Introduction: In India, crossbred cows incorporate the high production of B. taurus dairy breeds and the environmental adaptation of local B. indicus cattle. Adaptation to different environments and selection in milk production have shaped the genetic differences between B. indicus and B. taurus cattle. The aim of this paper was to detect, for milk yield of crossbred cows, quantitative trait loci (QTL) that differentiate B. indicus from B. taurus ancestry, as well as QTL that are segregating within the ancestral breeds.Methods: A total of 123,042 test-day milk records for 4,968 crossbred cows, genotyped with real and imputed 770 K SNP, were used. Breed origins were assigned to haplotypes of crossbred cows, and from that, were assigned to SNP alleles.Results: At a false discovery rate (FDR) of 30%, a large number of genomic regions showed significant effects of B. indicus versus B. taurus origin on milk yield, with positive effects coming from both ancestors. No significant regions were detected for Holstein Friesian (HF) versus Jersey effects on milk yield. Additionally, no regions for SNP alleles segregating within indigenous, within HF, and within Jersey were detected. The most significant effects, at FDR 5%, were found in a region on BTA5 (43.98–49.44 Mbp) that differentiates B. indicus from B. taurus, with an estimated difference between homozygotes of approximately 10% of average yield, in favour of B. indicus origin.Discussion: Our results indicate that evolutionary differences between B. indicus and B. taurus cattle for milk yield, as expressed in crossbred cows, occur at many causative loci across the genome. Although subject to the usual first estimation bias, some of the loci appear to have large effects that might make them useful for genomic selection in crossbreds, if confirmed in subsequent studies. Bos indicus and exotic Bos taurus dairy breeds, with an average yield of 7.22 kg/day, and an average herd size of two cows  between QTL and SNP is not expected to be the same in both.Given the time since the divergence, and the different environments in which they evolved, the two subspecies differ substantially for many adaptation traits such as heat tolerance and disease resistance , and theB. indicus versus exotic B. taurus origin (breed origin effect) 2) the effect of QTL in LD with SNP alleles segregating within B. indicus ancestral breeds, and 3) the effect of QTL in LD with SNP alleles segregating within B. taurus ancestral breeds.In crossbred cattle, SNP alleles can be assigned to their ancestral breed origin based on the inferred origin of the phased haplotypes in which they sit. This provides an opportunity to detect QTL that are segregating in crossbred cattle that are fixed for alternative alleles in the ancestral breeds and also detect QTL that are segregating within the ancestral breeds. The aim of this paper is to undertake a genome-wide association study (GWAS) for milk yield in crossbred cows that separates the effect of each SNP into: 1) the effect of indigenous baif.org.in) between 2016 and 2020. A total of 12,004 crossbred cows with four or more monthly test-day (TD) milk yield between 8 and 340 days after calving, in at least one lactation, were retained. Cows were crossbreds, of unknown breed compositions, between local indigenous B. indicus cattle and exotic B. taurus breeds. In the regions sampled, Holstein-Friesian (HF) and Jersey were the only B. taurus breeds known to have been used for crossbreeding. After data quality control (QC) (Phenotypes were collected within the Enhanced Genetic Gains program (EGP) of BAIF Development Research Foundation (rol (QC) , 11,510 rol (QC) . Fixed eB. indicus indigenous animals from a separate BAIF project (B. taurus autosomes (BTA). After applying genotype QC and retaining cows with milk yield that passed the phenotype QC, 4,968 genotyped crossbred cows with 123,042 TD yield were available for GWAS.The genotype data included 5,280 crossbred cows, of which 4,637 were genotyped with the GeneSeek Genomic Profiler (GGP) Bovine 50K BeadChip  and 643 genotyped with the Illumina 777k BovineHD BeadChip . Additional genotype data were available for 198 pure HF, 175 pure Jersey, and 158 pure indigenous animals (bulls and cows) from the BAIF bull stud herd, 389 of which were genotyped with the Illumina BovineSNP50 chip and the remaining genotyped with the GGP Bovine 50K chip. SNP positions were assigned according to ARS-UDC1.2 assembly of the bovine reference genome (GCA_002263795.2). After being subject to QC as described by (2021), animals genotyped with the 50K chip were imputed to high density (HD) using reference samples that had real HD genotypes. The reference population for imputation included 684 pure Jerseys, and 968 pure Holsteins from the Bovine HapMap , 666 pur project , and the project , which rB. indicus indigenous samples, representing the least related animals within a larger sample, based on low genomic relationships  or of three ancestors  were assigned to the phased haplotypes using PCAdmix software (B. indicus and pure B. taurus (HF and Jersey) validation samples. The last window in the chromosome was excluded if it contained less than 80% of the window size.Then, the breed origin of either two ancestors  using a window of 25 SNP to .997 (SD = .004) using a window of 500 SNP. The average estimate of B. taurus content in validated HF and Jersey animals was close to 1  when using 25 SNP window and increased slightly to .999 (SD = .001) when using 500 SNP window. The average estimated Jersey content for purebred Jersey animals was .975 (SD = .015) and the HF content for purebred HF animals was .985 (SD = .009), using a 500 SNP window. Increasing window size above 500 SNP did not alter the accuracy of assigning HF versus Jersey ancestry. A 500 SNP window was therefore chosen for all analyses. After excluding windows that contained less than 400 SNP, there were 674,972 SNP in 1,346 windows retained.The average estimate of B. indicus and B. taurus) ancestors, there are four possible combinations of ordered genotypes and four possible combinations of ordered breed origin at each locus. As a consequence, each cow at any given locus will have one of 16 different ancestry genotype combinations  the effect of QTL in LD with SNP alleles segregating within the B. indicus ancestral breeds; 3) The effect of QTL in LD with SNP alleles segregating within the B. taurus ancestral breeds. To do this, a simultaneous regression was performed on the allele count for 1) breed origin (B. indicus versus B. taurus), 2) SNP alleles of B. indicus origin, and 3) SNP alleles of B. taurus origin. The GWAS performed on categories 2) and 3) are equivalent to a within-breed GWAS for B. indicus and B. taurus respectively, except that they are based on expression in the crossbred cows of QTL that were originally segregating within B. indicus and B. taurus, respectively.Secondly, a GWAS was performed to separate the SNP effects into: 1) the effect of B. indicus and B. taurus), the allele count for breed origin was the number of copies  coming from B. indicus ancestor. For SNP alleles of B. indicus origin and of B. taurus origin that had a MAF > 1%, the following GWAS model was fittedB. indicus origin for B. taurus origin for B. indicus origin, B. taurus origin, and the other terms are as defined for Model 1.For the two-ancestor model  model, GWAS was performed on the allele count for indigenous breed origin, HF breed origin, Jersey breed origin, SNP alleles coming from indigenous ancestor, SNP alleles coming from HF ancestor, and SNP alleles coming from Jersey ancestor. Fitting indigenous breed origin conditional on Jersey breed origin estimates the effects that differentiate indigenous from HF and differentiate Jersey from HF, respectively. Similarly, fitting indigenous breed origin conditional on HF breed origin estimates the effects that differentiate indigenous from Jersey and differentiate HF from Jersey, respectively.For three -ancestor  was calculated for each SNP effect  by dividing the chi-squared test statistics by λ so that the median matches the expected value of one. For each effect, the false discovery rate (FDR) was then applied to control the rates of false positive associations.After performing GWAS, the genomic inflation factor (B. indicus and pure B. taurus (HF and Jersey) was very high for all the windows across the genome. The estimated B. indicus proportion in the B. indicus samples ranged from .953 to 1.00, with an average of .995 (SD = .007). The estimated B. taurus proportion in the B. taurus samples ranged from .938 to 1.00 with an average of .999 (SD = .004). When HF breed origin was assigned to haplotypes in the HF samples, the estimates ranged from .850 to 1.00, with an average of .983 (SD = .025), whereas the Jersey proportion in the Jersey samples ranged from .750 to 1, with an average of .975 (SD = .034).The accuracy of assigning breed origin to haplotypes in the validated samples of pure B. indicus breed origin in crossbred cows ranged from .30 to .48 across windows, with an average of .36 (SD = .01). The allele count for For the two-ancestor model, the frequency of the B. indicus origin and of exotic B. taurus origin in crossbred cows ranged from zero to one  when SNP alleles of indigenous origin had a MAF > 1%. However, the region only reached FDR 20% when GWAS was performed for SNP alleles of indigenous origin that had a MAF < 1% (Model 2a) when SNP alleles of exotic origin had a MAF > 1% (results not shown).versus HF origin and the difference of Jersey versus HF origin, respectively. Likewise, fitting indigenous breed origin conditional on HF breed origin (GWAS Model 4) estimates the difference of indigenous versus Jersey origin and the difference of HF versus Jersey origin, respectively. Therefore, Jersey breed origin (GWAS Model 3) and HF breed origin (GWAS Model 4) give identical estimates of SNP effects but with opposite sign. Similarly, the effects of SNP alleles of HF, of Jersey, and of indigenous origin  are identical for Model 3 and Model 4.Fitting indigenous breed origin conditional on Jersey breed origin (GWAS Model 3) estimates the difference of indigenous versus exotic breed origin detected on BTA5 using the two-ancestor model, was also found to be significant at FDR 5% using the three-ancestor model, differentiating indigenous from HF  detects linkage disequilibrium (LD) between SNP alleles and a QTL within a population. In a crossbred population resulting from n ancestors, there are 2n−1 possible forms of LD: the LD that existed within each of the n ancestors that is transmitted directly to their crossbred descendants, plus n−1 forms of LD with the newly created QTL, which are the loci that cause the functional differences between the ancestors, that were fixed or nearly fixed for a single QTL allele in the ancestral populations. Thus, while a single GWAA can be performed in a crossbred population, its interpretation is obscure. Moreover, it can have substantially lower power to detect QTL that were segregating in the ancestral populations, than undertaking a GWAA within pure ancestor populations, because the direction and size of LD is expected to vary between all but very closely related ancestral populations. The result is that no locus is expected to exhibit LD as high as in some of the ancestor populations. Several studies utilized breed origin information in their GWAS models , howeverB. indicus and .64 