-based analysis which integrates GWAS summary statistics with eQTL data in order to test whether the effect size of a SNP on the phenotype of interest is mediated by gene expression. We used this method as it does not require raw eQTL data to build the weights, so we were able to use the meta-analysis of eQTL data for the integration analysis. The gene and SNP positions for the summary statistics of the eQTL and mQTL datasets were standardized and aligned using an in-house summary statistics munging pipeline. The summary statistics were then converted into binary format (BESD) to perform the SMR analysis. The scripts used to create the binary (BESD) files for each dataset have been deposited in GitHub. The European subset of ADGC GWAS (phs000372.v1.p1) was used as the LD reference panel to perform the SMR analyses. The analysis was performed using default parameters as listed in the SMR tutorial. The genes below FDR threshold  and with heterogeneity P value > 0.05 were considered to be causal within each combination of analysis.To examine whether the GWAS variants associated with both AUD and DPW are mediated by changes in methylation and gene expression patterns, we conducted a summary data-based Mendelian randomization (SMR) analysis60 approach is implemented in Genome-wide Complex Trait Analysis (GCTA) software61 package and is valuable when the individual-level genotype data is not available for the conditional analysis. To perform the COJO analysis we used the summary statistics of AUD and DPW GWAS along with the European subset of COGA samples as the LD reference panel.We used a summary data-based conditional analysis approach to identify the independent lead SNP associated with AUD and DPW. This conditional and Joint analysis (COJO)We first performed a linear regression with alcohol intake as a dependent variable to identify possible covariates ). Gene-level analyses started with the featureCounts-derivedsample-by-gene read count matrix. The basic normalization and adjustment pipeline for the expression data matrix consisted of: (i) removal of low expression genes ; (ii) differential gene expression analysis based upon adjustment for the chosen covariates. We filtered out all genes with lower expression in a substantial fraction of the cohort, with 18,463 genes with at least 1 CPM in at least 50% of the individuals; note that only these genes were carried forward in all subsequent analyses. The log10 normalized alcohol consumption (from NSWBB brains) was used for differential expression analysis using the DeSeq2 program. The analysis was controlled for sex, age, PMI, Body mass index (BMI), RIN, batch and severity of alcoholism (AUDIT scores).−5; e/mQTL P 5 × 10−8; SMR P < 20%; Heidi P > 0.05). The results from this threshold were similar to our original analyses that focused on overlapping results after integration analysis  method to meta-analyze the summary statistics from DPW and AUD GWASs. Although, this method is robust to correlated multi-trait meta-analysis, the SMR analysis using the MTAG results was primarily driven by the DPW variants. To reduce this bias, we also focused on SMR results from MTAG using stricter threshold  were used to perform gene ontology and pathway enrichment analyses using the EnrichR and Ingenuity Pathway Analysis (IPA).The results of integration analysis for the DPW and AUD GWAS meta-analysis (SMR PWe used ShinyApp to create a database for query and visualization of the results of integration analyses. Users can create volcano, Manhattan plots, and heatmaps to visualize the results of eQTL, mQTL, and epigenetic integration analyses with summary statistics of AUD and DPW GWAS. Users will also be able to see whether the genes of interest are differentially expressed in the brains of people with AUD and controls.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting Summary


Is long-term prescription opioid therapy for noncancer pain associated with hepatitis C virus (HCV) seroconversion among individuals who initially had no history of injection drug use?In this cohort study of 382 478 individuals who had a baseline negative HCV test followed by at least 1 additional test, long-term prescription opioid therapy among those who were initially injection drug use–naive was associated with a statistically significant 3.2-fold higher risk of HCV seroconversion vs no long-term therapy. Overall, approximately 1% of exposed individuals seroconverted within 5 years.The findings of this study suggest that the risk of injection drug use initiation may be higher among people dispensed long-term prescription opioid therapy, leading to increased HCV seroconversion risk. Initiation of injection drug use may be more frequent among people dispensed prescription opioid therapy for noncancer pain, potentially increasing the risk of hepatitis C virus (HCV) acquisition.To assess the association between medically dispensed long-term prescription opioid therapy for noncancer pain and HCV seroconversion among individuals who were initially injection drug use–naive.A population-based, retrospective cohort study of individuals tested for HCV in British Columbia, Canada, with linkage to outpatient pharmacy dispensations, was conducted. Individuals with an initial HCV-negative test result followed by 1 additional test between January 1, 2000, and December 31, 2017, and who had no history of substance use at baseline (first