Urinary albumin excretion (UAE) is a risk factor for cardiovascular diseases, metabolic syndrome, chronic kidney disease, etc. Only a few genome-wide association studies (GWAS) for UAE have been conducted in the European population, but not in the Asian population. Here we conducted GWAS and identified several candidate genes harboring single nucleotide polymorphisms (SNPs) responsible for UAE in the Japanese population.We conducted GWAS for UAE in 7805 individuals of Asian ancestry from health-survey data collected by Tohoku Medical Megabank Organization (ToMMo) and Iwate Tohoku Medical Megabank Organization (IMM). The SNP genotype data were obtained with a SNP microarray. After imputation using a haplotype panel consisting of 2000 genome sequencing, 4,962,728 SNP markers were used for the GWAS.GRM7, EXOC1/NMU, LPA, STEAP1B/RAPGEF5, SEMA3D, PRKAG2, TRIQK, SERTM1, TPT1-AS1, OR5AU1, TSHR, FMN1/RYR3, COPRS, and BRD1) were associated with UAE in the Japanese individuals. A locus with particularly strong associations was observed on TSHR, chromosome 14 [rs116622332 (p = 3.99 × 10−10)].Eighteen SNPs at 14 loci  contains supplementary material, which is available to authorized users. Chronic kidney disease (CKD) is one of the most severe global public health problems . The proWhile a few genome-wide association studies (GWAS) on UAE have been conducted in individuals with type 2 diabetes mellitus  and typeThis research was conducted as a part of the residential cohort study of Tohoku Medical Megabank (TMM), a joint organization of the Tohoku University and the Iwate Medical University in Japan, established in 2011 after the Great East Japan Earthquake for creating an advanced medical system.Over 80,000 almost healthy adult individuals living in the Miyagi and Iwate Prefectures along the Pacific coast of the Tohoku district of northern Japan were recruited from May 2013 to March 2016 for the TMM Project. The participants were of 20–75 years old and completed questionnaires covering a wide range of topics including socio-demographic factors, lifestyle habits, and medical history. Blood and urine tests were performed at baseline survey. The participants living in the Miyagi Prefecture and Iwate Prefecture were recruited by Tohoku University and Iwate Medical University, respectively , 15. We From the 10,000 individuals whose data were collected up to 2013, we were able to obtain data of 9,966 individuals after excluding 34 people who withdrew their consent after collection. The data were released as dbToMMo 1.1. Among these 9,966 individuals, 4974 were from the Miyagi prefecture and 4992 were from the Iwate prefecture. Thus, both represented a roughly equal proportion.n = 1), genotype defect , or close relationship pairs  27], HbA1https://locuszoom.org]. We evaluated the result of the expression Quantitative Trait Loci (eQTL) analysis for some genome-wide significant SNPs by the Genotype Tissue Expression Project (GTEx) portal   ). One SNP was located in LPA on chromosome 6q25.3 [rs146871152 (p = 7.16 × 10–11)] ). One SNP was located in PRKAG2 on chromosome 7q36.1 [rs118160950 (p = 3.43 × 10−8)] ). Two SNPs were located in TPT1-AS1 on chromosome 13q14.13 [rs142317900 (p = 4.68 × 10−8) and rs151183316 (p = 3.43 × 10−8)] ). One SNP was located in EXOC1/NMU on chromosome 4q12 [rs75938525 (p = 6.93 × 10−10)] ). One SNP was located in STEAP1B/RAPGEF5 on chromosome 7p15.3 [rs146418897 (p = 1.49 × 10−10)] ). One SNP was located in SEMA3D on chromosome 7q21.11 [rs140221313 (p = 1.84 × 10−8)] ). One SNP was located in TRIQK on chromosome 8q22.1 [rs141491217 (p = 1.63 × 10−8)] ). One SNP was located in SERTM1 on chromosome 13q13.3 [rs79163227 (p = 2.84 × 10−8)] ). One SNP was located in OR5AU1 on chromosome 14q11.2 [chr14:21617499_TCTCA_T (p = 2.87 × 10−8)] ] ). One SNP was located in COPRS on chromosome 17q11.2 [rs148283070 (p = 8.42 × 10−9)] ). One SNP was located in BRD1 on chromosome 22q13.33 [chr22:49949123_GA_G (p = 7.22 × 10−9)] ).With respect to the SNPs meeting the significance level, we constructed a regional plot to visually examine LD with the surrounding SNPs (around 1 Mbp) and identified the gene in or around which the SNPs were located. We attached the result of eQTL analyses for genome-wide significant SNPs by GTEx Fig. , Fig S1.We performed GWAS for UAE in the Japanese general population and identified 18 SNPs, of which, 17 were not reported in any previous report. rs118160950 was already reported as a SNP related to UAE by GWAS performed in the European ancestry . In the CUBN gene locus. CUBN encodes cubilin protein acting as a receptor for vitamin B12-intrinsic factor complexes. It was hypothesized that the SNPs found in CUBN on chromosome 10 were significantly (p = 1.0 × 10−11) involved in UAE. However, these loci were not found to be significantly associated in our study. The reasons seem to be related to the difference in the studied population, because the reported significant covariates were not replicated in our population. We showed evidence of genetic differences between the Europeans and the East Asians. The other possibility is that these SNPs on CUBN are associated with UAE only in the diseased condition. There may be big differences in the mechanism between pathophysiological albuminuria and physiological albuminuria.In previous studies, rs10795433  were repWhen we evaluated the functional class of 18 SNPs, 6 SNPs  were intronic, 11 SNPs  were intergenic, and 1 SNP (rs142317900) was on the non-coding exons. No SNP was located on the coding exons. These candidates may possibly affect the factors regulating the transcription of the genes encoding the proteins involved in UAE.TSHR showed strong peaks. TSHR encodes TSHR (thyroid stimulating hormone receptor), and the TSH receptor is a member of the G protein-coupled receptor superfamily of integral membrane proteins which is coupled to the Gs protein [In our study, the SNPs located in  protein , 33. TSH protein , 35. In  protein . The oth protein –39. We ePRKAG2 coding 5′-AMP-activated protein kinase subunit gamma-2. AMP-activated protein kinase (AMPK) is a heterotrimeric protein composed of a catalytic alpha subunit, a noncatalytic beta subunit, and a noncatalytic regulatory gamma subunit [PRKAG2 did not indicate any stronger significance in our GWAS. However, we may consider that renal tubular dysfunction induced by glycol storage can affect the UAE. subunit . AMPK is subunit , 42, fam subunit , 44, and subunit . Studies subunit . Several subunit . PRKAG2 p value was less than 1.0 × 10–5, and also that has strong LD against each significant SNP (r2 > 0.2). There are significant eQTL of NMNAT1P1 pseudo-gene on TSHR gene locus in rs17111387, rs74771569, rs78176261, which three SNPs had strong LD against rs116622332 (the lead variant in Chromosome 14). Though NMNAT1P1 itself is a pseudo-gene, many significant eQTL variants are also shared with TSHR and NMNAT1P1. Additionally, about the genes around every lead variant, we can find the consistency between the peak of Manhattan plot and the peak of eQTL information about TSHR in thyroid , affects the expression of COG3. We cannot find other significant information in eQTL analysis about the other genes.We used the GTEx database to examine eQTL of signiicant variants and suggestive variants in each locus. Unfortunately, no eQTL was found in the lead significant variants Table S3). Then we extended the candidates for including suggestive variants whose . Then we. Second, many of the individuals analyzed in our study were affected by the Great East Japan Earthquake of 2011. We should consider mental disturbance and stress caused by this big disaster as a confounding factor. To conclude, we investigated the UAE associated SNPs in the Japanese population after adjusting for age, gender, hypertension, and impaired glucose tolerance. The 18 identified SNPs were uncovered to show a statistically significant effect on the UAE. There are limited studies evaluating the association with other candidate genes that we detected. The functional and biological roles exerted by each of the SNPs/genes are required to be elucidated in further studies.There are some limitations to our study. First, in this study, replication is lacking. Replication studies in other Japanese cohorts and/or other populations are requiredSupplementary file1 (DOCX 15329 kb)Below is the link to the electronic supplementary material.