B. taurus, made up of approximately .49 HF and .15 Jersey. As the power for detecting a QTL is proportional to m (1−m) where m is the minor allele frequency of the SNP . The power to detect additive QTL segregating within indigenous, within HF and within Jersey ancestral populations, is equivalent to undertaking GWAA in each of the pure populations of approximately .36, .49 and .15 the size of the crossbred population analysed here. Thus, for example, the power of detecting QTL segregating within B. indicus is equivalent to undertaking a GWAA on about 1,800 purebred B. indicus cattle, compared to the approximately 5,000 crossbred cattle analysed here. Furthermore, the power to detect QTL segregating within indigenous ancestors is lower than within HF and Jersey due to the lower average MAF of SNP within B. indicus  compared to typical intensive dairy systems in which most GWAA have been undertaken. This lower variation reflects the difficulty of obtaining frequent and accurate data in smallholder dairy systems, plus the substantial environmental fluctuations over time in smallholder systems, which are also more difficult to account for statistically because of the low herd size  indigenous cattle when producing crossbred animals rather than pure indigenous breeds. There has been little study of the variation of Desi cattle across India but visually they differ greatly between and even within the regions included in this study, and certainly cannot be considered as a single homogeneous breed. As such it is likely that LD between SNP and QTL will vary substantially between different populations of Desi, further reducing the power to detect such QTL in this crossbred population. We were unable to differentiate different sources of Desi using SNP marker data because of the remarkably low SNP variation observed between s breeds . The GWAerd size .B. indicus from B. taurus ancestral breeds, the estimate of location has low accuracy compared to a GWAA within a typical purebred population. This is because in recently created crossbred populations there have been few recombination events and LD between the newly created QTL and the ancestral origin of SNP remains very high over large genomic regions. The situation is essentially the same as the early days of QTL mapping in segregating populations, using techniques such as Haley-Knott regression , not just for B. taurus and B. indicus alleles but also for alleles of HF and Jersey origin. The lack of selection at this region within the crossbred population is confirmed by the frequency of B. indicus origin alleles ranging from .348 to .365, which is very similar to the average of B. indicus origin allele across the whole genome. GWAA using three ancestors confirmed that this region on BTA5 differentiates indigenous from HF and indigenous from Jersey, with the indigenous alleles increasing milk yield. The estimated effect is expected to have positive ascertainment bias. Nevertheless, the average difference between homozygous indigenous versus exotic in this region is .85 kg/day (SD = .05), which is about 11% of the average TD milk yield. If a large effect is confirmed in subsequent validation studies, this and other loci differentiating B. indicus from B. taurus effects on milk yield may prove valuable for genetic selection of crossbreds.This is reflected in the large region between 43.98 and 49.44 Mbp on BTA5 that differentiates versus HF origin effect between 39.76 and 40.25 Mbp on BTA25 at FDR 5%, with the HF alleles increasing milk yield. The average difference between homozygous indigenous and HF in this region is .52 kg/day (SD = .04), being ∼7% of the average yield. In the same region, no effect was detected between indigenous and Jersey nor between HF and Jersey. So, at this stage it cannot be determined whether this region might indicate a milk-increasing genetic variant that is unique to HF, or whether it is, like the region on BTA5, a B. indicus versus B. taurus difference.GWAA using a three-ancestor model detected an indigenous B. taurus breeds compared to between B. indicus and B. taurus breeds  cattle rather than indigenous dairy breeds. If some of the same genomic regions have been selected to produce both B. indicus and B. taurus dairy breeds, they will not show up in an analysis of Girolando data.Confirmation awaits more data from these Indian crossbred populations, which will be collected in the next several years, or analysis of other populations. The Girolando, which is a hybrid of Holstein and Gir, used widely in Brazil and for which high quality data recording exists, would be an interesting population to analyse . A diffeB. indicus breeds (B. indicus (Gir) cattle  cattle rather than from indigenous dairy breeds. Given that it is the B. indicus allele that increases milk yield in this region, it may be more likely that the causal gene is involved in environmental adaptation that allows expression of the milk production potential of crossbreds, rather than a gene that directly causes increased milk production.Because of the large confidence intervals attached to all putative breed origin effects identified in this study, we only discuss the largest effects detected, on BTA5 and BTA25. The region identified on BTA5 was previously reported as having been under selection in several Indian s breeds  and in A) cattle . Naval-Selopment  and has eef cows . RAP1B iMammalia . The higsistance . HoweverB. taurus ancestor, is contained within a QTL region previously reported for persistency of milk yield in Holstein cattle (The region identified on BTA25, with the increasing allele coming from the n cattle , and oven cattle  and Nortn cattle . Previoun cattle .B. indicus and B. taurus were detected across the genome, with variants that increase milk yield coming from both indigenous and exotic ancestors, and with some effects being of large size. Although acknowledging the weaker power for detecting genetic variation within the ancestral breeds, and the need for validation, these results suggest that genetic variation between ancestral breeds that now segregates in crossbred dairy cattle, may be important for future genomic selection of smallholder crossbred dairy cattle.A large number of genomic regions that define ancestral differences for milk yield between


Adolescent is the population whose age between 10–19 years old. They are undergoing rapid growth, development and are one of the nutritionally at-risk groups who should need attention. Adolescent undernutrition is a worldwide problem. Even if this stage brings the second window of opportunity to break the intergenerational cycle of undernutrition little is known specifically in the study area. This study was conducted to assess the prevalence of undernutrition and its associated factors among school adolescent girls in Abuna Gindeberet district, Central Ethiopia, 2021.p-value < 0.05.Institution-based cross-sectional quantitative study design was conducted in Abuna Gindeberet district among 10–19 years adolescent girls attending primary and secondary schools from January 1–30, 2021. A systematic random sampling technique was used to select 587 adolescent girls. Data were collected by using interviewer-administered structured and anthropometric measurements. Data were coded, then entered into the Epi-info version 7.2.2.6 and exported to SPSS version 25 and WHO Anthro plus for analysis. Logistic regression analysis was done to identify predictors of under nutrition. Level of statistical significance was declared at The overall magnitude of stunting and thinness were 15.4% [95% CI (12–18)] and 14.2% [95% CI (11–17)] respectively. Number of meals per day , adolescent girls of lower grades  and who did not begin menstruation  were significantly associated with stunting. Adolescent girls engaged in vigorous intensity activities , poor dietary diversity score  and adolescent age  were significantly associated with thinness among adolescent girls.Adolescent girl's undernutrition is a public health problem in the study area. The number of meals per day, adolescent girls of lower grades and who did not begin menstruation were significantly associated with stunting as well as adolescent girls engaged in vigorous-intensity activities; poor dietary diversity score and adolescent age were significantly associated with thinness among adolescent girls. Therefore, government and other stakeholders should focus on these identified factors to improve the nutritional status of adolescent girls. An adolescent is the population whose age between 10–19 years old and it is the period of gradual transition from childhood to adulthood that normally begins with the onset of signs of puberty, which is characterized by important psychological and social changes, not only physiological changes and classified into early adolescence (10–14 years) and late adolescence (15–19 years) .Globally, 1.8 billion adolescents account for 16% of the world's population and nearly 90% of them live in low and middle-income countries. AccordiNutrient requirements increase in adolescence to meet up the demands of pubertal growth. It is the age at which fast growth and development take place secondary to the infancy period. Adolescents gain up to 50% of their adult weight, more than 20% of their adult height, and 50% of their adult skeletal mass during these ages . In addiMalnutrition during adolescence manifests in three broad groups of conditions: undernutrition ; micronutrient deficiency or excess  and overweight or obesity . UndernuAccording to a different report, adolescent undernutrition is a problem in low and middle-income countries (LMIC). The global school-based study conducted in fifty-seven LMIC among 12–15 years adolescents reported an overall prevalence of 10.2% stunting and 5.5% thinness among boys and girls. From this 36.5% and 25.1% of stunting and thinness were reported among girls from Myanmar and Sri Lanka respectively . Some stPoor nutritional status during any stage of an adolescent can have consequences on cognitive development, resulting in decreased learning ability, poor concentration, and impaired school performance and can retard growth and sexual maturations especially in females it is associated with poor reproductive health outcomes , 26. UndChronic undernutrition among adolescents is commonly associated with poverty, poor maternal health and nutrition, frequent illness, or improper infant and young child feeding and care in early life , 29. A sThe study was conducted in Abuna Gindeberet district governmental schools. This is located in the West Shoa zone, Oromia regional state 192 km to the West of Addis Ababa capital city of Ethiopia and 134 km to North of Ambo town zonal capital city. There are eleven 1–4 grades, forty 1–8 grades and seven high schools (9–12 grades), and two 1–4 grades of private schools. The total adolescent girls in Abuna Gindeberet district school were 19,150. The study was conducted from January 1–30, 2021.An institution based cross-sectional quantitative study design was conducted. All adolescent girls (10–19 years) attending schools in Abuna Gindeberet district were source population and all randomly selected adolescent girls from randomly selected schools were study populations. All 10–19 years age girls in the schools of Abuna Gindeberet district, and self-reported pregnant and lactating girls were included and excluded from the study respectively.The sample size was calculated by using a single and