Me biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, COL27A1, with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease.Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic Bio Diseases often run in families. These disease are frequently linked to changes in DNA that are passed down through generations. Close family members may share these disease-causing mutations; so may distant relatives who inherited the same mutation from a common ancestor long ago. Geneticists use a method called linkage mapping to trace a disease found in multiple members of a family over generations to genetic changes in a shared ancestor. This allows scientists to pinpoint the exact place in the genome the disease-causing mutation occurred. Using computer algorithms, scientists can apply the same technique to identify mutations that distant relatives inherited from a common ancestor.Belbin et al. used this computational technique to identify a mutation that may cause unusually short stature or bone and joint problems in up to 2% of people of Puerto Rican descent. In the experiments, the genomes of about 32,000 New Yorkers who have volunteered to participate in the BioMe Biobank and their health records were used to search for genetic changes linked to extremely short stature. The search revealed that people who inherited two copies of this mutation from their parents were likely to be extremely short or to have bone and joint problems. People who inherited one copy had an increased likelihood of joint or bone problems.This mutation affects a gene responsible for making a form of protein called collagen that is important for bone growth. The analysis suggests the mutation first arose in a Native American ancestor living in Puerto Rico around the time that European colonization began. The mutation had previously been linked to a disorder called Steel syndrome that was thought to be rare. Belbin et al. showed this condition is actually fairly common in people whose ancestors recently came from Puerto Rico, but may often go undiagnosed by their physicians. The experiments emphasize the importance of including diverse populations in genetic studies, as studies of people of predominantly European descent would likely have missed the link between this disease and mutation. During the past two decades major advances in deciphering the genetic basis of human disease have resulted in thousands of disorders that are now understood at a genetic level . This prDespite this progress, most genetic diseases are still under-diagnosed  or misdiThe gold standard of genetic disorder diagnosis involves testing both patient and family members to confirm Mendelian segregation of the suspected underlying pathogenic variant . HoweverMe biobank, at the Icahn School of Medicine at Mount Sinai, New York City (NYC), is one of the most diverse cohorts ascertained at a single urban medical center under a uniform study protocol. Participants are largely from the local Upper East Side, Harlem and Bronx communities, and represent broad ancestral, ethnic, cultural, and socioeconomic diversity. We initially focused on adult height, which is easily measurable, stable over the adult life course, and one of the most abundantly recorded clinical parameters in EHRs. Height is known to be highly heritable and polygenic  by detecting pairwise identity-by-state using RELATEAdmix  with 26 global populations from the 1000 Genomes Project   as they clustered distinctly with an AJ reference panel and separately from other European ancestry groups in PCA space   and two  = 2504)  and Ashk = 2504)   . Based oCA space . The othCA space . An addiMe biobank participants, we estimated sharing of genomic tracts IBD >3 cM between every pair of individuals using the GERMLINE software (i.e. in founder populations)  . We summlations) . This islations)  and in slations) .Me H/L population, we leveraged self-reported and genetic information about sub-continental ancestry. By self-reporting, the H/L participants in BioMe were born in New York City (NYC) (40%), Puerto Rico (24%), Dominican Republic (19%), Central/South America (12%), Mexico (2%) or other Caribbean Island (2%) . Approximately 5086 NYC-born H/L individuals did not have recorded parental or grandparental country-of-origin, therefore we also devised a selection strategy using PCA analysis. We identified BioMe H/L participants on the cline between the African and European reference panels in PCA space coincident with Puerto Rican-born individuals. We excluded those on the same cline with ancestry from the Dominican Republic or another Caribbean Island  . We examand (2%) . We assen Island , and couMe =16.1). After making exclusions based on age , mean height measurements  were consistent with those reported for Puerto Rican populations in a recent global study on height  of the Mount Sinai Health System. We focused on height, a stable and ubiquitous health measure. Clinically, rare instances of growth failure or ‘short stature’ may be caused by a large heterogeneous group of genetic disorders  . We firsn height . We noten height  (range on height .