Plasmodium knowlesi parasites through zoonotic infection from macaque reservoir hosts. We obtained genome sequences from 28 clinical infections in Peninsular Malaysia to clarify the emerging parasite population structure and test for evidence of recent adaptation. The parasites all belonged to a major genetic population of P. knowlesi (cluster 3) with high genomewide divergence from populations occurring in Borneo (clusters 1 and 2). We also observed unexpected local genetic subdivision; most parasites belonged to 2 subpopulations sharing a high level of diversity except at particular genomic regions, the largest being a region of chromosome 12, which showed evidence of recent directional selection. Surprisingly, we observed a third subpopulation comprising P. knowlesi infections that were almost identical to each other throughout much of the genome, indicating separately maintained transmission and recent genetic isolation. Each subpopulation could evolve and present a broader health challenge in Asia.Most malaria in Malaysia is caused by  Plasmodium knowlesi  associated with different reservoir hosts: cluster 1 with long-tailed macaques (Macaca fascicularis) and cluster 2 with pig-tailed macaques (M. nemestrina)  and confirmed that all contained only P. knowlesi by using nested PCR assays, testing for all locally known malaria parasite species (https://www.thermofisher.com). We processed samples containing >300 ng of DNA for sequencing. We performed paired-end short-read genome sequencing by using Illumina MiSeq version 3 kits on the MiSeq platform  with a read length of 300 bp, and aligned reads to the P. knowlesi PKNH 2.0 reference genome sequence  and 5 laboratory isolates   (https://cran.r-project.org/web/packages/pegas/index.html) (FST). For sliding-window analysis genomewide, we calculated nucleotide diversity in nonoverlapping 50-kb windows. To scan for genes that might be under balancing selection, we calculated Tajima’s D on a gene-by-gene basis and excluded genes with <3 SNPs from analysis. We calculated FST values between each of the major parasite clusters for all individual SNPs across the genome with a minor allele frequency of >10%, and we calculated mean values in all nonoverlapping sliding windows of 500 consecutive SNPs across the genome.We used the packages adegenet  score, plotted until the EHH signal declined to <0.05 on each side. We merged overlapping windows of EHH containing some of the same SNPs to produce a contiguous overall putative selection window for that region, and we considered any gaps of >20 kb between SNPs with elevated |iHS| values to break a putative window of selection.We performed a scan for loci under recent positive selection by identifying SNPs with an allele associated with extended haplotype homozygosity, using the R package rehh , resulting in a total of 953,827 SNPs throughout the rest of the genome. After filtering out SNPs that had missing data in >10% of individual infection samples, we included 474,109 high-quality SNPs in subsequent analysis . This low proportion of mixed genotype infections was similar to that observed for the different P. knowlesi subpopulations (clusters 1 and 2) in Malaysian Borneo , whereas comparison of cluster 3 with cluster 2 reveals a genomewide mean FST value of 0.42,    Figure 2ixation) , panel Aixation) , panel Bixation) , panel CP. knowlesi cluster 3 clinical samples from Peninsular Malaysia into 3 different subclusters , B (10 infections), and C (3 infections), which are also apparent in neighbor-joining analysis of the cluster 3 samples alone  consisted of infections that were highly related to each other, virtually identical in many parts of the genome , panel Ce genome , panel Dding 0.2 , panel Dding 0.2  Figure 4P. knowlesi in Peninsular Malaysia, we summarized nucleotide site allele frequency spectra by calculating Tajima’s D index for all 4,742 genes with >3 SNPs. Overall, values were negatively skewed (mean −0.86) (csp) gene, which had the highest Tajima’s D value genomewide in cluster 1 P. knowlesi in Malaysian Borneo , the Duffy binding protein α , and the normocyte binding protein gene . These findings indicate that the mode or strength of selection on orthologous targets is not uniform in all malaria parasite populations, including among different P. knowlesi subpopulations.To scan for loci that might be under different selection pressures in n −0.86) , panel An −0.86) , panel Bn −0.86) , panel BSICAvar and kir genes that had been masked from SNP calling and analysis. The other 2 windows of extended haplotype homozygosity did not include SICAvar or kir genes but covered ≈28 kb (11 genes) on chromosome 9 and ≈315 kb (81 genes) on chromosome 12. The large region of elevated |iHS| values on chromosome 12 coincides with the region having the highest genomic divergence between cluster 3 population subclusters A and B  that is highly divergent genomewide from both of the clusters 1 and 2 in Malaysian Borneo . However, the cluster 3 clinical samples from Peninsular Malaysia themselves constituted 3 distinct subpopulations, and the cause of this local population genetic structure needs to be determined. No geographic separation is apparent; each of the three cluster 3 subpopulations was found in overlapping locations, and all were detected from among cases in the hospital that had most samples analyzed. The population structure might reflect >1 local zoonotic transmission cycle or could be a sign of recent selection and emergence of a subpopulation of P. knowlesi transmitted more effectively between humans.Genomewide sequence analysis of new clinical isolates has revealed unexpected parasite population structure and evidence of recent selection in P. knowlesi, whereas pig-tailed macaques are reservoir hosts for the cluster 2 population , represented by 3 infections highly related to each other throughout most of the genome. Although less common, clinical cases with this parasite type were identified in different hospitals in 3 different states in Peninsular Malaysia. Population genetic substructure also has been observed in the endemic malaria parasites P. falciparum (P. vivax (P. falciparum populations also has been observed in Cambodia, probably because of strong selection on locally emerging drug-resistant types in areas where transmission was low (P. knowlesi populations are probably substructured for other reasons, as previously observed in Malaysian Borneo, where the 2 divergent parasite genetic populations observed in human cases are associated with different reservoir host species (Even more unexpected is the observation of a separate P. knowlesi so far has mainly focused on parasites from Malaysia, where most reported cases of P. knowlesi malaria have been identified. However, cases of P. knowlesi malaria in humans have now been reported from all Southeast Asia countries. Whether other local zoonotic subpopulations exist throughout the region or whether all parasites belong to the major genetic populations observed in Malaysia is unknown. Our findings highlight the importance of monitoring population genetic changes in Malaysia and conducting comparable analysis in other areas where P. knowlesi has only very recently been realized to occur in humans (Population genomic analysis of Plasmodium knowlesi, peninsular Malaysia.Additional information about population genomic structure and recent evolution of Plasmodium knowlesi, peninsular Malaysia.Additional data for population genomic structure and recent evolution of


Sus scrofa chromosome 4 (SSC4), SSC6, SSC7, SSC8, SSC10, SSC12, SSC13, SSC14, SSC17, SSC18, and SSCX. Moreover, the JQ breed had more SNPs that were clustered into haplotype blocks than the other breeds examined in this study. Our shared and unique haplotype block analysis revealed that the Hongdenglong (HD) breed had the lowest percentage of shared haplotype blocks while the Shanzhu (SZ) breed had the highest. We found that the JQ breed had an average r2 > 0.2 at SNPs distances 10–20 kb and concluded that about 120,000–240,000 SNPs would be needed for a successful GWAS in the breed. Finally, we detected a total of 88 genes harbored by selected haplotype blocks in the JQ breed, of which only 4 were significantly enriched . These genes were significantly enriched in 2 GO terms , and 2 KEGG pathways . Most of these enriched genes were related to health. Also, most of the overlapping QTLs detected in the haplotype blocks were related to meat and carcass quality, as well as health, with a few of them relating to reproduction and production. These results provide insights into the genetic architecture of some adaptive and meat quality traits observed in the JQ pig breed and also revealed the pattern of LD in the genome of the pig. Our result provides significant guidance for improving the statistical power of GWAS and optimizing the conservation strategy for this JQ pig breed.The Jiangquhai (JQ) pig breed is one of the most widely recognized pig populations in China due to its unique and dominant characteristics. In this study, we examined the extent of Linkage disequilibrium (LD) and haplotype block structure of the JQ pig breed, and scanned the blocks for possible genes underlying important QTLs that could either be responsible for some adaptive features in these pigs or might have undergone some selection pressure. We compared some of our results with other Chinese and Western pig breeds. The results show that the JQ breed had the highest total block length (349.73 Mb ≈ 15% of its genome), and the coverage rate of blocks in most of its chromosomes was larger than those of other breeds except for  The pig population in China (435 million) accounts for 45% of the total population of pigs in the world  and the The JQ pig breed has existed since the early 19th century and has many characteristics such as strong fat deposition and excellent tasting, high-quality meat. It is a well known local pig breed used in producing ham in China , where tRecent studies had revealed high genetic diversity within the JQ pig breed . GenomicAdvancement in high-throughput genotyping technologies enables the use of large numbers of single nucleotide polymorphism (SNPs) in detecting haplotypes, which are products of introgression or selection during the domestication process of pigs . These hOver the years, haplotypes have proven to be more powerful in association studies than single-marker methods . Thus, tSince the extent of LD and haplotype blocks are of critical importance for genomic selection, marker-assisted selection, and conservation of animal genetic resources, the importance of constructing the haplotype blocks in the JQ pig breed and identifying the genes involved in them, especially those associated with economically important traits, cannot be overemphasized. Such information would help in understanding the genetic basis of breed distinction and adaptation and guide against incorporating haplotype blocks with deleterious gene effects into selection programs . To our n = 34), Shanzhu , and Hongdenglong  breeds, also from Jiangsu Province; Middle Meishan  and PudongWhite  pigs from Shanghai province; and, Duroc  and Yorkshire , which are western pig breeds. The Jiangsu pig samples from the conservation pig farms have been described in previous studies (1 (AvaII), and then ligated with a unique adapter barcode after which the samples were pooled and enriched through PCR to construct a sequencing library. Finally, the DNA sequence libraries  were sequenced using an Illumina HiSeq2500 (100 paired-end) sequencing platform according to the manufacturer’s protocol.A total of 192 pigs were