double population proportion formula for first and second objective respectively. For first objective by considering proportion of undernutrition among adolescent girls from previous study in the Hawzen district schools (thinness = 32.2%) and (stunting = 33.2%) (42), 95% CI, 4% marginal error. From this prevalence of stunting = 33.2% that yield the highest from the two proportions, sample size calculated as:where Z α/2 = critical value for normal distribution at 95% confidence level which equals to 1.96; P = prevalence of stunting from study in Hawzen district school, q = 1-p; d = margin of error.Then by adding 10% non-response rates sample size for first objective became 587.For the second objective, by using assumption of 80% power, 95% C.I, then by using Epi info statcalc for the most significant variable from previous study.From the calculated sample size, sample size for the first objective gave highest which were 587.th adolescent girls were selected from school registers until fulfilling the required sample size  and secondary school 9–12 grades). From forty primary schools, 12 schools (4–8 grades), and seven secondary schools (9–12 grade) three schools were selected by lottery method. The numbers of adolescent girls were obtained from the respective school director's office. From registration girl's age, 10–19 years were screened before actual data collections. The sample size was allocated to each selected school by using probability proportional allocation to total adolescent girls. Then k  by required sample size(n)). k = N/n = 6101/587 = 10. Then, from 1 to 10 random start were selected by lottery methods. The random start 4 were selected, then every 10 grades. Undernutrition is regarded as the presence of stunting and/ or thinness.Thinness is defined as the proportion of adolescent girls with value BAZ < -2 SDs .Stunting is defined as the proportion of adolescent girls with value HAZ < -2SDs .Dietary Diversity Score is the sum of food groups eaten by adolescent girls over the last 24 h .Meal pattern: the measure of whether they consumed their meal regularly or skipped some times.Primary school: In this study, institutional schools that have students from grades 4–8.Secondary school: Institutional schools that have students from grades 9–12.Data were collected by face-to-face interview using a pre-tested structured questionnaire adapted from previous studies, guidelines prepared by Food and Agricultural Organization, and anthropometric measurements , 34, 35.After training, data collectors were recorded height and weight by using a portable non-stretchable plastic height-measuring board with a sliding head bar following standard procedures and portable digital scales respectively. For height measurement, subjects were asked to stand erect with their shoulders level, hands at their sides, thighs and heels comfortably together, the buttocks, scapulae, heels, and head were positioned in contact with the vertical backboard with a sliding head bar. Then height was measured to the nearest 0.1 cm.For weight measurement, adolescent girls were asked to remove their shoes, wear light clothes (schools' uniforms) and then, trained data collectors have weighed the subjects on a calibrated portable digital scale and were record the value to the nearest 0.1 kg. Each measurement was standardized and calibrated by carefully handling, placing the weight scale on a flat surface, and confirmed reading at zero to ascertain accuracy every time before measurements.Data were collected by using pretested and structured questionnaires. The one-day training was given to both data collectors and supervisors. Language experts translated questionnaires to Afan Oromo and then back to English to check the consistency. Pretest was performed before actual data collection on 5% of sample size in neighbor district Gindeberet. Height and weight were taken two times to minimize intra and inter observer’s variability of the data collectors, relative technical error measurement (TEM) was calculated. The accepted relative technical measurement error for intra-observer and inter-observer were less than 1.5% and less than 2% respectively. The proper functioning of digital weight scales was checked every time before weight measurement. The data collectors assured the reading scale was exactly at zero before taking weight. Before data entry into the computer, every questionnaire was checked for completeness.Z-scores of the indices, BAZ and HAZ, by using WHO 2007 population references. Descriptive statistics such as frequency, proportions, mean and standard deviation were used to describe characteristics of the study population. Normality for continuous variables was checked and data were normally distributed. The presence of multicollinearity between independent variables was checked by using the variance inflation factor (VIF). However, there was no identified variable with multicollinearity problems. Bivariable and multivariable logistic regression analysis was carried out to identify predictors of undernutrition among adolescent girls. Model fitness was checked by Hosmer–Lemeshow goodness-of-fit test. An odds ratio with 95% confidence intervals was used to see the strength of association between each independent variable and outcome variable. Level of statistical significance was declared at p-value < 0.05.First, questionnaires were checked for completeness and consistency before data entry. Then, data were coded and entered onto the Epi-info version 7.2.2.6 and exported to SPSS version 25 for analysis. Anthropometric data and other essential variables were exported to WHO Anthro-plus software, a computer program that converts anthropometric data into After the briefing, the purposes of the study Research Ethical Committee (REC) of the Ambo University College of Medicine and Health Sciences have ethically approved it. Upon approval letter of permission was obtained from the colleges. For participants, less than 18 years of consent was obtained from their parents and assent from the students. For participants age greater than/equal to 18 years informed consent was obtained from students themselves. Confidentiality and privacy of the information were maintained.Five hundred eighty-three adolescent girls were included in the study with a response rate of 99.3%. The respondents’ age ranges from 10 to 19 years with the mean age of 14.62 (± 2.38 SD). Around 307 (52.7%) participants were found in the age range of 10 to 14 years. Around 389 (66.7%) and 194 (33.3%) of adolescent girls attend primary and secondary education respectively (Table Regarding the source of drinking water 269 46.1%), 132 (22.6%), and 182 (31.3%) were using water from a pipe, protected and unprotected well respectively. 428 (73.4%) of the respondents have a functional toilet in their compound. 222 (38.1%) of study subjects did not wash their hand after visiting the toilet. 235 (40.3%) of adolescent girls had information on adolescent nutrition. The sources of information were from schools 125 (21.4%), HEW 62 (10.7%), and mass media 48 (8.2%) respectively . Among the participants, 503 (86.3%) and 80 (13.7%) had good and poor dietary diversity scores respectively. The most common staple food among study subjects was teff 378 (64.8%), sorghum 93 (16%), maize 79 (13.5%), and wheat 33 (5.7%). Around 292 (50.1%) of the participant had eaten greater than or equal to three meals per day  of them started to see menstruation. The mean age of menarche was 13.71 (± 0.978SD). About 153 (26.2%) of the participant had a history of illness two weeks before data collection. Regarding the level of physical activities, 222 (38.1%) and 324 (55.6%) of adolescent girls were engaged in vigorous and moderate-intensity activities respectively ] and thinness 14.2% [95% CI = (11–17)] among school adolescent girls in Abuna Gindeberet district. Around seven (1.2%) of adolescent girls had both stunting and thinness. About 22 (3.8%) and 15 (2.6%) had severe stunting and thinness respectively ]. Adolescent girls of lower grades (4–8 grades) were 2 times more likely to develop stunting when compared to adolescent girls of higher grades (9–12 grades) . Adolescent girls who did not begin menstruation were 1.71 times more likely to develop stunting when compared to those who started to see menstruation  ]. Adolescent girls who had poor dietary diversity scores were 4 times more likely to develop thinness when compared to adolescent girls with good dietary diversity scores . Adolescent girls in the early stage (10- 14 years) were 3.77 times more likely to develop thinness when compared to adolescent girls in the late stage (15–19 years)  , Hawzen, Tigray region, Ethiopia (33.2%), Awash town, Afar region, Ethiopia (22.9%), and Amhara region, Ethiopia (31.5%) , 19, 23.The overall magnitude of thinness was 14.2% in the study area. This finding was in line with finding from Kavre district (14.94%), Amhara region (13.6%), Aksum town (12.6%), Asako district (14.8%), and Goba town (11.9%) respectively , 38, 40.The current finding on thinness was lower than the finding from West Bengal (50.89%) and Bangladesh (42.4%) , 37. TheThe study showed adolescent girls who ate less than three meals per day were 3.62 times more likely to develop stunting when compared to their counterparts. Studies conducted at Asako district, Adwa town, and Dangila district supported this finding , 30, 31.Adolescent girls of lower grades (4–8 grades) were 2 times more likely to develop stunting when compared to adolescent girls of higher grades (9–12 grades). This is in line with finding from the Tigray region Hawzen district and Jimma zone Southwest Ethiopia , 44. ThiAdolescent girls who did not begin menstruation were 1.71 times more likely to develop stunting when compared to those who started to see menstruation. This finding is in line with the study conducted at west Kenya and Adwa town school , 46. ThiThe study stated that adolescent girls who engaged in vigorous-intensity activities were 2.51 times more likely to develop thinness when compared to those who did not engage in vigorous-intensity activities. This is supported by a study conducted at Garhwali, India . This isAdolescent girls who had poor dietary diversity scores were 4 times more likely to develop thinness when compared to adolescent girls with good dietary diversity scores. This finding is supported by the study conducted at Adama town, Asako district, Goba town, Amhara region, Finoteselam town, Aksum town, and Awash town , 40, 50.Adolescent girls in the early stage (10–14 years) were 3.77 times more likely to develop thinness when compared to adolescent girls in the late stage 15–19 years). This is in line with finding from Adwa town, Aksum, Eastern Tigray, and the Amhara region –19 years, 40, 42.Adolescent girl's undernutrition is a public health problem in the study area. Number of meals per day, adolescent girls of lower grades and adolescents who did not begin menstruations were significantly associated with stunting as well as adolescent girls engaged in vigorous-intensity activities; poor dietary diversity score and adolescent age were significantly associated with thinness among adolescent girls. Therefore, government body, Adolescent girls, families and non-governmental organization should focus on importance of dietary diversity, frequency of meals per day and increased nutritional requirement during adolescent age to improve adolescent girls’ nutritional status.