−4) one IBD-clique achieved a genome-wide significant signal at the locus 9q32   , spanninCOL27A1, which encodes for Collagen Type XXVII, Alpha 1  , and dysmorphic features  than the population mean for height in the Puerto Rican cohort . The IBD Alpha 1 . We perf Alpha 1 . Examina0950220) . In silin family . First dfeatures .COL27A1.pG697R variant by genotyping all of the homozygotes and carriers of the top IBD-clique in the recessive model (N = 59), along with a panel of age- and sex-matched controls (N = 59). This demonstrated 100% concordance between the COL27A1.pG697R variant and the significant IBD-haplotype in homozygotes  by first imputing the 1KGP panel and re-running the recessive test as described above (n = 10007795 imputed and genotyped SNPs with an INFO score of >0.3 and at least two observations of homozygotes). The recessive model appeared to be well calibrated (λ = 1.02), however, we observed no genome-wide significant signal . Upon examination of the correlation between the imputed COL27A1.pG697R and the true carrier status of homozygotes, we noted a concordance of only 66.67%, indicating that the IBD haplotype was a better tag of the true COL27A1.pG697R homozygous state compared to 1KGP imputation in the Puerto Rican cohort  as part of the Polygeneic Architecture using Genomics and Epidemiology (PAGE) Study. The COL27A1.pG697R (rs140950220) variant was directly genotyped on MEGA, and an association of the variant under a recessive model resulted in a strong signal of association . The replication analysis revealed 51 additional BioMe carriers and two individuals that were homozygous for the variant. Both carrier and affected status was confirmed via independent genotyping and Sanger sequencing . The two homozygous participants were both short statured (2.4 and 3.6 s.d from the sex specific population mean).The association between COL27A1.pG697R variant and Steel syndrome, a clinical expert manually reviewed the electronic health records (EHR), including clinical diagnoses, surgical procedures, and radiology reports of the five participants  homozygous for the COL27A1.pG697R variant that we had identified through the discovery and replication efforts. Of note, there was no evidence that any of the five patients had a clinical diagnosis of Steel syndrome. In all five individuals, however, we found EHR-documented evidence of several previously described Steel syndrome characteristics, including developmental dysplasia of the hip , carpal coalition, scoliosis, and cervical spine anomalies  with COL27A1.pG697R carrier status, or Phenome-Wide Association Study (PheWAS) (COL27A1.pG697R carriers and N = 4480 non-carriers). The five homozygotes were excluded. The ICD9 code was set as the outcome variable and COL27A1.pG697R as the primary predictor variable, including age, sex and the first five PCAs as covariates in all tests. To avoid spurious associations, we restricted the analysis to diagnosis codes with at least 3 observations (n = 367 ICD9 codes) amongst carriers.To test for clinical features of Steel syndrome in (PheWAS) . PheWAS (PheWAS)  to accou(PheWAS)  to accou(PheWAS) . Each teGLM < 7.1 × 10−6; odds ratio (OR) = 34.5; 95% Confidence Interval (CI) = 7.4–162), 721.0 , and 716.98 . ICD9 730.08 encodes for ‘acute osteomyelitis, other specified sites’. Manual review of chart records for these patients revealed that this code referred to vertebral osteomyelitis in the three carriers with the ICD9 code. ICD9 721.0 encodes for cervical spondylosis without myelopathy. Cervical spondylosis refers to degenerative changes of the cervical spine, which can eventually progress to encroach on the cervical canal, causing myelopathy . A third diagnosis code, 716.98, encodes for ‘arthropathy, unspecified, or involving other specified sites’. Manual review of chart records