used in this study. Of the total pig population, thirty-eight (38) were Jiangquhai (JQ) pigs from the pig conservation farm in Jiangsu province. Other pig breeds used as a reference population were; Huaibei , respectively, and N is the number of loci tested. The genetic distance (D) between all pairwise combinations of individuals was calculated as follows: 1-Dst. Pairwise genetic differentiation    between ex, FST) . Based osoftware .p-value ≤ 1 × 10–6), MAF < 0.05, and LD (linkage disequilibrium) greater than 0.5 across populations (command: PLINK indep-pairwise 50 5 0.5), were used for population structure analysis using ADMIXTURE v1.3 software  of each breed was estimated using the SNP data from the admixture analysis. Ne was estimated using the software SNeP ]–1, and ct is the recombination rate for specific physical distance between markers, measured in Morgan  for all retained SNPs within each breed using the command line “–ld- window-r2 0” in PLINK v1.9  between all autosomal SNPs was, however, divided into the following bin distances: 0–10, 10–20, 20–40, 40–60, 60–100, 100–200, 200–500, and 500–1000 kb.Where LDon in R3 . They wer2) value between SNP pairs, using the HAPLOVIEW software (A Hidden Markov Model implemented in the program BEAGLE v4.1 software  was usedsoftware .fi is the frequency of the ith haplotype. To gain insight into the haplotype diversity within the block region (with the maximum number of SNPs in JQ breed), we applied the “four-gamete rule block definition algorithm” implemented in haploview software. This algorithm computes the observed frequency of the four possible two-marker haplotypes for each pair of SNPs and defines a block when the frequency is 0   . A frequThe shared and unique haplotype block regions between breeds were also detected. Shared haplotype blocks were defined as the overlapping block regions shared by two populations or more, while the unique haplotype blocks were the block regions specific to each population. Both the shared and unique haplotype block regions were detected and visualized using the R Bioconductor package “GenomicRanges”  and ‘ggbTo detect the possible genes and important QTLs that span the haplotype blocks, we hypothesized that important traits under selection for adaptation of the JQ pig breed could be harbored in haplotype block regions with the highest block length. We also theorized that haplotype blocks with the highest number of SNPs could reveal some important genetic variations in the JQ breed. Therefore, we chose the first ten haplotype block regions within each respective criterion for functional annotation. In total, 20 block regions were annotated for possible QTLs and genes.4. To ensure efficient processing and control the volume of QTLs detected, we filtered out QTL regions to lengths ≤ 10 Mb, afterward, a Perl homemade script was used to detect haplotype block regions with more than 50% overlap with the filtered QTL regions. The QTLs that fall within the selected haplotype block regions or the haplotype block regions that fall within the QTLs are defined as overlap.The QTL regions spanned by the haplotype block of the JQ breed were detected by mapping selected haplotype block regions onto QTL sections using data from the Pig QTL database5. Thereafter, the detected genes were functionally annotated by performing the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway , and then selected the most significantly enriched genes with FDR  < 15%. We also established the relationship between the likely candidate genes and QTLs detected in the haplotype blocks. This result potentially reveals the genes and characters that might have either undergone artificial or natural selection pressure in the JQ breed or the genes involved in complex traits of the breed.Furthermore, we performed a gene set enrichment analysis (GSEA) to further elucidate the biological function of the selected haplotype block regions above. We mapped the selected haplotype block regions and genes using gene annotation data for pigs from the Ensembl gene database 98 pathway  and Gene pathway  enrichmethway  within the 8 populations were 0.210 (JQ), 0.181 (HD), 0.198 (HB), 0.255 (SZ), 0.164 (MMS), 0.203 (PD), 0.133 (D), and 0.167 (Y). The highest genetic differentiation (0.441) between breeds was found between MMS and D breed, while the lowest was found between MMS and PD breed (0.093) . A Neighin K = 7 , before in K = 7 . Consistin K = 7 , K = 2 sNe trend of each pig breed across different generations is shown in Ne, that over shorter distances provided ancestral Ne  were only utilized in characterizing the LD and haplotype block structure of the breeds.r2 between adjacent SNPs on the autosomes was largest for D breed (r2 = 0.39), followed by Y (r2 = 0.34), whereas other pigs exhibited a smaller average r2, ranging from 0.22 (SZ) to 0.32 (HD). The average autosomal LD (r2) for the following bin distances 0–10, 10–20, 20–40, 40–60, 60–100, 100–200, 200–500, and 500–1000, is presented in r2 value observed for both D (r2 = 0.45) and Y (r2 = 0.37) breed was also the highest. However, the average LD (r2) decreased in other breeds; PD (r2 = 0.32), HD (r2 = 0.32), SZ (r2 = 0.31), MMS (r2 = 0.29), HB (r2 = 0.27), and JQ (r2 = 0.23). Overall, on the autosome of the JQ breed, about 28% of adjacent SNP pairs had r2 > 0.3 and 36% had r2 > 0.2. The corresponding percentages for HB, SZ, HD, MMS, PD, D, and Y were about 32 and 41%, 25 and 33%, 40 and 41%, 34 and 41%, 32 and 39%, 48 and 56%, 42 and 50%, respectively.The average r2) across breeds decreased with increasing SNP pair distance  while HB, SZ, HD, MMS, PD, D, and Y had 300.83 Mb (12.35%), 92.20 Mb (3.79%), 330.41 Mb (13.57%), 167.88 Mb (6.90%), 211.38 Mb (8.68%), 33.04 Mb (1.36%), and 176.35 Mb (7.24%) total autosomal block length, respectively. Moreover, fewer haplotype blocks  were observed on the autosome of D breed compared to others , possiblSus scrofa chromosome 4 (SSC4), SSC6, SSC7, SSC8, SSC10, SSC12, SSC13, SSC14, SSC17, SSC18, and SSCX  gene, which suggests that it is associated with oncogenic processes , while the lowest amount of SNPs on the autosomes of these pig breeds was 4,327, 3,083, and 3,013 on chromosome 11 of JQ, HB, and HD; and 2,276 on chromosome 16 of SZ breed. Conversely, the highest number of SNPs in other pigs was 7,940 and 6,274 on chromosome 6 of PD and MMS; 1,183 on chromosome 1 of D; and 7,799 on Chromosome 6 of Y breed. However, the lowest  number of SNPs in blocks (formed on the autosome) was 2,488, 1,580, 338, and 2,070, and was found on chromosome 16, 16, 18, and 16 of PD, MMS, D, and Y breed, respectively .As shown in In general, the total length of haplotype block shared across the autosome of all the Jiangsu province pigs  was 7.89 Mb . These sMycoplasma hyopneumoniae antibody titer, and toll-like receptor 9 level  downloaded from the pig QTL database. Consequently, 112 porcine QTLs were detected to overlap with the haplotype block regions of this breed. Interestingly, we found that most of the detected QTLs were related to meat and carcass quality, health, and a few reproduction and production-related QTLs. We detected QTLs related to traits such as feed conversion ratio, loin muscle area, body weight, intramuscular fat content, scrotal/inguinal hernia, teat number, total number born alive, change in  9 level , which sp-value ≤ 0.05). These genes were significantly enriched in 2 GO terms and 2 KEGG pathways (ACVRL1 and ACVR1B) found on SSC5 were enriched (GO:0003840) in the molecular functional process  associated with activin receptor activity, type I. However, the other 2 genes (GGT5 and GGT1) which are located on SSC14 were enriched in the molecular functional process gamma-glutamyltransferase activity . These 2 genes (GGT5 and GGT1) were also enriched in the signaling pathway related to ssc00460: Cyanoamino acid metabolism, and ssc00430: Taurine and hypotaurine metabolism .Furthermore, we identified a total of 88 genes harbored by the selected haplotype blocks , of whicpathways  which weThe evolutionary history of some pigs in certain regions of developing countries like China is poorly understood. The emergence of new breeds or sub-populations is a result of natural (adaptation) or artificial selection and this selection pressure plays a major role in shaping the genetic architecture and gene pool of extensively raised livestock species . DespiteConservation of animal genetic resources from a global perspective, focuses not only on endangered breeds but also on those that are not well utilized. Locally adapted breeds are always at risk of extinction, particularly when local populations have a preference for imported breeds. Generally, only a small proportion of breeds, particularly in developing countries, are involved in planned genetic improvement programs that aim to ensure efficient and sustainable utilization of these breeds. Therefore, developing countries like China should ensure that commercial pig strains are developed, while also maintaining the genetic diversity within the purebred population.Mycoplasma hyopneumoniae in the JQ breed (Mycoplasma hyopneumoniae antibody titer (ID = 12330) and changes in Mycoplasma hyopneumoniae antibody titer (ID = 12331). Generally, JQ pigs are excellent producers of quality meat, used in the production of Rugao ham, and characterized by large body size and high lean percentage  related to Taurine and hypotaurine metabolism. Taurine is known to affect the cholesterol level in the body and can be found in various meat products  gene in the JQ breed, to our surprise, it was not significantly enriched in any pathway or ontology. We infer that there could be more health-related genes in this block region (M. hyopneumoniae in JQ porcine lean strain (JQHPL) and concluded that JQHPL pigs exhibited higher resistance to M. hyopneumoniae than the western strains in the study, possibly due to the faster and stronger mucosal immunity phenotype of the strain , C3c concentration (C3C), and Hemolytic complement activity  (AH50), while, GGT5 and GGT1 gene overlapped QTLs linked to Interferon-gamma to interleukin-10 ratio (IFNGIL10), Calcium level (BCAL), Creatinine level (CREAT), Potassium level (BPOTASS), C3c concentration (C3C), Haptoglobin concentration (HAPT), and Melanoma susceptibility (MELAN). This suggests an association of the haplotype blocks in these genomic regions with health-related traits in the JQ breed.In a bid to establish a relationship between the candidate genes and QTLs detected in haplotype blocks, we linked the genomic regions of the detected candidate genes to the corresponding QTL region. Surprisingly, we found that only one gene overlapped these QTL regions. This is because the length of each QTL in these regions is greater than 10 Mb (ranging from 33.11 to 131.16 Mb). We premise this observation on the filtering of all QTLs with length > 10 Mb during our QTL annotation. However, we made some compromises (in QTL length) to enable us to have an overview of the quantitative traits in these genomic regions. In summary, A full understanding of the LD properties in domesticated animals like pigs is of importance because it underlies all forms of genetic mapping  and can r2 value as a measure of LD between each locus of a chromosome. Generally, we observed a lower LD level, at larger SNP distances, on both autosomes and SSCX of JQ breed. A similar result was also found in the SZ breed (r2) depends on MAF, it is expected that there would be a little difference in the r2 value obtained from both studies, partly due to SNP ascertainment bias on SNP chip data (r2 > 0.3) at SNPs-distance of 36.10 kb for the