Glycine max (L.) Merr.] seeds have an amino acid profile that provides excellent viability as a food and feed protein source. However, low concentrations of an essential amino acid, methionine, limit the nutritional utility of soybean protein. The objectives of this study were to identify genomic associations and evaluate the potential for genomic selection (GS) for methionine content in soybean seeds. We performed a genome-wide association study (GWAS) that utilized 311 soybean accessions from maturity groups IV and V grown in three locations in 2018 and 2019. A total of 35,570 single nucleotide polymorphisms (SNPs) were used to identify genomic associations with proteinogenic methionine content that was quantified by high-performance liquid chromatography (HPLC). Across four environments, 23 novel SNPs were identified as being associated with methionine content. The strongest associations were found on chromosomes 3 , 8 (ss715599541 and ss715599547) and 16 (ss715625009). Several gene models were recognized within proximity to these SNPs, such as a leucine-rich repeat protein kinase and a serine/threonine protein kinase. Identification of these linked SNPs should help soybean breeders to improve protein quality in soybean seeds. GS was evaluated using k-fold cross validation within each environment with two SNP sets, the complete 35,570 set and a subset of 248 SNPs determined to be associated with methionine through GWAS. Average prediction accuracy (r2) was highest using the SNP subset ranging from 0.45 to 0.62, which was a significant improvement from the complete set accuracy that ranged from 0.03 to 0.27. This indicated that GS utilizing a significant subset of SNPs may be a viable tool for soybean breeders seeking to improve methionine content.Soybean [ Glycine max (L.) Merr.] has an ideal amino acid profile among the protein sources used in livestock feed and human food. All nine essential amino acids, histidine (His), isoleucine (Ile) leucine (Leu), lysine (Lys), methionine (Met), phenylalanine (Phe), threonine (Thr), tryptophan (Trp), and valine , are present in soybean seeds  utilizes similar statistical models as GWAS, but it seeks to exploit larger genomic variations than individual genomic regions . GS has Additionally, A total of 500 soybean accessions were selected from the USDA Soybean Germplasm Collection to represent maximum genetic variability in maturity groups IV and V based on genetic distance . Among tAll seed samples were cleaned by removing moldy, mottled, discolored, or off-types seeds. Dry-matter based protein content and moisture were measured using the DA 7250 NIR Analyzer spectrophotometer (PerkinElmer Inc.) through near-infrared reflectance spectroscopy (NIRS). For NIRS, the manufacturer’s annual updated calibration module was used and protein content was recorded for each sample.Samples were ground using a water-cooler Foss 1095 Knifetec mill to a consistent particle size. Subsamples of 0.01 g were weighed into glass digestion tubes and subsequently hydrolyzed using a modified method 994.12  to break–1 cp) basis. Data were fit with an ANOVA using standard least squares that included accession, location, and year as fixed effects.Concentrated samples were rehydrated with water into vials for high-performance liquid chromatography (HPLC) analysis. HPLC was performed using online derivatization with o-phthalaldehyde (OPA), ultra-violet (UV) detection, and the Agilent AdvanceBio Amino Acid Analysis (AAA) 4.6 × 100 mm, 2.7 μm LC column and 4.6 × 5 mm guard columns with the Agilent HPLC model 1200. Each sample had two technical replicates that were averaged to account for biological and equipment variation. To better describe proteinogenic concentrations, Met was reported on a g/kg crude protein  was used to identify an optimal number of clusters. The function dapc was then used by retaining an optimal number of principal components to maximize cumulative variance without overfitting, and all discriminant functions and eigenvalues were retained. A kinship matrix was also created with the software TASSEL 5  using the  package  in R to  package . SuccessTASSEL 5  using thTASSEL 5 .sid = 1−(1−α)m)(1/) for multiple testing was used to identify significant associations. The effective number of markers (Meff) was calculated to be 4,191 using the poolr package in R with the Li and Ji method  > 4.91 and −log10(P) > 4.16, respectively. QQ and Manhattan plots were used to visualize results with the qqman package  and general linear model (GLM). Predominantly, MLM was used to incorporate a kinship matrix (K) jointly with population structure (Q) for increased statistical power through the Q+K approach . GLM wasi method . Meff re package . Gene mote genes . Gene dethaliana . If TAIRthaliana . Expressthaliana .k = 5 with 20 iterations, and the coefficient of determination (r2) was collected for each fold. Each environment’s dataset underwent GS using all 35,570 SNPs as well as a subset of 248 SNPs generated with a significance threshold of −log10(P) > 3 from the GWAS  with the TASSEL 5 genomic selection function. Similar to the GWAS, the Q+K approach was used to fit a mixed model with population structure and a kinship matrix as covariates. K-fold cross validation was performed using the GWAS . A T-tes–1 cp and an average standard deviation (SD) of 2.84 g kg–1 cp. –1 cp and 3.36, 1.73, and 2.61 g kg–1 cp, respectively. Warsaw, VA exhibited significantly higher average Met than both other locations, while Blacksburg, VA also possessed significantly higher average Met than Clayton, NC. Samples grown in 2019 showed significantly higher Met content than 2018, but accessions were not shown to have a significant impact on Met content.Methionine concentrations across all environments displayed normal, continuous distributions with a grand mean of 9.06 g kgk = 4 was determined as the optimal number of clusters (n = 76) contained 55 accessions (72.4%) that originated from China, 11 from Vietnam (14.5%), five from Japan (6.6%), three from Taiwan (3.9%), and one from Indonesia (1.3%). Cluster I also contained 52.6% of accessions from maturity group (MG) V. Cluster II (n = 62) contained 54 (87.1%), four (6.5%), two (3.2%), one (1.6%), and one (1.6%) accessions from China, Japan, the United States, Georgia, and South Korea, respectively, and 83.9% of those belonged to MG IV. Cluster III (n = 47) contained 37 (78.7%) accessions from the United States, three (6.4%) from South Korea, two (4.3%) from Japan, and one (2.1% each) from Australia, Brazil, and Costa Rica. Cluster III also contained 78.7% of accessions from MG IV. Cluster IV (n = 126) contained 65 (51.6%), 15 (11.9%), 14 (11%), 11 (8.7%), and seven (5.6%) accessions from China, Japan, South Korea, the United States, and North Korea, respectively, as well as two (1.6% each) accessions from Georgia, Uganda, and Vietnam and one accession (0.8% each) from Brazil, India, Morocco, Nepal, Russia, and Taiwan. Within cluster IV, 77% of accessions belonged to MG IV. Clusters were not shown to have a significant effect on Met content. Although, the clusters displayed that accession were stratified predominantly by geographic origin which proved useful in identifying genetically similar accessions.Through DAPC, 150 principal components that accounted for 78% of cumulative variance were retained, and with the smallest BIC, clusters . Countryclusters . Cluster–1 cp) in soybean seed (10(P) > 4.91]: ss715586112, ss715586120, ss715586126, ss715586203, ss71558 6204, ss715599541, ss715599547, and ss715625009. The remaining 15 SNPs displayed −log10(P) > 4.16 which was above the suggestive threshold: ss715585365, ss715586063, ss715 586201, ss715589347, ss715589348, ss715589349, ss715590327, ss715593682, ss715593752, ss715625002, ss715625007, ss715625012, ss715625013, and ss715625017. Chromosome (Chr) 3 contained the most associations , followed by Chr 16 , Chr 4 (three suggestive), Chr 6 (two suggestive), Chr 8 (two significant), Chr 5 (one suggestive), and Chr 12 (one suggestive). When including all environments, an MLM did not identify any SNPs above the significance or suggestive threshold.A total of 23 SNPs were identified as being associated with proteinogenic Met concentration  > 3] with Met content. The 248 SNP subset is displayed in r2) between GEBVs and observed values varied throughout environments, but the subset of 248 SNPs consistently outperformed the larger SNP set .Genomic best linear unbiased prediction through TASSEL estimated GEBVs using two different sets of SNPs: a complete set with 35,570 SNPs and a subset of 248 SNPs with some association [−log SNP set . Using t–1 cp) in soybean seeds using accessions from MG IV and V that complements current genomic knowledge. Furthermore, we discovered that GS with a subset of significantly associated SNPs improved the genomic prediction accuracy for Met.Soybean protein content and amino acid profiles are critical objectives for plant breeders. For this reason, many resources have been allocated to unlock genomic controls for these traits. As suggested by Previous studies have identified genomic associations with Met content on chromosomes 1, 2, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 18, and 20 . AlthougOur analyses identified associations greater in number and significance from the 2019 dataset when compared to 2018 measurements. This is likely caused by substantial differences between Met concentrations between environments including soil type and rainfall. Environment temperature was also considered, but there was little to no difference between locations besides slightly lower temperatures in Blacksburg, VA as a function of elevation. As shown in 10(P) greater than the suggestive threshold, but not the significance threshold. Although, ss715590327 (suggested from combined 2018 environments) was within 10 kb of Glyma.05g104400, a gene model involved in peptidyl-amino acid modification. The 20 SNPs identified from our 2019 datasets provide superior evidence for associations to Met concentrations. The strongest associations occurred on Chr 3 with a set of four SNPs  within a distance of 710 kb and another set of three SNPs  within a distance of 20 kb. Within immediate proximity to the former set, nine gene models of relevant protein functions are present with ss715586126 being inside the coding region of Glyma.03g18980, a leucine-rich repeat protein kinase family protein that is highly expressed in pod walls. The latter set is close to four gene models including Glyma.03g204000, a Mal d 1-associated protein expressed highly in the root system and moderately in pods and developing seeds, where ss715586203 is within the coding sequence.The three SNP associations from 2018 data exhibited a -logWhile only suggestive associations, two SNPs on Chr 6 are within a 300 kb distance, and ss715593682 is part of the coding region for a S-adenosyl-L-methionine-dependent methyltransferase, Glyma.06g193300. The two significant SNPs found on Chr 8 (ss715599541 and ss715599547) are within 31 kb of each other and are proximal to seven various genes. Interestingly, ss715599541 is a part of the 3’ untranslated region of Glyma.08g177100, a gene model with unknown function. Chr 16 contains one significant SNP association (ss715625009) that is flanked by five other suggestive associations, all within a 124 kb region. Within this region, ss715625012 can be found in the coding sequence of Glyma.16g220200, a serine/threonine protein kinase.Arabidopsis thaliana into soybean; Arabidopsis thaliana, When our results are combined with previously identified marker-trait associations, genomic regions impacting Met concentration in soybean seeds can be found on all chromosomes except Chr 19. This creates a complicated framework for increasing Met content through marker-assisted selection (MAS), transgenic, or genome editing approaches. In breeding applications, our study suggests that GS may be a useful tool for selecting varieties with increased Met content. GS success is mainly determined by prediction accuracy  and impaIn summary, this project included a GWAS that not only identified many SNPs associated with Met content but also characterized several genomic regions that appear relevant. Within these regions, numerous gene models are present and their expression may correlate to the desired trait. GS was also evaluated as a potential method for selecting soybean lines with higher Met content. GS appears to be useful in certain environments with a subset of SNPs and could complement or outperform MAS. However, GxE limitations are still present and may impact which genes are influencing the final Met concentrations. This will require further research to elucidate genomic control of Met concentrations in soybean seed.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.WS collected the data, performed the analyses, and wrote the manuscript. QS assisted in germplasm selection. ZS assisted in germplasm population maintenance. DY, HH, CS, and MR assisted in designing phenotypic quantification. BZ and MM provided study formulation and analysis expertise. All authors edited and reviewed 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