for these patients revealed that this code referred to knee arthropathy in all four patients. Finally, two other ICD9 codes were significantly associated with the COL27A1.pG697R variant; 622.10 , which encodes for cervical dysplasia, and 789.1 , which encodes for hepatomegaly. Presently, it is unclear whether these two are related to a COL27A1.pG697R carrier phenotype, or are spurious associations.Results of the GLM test are shown in GLM=1.59; λSPATest=1.20; λGCTA=1.36; λFirth=2.09), indicating that no single model fully accounts for the confounding effects of distant relatedness, case:control imbalance and rare variants/ICD9s , but only remains significant after genomic control adjustment in one of the tests (pGCTA_adjusted < 4.6 x 10−5). Neither codes linked to cervical spondylosis ; pGCTA < 3.3 × 10−3 (8th)) or knee arthropathy ; pGCTA < 3.5 × 10−3 (9th); pFirth < 0.001 (35th))) were significant after genomic control correction. Therefore, while PheWAS analysis provided preliminary support of Steel syndrome-associated clinical features in carriers, best practices for PheWAS models for rare variants/ICD9 codes, and in the presence of population structure, remains an open problem for the genomics community. It is also possible that some relevant clinical features of Steel syndrome might be poorly captured by or absent from medical billing codes.We observed over inflation in the distribution of the PheWAS test statistic, measured by lambda (λ), for all four score based models  to reduce confounding from age-related related symptoms of spine and joint pain. We also selected 31 age and sex matched Puerto Rican non-carriers for comparison (mean age 40.6 years). Utilizing the same criteria used to characterize Steel syndrome cases, we found no evidence of clinical short stature or hip dislocation in carriers, but did observe a trend of elevated rates of major joint and spine degradation , the Americas (N = 45685), Asia (N = 5311), Europe (N = 209), the Middle East (N = 163) and Oceania (N = 28) genotyped on MEGA in the PAGE Study. This included; 13050 in the Multi-Ethnic Cohort (MEC) (Me biobank participants (including the 1775 Puerto Ricans on MEGA described above); and a Global Reference Panel from Stanford University including the Human Genome Diversity Panel . We estimate the carrier rate of COL27A1.G697R to be 1:51 in Puerto Rican-born individuals  = 1.1%); 1:9 in individuals born on the island of St. Thomas (MAF = 11%); 1:346 in Hispanic/Latino/as in the US (MAF = 0.29%) and 1:72 in BioMe Hispanic/Latino/a populations from New York City (MAF = 0.7%); and 1:746 in individuals born in the Dominican Republic (MAF = 0.067%)   Study; 1rt (MEC) ; 12852 irt (MEC) ; 13044 aty Panel  (N = 986ulations . The COL 0.067%) . We note 0.067%) , likely COL27A1.pG697R risk haplotype. First, by visual inspection, we were able to discriminate a single haplotype of 107.5 kb in length that contained 55 SNPs, which uniquely tagged the COL27A1.pG697R variant (R2 = 1). This haplotype was present only in individuals born in Puerto Rico (N = 25), the Dominican Republic, (N = 2), Columbia (N = 1), New York City (N = 40) and St. Thomas (N = 1). Genotyping determined that only the haplotype carriers from Puerto Rico, New York City and St. Thomas also carried the COL27A1.pG697R variant . Second, we inferred continental ancestry along the genomes of the three Puerto Rican homozygotes in the discovery cohort. Local ancestry inference is the task of assigning continental ancestry to genomic segments in an individual with recent ancestors from multiple continents. For the Puerto Rican homozygotes, we estimated local haplotypic similarity with a reference panel of African, European and Native American genomes using RFMix  of the Puerto Rican discovery cohort (N = 2816) . This is consistent with the timing of European immigration and slave trading on the Island, resulting in admixture and population bottlenecking, followed by demographic growth post-contact  . The IBD-contact . Finally-contact  across che locus . TogetheCOL27A1 gene had been previously implicated as the genetic variant underlying Steel syndrome , we discovered a recessive haplotype on 9q32 conferring extreme short stature. Whole genome sequencing revealed that a mutation (Gly697Arg) in the syndrome . Populatisorders . Taken tCOL27A1.pG697R variant was first described very recently .This is consistent with our finding, supported by previous work , demonstrecently . This