JQ breed. Moreover, using the MUC4  gene sequences, r2 > 0.3 extended up to 20 kb distance in the JQ breed, validating to some extent, the reliability of the LD value obtained in our study.In this study, we looked at the extent of LD in the JQ breed and compared it to the one obtained in other breeds. We used SZ breed . HoweverSZ breed . Generalhip data . This kiK = 7  extended up to 500–1000 kb in HD (0.23), D (0.29), and Y (0.26) breeds , reported for the D and Y in our study was higher compared to the one reported by r2 for the autosomes of these breeds is still comparable to a previous study that used larger sample sizes (>100)  breeds . This sus (>100) . These dkb in JQ , indicatThe ability of an animal to survive in a changing environment, and also keep up with changes in selection preference, depends on the richness (genetic diversity) of its gene pool. This can be affected by several occurrences, including natural and artificial selection. There are various parameters for measuring genetic diversity in a population, including population-gene-frequency based statistics like average expected heterozygosity (He), the proportion of polymorphic loci (Pn), and allelic richness (Ar). However, alternative statistics based on allelic diversity  can also provide insight into the genetic diversity in a population and be a better predictor of long-term adaptation and total response to selection in an unpredictable future scenario . TherefoFurthermore, our block analysis revealed that the JQ breed had more SNPs that were clustered into haplotype blocks than any other breed in this study , an indiThe variation in the number of unique haplotype blocks within a population can shed more light on the independent genomic sub-structuring and evolution of such populations . MoreoveThe present study confirms previous findings  and contWe analyzed the LD and haplotype block structure of the JQ pig breed and also detected some underlying QTLs and genes spanned by these blocks. The present study revealed some blocks that might be associated with some quantitative or adaptive traits of the JQ pig and also provides useful information that could contribute to more informed, strategic management decisions in conserving and utilizing this breed. This result might also be useful in selecting variants for further association studies of these traits. We also reported a high level of introgression of Y haplotypes into the SZ pig breed and concluded that the later breed might be at the point of losing its genetic uniqueness.10.6084/m9.figshare.11984010.All SNP data in the present study can be found in the FigShare Repository: The animal study was reviewed and approved by the Institutional Animal Care and Use Committee of Shanghai Jiao Tong University (contract no. 2011-0033).YP and QW conceived and supervised the study. FO analyzed the data and wrote the manuscript while other authors helped FO during the analysis and also revised the manuscript. All the authors revised and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


ContamLD leverages the idea that contaminants should have haplotypes uncorrelated to those of the studied individual. Using simulated data, we confirm that ContamLD accurately infers contamination rates with low standard errors: for example, less than 1.5% standard error in cases with less than 10% contamination and 500,000 sequences covering SNPs. This method is optimized for application to aDNA, taking advantage of characteristic aDNA damage patterns to provide calibrated contamination estimates, and is available at  Ancient DNA (aDNA) data has emerged as a powerful tool for learning about ancient population history, allowing the direct study of the genomes of individuals who lived thousands of years in the past –3. UnforAccurate measurement of the proportion of contamination in ancient DNA data is important, because it can provide guidance about whether the analysis should be restricted to sequences that show the characteristic pattern of C-to-T mismatch to the reference genome of authentic aDNA (if contamination is high) , or carrANGSD software, although there are also other software packages [Current methods for estimating contamination have significant limitations. Methods based on testing for heterogeneity in mitochondrial DNA sequences  can be biased, because there are several orders of magnitude of variation in the ratio of the mitochondrial to nuclear DNA copy number across samples. Thus, samples that have evidence of mitochondrial contamination can be nearly uncontaminated in their nuclear DNA, while samples that have no evidence of mitochondrial contamination can have high nuclear contamination . A more packages –10. HoweContEst [ContaminationDetection [DICE, expanded on this approach and jointly estimates contamination rate and error rate along with demographic history based on allele frequency correlation patterns [DICE required over 3× genome sequence coverage and solved the distinctive problem of measuring contamination of present-day humans in a Neanderthal genome.Several methods for estimating contamination rates in nuclear DNA from modern genomes have been published, including ContEst  and Contetection . Howeveretection  and Neanetection  by produpatterns . Howeverpatterns , DICE reContamLD, is based on the idea that when sequences from one or more contaminating individuals are present in a sample, LD among sequences derived from that sample is expected to be diminished, because the contaminant DNA derives from different haplotypes and therefore should have no LD with the authentic DNA of the ancient individual of interest. Thus, the goal of the algorithm is to determine the LD pattern the ancient individual would have had without contamination and compare it to the LD pattern found in the sample. The LD patterns of ancient individuals are determined using reference panels from 1000 Genomes Project populations to compute approximate background haplotype frequencies, where haplotypes are defined as pairs of SNPs with high correlation to each other. Contamination is then estimated by fitting a maximum likelihood model of a mixture of haplotypes from an uncontaminated individual and a proportion of contamination (to be estimated from the data) from an unrelated individual. ContamLD corrects for mismatch of the ancestry of the ancient individual with the reference panels using two different user-specified options. In the first option, the mismatch is corrected using estimates from damaged sequences (which we assume lack present-day contaminants). In the second option, ContamLD performs an “external” correction by subtracting the sample’s contamination estimate from estimates for individuals of the same population believed to have negligible contamination . The second option has more power than the first option and allows detection of cross-contamination by other ancient samples, but it could be biased if a reliable estimate from an uncontaminated individual from the same population is not available for the external correction.We report a method for estimating autosomal aDNA contamination using patterns of linkage disequilibrium (LD) within a sample. This approach, implemented in our software ContamLD accurately infers contamination in both ancient and present-day individuals of widely divergent ancestries with simulated contamination coming from individuals of different ancestries. The contamination estimates are highly correlated with estimates based on X chromosome analysis in ancient samples that are male, as assessed using ANGSD [ContamLD run with the first option has standard errors less than 1.5% in samples with at least 500,000 sequences covering SNPs . The damage status  of each sequence was also determined based on the status of the ancient reference individual. Contaminating sequences were then “spiked-in” at varying proportions (0 to 40%), using an additional present-day individual from the 1000 Genomes Project to determine the contaminating allele type  provided with the software that we recommend for shotgun-sequenced samples, which provides more power to measure contamination.For most of the analyses reported in this study, we simulate data for SNP sites targeted in the 1.24 million SNP capture reagents  that intContamLD is run on samples with 0%, 7%, and 15% simulated contamination. Additional file ContamLD often overestimates contamination, but in practice, samples with above 10% contamination are generally removed from population genetic analyses, so inaccuracies in the estimates at these levels are not a concern in our view . ContamLD assumes that the individual making up the majority of the sequences is the base individual, so we do not explore contamination rates greater than 50% in these simulation studies.We first analyzed data generated using a reference individual from the 1000 Genomes CEU population (Utah Residents (CEPH) with Northern and Western European Ancestry) and the SNP coverage profile of a 1.02× coverage ancient individual of West Eurasian ancestry . Additional file We observe a linear shift in the contamination estimates such that most estimates are biased to be slightly higher than the actual value, with even greater overestimates occurring at higher contamination rates , [ContamLD could be further improved by using an estimate of ROH from software like this as an input parameter.In contrast to the upward bias in contamination estimates due to mismatch of the individual’s haplotypes with the reference panel haplotype frequencies, we observe negative shifts for children of two closely related parents, as expected because hapRHO),  and ContContamLD only detects contamination where the contaminant sequence differs from the target individual’s sequence. If the contaminant carries the same haplotypes as the target individual, in the most extreme case as expected for an identical twin, then the existence of contamination will be missed altogether. In general, contamination from closely related individuals is unlikely to be a concern for many population genetic analyses, as close relatives usually (but with important exceptions) have very similar ancestry.A final type of bias could be expected to arise if the contamination comes from an individual related to the target individual. In this case, the true contamination rate is expected to be underestimated, because ContamLD.In our implementation, we correct for these systematic biases in two ways, implemented as different options in ContamLD estimate based on undamaged sequences is corrected by estimates based on the damaged sequences . In the second option, we allow the user to subtract the contamination estimate from the estimate of an individual of the same ancestry assumed to be uncontaminated. An advantage of the second option compared to the first is that it has smaller standard errors .The first option leverages information from sequences that contain evidence of the C-to-T damage that is characteristic of ancient sequences. This option assumes these sequences are authentically ancient and not derived from a contaminating source , so the ors Fig. , reflectContamLD includes both methods as options. The uncorrected score also forms the basis for warning output by the software, namely high contamination or possible contamination with another ancient sample leading to an inaccurate damage correction estimate.In what follows, we report the results of analyses based on the first option, but ContamLD is designed to work on ancient individuals, so we simulated contamination of real ancient individuals  Figure ContamLD produces highly accurate contamination estimates for this simulation.le