R = 0.89 and 0.85). By investigating population structure between these 12 pointer setter breeds we showed that GLP is a breed distinct from other pointers and shares common ancestry with a few other pointing breeds. Finally, we identified selection signatures in GLPs using the runs of homozygosity islands method. The most significant runs of homozygosity island was detected on chromosome 30 harboring the genes RYR3, FMN1, and GREM1. The RYR3 gene plays a role in skeletal muscle contraction while the FMN1 and GREM1 genes are involved in limb development. The selection on these three genes could have contributed to the excellent athletic performance of GLPs, which is an extremely important characteristic for this hunting dog.The German Longhaired Pointer (GLP) breed is a versatile pointer dog breed. In the current study, we investigated the genetic diversity of these dogs based on SNP array data and compared it to 11 other pointer setter breeds. The results show that GLPs have a relatively low level of inbreeding among these pointer breeds. In addition, with the availability of pedigree information of the GLPs, we demonstrate that the correlation between pedigree‐based inbreeding and genotype‐based inbreeding coefficients was high ( Pointer dogs use their instinct to point game (quarry etc.) by stopping and aiming its muzzle towards game. There are 36 pointer breeds according to FCI. These pointer dogs have one recognized ancestor, the Old Spanish Pointer, which was probably established in 100–250 BC and is now practically extinct  is a type of multipurpose gundog, which is very active and athletic in general. GLP was assigned as a Spaniel type of continental pointer dogs by Fédération Cynologique Internationale  was estimated using the CFC  program  were set to unknown. Date of birth of dogs whose parents were born after their birth was also set to unknown. A pedigree loop involving a GLP was detected. To correct this, the mother of that GLP was set to unknown. The Retriever program   >0.000001.The overlap between the three  SNP genotype sets was determined based on exact location (chromosome + position) of markers and used to merge all genotype data into a single SNP genotype dataset. plink (v1.9) and r (v4.0.3). Firstly, genetic distance between dogs was calculated using ‘‐‐distance‐matrix’ in plink (v1.9). Then, classic multidimensional scaling of the distance matrix was performed using ‘cmdscale’ function in R and the first and second component were plotted using R. To investigate the genetic similarities between all dog breeds (GLP + 161 other dog breeds), a phylogenetic tree was constructed. Firstly, the pairwise distance between dogs was estimated using plink with command ‘‐‐distance 1‐ibs’. The ape package .To determine the relationship between the pointer setter breeds, a principal component analysis (PCA) was performed using Fped, the inbreeding coefficient was also estimated based on SNP array data and compared between the pointer breeds. Homozygous genotype‐based inbreeding coefficient (FHOM) was estimated by plink with command ‘‐‐het’, which is based on expected and observed autosomal homozygous genotypes. ROH across the genome were detected using plink (v1.9) through a sliding window approach. ROH were defined according to the following criteria: (i) the minimum count of SNPs in a sliding window was 50; (ii) the minimum ROH length was set to 1 Mb; (iii) the maximum inverse density was 50 kb per SNP; (iv) To avoid the effects of low SNP density region, the maximum gap length between consecutive SNPs was 1 Mb; (v) The minimum hit rate of all scanning windows containing the SNP was set to 0.05; (vi) at most one heterozygous call allowed per scanning window; (vii) at most five missing calls allowed per scanning window. Total length of ROH for each dog was plotted by breeds. FROH was estimated by total length of ROH segments on auto‐chromosomes divided by total length of auto‐chromosomes (2200 Mb). To eliminate the possibility that SNP chip ascertainment bias results in false positives and to validate ROH hotspots, ROH were also identified based on WGS data in plink according to the following criteria: (i) the minimum count of SNPs in a sliding window was 50; (ii) the minimum ROH length was set to 500 kb; (iii) the maximum inverse density was 30 kb per SNP; (iv) to avoid the effects of low SNP density region, the maximum gap length between consecutive SNPs was 1 Mb; (v) the minimum hit rate of all scanning windows containing the SNP was set to 0.05; (vi) at most five heterozygous call allowed per scanning window to account for false heterozygous calls from WGS data; and (vii) at most three missing calls allowed per scanning window.Besides admixture (v1.3.0) program  from 2 to 12. The best k value was then determined when a smallest cross‐validation error was achieved from the observed data.To investigate the genetic relationship between these pointer breeds, the r2 between pairs of markers. poplddecay  decay analysis. The LD was measured using clusterprofiler package . We plotted the percentage of dogs with the SNP in a ROH against the chromosome location. The ROH island was defined to be genomic region where frequency of presence of SNPs in a ROH is in the top 1% of distribution of that frequency among all GLPs. Genes within the overlapping ROH islands identified based on SNP array and WGS data were extracted and a gene set enrichment analysis was performed using Pedigree information from 58 533 GLPs worldwide was available where the ancestor can be traced back to 1876. Yearly average complete generation equivalent of dogs born after 1990 was more than 10. However, most dogs, especially dogs born before 2013, have a complete generation equivalent of <5. Meanwhile, average complete generation equivalent has not gone up since 1960s Figure . These sFped increased steadily . However, the pedigree completeness of GLPs born in Germany is lower than that of GLPs born in the Netherlands  146 324 common SNP markers were selected and finally 126 144 SNPs remained after quality control. To determine the phylogenetic relationship between GLP and 161 other dog breeds, a neighbor‐joining tree was constructed Figure . The GLPadmixture was run from k = 2 to k = 12 , but are not present in English setters and Irish setters. This suggests that those 10 breeds (except for ESET and ISET) may have shared ancestry and GLP may resemble most the ancestral breed. Among all these breeds, LMUN genetically share most with GLPs, which was consistent with the PCA result. According to the breeding history, LMUN was separated in 1909 from GLP according to a difference in coat color (black color). From FROH and FHOM of each dog and compared the results between the pointer setter breeds. Among the 12 pointer setter breeds, GLPs have relatively low average inbreeding level based on both FROH and FHOM .To assess the inbreeding level of GLPs, we estimated the M Figure . The avee Figure , and FRTo reduce the bias on estimates of LD decay because of unbalanced sample size, we randomly sampled 10 GLPs for LD decay analysis. Meanwhile, SPIN, LMUN, and GWHP were not included in this study because of a small sample size (two or three dogs per breed). The extent of LD, in general, corresponds well to the inbreeding level of the breed Figure . ISET anIn total, 2040 ROH with an average length of 6.57 Mb (ranging from 1.29 to 64.72 Mb) were identified in the 37 GLPs based on the SNP array data. ROH are divided into six groups according to size  gene is involved in skeletal muscle contraction by releasing calcium from the sarcoplasmic reticulum followed by depolarization of T‐tubules  gene was reported to associate with limb deformity in mouse  gene is also known to play a role in limb outgrowth and development in mammals , EBPL (emopamil binding protein like), RCBTB1 (RCC1 and BTB domain‐containing protein), SETDB2 (SET domain bifurcated histone lysine methyltransferase 2), and CAB39L .Moreover, a ROH island on chr22 was also identified and the corresponding region is an outlier not only in GLPs, but also in many other dog breeds Figure . InteresWe also investigated ROH islands identified on other chromosomes . These regions are less homogeneous among GLPs Figures , implyinIn this study, we investigated the phylogenetic relationship and population structure of GLP and 11 other pointer setters. The GLP belongs to the pointer setter cluster on the phylogenetic tree as expected. The phylogenetic tree and PCA analysis show that LMUN is the breed closest to GLP, followed by the GWHP, GSHP, WHPG, and VIZS. This corresponds well to the recorded breeding history of these dog breeds.FROH and FHOM and compared them between 12 pointer setter breeds included in this study. The average inbreeding of three breeds, GLP, GSHP, and GWHP, was lower compared to the other pointer breeds, while a big variation within the breeds was also observed. One thing to note is that the relatively low inbreeding of GLP, GSHP, and GWHP among 12 pointer setter breeds included in the current study does not indicate that these three breeds have absolutely low inbreeding and do not suffer from adverse effects of inbreeding. It is known that pedigree dogs have high inbreeding rates in general due to two bottlenecks in the history , and not in herding dogs (Berger des Pyrenees and Schapendoes). However, our analysis does not support their conclusion. According to our analysis, this selection signal is present not only in pointer breeds, but also in many other breeds. Approximately 51% of all dogs (GLPs and 1346 other dogs) capture the same haplotype detected in GLPs. Especially, we found that some terrier and retriever dog breeds—Bedlington Terrier, Border Terrier, Newfoundland, Irish Water Spaniel, Scottish Terrier, Soft Coated Wheaten Terrier, and Norfolk Terrier—carry the same haplotype as GLP . KPNA3 mediates nuclear import  was also reported in previous studies. Akkad et al.  indicateP Figure . TherefoRYR3, FMN1, and GREM1.In summary, we comprehensively characterized the genetic composition of GLP breed. The phylogenetic analysis shows that GLP is closest to pointer breeds originating from Germany in comparison to all the other breeds included in this study. Through inbreeding analyses, we showed that Dutch GLPs have relatively low inbreeding in comparison to 11 other pointer setter breeds. However, inbreeding varies considerably and is associated with the incidence of thyroid cancer within GLPs, suggesting that inbreeding control is essential for breeding healthy GLPs. We also identified a genomic region under selection on chromosome 30 that might contribute to hunting performance of GLPs, harboring the genes The authors declare no conflict of interest.Figures S1–S12Click here for additional data file.