surecently , and othrecently , other vn Indian  and Emern Indian  familiesn Indian . We estivia IBD mapping was better powered for discovery of the COL27A1 variant compared to a more typical GWAS approach . As sample sizes increase in health systems and biobanks, the odds of a new individual being a direct or distant relative of someone already in the database increases exponentially , and imThis work also highlights some of the current challenges in the emerging field of genomic medicine. We demonstrated that evidence from EHRs could be readily extracted and retrospectively used to characterize clinical features of a musculoskeletal genetic disorder. However, features of many other genetic disorders may not be detectable via routine clinical exam, lab tests and radiologics, and may not be amenable to such an approach. Furthermore, statistical methods for population-scale disease variant discovery, which were predominantly developed for cohorts collected for genetic research, may not be optimally calibrated for discovery in patient populations encountered in health systems. Finally, many genetic disorders are very rare, or have more complex genetic underpinnings, which would reduce power for detection using the strategy we have described. However, recent efforts, such as the Precision Medicine Initiative, that focus on the broad adoption of genomics in medicine, combined with international efforts to catalog rare genetic diseases, are primed to increase the rate of incidental genetic diagnosis of disease.COL27A1 variant is very rare in current large-scale genomic databases used for clinical research. Thus traditional association strategies and ascertainment bias focused on populations of European descent would have failed to identify and characterize this disorder and its public health burden. As ours and other recent studies have demonstrated, EHR-embedded research will be increasingly important for disentangling the pathology of rare genetic disorders, and understanding the continuum of complex and Mendelian disease. As studies grow in size, and healthcare systems learn to leverage the wealth of information captured in the EHR, there is a need to provide relevant medical information to any patient entering the clinic anywhere in the world. Methods like that described here allow for precision medicine with a truly global outlook.In summary, this work demonstrates the utility of biobanks for exploring full medical phenomes, and highlights the importance of documenting a wider spectrum of genetic disorders, in large and diverse populations of humans. In particular, this method provides a bridge between classical medical genetic methods and those employed in population-level GWAS. Here we note that the Me Biobank Program of The Charles Bronfman Institute for Personalized Medicine at Mount Sinai Medical Center from 2007 onward. The BioMe Biobank Program  operates under a Mount Sinai Institutional Review Board-approved research protocol. All study participants provided written informed consent. Of the approximately 38000 participants currently enrolled in BioMe, N = 10511 unrelated are genotyped on the Illumina Infinium OmniExpress (OMNI) array. 5102 of these participants self-report as ‘Hispanic or Latino/a’, 3080 as ‘African American or African’, 2078 as ‘White or Caucasian’, 89 as ‘Mixed’ and 162 as ‘Other’. Country of origin information is available for all but N = 5 participants, with N = 6553 reporting being born in the United States and the remaining N = 3953 report being born outside of the US. Parental and grandparental country of origin information is only available for a small subset of individuals genotyped on the OMNI array (N = 43). An additional N = 10471 participants were genotyped on the Illumina Infinium Multi Ethnic Genotyping array (MEGA) v1.0. Of these, approximately 4704 self-reported ‘Hispanic or Latino/a’ ethnicity, 3143 self-reported as ‘African American or African’, 22 self-reported as ‘White/Caucasian’, 708 self-reported as ‘Mixed’ and 1894 self-reported as ‘Other’. Country of birth information was available for all but a small number of participants (N = 228), with 5190 reporting being born in the United States, and the remaining 5053 self-reporting being born elsewhere. Parental and grandparental country of origin information is available for 4323 individuals genotyped on the MEGA array.Study participants were recruited from the BioMe participants was performed on the Illumina Infinium