ANGSD   and YRI  panels were used instead . We thus recommend that users take care to choose an appropriate panel that is within the same continental ancestry as their ancient individual. Nevertheless, we note that we were able to obtain reasonably accurate estimates for Upper Paleolithic European hunter-gatherers, such as the Kostenki14 individual [There are many potential cases in which ancient individuals can come from populations with very different genetic profiles compared to present-day 1000 Genomes populations, leading to an ancestry mismatch to the haplotype reference panels. atistics  to deterdividual , who is ContamLD overestimated contamination .Contamination can come from a wide variety of sources, including, but not limited to, members of the archeological excavation team, the aDNA laboratory, or residual human DNA on the plastic and glassware or in laboratory reagents. Thus, we sought to understand the effect of mismatch in the ancestry of the true sample and the contaminating individual in our contamination estimates. We found that as the ancestry of the two diverged, ion Fig. : Fig. S4ion Fig. : Fig. S4ion Fig. : Fig. S5ContamLD relies on measuring the difference between the LD pattern of the sample and that expected from an uncontaminated individual. However, individuals from groups recently admixed between two highly divergent ancestral groups have LD patterns that are similar in some ways to that of an unadmixed individual with contamination from a group with ancestry diverged from that of the individual of interest. To understand how this would impact ContamLD, we ran the software on an ASW (Americans of African Ancestry in Southwest USA) individual with different levels of added CEU contamination. When we ran ContamLD with a YRI panel and no correction on an individual with no contamination, the individual was inferred to have a contamination of ~ 20% . Using an ASW panel did not perform any better. However, the concerns were mostly addressed by the damage-restricted correction (option 1) at low contamination levels  down-sampled to 0.5x coverage contaminated with another ancient West Eurasian ancestry individual I10895) as above with damage rates simulated to be between 0.005 to 0.075. We found that standard errors decreased as damage rate increased  when no artificial contamination was added . We then analyzed the individuals with ContamLD and ANGSD and found that compared to ANGSD, ContamLD consistently had similar errors relative to the true contamination level  into the BAM files of 65 ancient male individuals of variable ancestries and ages . We chose ancient male individuals that had average coverage over 0.5× and X chromosome contamination estimates under 2% , and ContamMix  without adding additional contamination. For this analysis, we included published data generated with the ~ 1.24 million SNP enrichment reagent, as well as data from libraries that failed quality control due to evidence of contamination  estimates, showing high contamination in some libraries with low nuclear contamination, and low mitochondrial contamination in some libraries with high nuclear contamination, likely reflecting the known biological phenomenon of orders of magnitude of variation in the mitochondrial to nuclear DNA ratio , and all samples with X chromosome estimates over 5% were flagged with this warning and/or had estimates of over 5% contamination with ContamLD . It is unfortunately not possible to know the true contamination of the samples we tested in Fig. We tested 439 ancient males with tio Fig. a. In conion Fig. b. This pContamLD until the contamination reached 15% at which point the “Very_High_Contamination” flag came up . To understand these issues better, we performed a simulation in which an ancient Iberian I3756) was contaminated with another ancient West Eurasian individual I10895), and the damaged sequences were set to be a 5% down-sampling of the set of contaminated sequences . We found that, as expected, the contamination from the ancient individual was not detected  by the damage-restricted correction version of 0895, and6 was conContamLD, for estimating rates of autosomal DNA contamination in aDNA samples. ContamLD is able to measure contamination accurately in both male and female individuals, with standard errors less than 1.5% for individuals with coverage above 0.5× on the 1240K SNP set (for contamination levels less than 10%) for the damage-restricted correction method (option 1). On the shotgun panel we provide, standard errors are less than 1.5% for coverages above 0.1×. ContamLD is best suited to scenarios in which the contaminant and the ancient individual of interest are similar ancestries, which is useful, because DICE [ContamLD works even for recently admixed individuals. Lastly, ContamLD can detect cases of contamination from other ancient individuals, though this works best if it is large amounts of contamination that can reach the threshold required for the “Very_High_Contamination” flag.We have presented a tool, use DICE  and manyuse DICE ) are betContamLD in multiple simulation scenarios to determine when bias or less reliable results could be expected. When applied to the situation with a test individual (ancient or present-day), contaminant, and haplotype reference panel all from the same continental ancestry, ContamLD provides an accurate, unbiased estimate of contamination. When the contaminant comes from a population that is of a different continental ancestry from the population used for the base and haplotype panel, the contamination appears to be slightly overestimated, particularly for higher contamination. This should not be a large problem in analyses of real  data, because the effect is small at the contamination levels of interest (< 5%). When we varied haplotype panels, we found that the estimator is robust when applied to simulated datasets using haplotype panels that are moderately divergent from the base sample (within-continent levels of variation). We provide users tools for automatically determining the panel that shared the most genetic drift with the sample so that the user can select the panel most closely related to the sample. In other simulations, we found that the performance of the algorithm declines as the coverage of the sample decreases. The estimates are not biased, but the standard errors substantially increase when fewer than 300,000 sequences are available. In these cases, if the individual was shotgun sequenced, we recommend that users choose the shotgun panel, which will substantially increase the power for the analyses.We tested We applied the algorithm to estimate contamination levels in dozens of ancient samples and compared them to X chromosome-based contamination estimates. There was generally good correlation with the X chromosome estimates, except that when the true contamination was very high, the LD based estimates were sometimes estimated incorrectly, likely because the contamination was due to cross-contamination from another ancient individual and there was over-correction from the damage estimates. This problem is mitigated, however, because the software indicates if the uncorrected estimate is very high so users can identify highly contaminated samples and remove them from further analyses. A difficult case for the software is if there is contamination in part from another ancient sample. This can cause an over-correction and lead to an underestimate of the contamination. The “Very_High_Contamination” warning catches very high contamination from other ancient samples, but it will miss cases of moderate levels of contamination from other ancient samples, because it will not reach the threshold required for the warning. In theory, the user can determine the true contamination in these cases using the external correction, but the external correction can be difficult if the user does not have an adequate sample to correct the estimate of the sample of interest. The damage correction of the software also does not work if the samples have undergone full UDG treatment (which results in very few damaged sequences), and for this case, the external correction is the only option.The software run time is dependent on SNP coverage. If ~ 1,000,000 SNPs are covered (the depth of the coverage on each SNP does not affect run time), the analysis takes approximately 2 h if 3 cores are available on CentOS 7.2.15 Linux machines (~ 25 GB of memory). The software is designed for samples to be run in parallel, so the total time for analysis even for large numbers of samples is often not much greater than the time for a single sample.ContamLD is able to estimate accurately autosomal nuclear contamination in ancient DNA with standard errors that depend on the coverage of the sample. This will be particularly useful for female samples where X chromosome estimates are not possible. As a general recommendation for users, we believe in most cases all samples with a contamination estimate that is greater than 0.05 (5%) should be removed from further analyses, or the contamination should be explicitly modeled in population genetic analyses.In summary, Genome-wide datasets from individuals that were part of the 1000 Genomes Project  were useWe analyzed mitochondrial and X chromosome contamination estimates , 27 fromContamLD.For each ancient individual, we generated the sequence depth data from the sample bam file, counting the number of reference and alternative alleles at each SNP site in the analysis dataset. Damage-restricted data were generated by restricting to sequences with PMD scores greater than or equal to 3 . Our sofr2 cutoff of 0.2.  We then calculated the frequencies of each SNP in all of these pairs as well as the haplotype frequencies at each of these pairs while holding out the present-day individuals used for contamination simulation.To create haplotype panels, we obtained all SNP pairs in high LD for each 1000 Genomes population using PLINK version 1.9  with r2 α, the level of contamination, by examining the frequencies of allele pairs that should be in LD  and determining how much their frequencies differ from what would be expected under no contamination. To estimate this, we need both the distribution underlying the haplotypes (q) that an uncontaminated test sample should have as well as the distribution of “unrelated haplotypes” . Thus, for this analysis, when examining a pair of SNPs, it is equally likely for the SNP pair to have been formed from the true haplotype  or the background distribution . We therefore can estimate q as:h is the distribution of true haplotypes and h is more than 1/2, because the two parental chromosomes are more related, but this can generally be corrected (see below).To determine h can be estimated from an external reference panel using a maximum likelihood estimator (MLE) to obtain haplotype frequencies in the population from the counts (necessary because the panels are not phased). The MLE setup is:P is the (unknown) diploid count distribution of the haplotypes of the population the test individual is from , n is the number of SNP pairs, c is the vector of observed haplotypes in the diploid count panel (from 1000 Genomes), i sums over all 4 haplotype possibilities, ha,b) haplotype distributions of the parents of the test individual (the haploid chromosomes they pass on to their child), and a, b→ implies that a1 + a2 = i and b1 + b2 = j, meaning that one adds up all cases where the haplotype combination would lead to a particular diploid count  configuration of the father’s chromosome contributed to a particular diploid count . In other words, given the observed