HCV-negative test), were included. Participants were followed up from baseline to the last HCV-negative test or estimated date of seroconversion (midpoint between HCV-positive and the preceding HCV-negative test).Episodes of prescription opioid use for noncancer pain were defined as acute (<90 days) or long-term (≥90 days). Prescription opioid exposure status (long-term vs prescription opioid–naive/acute) was treated as time-varying in survival analyses. In secondary analyses, long-term exposure was stratified by intensity of use (chronic vs. episodic) and by average daily dose in morphine equivalents (MEQ).Multivariable Cox regression models were used to assess the association between time-varying prescription opioid status and HCV seroconversion.A total of 382 478 individuals who had more than 1 HCV test were included, of whom more than half were female (224 373 [58.7%]), born before 1974 (201 944 [52.8%]), and younger than 35 years at baseline (196 298 [53.9%]). Participants were followed up for 2 057 668 person-years and 1947 HCV seroconversions occurred. Of the participants, 41 755 people (10.9%) were exposed to long-term prescription opioid therapy at baseline or during follow-up. The HCV seroconversion rate per 1000 person-years was 0.8 among the individuals who were prescription opioid–naive/acute  and 2.1 with long-term prescription opioid therapy . In multivariable analysis, exposure to long-term prescription opioid therapy was associated with a 3.2-fold  higher risk of HCV seroconversion (vs prescription opioid-naive/acute). In separate Cox models, long-term chronic use was associated with a 4.7-fold higher risk of HCV seroconversion , and long-term higher-dose use (≥90 MEQ) was associated with a 5.1-fold higher risk .In this cohort study of people with more than 1 HCV test, long-term prescription opioid therapy for noncancer pain was associated with a higher risk of HCV seroconversion among individuals who were injection drug use–naive at baseline or at prescription opioid initiation. These results suggest injection drug use initiation risk is higher among people dispensed long-term therapy and may be useful for informing approaches to identify and prevent HCV infection. These findings should not be used to justify abrupt discontinuation of long-term therapy, which could increase risk of harms. This cohort study examines the incidence of hepatitis C seroconversion in individuals prescribed opioid therapy for noncancer pain. In the US, the estimated annual number of acute HCV diagnoses increased by 280% between 2010 and 2017 (from 11 800 to 44 700).5 Canada and the US have consistently been 2 of the highest consumers of prescription opioids globally over the past 2 decades,5 despite evidence suggesting that prescription opioids provide limited benefit in managing chronic noncancer pain.7 Long-term prescription opioid therapy is also associated with a dose-dependent risk of adverse outcomes, including dependence and overdose.8 In recent years, several policies and interventions have been introduced to reduce prescription opioid–related harms, but evidence of their effectiveness is limited.9Since the early 2000s, the use of prescription opioids to manage pain in North America has increased substantially.10 potentially leading to an increased risk of HCV acquisition if drug use equipment is shared. The association between self-reported nonmedical prescription opioid use (prescription opioids primarily obtained from sources other than a physician) and initiation of heroin, a commonly injected drug, is well established,12 although the overall rate of transition appears to be infrequent .13 Less is known about medically dispensed prescription opioids,14 although a recent cohort study found long-term prescription opioid therapy for pain to be associated with initiation of injection drug use.10 Although these associations may not be causal, qualitative studies suggest that some people attribute their initiation of heroin/injection drug use to prior medical or nonmedical prescription opioid use.19 In particular, sudden discontinuation of prescription opioid therapy, potentially owing to more stringent policies and guidelines, may lead some individuals to initiate heroin/injection drug use and inadvertently increase the risk of harm.20 There is also concern that the introduction of tamper-resistant formulations may have had a similar effect.21 Epidemiologic changes in HCV in parts of the US can be characterized by the emergence of a cohort of young people (aged <30 years) who inject drugs and often report a history of oral nonmedical prescription opioid use prior to injection drug use initiation.24Initiation of injection drug use may be more frequent among people with a history of medically dispensed prescription opioid therapy for pain,21 and 2 administrative case-control analyses have identified prior dispensation of prescription opioids as a risk factor for HCV diagnosis.26 However, these studies assessed HCV diagnosis, and most analyses of HCV seroconversion have focused on the association between nonmedical prescription opioid injection and HCV acquisition among people who already inject drugs.28 In this present analysis, we examined the association between medically dispensed prescription opioid therapy for noncancer pain and HCV seroconversion among individuals who were initially injection drug use–naive in a large population-based