OmniExpress plus HumanExome array. Calling was performed using the GenomeStudio software. A total of 1093 individuals were removed prior to zCall due to plate failure (N = 672), unambiguous discordance between genetic and EHR recorded sex (N = 693), a call rate of <98% (N = 834), or deviances in levels of heterozygosity . This was defined as having either an inbreeding coefficient outside the range −0.1 and 0.3 for common alleles (MAF >1%), or between 0.4 and 0.9 for rare alleles (MAF <1%). Additional quality control of 11656 individuals was performed using PLINK1.7 (SCR_001757). An individual with a call rate of <99% was also excluded (N = 1), along with intentional genetic duplicates . Site-level quality control consisted of the removal of SNPs with a call rate of <95% (n = 42217), and the removal of sites that were significantly out of Hardy-Weinberg equilibrium  when calculated for self-reported EA, AA and H/L separately. Palindromic sites and those that deviated considerably from the 1KGP allele frequencies (<40% versus >60%) were also removed to ensure uniform stranding across datasets. After QC steps, 11212 participants and 866864 SNPs remained for downstream analysis.Genotyping of 12749 BioPLINK1.7  (RRID:SCMe participants (N = 11212) using the RELATEAdmix software (SCR_001263) H/L samples were merged with the Utah Residents (CEPH) with Northern and Western European Ancestry , Yoruba in Ibadan, Nigeria  and Native American  and used as input for the ADMIXTURE software, which was run unsupervised at k = 3. ADMIXTURE analysis confirmed that NA reference panel comprised >99% proportion Native American genetic ancestry  and run unsupervised at k = 2. AA samples were merged with the CEU and YRI reference panels and run unsupervised at k = 2. After intersecting with reference panels 99296 SNPs were used as the input for RELATEAdmix.Pairwise IBS-based relationship estimates were derived for Biosoftware , which asoftware  (RRID:SCancestry . EuropeaSCR_004965) (Me participants. Regions containing the Human Leukocyte Antigen (chr6: 27000000–35000000 (NCBI37/hg19)), Lactase gene (chr2:135000000–137000000 (NCBI37/hg19)) and a common inversion (chr8:6000000–16000000 (NCBI37/hg19)), all of which are regions known to confound PCA analysis were removed from the genotype data prior to analysis. Data were merged with a reference panel of 2504 individuals from Phase 3 of the 1KGP (RRID:SCR_006828)  was performed using the SMARTPCAv10210 software from the EIGENSOFTv5.0.1 (RRID:_004965)  in 10511_006828)  that wasSCR_001720)  using the following flags: ‘-min_m 3 -err_hom 0 -err_het 2 -bits 25 –haploid’. IBD haplotypes that fell within or overlapped with centromeres, telomeres and regions of low complexity were removed from the GERMLINE output using an in-house Ruby script. Additional quality control measures consisted of the exclusion of regions of the genome where the depth of IBD-sharing  exceeded 4 standard deviations from the genome-wide mean (Phased genotype data were filtered to MAF >0.01 and converted to PLINK format using the FCGENE software  (we avoi_001720)  was usedide mean .IBD clustering to identify ‘cliques' of three or more IBD haplotypes shared between multiple individuals was then performed using the efficient connect-component-based clustering version of the Dash Associated Shared Haplotypes algorithm (DASH) , using tN), we first binned pairwise IBD tracts by length bin in 0.01 cM increments. We then summed the number of pairwise IBD tracts falling into each length bin (x), and divided this number by the number of possible pairwise IBD sharing for each population: N*(N-1)/2.We calculated the length of any pairwise IBD tract  for each IBD sharing pair within each population to obtain an estimate of the mean and variance of pairwise sharing per population. To compare the tract length distribution between populations . Height data was then log transformed and converted to age-adjusted Z-scores. Participants were excluded on the basis of age reported at the point of enrollment, with a minimum cut-off of 18 years old for females (N = 0) and 22 for males (N = 0), and a maximum cut off of 79 years old for both sexes (N = 194) leaving a total of n = 2622 PR.A self-reported measurement of height in feet and inches was recorded for each participant at enrollment into the Bio‘--linear recessive’ flag. Age and sex adjusted Z-scores for height were used as the outcome variable. IBD clique membership was used as