haplotype counts in the reference panel, how many times would it be expected that a particular haplotype configuration  in one of the parents contributed to those counts?The MLE would be computationally intractable to solve due to our lack of knowledge of which parent contributed to each count, so we instead used an EM algorithm to obtain n1 and n2) of the haploid parents are obtained, they are added together to produce the diploid individual . Then, the expected value of the haplotype distribution can be maximized by averaging over the possible haplotype distributions. Thus, the maximization step is:D is the sum of the probabilities of a particular haplotype configuration over all diploid count configurations.Once the counts  to be 0.25 and then iterated through the algorithm until convergence . We then used this estimate of h to get an estimate of q (based on the first equation above).We initially set all α, we used the equation:To estimate T is the expected distribution underlying the observed haplotypes of the sample, which is a mix of the test individual and contaminant. This means that assuming the test individual comes from a population with a haplotype distribution (frequency of the different haplotype possibilities at each SNP pair throughout the genome) that can be approximated by the chosen reference panel (and estimated as above), T is the haplotype distribution expected for the sample given a particular amount of contamination . q is the haplotype distribution for an uncontaminated sample. A fraction (1 − α′)2 + α′2 of the distribution should look like this, where (1 − α′)2 is the probability that two uncontaminated sequences form the SNP pair and α′2 is the probability that two contaminated sequences form the SNP pair, assuming the contaminating sequences are from a single individual, which would “re-form” a SNP pair with LD . α′(1 − α′) of the distribution should look like this (the probability that the SNP pair is formed from a contaminated sequence and an uncontaminated sequence).Here, α′ = 0 and the alternative hypotheses of different α′. A LOD score is assigned to each estimate of the contamination rate (α) between − 0.1 and 0.5 . The grid of α′ is scaled by intervals of 0.0001. The α′ with the highest LOD score is the best estimate of α and is returned. When we have multiple sequences on the same SNP, we assume independence of the sequences, which provides additional power. The assumption of independence does not bias the error estimation for the same reason as explained above for the independence of SNP pairs.This expression can be used to solve for α′ by maximizing the log of the odds (LOD) scores under the null hypothesis that α′ we obtain is not equal to the true α, because the reference panel does not perfectly capture the SNP and haplotype frequencies of the test sample. We found that this difference causes a linear shift in contamination estimate where the mismatch between the sample individual and the reference panel leads to a positive shift while inbreeding leads to a negative shift. These biases can be addressed in either of two ways.In practice, the α′ estimate only on alleles from sequences with evidence of damage characteristic of ancient samples. Under the assumption that these sequences are not affected by present-day contamination, the inferred α′ would be an estimate of the bias, which can be subtracted out from the estimate based on all sites. We separately analyzed the following pairs of SNPs: UU (both SNPs at undamaged sequences), DU (one site damaged and the other undamaged), and DD (both SNPs at damaged sequences). For the UU pairs, the value we calculate would be α + k, where k is the linear shift. For DU pairs, the value calculated would be α/2 + k, and for DD pairs, the value calculated would be k. We added the likelihoods for these pairs and maximized the likelihood to solve for α and k. After solving for α, we multiply by (1 damage rate) to obtain the contamination level across all sequences, because α is the contamination rate at undamaged sequences.First, for the “damage correction” approach, we performed an ANGSD using method 1 as developed first by Rasmussen et al. [α′. We assumed a true contamination of 0 for these samples and thus subtracted this α′ from all other contamination estimates. We caution that this method does not correct for uncertainty in the contamination estimate in the external sample used for benchmarking.Second, for the “external correction” approach, we took individuals from the test individual’s population that were high coverage and samples we believed had very low contamination  and meaContamLD is similar to that of Vohr et al. [ContamLD searches for breaks in LD in the sequences of a single sample to determine if sequences from other individuals are present in the sample.The approach of r et al.  except tTo test the accuracy of the algorithm, we applied it to a variety of scenarios with both present-day DNA as well as real aDNA samples that had simulated present-day DNA contamination. In all our simulations with 1000 Genomes individuals, we removed the individual being used from our haplotype panel before performing the analyses.We first simulated contamination of present-day individuals with other present-day individuals as contaminants . In order to best approximate the distribution of both the damaged and undamaged sequences that is characteristic of aDNA data, we used sequence depth information from an ancient individual as a reference. At each SNP, the total number of simulated “damaged” and “undamaged” sequences was determined based on the number of damaged and undamaged sequences at the SNP in the reference ancient individual. The identity of each allele for the present-day “base” sample was randomly chosen based on the genotype of the “base” present-day 1000 Genomes individual at each SNP, as described above for the contamination. The addition of contaminant sequences to the dataset was performed using the method described above. In order to reduce bias caused by the damage correction procedure, the damage-restricted dataset was generated only once for each simulation type (which included multiple simulations across varying contamination rates) and combined with the undamaged dataset to produce the overall dataset. This method was used to generate a simulated individual using present-day CEU (NA06985) or ASW NA19625) from the 1000 Genomes dataset as the “base” sample from the sequence distributions of a 1.02× coverage ancient Iberian individual (I3756) (the “reference”) [625 from α, from 0 to 15%.We generated simulated data with contamination from multiple sources by adjusting the present-day contamination simulation method to randomly sample from two or more present-day source contaminant genomes with equal probability. In each case, a 1000 Genomes Project CEU individual (NA06985) was used as a “base” genome with the sequence distribution of I3756 (the “reference”). In the case of 2 sources of contamination  to contaminate the BAM files directly.We performed two sets of simulations contaminating different ancient individuals. In both cases, we selected ancient male individuals with minimal contamination (as assessed by X chromosome contamination levels from ANGSD ) to act  dataset  to assesα/(1 − α), where α is equal to the contamination rate . The identity of the added contaminant allele was randomly chosen based on the genotype of the chosen “contaminant” present-day genome at the site . This method maintains the underlying distribution of “uncontaminated” reference and alternative alleles at each SNP site, while adding additional “contaminant” alleles to each site, producing an overall contamination rate of α in the undamaged sequences.In the first simulation set, we assumed that sequences with C-to-T damage are highly unlikely to be the product of contamination . Thus, we exclusively added contamination to the “undamaged” fraction of sequences. At each SNP site, we classified sequences present in the damage-restricted dataset as “damaged” and added to the simulated data. We classified all other sequences as “undamaged” and also added them to the simulated data, but for each “undamaged sequence,” we added a contaminant sequence to the simulated SNP data with probability For each simulation, we generated two output files: (1) a file reporting the total number of sequences carrying reference and alternative alleles at each SNP and 2) a damage-restricted file reporting the total number of damaged sequences carrying reference and alternative alleles at each SNP. We used a 1.02× coverage ancient Iberian individual I3756) , we created the “damaged” sequence set as a randomly chosen 5% of the sequences from the non-contaminant individual. We then analyzed the data with ContamLD  and ANGSD using default settings (method 1). We also performed simulations with a 1.0× coverage ancient West Eurasian ancestry individual  [For the second simulation set, we analyzed 65 ancient individuals of average coverage over 0.5× and baseline ividual)  down-samContamLD on the resulting data with damage-restricted correction, option 1.As the last simulation, we examined the case of an ancient individual contaminating another ancient individual where some of the damaged sequences would also come from the contaminating individual. In this simulation, we analyzed a 1.02× coverage ancient Iberian individual (I3756) and contaminated the BAM with sequences from a ~ 1200BP ancient West Eurasian individual (I10895) in the proportions 0.000, 0.005, 0.010, 0.020, 0.025, 0.030, 0.040, 0.050, 0.060, 0.070, 0.080, 0.090, 0.100, 0.150, 0.200, and 0.300. We then down-sampled the BAM, taking a random 5% of the sequences of these contaminated BAM files to act as the “damaged” sequences, because this would correct for any baseline contamination in the I3756 individual yet would simulate additional contamination of I3756 by an ancient individual with the same damage rate as I3756 . We then performed the standard processing of both the full contaminated BAMs and the 5% down-sampled BAMs (simulated to be “damaged” sequences), removing two base pairs from the end of each sequence and carrying out a “pseudo-haploid” genotype pulldown. We ran ContamLD to examine the shotgun-sequenced individuals analyzed at the1240K SNP set and the large 5.6 million SNP shotgun panel. The ancient shotgun sequenced individuals were of 0.1–0.5× coverage from Allentoft et al. [ContamLD (damage-corrected version), ANGSD [ContamMix [As our last set of analyses, we directly measured the contamination levels in ancient individuals without simulated contamination. We used t et al. ; Damgaart et al. ; and de t et al. . In addi), ANGSD , 39 usinAdditional file 1: Supplementary Figures. 10 supplementary figures showing results of additional analyses.Additional file 2: Table S1. Ancient Individual Meta-Data. Excel spreadsheet detailing information about the ancient individuals analyzed.Additional file 3: Table S2.ContamLD and ANGSD Estimates. Excel spreadsheet providing ContamLD and ANGSD estimates from simulations of ancient individuals with meta-data of the ancient individuals also included.Additional file 4: Table S3.ContamLD, ContamMix, and ANGSD Estimates. Excel spreadsheet providing ContamLD, ContamMix, and ANGSD estimates from ancient male individuals with meta-data of the ancient individuals also included.Additional file 5: Table S4. Simulation with Damaged Contaminated Reads. Excel spreadsheet providing ContamLD estimates for a simulation where the damaged reads of the contaminant individual were also included in the contamination.Additional file 6. Review history.