cohort of people who had more than 1 HCV test.29To date, few studies have directly assessed the association between medically dispensed prescription opioid therapy for pain and HCV seroconversion among individuals who initially have no history of injection drug use (injection drug use–naïve). An ecological study has implicated prescription opioid policies in increasing HCV diagnoses in the US29 The databases integrated within the IDEAs platform and their linkage have been described  between 1992 and 2015. The BCCDC-PHL performs more than 95% of all HCV testing in the province. These data are linked to information on medical visits (1990-2015), hospitalizations (1985-2015), emergency department visits (2012-2015), cancers (1923-2015), pharmacy dispensations (1996-2015), deaths (1985-2015), and HCV laboratory testing (1992-2017).We used the IDEAs data platform .STROBE) reporting guideline for cohort studies. This study was reviewed and approved by the University of British Columbia Research Ethics Board. Informed consent is not required for secondary analysis of administrative deidentified data.Reporting of this analysis followed the Strengthening the Reporting of Observational Studies in Epidemiology .We excluded individuals with (1) no follow-up HCV test  before December 31, 2017, (2) a first HCV antibody–negative test conducted when the participant was younger than 18 years or older than 65 years, or (3) a history of substance use, opioid agonist therapy, or HIV infection prior to the first HCV-negative test .31 To further increase sensitivity, we excluded individuals with a history of an alcohol use problem or who were living with HIV.A history of a substance use problem was identified using diagnostic codes from hospitalization, medical visit, and ambulatory care records , days of drug supply , and episode intensity (percentage of episode days covered by drug supply). These measures were used to categorize episodes as acute or long-term (episodic or chronic) , prescription opioid formulations primarily used for cough suppression or opioid agonist therapy, injectable prescription opioid formulations, and prescription opioid dispensations occurring after a cancer/palliative care record were not considered.35We used an episode approach to assess patterns of prescription opioid use among participants.chronic) .33,34,35In this analysis, an individual’s prescription opioid exposure status was treated as time-varying based on episodes initiated between 1996 and 2015. We classified prescription opioid exposure status as either unexposed (prescription opioid-naive or acute use) or exposed (long-term prescription opioid therapy). At baseline (first HCV-negative test), an individual was considered exposed only if they had an ongoing long-term episode. During follow-up, baseline prescription opioid status could change from unexposed to exposed if an individual initiated a long-term episode. Status could not change from exposed to unexposed. In secondary analyses, long-term exposure was stratified by intensity of use (episodic vs chronic)  and by a37 We believed that combining prescription opioid-naive and acute-exposed individuals was justified because a previous analysis found that most (70%) acute episodes contain only 7 or fewer days’ supply of opioids.34We focused on long-term prescription opioid therapy as our exposure of primary interest because research suggests it is associated with limited benefit and increased risk. Prescription opioids are generally not recommended as first-line treatment for chronic noncancer pain.We measured individual-level covariates covering different sociodemographic factors  and comorbidities . Covariates were assessed using linked data sets. Classification of race used a name recognition algorithm. This algorithm could lead to individuals who are not White to be misclassified as White because they have anglicized names, but the algorithm has high specificity for Asian names; therefore, 3 categories were used: East Asian, South Asian, and other. More information on covariate definitions can be found in eTable 2 in the We followed up eligible individuals from the date of their first HCV-negative test (baseline) to the date of HCV seroconversion or last HCV-negative test. If their last HCV-negative test or estimated date of HCV seroconversion occurred after 2015, follow-up was censored at the end of 2015. Individuals who initiated a long-term prescription opioid episode during follow-up but had developed a substance use problem between baseline and initiation of prescription opioid therapy were censored at the date of episode initiation to limit analysis to individuals who were injection drug use–naive at initiation of a long-term prescription opioid episode.38 Multivariable Cox regression models were used to quantify the association between time-varying prescription opioid status and HCV seroconversion.39 All covariates in the model (except for sex and race) were treated as time-varying. Time-varying covariates were measured at baseline and, if applicable, updated at initiation of a long-term prescription opioid episode. We allowed for a 90-day lag to identify substance use problems (exclusion criteria) to account for potential delays in diagnosis. Cox regression models were stratified by age and calendar year of study start owing to violation of the nonproportionality assumption. With 2-sided, unpaired