ROH) and homozygosity (FHOM). Finally, we identified genomic regions with high frequencies of ROHs and annotated candidate genes contained therein.Runs of homozygosity (ROH) are continuous homozygous regions typically located in the DNA sequence of diploid organisms. Identifications of ROH that lead to reduced performance can provide valuable insight into the genetic architecture of complex traits. Here, we systematically investigated the population genetic structure of five Anhui indigenous pig breeds (AHIPs), and compared them to those of five Western commercial pig breeds (WECPs). Furthermore, we examined the occurrence and distribution of ROHs in the five AHIPs and estimated the inbreeding coefficients based on the ROHs  in AHIPs and WECPs. A total of 27 regions had a high frequency of ROHs and contained 17 key candidate genes associated with economically important traits in pigs. Among these, nine candidate genes  found in WECPs were related to muscular and skeletal development, whereas eight candidate genes  found in AHIPs were associated with health, reproduction, and fatness traits.The WECPs and AHIPs were clearly differentiated into two separate clades consistent with their geographical origins, as revealed by the population structure and principal component analysis. We identified 13,530 ROHs across all individuals, of which 4,555 and 8,975 ROHs were unique to AHIPs and WECPs, respectively. Most ROHs identified in our study were short (< 10 Mb) or medium (10–20 Mb) in length. WECPs had significantly higher numbers of short ROHs, and AHIPs generally had longer ROHs. FOur findings provide a useful reference for the selection and assortative mating of pig breeds, laying the groundwork for future research on the population genetic structures of AHIPs, ultimately helping protect these local varieties.The online version contains supplementary material available at 10.1186/s12864-022-08583-9. Long RO.  and Herr. [.  used ROH. [. . Inbreed. [. . Therefo. [. .PED), whose accurate estimation relies heavily on the accuracy, completeness, and depth of pedigree information. However, pedigree errors are common in many livestock populations [GRM), homozygosity (FHOM), and ROH (FROH). The genomic coefficients derived from animals/populations can be calculated without pedigree records or incomplete pedigree information. In addition, genomic F may provide a more accurate measure of inbreeding levels, even with missing pedigree information [ROH is the most powerful and accurate method for detecting inbreeding effects and is closest to the true inbreeding coefficient [. Thus, FROH has been widely used to estimate genomic inbreeding in livestock in recent years [Traditionally, the inbreeding coefficient has been estimated based on pedigree information , including Landrace (LAN) , Large WROH) between WECPs and AHIPs using ROHs; (4) to identify and compare potential ROH regions associated with economically important traits in AHIPs and WECPs. Our results could help preserve the genetic diversity of AHIPs, promoting sustainable breeding programmes for genetic improvement in these breeds.Although ROH has been used for breeding estimates in many Western commercial and Chinese indigenous pig breeds, it has been used less frequently in Anhui indigenous pig breeds (AHIPs), including the Wei (YZ), Wannan black (WNHZ), Huai (HZ), Wannanhua (WNHUAZ), and Six White (LB). These breeds have improved meat quality , diseaseUsing the genetic background information of the ten pig breeds, we examined the relatedness among populations of indigenous breeds  collected from Anhui Province, China. In addition, samples were collected from the five WECPs  and comparatively analysed Fig. A. PrinciA descriptive summary of the ROH numbers and length categories  in each pig breed is listed in Table ROH) and homozygous-based (FHOM) inbreeding coefficients in different length categories are listed in Table HOM varied from 0.0971 ± 0.0531 (LB) to 0.3079 ± 0.0492 (LAN), and the values of FROH(ALL) varied from 0.064 ± 0.007 (YZ) to 0.289 ± 0.008 (LAN). We also found a high correlation between FHOM and FROH in all ten breeds , and the average correlation between FROH and FHOM in the ten breeds was 0.967. The genomic inbreeding coefficients (FROH and FHOM) were highest in the LAN, BER, and PIE breeds of WECPs, and lowest in the HZ and YZ breeds of AHIPs  in WECPs were found to be closely associated (Padj < 0.05) with skeletal muscle tissue development, embryonic skeletal morphogenesis, and cellular response to growth factor stimulus. Eight candidate genes  related to sex hormones and reproductive development, fatty acid biosynthesis metabolism, and immune response regulation were selected for subsequent analyses  enrichment analysis was performed separately for WECPs and AHIPs Fig. B. Genes ses Fig. C. SimilaROH values in WNHUAZ and WNHZ also supported the notion that these breeds originated from a common population. However, due to the small sample size and marker density, the results of population genetic structure in the WECP and AHIP breeds are insufficient and need further investigation.The Anhui Province is one of the top ten provinces that traditionally produce pigs in China, and it has abundant genetic resources of indigenous pig breeds . Due to long-term natural adaptation and artificial selection, the AHIPs have gradually evolved high fertility, high fat content, excellent meat quality , diseaseThe abundance, length, and genomic distribution of ROHs provide valuable information about the demographic history of livestock species . In thisROH estimates are more accurate for estimating autozygosity and detecting inbreeding effects than pedigree data [ROH and FHOM, to estimate inbreeding coefficients in AHIPs and WECPs. Previous studies have reported that FROH generally highly correlates with FHOM  consistent with our results  and previous studies [HOM values were higher than FROH values in all ten pig breeds because the FHOM estimate cannot distinguish identity by descent alleles from identity by state alleles, inevitably overestimating inbreeding levels [HOM to estimate the inbreeding coefficient is not sufficiently accurate, FROH can alleviate the issues mentioned above. Thus, theoretically, FROH may be a more effective and accurate alternative for quantifying relatedness and inbreeding levels [ROH of AHIPs is generally expected to be lower than that of WECPs. The contradictory results of our study may be explained by the small effective population size and random sampling errors in WECPs, resulting in higher inbreeding estimates for WECPs in recent generations [With the development of high-throughput genotyping technologies, genetic markers can provide a more accurate estimate of population relationships in pigs than pedigree data, which may have missing or incorrect parent information , 22. In ree data , providi studies , 37. Morg levels . Althougg levels . Furthererations .SULT1E1, LIPC, and MCEE) were involved in fat deposition, three  were involved in reproduction, and two (STAP1 and DUSP1) were immune system-related. LIPC encodes hepatic lipase and affects the metabolism, composition, and expression of several lipoproteins [SULT1E1, a negative regulator of adipogenesis [SULT1E1 is also reportedly linked to foetal development [SULT1E1 gene causes placental thrombosis and spontaneous foetal loss [ZNF366 plays an important role in regulating the expression of target genes in response to oestrogen [TJP1 has been related to testis weight, spermatogenesis, and the development of ovarian and cystic follicles [CSN1S1 is an effective molecular marker for litter size in goat breeding [STAP1 [DUSP1 [CDH13, PROX1, EGR2, CCNT2, SETD2, and MYL3) were related to muscular development, and three  were involved in skeletal morphogenesis. Among these candidate genes, miR-15a [CCNT2. High expression levels of CDH13 promote muscle-type identity, as CDH13 plays an active role in myogenesis [MYL3 [EGR2 [PROX1 is involved in muscle fibre conversion, and is a promising candidate gene affecting pork quality traits [FGF18 [FGF20 [FLVCR1 deficiency results in Diamond–Blackfan anaemia, often associated with skeletal malformations [We found that the ROH islands harboured several candidate genes controlling economically important traits in pigs. In particular, we identified 27 genomic regions with a high frequency of ROHs, harbouring 17 key candidate genes in WECPs and AHIPs. Furthermore, we identified eight candidate genes in the AHIPs, of which three ; (2) SNPs with a minor allele frequency of < 0.05 were removed (–maf 0.05); (3) data from individuals with a call rate of < 0.90 were discarded (–mind 0.1); (4) Hardy–Weinberg Equilibrium (HWE) P-value < 1 × 10–6 (–hwe 0.000001). The SNP genome coordinates were obtained from the Sus scrofa 11.1 porcine genome reference assembly. After genotype quality control, 1158 markers were excluded based on the HWE test (p ≤ 1 × 10–6), 7231 SNPs failed the missingness test (GENO > 0.1), 9788 SNPs failed the frequency test (MAF < 0.05), yielding 320 individuals and 54,075 SNP for further analysis.A total of 320 pigs were used in this study: 170 WECPs  and 150 AHIPs from the Anhui Province, China. Detailed information on the sampled pig breeds in this study, including the regions of recollection, breed names and abbreviations, and sample size, are presented in Table Sggmap package [http://itol2.embl.de/personal_page.cgi) [The geographical distributions of five WECPs and five AHIPs were estimated using the  package  in R sta package . A scatt package  was usedage.cgi) .