TMEM161A and another five SNPs significantly correlated to EYC were constructed in haplotype block on chromosome 5 from 21,069,009 to 21,363,580, which annotated by TMEM161A, CALCR, TFPI2, and GLP1R. Those genes were enriched in epidermal growth factor-activated receptor activity, regulation of epidermal growth factor receptor signaling pathway. The SNPs, haplotype markers, and candidate genes identified in this study can be used to improve the accuracy of marker-assisted selection for the reproductive performance and egg quality traits of geese. In addition, the candidate genes significantly associated with these traits may provide a foundation for better understanding the mechanisms underlying reproduction and egg quality in geese.Geese are one of the most economically important waterfowl. However, the low reproductive performance and egg quality of geese hinder the development of the goose industry. The identification and application of genetic markers may improve the accuracy of beneficial trait selection. To identify the genetic markers associated with goose reproductive performance and egg quality traits, we performed a genome-wide association study (GWAS) for body weight at birth (BBW), the number of eggs at 48 weeks of age (EN48), the number of eggs at 60 weeks of age (EN60) and egg yolk color (EYC). The GWAS acquired 2.896 Tb of raw sequencing data with an average depth of 12.44× and identified 9,279,339 SNPs. The results of GWAS showed that 26 SNPs were significantly associated with BBW, EN48, EN60, and EYC. Moreover, five of these SNPs significantly associated with EN48 and EN60 were in a haplotype block on chromosome 35 from 4,512,855 to 4,541,709 bp, oriented to  As one of the most economically important waterfowl, geese are widely raised for their eggs, meat, liver, feathers, and other byproducts . Geese aEggs are inexpensive source with essential vitamins and minerals . In an eIn birds, the body weight at birth, egg production and egg yolk color traits are quantitative traits, presumed to be controlled by numerous genes or loci in the genome with minor effects, and also influenced by multiple factors, including genetic factors, diet, housing, environment, management, disease, and other factors, such as environment, diet, housing practice . CompareGenome-wide association studies (GWASs) are often used to screen for genes or markers associated with specific traits . To dateIn this study, we employed a whole-genome resequencing approach to identify single nucleotide polymorphisms (SNPs) in the genome of the Sichuan goose; a GWAS analysis was then used to screen the corresponding genomic regions and candidate genes for the reproductive performance and egg quality traits. Moreover, MALDI-TOP MS method were used to verify the SNPs and haplotype blocks that are significantly associated with the traits in goose population. This work will lay a foundation for further studies on goose traits to facilitate genetic selection.All experiments involving animals were performed according to the laws and regulations established by the Ministry of Agriculture of China . This study was approved by the Animal Care and Welfare Committee of the Chinese Chongqing Academy of Animal Science .A total of 209 Sichuan white geese were used in this study. Each bird was reared from birth to the non-laying period (65 weeks). All individuals were housed in single cages (600 mm × 800 mm × 900 mm) at the AnFu Waterfowl Breeding Base in Chongqing City, China . All geese were raised under natural temperatures and artificial lighting (12 h of light per day) and fed the same diet with free access to water. At week 28, blood samples were collected from the wings of all geese using vacuum tubes containing ethylenediaminetetraacetic acid. These blood samples were used for DNA extraction.During the egg-laying period (weeks 28–64), we collected eggs four times a day and marked the eggs, which allowed us to trace the traits to the specific one in the future study. The body weight at birth (BBW) and the number of eggs at 48 weeks of age (EN48) or 60 weeks of age (EN60) were individually recorded and statistic. The three consecutive laid eggs from 209 individuals in a week during the experimental period (from 35 to 40 weeks) were used to evaluate the egg yolk color (EYC) using the Roche Yolk Color Fan (1–15: light yellow to orange) .We extracted genomic DNA from whole blood samples using a genomic DNA extraction kit . The DNA concentration and quality were determined using a NanoVue spectrophotometer  and agarose gel electrophoresis to verify the quality requirements of the Illumina sequencing platform.Whole-genome resequencing data were obtained using the Illumina HiSeq X Ten platform . After read filtering, the high-quality reads were mapped to the goose genome at the chromosome-level  using BW–7). After quality control, 9,279,339 SNPs were identified of 209 individuals. Then, we performed principal component analysis (PCA) to assess goose population stratification with the application of Plink. Furtherly, the top two PCs (PC1 to PC2) were picked and plotted with R.Quality control was conducted at both the individual and SNP levels using Plink  with pary = Wα + xβ + ε, where y is the phenotypic value for all individuals, W is a covariance matrix used to control the population structure (fixed effects: PC1 and PC2), α is a vector of the corresponding coefficients including the intercept, x is the genotype of the SNP or haplotype marker, β is the effect size of the SNP or haplotype marker for the phenotypes, and ε is a vector of random residuals. The Wald test statistic was used to calculate the significance of the associations between the SNPs and phenotypes, while the association analysis results were corrected using Bonferroni’s correction .In this study, we performed GWAS assuming a random population without considering the familial relationships. The GWAS analysis between the traits  and the SNPs was performed with genome-wide efficient mixed model association (GEMMA) software  using a The genotypes of the significantly associated SNPs identified by GWAS were determined by iPLEX matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) of the MassARRAY genotyping platform . Amplification and extension primers  were des1, and quantile–quantile (QQ) plots were drawn using the qqman package2; both were performed in R project software. We used the GenABEL package to calculate the extent of false-positive signals in the results using the genomic inflation factor (k)  .3  structure between significantly associate SNPs identified by MALDI-TOF MS method using Haploview software (version 4.2)3 . Associa4 (BEDTools software  was used4 .The body weight (BBW), laying performance (EN48 and EN60), and egg quality (EYC) of all 209 Sichuan white geese were recorded during the laying period . A totalPrincipal component analysis revealed slight stratification within the goose population . Therefop < 0.05) for the EN48 and EN60 traits and included “response to radiation” , “response to light stimulus” , and “cellular response to hormone stimulus”  (p < 0.05) for the EYC trait, including “regulation of epidermal growth factor-activated receptor activity” , “regulation of signaling receptor activity”  and “regulation of epidermal growth factor receptor signaling pathway”   and egg quality traits (EYC). The 37 Gene Ontology (GO) terms were significantly enriched ( × 10–2) . The 21  × 10–4) .TMEM161A), located within a 28.85 kb genomic region  of chromosome 35, were significantly associated with EN48 and EN60 (Haplotypes were constructed for the regions containing the significantly associated SNPs. We found that 5 SNPs in transmembrane protein 161A (and EN60 . The 5 Sand EN60 . Haplotyand EN60 .GLP1R), calcitonin receptor (CALCR), and tissue factor pathway inhibitor 2 (TFPI2) genes that were significantly associated with EYC. The 5 SNPs and genotypic distributions were statistically analyzed, and associations between the genotypes and EYC traits were determined  was constructed based on 5 SNPs  in the gtermined . Haplotytermined .Sichuan white goose is a popular Chinese goose breed for meat and egg purposes. The number of eggs at 60 weeks for the experimental geese were about 60 per individual, which was consistent with that described previously and laid the foundation for the further study . While ede novo assembly of the goose genome (There were two advantages than most of the previous studies. (1) The goose is an economically important waterfowl with distinctive characters. However, the habitat preference for water, large body size and the seasonal reproduction restricts most of the goose rearing in free-range or large groups. Compared with most of the previous studies, one of advantages in this study is that the individuals were reared in single cages (600 mm × 800 mm × 900 mm), which allowed the traits to be accurately recorded and statistically analyzed for each goose. (2) Our lab has generated a chromosome-level e genome . Our assThere was a population stratification phenomenon in the experimental goose population in this study maybe due to the limitation of the population size (209). Considering the number maybe the limitation for this study, to identify the SNPs associated with the EN48, EN60, and EYC traits, we employed the MALDI-TOF MS method to detect some selected SNP sites, and analyzed the frequency of candidate SNPs, which was consistent with the GWAS analysis results. In addition, the haplotype blocks constructed by some SNPs from GWAS analysis were also significantly associated with the traits. For instance, SNP16, SNP17, SNP18, SNP281, and SNP282 were significantly associated with the EN48 or EN60 , and theSMAP2) was significantly associated with BBW. SMAP2 is a member of the ArfGAP subfamily. The protein encoded by this gene exerts a vital function in vesicle trafficking and plays a crucial role in sperm formation , is involved in several cellular processes, including the adaptive response to oxidative stress , DCC-interacting protein 13-beta (DP13B), RNA-directed DNA polymerase from mobile element jockey (RTJK), and S2542 . Previous studies have identified many candidate genes corresponding to goose reproductive traits , response to a light stimulus (GO: 0009416), cellular response to a hormone stimulus (GO: 0032870), and response to a peptide hormone stimulus (GO: 0043434) . Geese aCALCR, TFPI2, and GLP1R. CALCR is a receptor that binds to the peptide hormone calcitonin and is involved in calcium homeostasis, bone formation and metabolism, and lipid metabolism , CB042 (known as uncharacterized protein C2orf42), voltage-dependent L-type calcium channel subunit beta-2 (CACB2), cerebral cavernous malformations protein 2 homolog (CCM2), and zinc finger and BTB domain-containing protein 46 (ZBT46). The effects of these genes on EYC require further study.Egg yolk color is a crucial economic egg quality index because consumers link yolk color to the nutrition contained in an egg . Studiestabolism . GLP1R, ecretion . Interesght loss . TFPI-2  lesions . In addiigration . Therefoigration . In agreThe GO pathway analysis revealed that the “regulation of epidermal growth factor-activated receptor activity” (GO: 0007176) and “regulation of epidermal growth factor receptor signaling pathway” (GO: 0042058)  were remIn the current study, a total of 26 SNPs significantly associated with egg production and quality trait were identified by GWAS. Additionally, these SNPs with significant effects were further verified using the MALDI-TOP MS method in the same population. Furthermore, 14 annotated genes were detected as candidate genes for the four traits based on their basic function. These results supplied the new candidate genetic markers and genes for the marker-assisted selection of geese, and laid the foundation for the genetic basis of the reproduction performance and egg quality traits.SRR10687319–SRR10687346, SRR10687348–SRR1068 7383, SRR10687385–SRR10687397, SRR10687399–SRR106874 03, SRR10687405–SRR10687410, SRR10687412–SRR10687417, SRR10687419, SRR10687421–SRR10687423, SRR10687425, SRR 10687427–SRR10687433, SRR10687436–SRR10687445, SRR106 87447–SRR10687464, SRR10687466–SRR10687473, SRR106874 75, SRR10687478, SRR10687483–SRR10687486, SRR10687488–SRR10687510, SRR10687513–SRR10687517, SRR10687519–SRR 10687528, SRR10687530, SRR10687532–SRR10687535, and SRR 10687537–SRR10687554.The raw whole-genome resequencing data have been deposited in the NCBI Short Read Archive (SRA) under accession numbers: The animal study was reviewed and approved by all experiments involving animals were performed according to the laws and regulations established by the Ministry of Agriculture of China . The study was approved by the Animal Care and Welfare Committee of the Chinese Chongqing Academy of Animal Science .GG, QW, and XZ provided funds, and designed and supervised the projects. GG, XZ, JL, and YX collected the samples and the phenotypic data. GG, DG, SH, SX, and RW performed the bioinformatics analysis, data uploaded, and drawn the figures. GG, KZ, RW, and CY wrote the manuscripts. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer YW declared a shared affiliation, with no collaboration, with one of the authors DG to the handling editor at the time of review.