testing, significance was set at P < .05. All analyses were conducted using SAS/STAT software, version 9.4 (SAS Institute Inc).We created pseudocumulative incidence curves by applying the clock reset approach to unexposed individuals initiating long-term prescription opioid therapy after the start of the study.Between 2000 and 2015, a total of 1 153 433 individuals tested negative on an HCV antibody test. We excluded 655 959 individuals who did not have a follow-up test before the end of 2017; 52 411 whose first HCV-negative test was conducted when they were younger than 18 years or older than 65 years; 61 918 with a history of a substance use problem , opioid agonist therapy, or HIV; and 667 who were missing information on sex or geography.The final study population included 382 478 individuals, of whom more than half were female , born before 1974 (201 944 [52.8%]), and younger than 35 years at baseline (196 298 [53.9%]) . Mean S. Median Most participants were classified as prescription opioid–naive at study start (370 889 [97.0%]), with the remainder having an ongoing episodic (7713 [2.0%]) or chronic (3876 [1.0%]) episode at baseline. A total of 30 166 individuals who were prescription opioid–naive/acute at baseline initiated a long-term prescription opioid episode during follow-up. Most of these long-term episodes were episodic (26 968 [89.4%]) and the remainder were chronic (3198 [10.6%]). A further 15 468 individuals began a long-term episode during follow-up but had developed a substance use problem between baseline and initiation of therapy and were therefore censored at initiation of therapy. Individuals who initiated long-term therapy (41 755) were more likely to be older, of other race, have greater social or material deprivation, and have a history of chronic pain and major mental illness .Characteristics of the first long-term episode initiated by study participants are described in eTable 3 in the There were a total of 1947 HCV seroconversions during follow-up, yielding an overall incidence rate of 0.9 seroconversions per 1000 person-years . Of indiMost HCV seroconversions (1489 [76.5%]) were among prescription opioid–naive/acute individuals, with the remainder among individuals exposed to long-term therapy (458 [23.5%]) . The ratIn the main multivariable Cox regression model, long-term prescription opioid therapy was associated with a 3.2-fold higher risk  of HCV seroconversion vs prescription opioid–naive/acute . Other c26 One of these analyses found that older individuals (age >40 years) with a history of prescription opioid use were 11 times more likely to test positive on an HCV antibody test,26 and the other analysis found prior prescription opioid dispensation to be the second most important variable for predicting HCV diagnosis in a machine learning prediction model.25 Our analysis builds on these studies by using a longitudinal approach with HCV seroconversion as the outcome and by distinguishing between prescription opioid dispensations for pain vs opioid agonist therapy.In, to our knowledge, one of the largest cohorts of individuals tested for HCV in the world, use of long-term prescription opioid therapy for noncancer pain was associated with a higher risk of HCV seroconversion among individuals who had more than 1 HCV test and who were injection drug use–naive at baseline or initiation of therapy. Overall, the rate of HCV seroconversion among people dispensed long-term prescription opioid therapy was infrequent . However, seroconversion risk was 3.2-fold higher with long-term vs no long-term prescription opioid therapy and there was evidence of higher risk with more intense long-term use (chronic vs episodic) and at higher daily doses (≥90 vs <90 MEQ). Our findings support 2 recent case-control analyses identifying an association between medically dispensed prescription opioids and HCV diagnosis.12 and a recent cohort analysis identifying an association between medically dispensed long-term prescription opioid therapy and initiation of injection drug use.10 Although we were unable to establish a causal relationship between prescription opioids and HCV seroconversion or formally assess the role of injection drug use initiation as a mediator, several reasons or motivations for transitioning from prescription opioids to heroin or injection drug use  have been identified in qualitative studies.19 To evaluate our hypothesis that injection drug use initiation risk is higher among people dispensed prescription opioids, we used an administrative algorithm with high sensitivity for identifying injection drug use31 to limit our analysis to individuals who were injection drug use–naive at prescription opioid initiation. Furthermore, we expected most HCV seroconversions during follow-up to be related to injection drug use because screening of blood products for HCV was implemented in the early 1990s and our analysis was limited to people with an HCV-negative antibody test in 2000 or later. We found that three-quarters of individuals who seroconverted had evidence of injection drug use after the start of the study, highlighting the role of injection drug use transition in HCV seroconversion.Our analysis suggests that injection drug use initiation may be more frequent among people dispensed long-term prescription opioid therapy and that this may lead to a higher risk of HCV acquisition. This finding supports the