–homozyg-kb 1000); (2) a minimum of 50 consecutive SNPs were included in an ROH (–homozyg-snp 50), which was calculated using the equation proposed by Lencz et al. [ROHs were identified for each individual using the PLINK software (v1.90), which uses a sliding window technique to scan each individual’s genotype at each marker position to detect homozygous segments . We defiz et al. :\documenns is the number of SNPs per individual, ni is the number of individuals, and het is the heterozygosity across all SNPs. After calculation, the minimum number of SNPs constituting an ROH was set to 50; (3) the maximum gap between consecutive SNPs was set to 1 Mb (–homozyg-gap 1000); (4) the minimum SNP density was set to 1 SNP every 100 kb (–homozyg-density 100); (5) the minimum number of SNPs in a sliding window was set to 50 (–homozyg-snp 50); (6) one heterozygous genotype and no more than five missing SNPs were allowed per window ; (7) the window threshold was set to 0.01 (–homozyg-window-threshold 0.01). In this study, we classified ROHs into five different categories according to their physical length: 1 to < 5 Mb, 5 to < 10 Mb, 10 to < 20 Mb, 20 to < 40 Mb and > 40 Mb. For each length category, we computed the frequency of ROH numbers and the average length of an ROH in every breed.where α is the percentage of false-positive ROHs (set to 0.05 in the present study), ROH, we evaluated the genomic coefficients for the ten pig breeds using two methods: (1) PLINK v1.90 software was used to measure the inbreeding coefficient based on the difference between the observed and expected numbers of homozygous genotypes (FHOM) [FHOM) was calculated as follows:To verify the accuracy of Fs (FHOM) . The inbL is the number of genotyped autosomal SNPs, E is the number of homozygotes expected by chance, and O is the number of observed homozygotes. (2) Genomic inbreeding coefficients were also estimated based on ROH (FROH). The FROH was calculated as follows:where LROHi is the total length of ROHi on autosomes, and Lauto is the autosomal genome length covered by the SNP chip. Furthermore, the correlation between FROH and FHOM for each breed was calculated using Pearson’s correlation.where . [http://asia.ensembl.org/Sus_scrofa/Info/Index) to annotate the genes in the ROH islands. In addition, pig QTLdb  was used to annotate the genes in the ROH islands. GO enrichment analysis of genes in the ROH islands was performed using g:Profiler (https://biit.cs.ut.ee/gprofiler/gost), and terms with a P-value greater than 0.05 were filtered. The biological function of each annotated gene within the ROH islands was determined through an extensive literature search.To identify genomic regions with a high frequency of ROHs, we calculated the percentage of occurrences of SNPs in ROHs by counting the number of times an SNP was detected in those ROHs across individuals. In this study, the threshold used to define an ROH hotspot in the genome was 40%, in agreement with a previous report by Rui et al. . Adjacen. , 76. We Additional file 1: SupplementaryFigure 1. The percentage of chromosome coverage (%) by ROHs in each breed. YZ, Wei pigs; WNHZ, Wannan black pigs; HZ, Huai pigs; WNHUAZ, Wannanhua pigs; LB, Six White pigs; LAN, Landrace pigs; DUR, Duroc pigs; PIE, Piétrain pigs; LWY Large White pigs; BER, Berkshire pigs.Additional file 2: SupplementaryFigure 2. Manhattan plot of the occurrence (%); of SNPs in ROHs in ten pig breeds.The x-axis represents the SNP genomic coordinate in each chromosome, and the y-axis shows the proportion of overlapping ROHs shared among individuals, based upon the number in population. Colourful data points indicate SNPs, and the dashed line represents the 40% threshold. YZ, Wei pigs; WNHZ, Wannan black pigs; HZ, Huai pigs; WNHUAZ, Wannanhua pigs; LB, Six White pigs; LAN, Landrace pigs; DUR, Duroc pigs; PIE, Piétrain pigs; LWY Large White pigs; BER, Berkshire pigs.Additional file 3: Supplemental Table 1. The percentage of chromosome coverage (%) by ROHs in each breed. Supplemental Table 2. Candidate genes located in genomic regions with a high frequency of ROHs. Supplemental Table 3. Pig QTLs located in genomic regions with a high frequency of ROHs. Supplemental Table 4. The summary of Sample information for each breed.


Triticum aestivum L.) is the main staple crop in the world and a major source of carbohydrates and proteins, functional genomics and allele mining are still big challenges. Given the advances in next-generation sequencing (NGS) technologies, the identification of causal variants associated with a target phenotype has become feasible. For these reasons, here, by combining sequence capture and target-enrichment methods with high-throughput NGS re-sequencing, we were able to scan at exome-wide level 46 randomly selected bread wheat individuals from a recombinant inbred line population and to identify and classify a large number of single nucleotide polymorphisms (SNPs). For technical validation of results, eight randomly selected SNPs were converted into Kompetitive Allele-Specific PCR (KASP) markers. This resource was established as an accessible and reusable molecular toolkit for allele data mining. The dataset we are making available could be exploited for novel studies on bread wheat genetics and as a foundation for starting breeding programs aimed at improving different key agronomic traits.Although wheat ( Triticum aestivum L. 2n = 6x = 42, AABBDD) is a major staple crop that provides about 20% of daily calories and 21% of protein needs consumed by the world population (http://www.fao.org/3/a-i6583e.pdf). However, to meet global food demand without expanding acreage, wheat grain production is projected to increase by at least 50% within the next few decades , the next challenges appear to be very demanding for wheat breeders and scientists  markers to detect and distinguish specific alleles in the entire population. This resource has been primarily established to be used for allele mining  and for F6:7), previously studied for five high-correlated morpho-physiological traits   , were anneration . The twoneration .Lankaodali is an early bread wheat cultivar of Chinese origin with very large kernels, low tillering ability, and poor qualitative attributes. Rebelde is an Italian cultivar with late flowering, small kernels, high tillering ability and excellent grain quality traits. During the 2020–2021 growing season, the leaves of 14-day-old seedlings of each of 48 individuals were collected and ground using liquid nitrogen. DNA was extracted using the Quick-DNA Plant/Seed Miniprep Kit  according to the manufacturer’s instructions. DNA quality and quantity were estimated using the NanoDrop ND-1000 spectrophotometer  and the Qubit fluorometer , respectively.http://www.arborbiosci.com). Bait design was based on the Chinese spring wheat genome v1.0 .In-solution-based hybridization was applied to capture the target loci. Baits  were designed to specifically capture over 250 Mb of coding DNA sequences (CDS)  to 230 M reads (sample 31) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and reads with a base quality score below 20 (Q < 20) were removed using Trimmomatic , to remove duplicate reads. Reads with mapping quality scores below 30 (Q < 30) were also filtered out. QualiMap (The extracted DNA (250 ng −1 μg) was subjected to random mechanical shearing to obtain fragments with an average size of 400 bp. The fragments underwent an A-tailing reaction at 3’ of the bluntmple 31) . Overallmmomatic   was used to derive the depth of coverage of the target regions for each individual. On average, more than 50% of the bases in the bait regions were covered at a depth greater than 20x  and remove the latter from the raw VCF  file. A total of 15,046,465 SNPs with a sequencing and alignment quality score ≥30  and a coverage ≥10 were identified. The VCF-stat utility in VCFtools  version 4.0 , followiVCFtools  was usedVCFtools   reaching a final annealing temperature of 55°C, followed by 26 additional annealing cycles . Fluorescence readings were performed with a temperature below 37 °C and allelic discrimination plots were drawn using the SNP viewer software (https://www.biosearchtech.com/support/tools/genotyping-software/snpviewer). Individuals with contrasting alleles at each SNP locus  were genotyped using the KASP assays  assessed in other environments , and, thus, detect stable QTL based on genotype-environment (GxE) interactions.Indeed, SNP discovery supports the use of bulked segregant analysis as a powerful tool to accelerate gene identification and QTL mapping cost-effectively.In addition, the data described here could be used to dissect naturally occurring allelic variation at candidate genes controlling key agronomic traits, identify useful alleles  and understanding their function.