the primary predictor variable and the first five PCA eigenvectors were used as covariates. The model was run across a total of 2622 PR ancestry individuals and a total of 480 IBD-cliques where at least 3 individuals were homozygous for the IBD haplotype.Association of IBD clique membership with height as a continuous trait was performed under a recessive model using PLINKv1.9  using thMe individuals ascertained on the Illumina OMNI Express array (N = 11212) were phased together using SHAPEIT2 (SCR_013055)  via the f‘--linear recessive’ flag. Age and sex adjusted Z-scores for height were used as the phenotypic outcome and the first five PC eigenvectors were used as covariates. Analysis was restricted to SNPs with >= 2 observations of individuals homozygous for the minor allele (as the only 2 of the 3 homozygotes had been imputed correctly), and SNPs with an INFO score of >= 0.3 .We ran a recessive GWAS on the same 2622 inferred Puerto Rican ancestry individuals used in our recessive IBD-mapping model. The association was run over hard-called data using the PLINKv1.9 software using the ‘-out_mode EMIT_ALL_SITES’ flag in GATKv3.2–72 (RRID:SCR_001876). For summary statistics of whole genome sequencing (WGS) see Genomic libraries were prepared from DNA obtained for the four IBD homozygous individuals. DNA was sheared to 300 bp on a Covaris E220, libraries were made using the NEBNext Ultra DNA Library Prep kit for Illumina. The libraries were submitted for Whole Genome Sequencing (WGS) at the Mount Sinai Genomic Core using the Illumina HiSeq 2500 system, performed by the Genomics Core Facility of the Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai. Reads were aligned to the NCBI37/hg19 reference genome and variants were called using the sequence analysis pipeline by SCR_005191) as part of the sequence analysis pipeline published by COL27A1  which was present in 7 copies . The rs140950220 G/C allele status was validated by Sanger sequencing of exon 7 in the COL27A1 gene in all four individuals. We also validated COL27A1.pG697R status in individuals carrying the significant IBD-clique at 9q32 using the Fluidigm SNPType assay adhering to the standard protocol. All individuals carrying at least one copy of the top IBD-haplotype (N = 59) were genotyped for the rs140950220 variant in addition to a panel of age and sex matched Puerto Rican ancestry controls (N = 59).WGS variant calls were annotated with allele frequency information and in silico prediction scores for SIFT, PhyloP, GERP generated using snpEffv3.0 (RRID:COL27A1.pG697R (dbSNP = rs140950220) variant in the Population Architecture using Genomics and Epidemiology (PAGE) study. The PAGE study comprises a diverse global reference panel from five studies. African-American and Hispanic/Latina women from the Women’s Health Initiative (WHI), a multi-center cohort study investigating post-menopausal women’s health in the US and recruited women at 40 centers across the US. Self-identified Hispanic/Latino/as from four sites in San Diego, CA, Chicago, IL, Bronx, NY, and Miami, FL as part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). African American, Japanese American, and Native Hawaiian participants from the Multiethnic Cohort (MEC) prospective cohort study recruiting men and women from Hawaii and California. The Global Reference Panel (GRP) created by Stanford University contributed samples including; a population sample of Andean individuals primarily of Quechuan/Aymaran ancestry from Puno, Peru; a population sample of Easter Island (Rapa Nui), Chile; individuals of indigenous origin from Oaxaca, Mexico, Honduras, Colombia, the Nama and Khomani KhoeSan populations of the Northern Cape, South Africa; the Human Genome Diversity Panel in collaboration with the Centre Etude Polymorphism Humain (CEPH) in Paris; and the Maasai in Kinyawa, Kenya (MKK) dataset from the International Hapmap Project hosted at Coriell. Finally, the BioMe biobank in the Mount Sinai health system, New York City, contributed African-American, Hispanic/Latino/a, and participants who reported as mixed or other ancestry to the PAGE study,~50% of whom were born outside New York City and for whom country-of-birth information was available. In all, participants in the PAGE Study represent a global reference panel of 112 populations ranging from 4 to 17773 individuals in size  CIDR technical filters, (2) SNPs with missing call rate >= 2%, (3) SNPs with more than 6 discordant calls in 