N = 1515, 51% girls, M age = 13.0 and 14.3 years at T1 and T2, respectively). Multi-group analyses revealed that the negative links between T1 parental control and T2 adolescent delinquency, T1 parental solicitation and T2 adolescent conduct problems and delinquency, and T1 emotional problems and T2 adolescent disclosure were moderated by the family’s emotional climate. When the family’s emotional climate was positive, the parenting strategies had a more positive effect on adolescent psychosocial functioning, and adolescents with emotional problems communicated more openly with their parents. These findings suggest that the relational context in the family is an important protective factor and add specificity to the previously established role of parent-adolescent communication in adolescent psychosocial development. In terms of preventive interventions, strategies to enhance the family’s emotional climate should be considered prior to teaching specific parenting strategies.The current study was designed to extend the parenting literature by testing the moderating role of the family’s emotional climate, operationalized with parent-adolescent emotional closeness and adolescent feelings of being overly controlled by parents on the longitudinal associations between parent-driven communication efforts , adolescent-driven communication efforts (i.e. adolescent disclosure and secrecy) and adolescent psychosocial functioning . We conducted a series of cross-lagged models controlling for adolescent gender and ethnicity using a two-wave Swedish longitudinal set of self-report data ( The family’s emotional climate is a key context for adolescent development in general and adolescent-parent communication in particular. The history of interactions between parents and their children, as well as the goals and attributes that parents bring to their parenting socialization, rest within the context of the family’s emotional climate  and parental behavioral control  beginning in sixth grade (n = 781) and seventh grade (n = 734), respectively. The mean ages were T1: M = 13.01 years (SD = 0.60); T2: M = 14.33 years (SD = 0.64). Most lived with both parents (80.6%) and were of Swedish ethnicity (80.5%). Nearly 20% were persons of other Western origin, Middle East or Africa. Most of the adolescents (62.8%) reported having as much money as their classmates, while 20.3% reported that their family had more money than their classmates’ families, and 16.8% reported that their family had less money than the families of their classmates. The participants included at T1 and those who opted out were compared using available register data on demographics (gender and immigration status) and school performance (absenteeism and merit points based on grades) to assess the representativeness of the sample used. There were no significant differences in gender (p = 0.22), immigrant status (p = 0.07), merit points (p = 0.15), or absence from school (p = 0.60) which indicates that the sample is representative for the target sample based on gender, immigrant status, and school performance.The sample for the current study is based on two waves (from now on referred to as T1 and T2) of self-reported data with In 2013, we established contact with all middle schools in the participating municipalities and with the parents of the students. Students, as well as their parents, were sent letters with information about the study. The letter, translated into 32 different languages, explained the nature of the survey, and informed about opt-out parental consent, where the parents were given the opportunity to decline their child’s participation. The students replied annually to questionnaires, which were collected in the classrooms by the research team. The study received ethical approval from the Regional Research Review Board in Gothenburg before each data collection wave.Never) to 3 (Always). Measure has proven to be internally consistent in samples of adolescents with alphas ranging from 0.64 to 0.82  to 2 (Completely true). Although alpha for the conduct problems scale was poor (α = 0.48), the measure has been proven to have good test-retest reliability and predictive validity  were used to assess adolescent emotional problems and conduct problems  to 3 . The measure has been proven to be internally consistent with alphas ranging from 0.67 to 0.79.The measure was taken from the annual school survey conducted by The Swedish National Council for Crime Prevention Ring  and asseery happy) to 4 (Very unhappy), and “I think that my life has purpose and meaning” with ratings ranging from 1 (Completely agree) to 4 (Completely disagree). The responses were later reversed so that higher values indicated higher levels of wellbeing. The measure has been proven to be internally consistent with Spearman Brown coefficients ranging from 0.73 to 0.75. The items used are similar to items measuring subjective and psychological well-being in the Mental Health Continuum Short Form  to 3 (Completely true) because reports for mothers and fathers were substantially correlated (r (1464) = 0.75, p < 0.01) and subsequently averaged. The measure has been proven to be internally consistent in samples of adolescents with alphas ranging from 0.85 to 0.91.The scale, developed by Biesecker  and use measuredNot true) to 3 (Completely true). The measure has been proven to be internally consistent in samples of adolescents with alphas ranging from 0.69 to 0.82 and good test-retest reliability  to confirm the internal structure of the scales during T1 and T2. All factors provided an acceptable model fit indicated by CFI > 0.90, TLI > 0.95 and RMSEA <0.08. After the CFAs were completed, for each measure we compared models with unconstrained and constrained factor loadings over time in order to test measurement invariance. The relative fit of the constrained model was evaluated based on change in CFI (ΔCFI). The change in each model was <0.01 which indicated an equivalent fit between the models  continued to provide data at T2. The attrited adolescents reported lower levels of adolescent disclosure  and higher levels of conduct problems  at baseline. Attrited adolescents did not significantly differ from the retained adolescents in terms of gender, ethnicity, parent-driven communication efforts, adolescent secrecy, feelings of being overly controlled, parent-adolescent emotional connectedness, emotional problems, delinquency, or wellbeing at baseline. As Cohen’s d effect sizes were small  was significant, however the normed chi-square (χ2/df) was low (1509.587/1103 = 1.37), implying a low violation of the MCAR assumption. Further attrition analyses showed that 75.3% of the original sample (ct Cohen ), we incct Cohen .We calculated bivariate correlations between the study constructs. Subsequently, we implemented structural equation modelling using AMOS 23.0 in four steps. First, we fitted separate measurement models to evaluate autoregressive cross-lagged associations between each parent-adolescent communication measure  and each outcome measure . Thus, the latent constructs of each T1 parent-adolescent communication measure were regressed on the latent constructs of each T2 adolescent psychological health measure, and the latent constructs of each T1 adolescent psychological health measure were regressed on each T2 parent-adolescent communication measure. Next, to obtain the most parsimonious model, in the final model we constrained the factor loadings in the constructs to be the same across time points. Evaluation of model fit was based on recommended fit index cut-off values  would indicate a moderation effect  was positively linked to T2 adolescent delinquency, while T1 parental solicitation was negatively linked to T2 adolescent emotional problems (β = −0.09 p = 0.001), T2 adolescent conduct problems (β = −0.10 p = 0.009) and T2 adolescent delinquency (β = −0.11 p < 0.001). T1 adolescent disclosure was negatively linked to T2 emotional problems (β = −0.12 p = 0.012) and T2 adolescent delinquency (β = −0.08 p = 0.011). T1 adolescent secrecy was positively associated with T2 adolescent emotional problems (β = 0.10 p < 0.001), T2 adolescent conduct problems (β = 0.21 p < 0.001) and T2 adolescent delinquency (β = 0.19 p < 0.001) and negatively linked to T2 adolescent wellbeing (β = 0.10 p = 0.012). T1 adolescent emotional problems were negatively linked to T2 adolescent disclosure (β = −0.08 p = 0.015).Table 2 (1) = 4.21, p = 0.040) only in adolescents who rated their feelings of being overly controlled by parents as low (β = −0.16 p = 0.011) compared to those who rated their feelings of being overly controlled by parents as high (β = −0.01 p = 0.883). Moreover, T1 parental behavioral control was linked to higher levels of T2 adolescent delinquency (Δχ2 (1) = 10.59, p = 0.001) only in adolescents who rated their connectedness to their parents (β = 0.21 p < 0.001) as low compared to those who rated their connectedness to their parents as high (β = 0.01 p = 0.789). T1 parental solicitation was linked to lower levels of T2 adolescent delinquency (Δχ2 (1) = 4.61, p = 0.032) only in adolescents who rated their feelings of being overly controlled by parents as low (β = −0.19 p < 0.001) in contrast to those who rated their feelings of being overly controlled by their parents as high (β = −0.07 p = 0.184). Finally, T1 adolescent emotional problems were linked to lower levels of T2 adolescent disclosure (Δχ2 (1) = 6.55, p = 0.010) only in adolescents who rated their connectedness to their parents as low (β = −0.19 p = 0.010) in contrast to those who rated their connectedness to their parents as high (β = 0.07 p = 0.229).For each of the bivariate models we applied separate multi-group analyses to test the moderation brought about by adolescent connectedness to parents and feelings of being overly controlled by parents, as measures of a family’s emotional climate, on the links between parent-adolescent communication efforts and adolescent psychosocial functioning. The moderation by the family’s emotional climate emerged in links between T1 parental solicitation and T2 adolescent conduct problems, T1 parental behavioral control and T2 adolescent delinquency, T1 parental solicitation and T2 adolescent delinquency and T1 adolescent emotional problems and T2 disclosure. T1 parental solicitation was linked to lower levels of T2 adolescent conduct problems  and should therefore be interpreted with caution. This link was not moderated by the family’s emotional climate. Thus, whereas a family’s emotional climate moderated the links between parents’ actions and adolescent externalizing problems, this was not the case regarding adolescent internalizing problems. A similar result was shown in a study by Formoso et al. , who fouCorroborating results from other studies, we found that adolescent disclosure was linked to less adolescent emotional problems  on adolescent psychosocial functioning is not isolated from the context of the family’s emotional climate. When used within the context of a family’s positive emotional climate, parents’ questions and requests for information are protective against adolescent externalizing problems over time. On the other hand, parents’ rules and behavioral expectations are disadvantageous for adolescent behavioral development when used within the context of a family’s poor emotional climate. In other words, when the family’s emotional climate is positive, parental actions can result in advantageous developmental outcomes in adolescents. In contrast, when a family’s emotional climate is poor, the effect of parental actions is questionable or even disadvantageous to adolescent developmental outcomes. Such findings contribute to the previous research by adding specificity to the previously established role of parent-adolescent communication in adolescent psychosocial development.Our findings indicate further that adolescent disclosure is protective of adolescent psychosocial functioning, while adolescent secrecy is linked to higher levels of adolescent psychosocial problems and a lower sense of wellbeing over time. Adolescent-driven communication efforts have an effect on adolescent developmental outcomes regardless of the state of adolescents’ perceptions of the family’s emotional climate. As adolescent disclosure and secrecy are adolescent-induced mechanisms, we suggest that other cognitive processes, such as adolescent beliefs and expectations (Chaparro and Grusec The findings in the current study underpin the importance of considering the context of a family’s emotional climate when providing recommendations to parents and in parenting programs. Put simply, prior to teaching specific parenting strategies such as setting clear rules, professionals should be offering strategies to promote the family’s emotional climate, including emotional connectedness between parents and their adolescent children. This could particularly be important in families with unstable home environments. At the same time, it should be noted that more studies are needed to identify predictors of a family’s emotional climate in families with adolescents. It is likely that the family’s emotional climate is established during earlier stages of the family life cycle (Rodgers and White