Mycobacterium tuberculosis (Mtb) bacterial genotype on the outcome of the infection. Several studies have found associations of human genetic polymorphisms with Mtb phylo-lineages, but studies analysing genome-genome interactions are needed. By implementing a phylogenetic tree-based Mtb-to-human analysis for 714 TB patients from Thailand, we identify eight putative genetic interaction points (P < 5 × 10−8) including human loci DAP and RIMS3, both linked to the IFNγ cytokine and host immune system, as well as FSTL5, previously associated with susceptibility to TB. Many of the corresponding Mtb markers are lineage specific. The genome-to-genome analysis reveals a complex interactome picture, supports host-pathogen adaptation and co-evolution in TB, and has potential applications to large-scale studies across many TB endemic populations matched for host-pathogen genomic diversity.The genetics underlying tuberculosis (TB) pathophysiology are poorly understood. Human genome-wide association studies have failed so far to reveal reproducible susceptibility loci, attributed in part to the influence of the underlying  Mycobacterium tuberculosis genetics to find genome-genome interactions associated with infection.Few genetic loci have been associated with tuberculosis infection, possibly because of the influence of genetic variation in the pathogen. Here, the authors integrate human and  Mycobacterium tuberculosis (Mtb) bacteria, with a wide spectrum of outcomes, probably reflecting differences among human host and pathogen genomes, as well as environmental factors such as immune antigen exposure history. Mtb genomic variation, including single nucleotide polymorphisms (SNPs), has helped define Mtb lineages and sub-lineages2. Mtb lineages are endemic in different locations around the globe2, with a phylo-geographical structure, leading to the hypothesis that the strain-types are specifically adapted to people of these global locations and thus different human genetic backgrounds4. Isolates within lineages 1, 5, and 6 are considered “ancient” strains because they were the first to diverge from the common ancestor, while those in lineages 2 to 4 are called “modern” with some spreading globally and acquiring drug resistance2. Several studies have reported that lineages may vary in their propensity to transmit and cause severe disease5; but results are inconsistent and there is considerable inter-strain variation within lineages6.Tuberculosis (TB) is a complex disease, caused by 8, this approach has proven difficult for TB12, with the susceptibility loci identified not replicated in different populations14. Despite these difficulties, various studies have demonstrated an association between certain alleles of the HLA class II region and pulmonary TB, possibly through reduced presentation of protective Mtb antigens to T cells15. Other GWAS studies have identified loci related to innate immunity13, known to be important in determining Mtb infection and disease outcome10. One reason for the difficulty to replicate hits could be the concept of “genetic heterogeneity”, whereby the underlying genetic causes for a trait, in this case susceptibility to TB, may be different across populations. An appealing interpretation of the heterogeneous effect of polymorphisms in different populations implicates the influence of the bacterial genotype on the outcome of the infection for a particular host genotype16. Several studies have found associations of some host genetic polymorphisms with particular Mtb lineages or strain families20. In particular, associations have been found using targeted or genome-wide host genotyping approaches combined with traditional Mtb typing approaches using long sequence polymorphisms or spoligotypes24. These results suggest that, at least to some extent, TB clinical phenotypes can be explained by the interaction between human and Mtb genetic variation. However, these approaches used Mtb typing methods that do not fully represent the strain types on a high resolution.Host genetics has the potential to inform about TB disease susceptibility and thus reveal genes important for successful host defence strategies. However, despite the GWAS successes in other diseases25, hepatitis C virus infection26 and Epstein-Barr virus27. However, these approaches have not been applied to TB, where genetic studies have predominantly considered human and Mtb genomes separately. Here, we sought to reveal insights into human-Mtb interaction points using “genome-to-genome” analysis, using a novel approach guided by the Mtb phylogenetic structure, thereby controlling for confounding effects of population structure. To this end we integrated the human and Mtb genomics data from a well-characterized Thailand cohort (n = 714) of TB patients, with an analysis revealing eight putative genetic interaction points (P < 5 × 10−8), including the involvement of genes related to host immunity.Host-pathogen interaction genomics, using genome-to-genome analytical approaches, has already begun to be used to identify pathogenic mechanisms associated with other diseases, including HIVn = 714) of TB pulmonary patients were predominantly male (73.7%), all HIV negative and more than 14 years of age (median age: 46 years), with 95% coming from the Chang Rai district in northern Thailand  being rare with minor allele frequencies (MAF) <2%. Mtb phylogenetic reconstruction using the 56k SNPs resulted in a tree with 4 major clades, corresponding to the main lineages. Further, subclades defined by long internal branch lengths were also identified , indicating that the diversity measured broadly reflects the genetic diversity in the East Asian region. However, the individuals did not cluster with collection site, suggesting that these clusters are not driven by geography , which suggests nonrandom associations in the human to Mtb pairings in our patient population. This could be either due to non-random transmission patterns  or due to specific human to Mtb genetic interaction patterns. The non-random transmission scenario seems unlikely since nearly all isolates are from the relatively small region of Chang Rai and non-familial related individuals . This method allowed us to test for associations between internal nodes on the Mtb phylogenetic tree (144 with a minimum clade proportion > 2%) and human variants (~7.4 million with MAF > 2%), adjusting for the confounding effects of both Mtb and host population structure (see METHODS). The analysis revealed associations involving 105 human SNPs (in 44 loci) and 30 Mtb phylogenetic clades  described by Thye et al.12, and in the HLA region on chromosome 6  reported by Sveinbjornsson et al.9. Although none of these sequence variants in our analysis reach the significance cut-off, a potential marginal peak was observed around HLA-DQB1 (P = 4.92 × 10−7) , but this did not reach genome-wide significance , reinforcing a highly complex dynamic between human host and pathogen across geography, and the need to consider both organisms to understand TB biology, pathophysiology, and epidemiology.To reveal if the putative interactions were human cluster specific, host-pathogen pairs from just the main PC group , consistent with the interaction effect not being driven by an analysis of the main cluster. The other hits showed no association. As a benchmark, the same analysis was also run on the MHC region, where selection signals are expected, and revealed high XP-EHH values (>3) across all three pairwise cluster comparisons  had elevated XP-EHH values  studies in different populations. Our analysis suggests that large-scale numbers across paired samples matched for host-pathogen genomic diversity would facilitate replication. The resulting insights from such investigations will be instrumental for designing and informing treatment and vaccine-design decisions for TB that may be more specific to infecting strains or host genetics, thereby providing much-needed control measures to assist disease elimination.By testing associations between all possible combinations of human SNP variation and The 714 TB cases were recruited from hospitals in Chiang Rai, Lampang and Bangkok provinces in Thailand  between 2010 and 2012. These included a minority of travellers from neighbouring Laos and Myanmar. The patients had no previous history of TB disease, were primarily male (73.9%), and aged > 14 years. The TB diagnosis was confirmed by microscopy and culture, and all individuals are HIV seronegative  were generated on Illumina Human610-Quad and Illumina HumanOmniExpressExome-8 v1.2 BeadChips, complemented by imputation of >8.4 million genomic sites using Beagle4.1 software34 and a 1000 Genomes reference panel35. Human leukocyte antigen (HLA) protein alleles were imputed using SNP2HLA software (v1.0.3) and a pan-Asian reference36. SNPs were removed if there was: (i) deviation in genotypic frequencies from Hardy-Weinberg equilibrium (HWE) as assessed using a chi-square test (P < 0.00001); (ii) high genotype call missingness (>10%); (iii) low minor allele frequency (<5%); or (iv) low imputation quality . The final number of SNPs was 5,948,940. The population structure was explored using principal component (PC) analysis (PCA) inferred from pairwise SNP genotype differences between individuals using Plink2 (v2.00a3.7LM) software (settings --pca). This analysis led to three clear human PC clusters. To check the extent of this variability compared to global genetic variation, the data was merged with the 1000 Genome project set and PCA was performed on the combined dataset as well as with only the East Asian (EAS) populations.Human genotypes for the Thai TB cases (M. tuberculosis (Mtb) sequence data was generated at the Sanger Institute using an Illumina HiSeq2000 machine. The raw sequencing data were aligned to the H37Rv reference genome (Genbank accession number: NC_000962.3) using the BWA-MEM algorithm (v0.7.17-r1188)37. SAMtools/BCFtools38 software was used to call SNPs and small indels using default options. Alleles were additionally called across the whole genome (including SNP sites) using a coverage-based approach. A missing call was assigned if the total depth of coverage at a site did not reach a minimum of 20 reads or none of the four nucleotides accounted for at least 75% of the total coverage. Samples or SNP sites having an excess of 10% missing genotype calls were removed. This quality control step was implemented to remove samples with low quality genotype calls due to poor depth of coverage or mixed infections. The final discovery dataset included 720 Thai isolates and ~59k genome-wide SNPs. SNPs were combined within a multifasta format file and used as input to iq-tree software for phylogenetic reconstruction . Custom scripts were used to traverse the tree and create input files for Plink2 software to run the association analysis detailed below . Lineages and drug resistance were predicted using the TB-Profiler tool (v4.2.0)39. The Mtb SNP analytical pipeline is described in greater detail elsewhere40. To look for evidence of recent transmission between samples, the number of SNP differences between all pairs of samples was calculated. A cut-off of 12 SNPs was used to infer transmission41, and putative transmission clusters were visualised using a customised javascript script.2 test was applied. The genome-to-genome analysis was performed using logistic regression with the Mtb phylogenetic clade (binary variable: in or not in clade) as the outcome and human genotypes as predictors. Mtb clades were included in the analysis if their nodes represented > 2% of the total number of samples. A separate logistic model was fitted for each human SNP genotypic effect , and the minimum p-value across the tests for each Mtb clades was retained. The two main human PCs were included in the model to adjust for population structure in the analysis of “all” Thailand samples. A sensitivity analysis was performed on the main association hits, and assessed whether highly significant P values were robust to models that included 0, 2, 5 or 10 PCs. All association hits were robust to applying these different numbers of PCs. As the PCA revealed three distinct clusters with the main cluster representing ~60% of the data, the analysis steps were repeated using only samples from this cluster without PC adjustment in the model. This analysis is referred to in the text as the “main cluster” analysis as opposed to the “all” Thai sample analysis. P values were used to rank association hits. Given the complexity of establishing significance thresholds in host-pathogen genomics, a cut-off of P < 5 × 10−8 was used as a guide to establish and present the most significant hits from the analysis. This significance threshold is similar to a recent human genome-to-pathogen genome study26. Regional association plots were generated using locuszoom42 using the minimum P value across the different genotypic models and tested clades for each human variant, to produce a single Manhattan plot for the analysis of all data and for the main cluster analysis. For comparisons, Mtb (sub-)lineage data was available for 32k globally sourced isolates1. Selection analysis was performed on human genotypes using a between-population XP-EHH approach implemented in selscan software , and attempted to establish if the association hits were in genetic regions with evidence of recent positive selection. Three separate analyses were conducted in pairwise comparisons of the human clusters identified using the host PCA. Annotated maps were generated using the maps library package within the R statistical software tool.To uncover effects between lineages and the human PC group overall, a χFurther information on research design is available in the Supplementary InformationReporting Summary