well-established association between nonmedical prescription opioid use and heroin,40 potentially leading to an increased baseline risk of injection drug use initiation and residual confounding. Confounding by indication is another potential issue, as uncontrolled chronic pain may lead to injection drug use initiation in and of itself.12 Other mechanisms could explain the association between prescription opioids and HCV: people with a history of prescription opioid dependence prior to injection drug use initiation tend to have higher-risk injection practices41 and episodic prescription opioid therapy may be indicative of an inability to access a constant supply of prescription opioids to manage pain . Furthermore, although we attempted to limit our analysis to individuals who were injection drug use–naïve and accessing prescription opioid therapy for noncancer pain, we could not rule out the baseline inclusion of people who inject drugs  and/or the inclusion of people accessing prescription opioid therapy for recreational purposes or off-label treatment of opioid use disorder. If people who inject drugs were included at baseline, prescription opioid therapy may be a proxy for inadequate access to harm-reduction supplies and/or the high prevalence of chronic pain in this population.43 In addition, owing to the inability to identify the exact date of seroconversion, some individuals who initiated prescription opioid therapy during follow-up may have done so after acquiring HCV; thus, prescription opioid therapy could be a proxy for HCV-related pain.44Prescription opioid therapy may also be a proxy for injection drug use or HCV diagnosis/acquisition. People receiving prescription opioid therapy can have complex medical and sociodemographic profiles ,45 and may suggest interventions to reduce the risk of prescription opioid dependence 37 could prevent injection drug use initiation and HCV acquisition. However, interventions to promote appropriate prescription opioid use should consider that unmanaged pain or withdrawal  could increase the risk of harm and may facilitate illicit drug use in some individuals.42 There is a need to support shared, patient-centered decision-making with regard to initiating, tapering, and discontinuing prescription opioid therapy.46 Provision of opioid agonist therapy and other harm-reduction interventions to individuals who develop prescription opioid dependence and/or transition to injection drug use can prevent further harms. A systematic review published in 2011 found that harm-reduction strategies combining opioid agonist therapy, sterile drug use equipment, and other supports can reduce HCV incidence by 75% among people who inject drugs.47 These interventions should be acceptable, accessible, and affordable and developed through peer engagement.48 Provision of prescription opioids as an alternative to the increasingly toxic drug supply may also reduce overdose risk in individuals who initiate use of illicit drugs.49Our findings may support the offer of an HCV test to people with a history of long-term prescription opioid therapy50 Although most HCV infections were likely acquired through injection drug use, some may have been acquired through noninjection drug use routes .52 In addition, some evidence suggests certain prescription opioids may suppress the immune system, thereby increasing an individual’s vulnerability to infectious diseases.53 We censored individuals who were prescription opioid–naive/acute-exposed who developed a substance use problem between baseline and initiation of long-term prescription opioid therapy to limit the analysis to those who were injection drug use–naive at initiation of therapy, potentially introducing selection bias. We also lacked information on the reason for HCV testing, making it difficult to fully characterize our study population. Our initially injection drug use–naive study population was likely a heterogeneous mix of individuals who received their initial HCV test for different reasons .Our study has limitations. Drawbacks of restricting our analysis to individuals who had more than 1 test include exclusion of people whose first HCV test was positive, inability to include all people who were dispensed long-term prescription opioid therapy, and a bias toward including people who transitioned to injection drug use after their HCV-negative test and an elevated rate of HCV seroconversion, limiting generalizability of the study findings. However, given the asymptomatic nature of HCV infection and the lack of a test to define acute infections, the approach we used is necessary to identify seroconversions. For cautious comparison, the annual rate of HCV diagnosis in British Columbia between 2003 and 2015 ranged from 0.5 to 0.8 per 1000 population vs 0.9 per 1000 person-years in our analysis.Our large study of persons who received more than 1 HCV test identified an association between medically dispensed long-term prescription opioid therapy for noncancer pain and a higher risk of HCV seroconversion among individuals who were initially injection drug use–naive . This finding suggests injection drug use initiation is more frequent among people who received long-term prescription opioid therapy. Our findings may be useful for informing HCV testing and prevention initiatives. However, abrupt tapering or discontinuation of prescription opioid therapy could increase the risk of harm.