988 study duplicates, (4) SNPs with greater than 1 Mendelian errors in 282 trios and 1439 duos, (5) SNPs with a Hardy-Weinberg p<10−10, (6) positional duplicates.We estimated the frequency of the  in size . Samples in size  and retuwww.pdb.org) on January 21, 2017. Visualization and modeling of the missense variant were performed in PyMol .We downloaded X-ray crystal coordinates  with EHR-derived ICD9 billing codes as the phenotypic outcome. In the association model, for each individual ICD9 codes were encoded as ‘1’ if the ICD9 was present in their EHR, and ‘0’ if the ICD9 code was absent. Carrier status for COL27A1.pG697R was used as the primary predictor variable, with heterozygous individuals encoded as ‘1’, non-carriers encoded a ‘0’ and homozygotes excluded from the analysis. We restricted the analysis to carriers of COL27A1.pG697R (n = 106) and non-carriers (n = 4480) who either reported being born in Puerto Rico or who were US-born, self-identified as H/L and overlapped with Puerto Rican born individuals in principal component analysis. Age, sex and the first 5 principal components were included as covariates in our model. The regression was performed using four methods; Generalized Linear Models (GLM) using the glm function in Rv3.2.1; a score test based on the saddlepoint approximation (SPATest) using the SPAtest function in Rv3.2.1; a score test using a base adjustment for rare variants (Firth test) using the logistf function in Rv3.2.1; and a linear mixed model using the GCTAv1.24.2 software with a genetic relationship matrix constructed from 281666 SNPs shared between the OMNI and MEGA arrays (MAF >= 1%). To adjust for multiple tests, raw p-values were adjusted for false discovery rate using the p.adjust function in R, and only those below an FDR adjusted p-value of 0.05 were reported as significant.To test for clinical symptoms of Steel syndrome in COL27A1.pG697R carriers (mean age 42), and compared their findings to 31 randomly selected age and sex matched Puerto Rican non-carriers, and also to published reports of population prevalences of key clinical features for similar age groups where available.Information from inpatient, outpatient, emergency and private practice settings housed in the Mount Sinai health system since 2004 was reviewed by two clinical experts independently. This data includes laboratory reports, radiological data, pathology results, operative and inpatient/outpatient progress notes, discharge summaries, pharmacy, and nurses reports. The clinical experts examined for clinical features similar to those reported for Steel syndrome cases in Due to the process of recombination, individuals from populations that have undergone recent admixture can exhibit a mosaic of genetic ancestry along their genome. Their genetic ancestry at a given genomic segment , can be inferred from genotype data with the use of non-admixed reference panels of known continental ancestry. We calculated local ancestry in the three homozygous Puerto Rican individuals genotyped on OMNI by first extracting the intersecting sites of the Affymetrix 6.0 array  and merging them with 3 ancestral reference panels. These reference panels consisted of the CEU and YRI samples from the 1KGP in addition to the Native American reference panel described previously that were used as a proxy for European, African and Native American ancestral source populations, respectively. RFMix  was usedTo investigate evidence of a founder effect in Puerto Ricans we ran the IBDNe software  in 2816 https://github.com/gillian-belbin/ibdmapping_ehr_figscripts [https://github.com/elifesciences-publications/ibdmapping_ehr_figscripts). The data frames of analysis results that were used to generate the main and supplementary figures are located here: http://research.mssm.edu/kennylab/data_source_file.tar.gzAll scripts used to generate main and supplementary figures for this manuscript are available for download on GitHub  have raised some questions about aspects of the statistical and methodological approaches, and identified some weaknesses in the methods description, reported sample sizes and in referencing to tables and figures. Furthermore, we felt that some aspects of the work, like IBD detection, need to be explained more effectively to non-expert eLife readers.The various contributors to the Below, we provide a condensed list of Essential Revision actions that we consider need to be addressed in any revised submission.Essential revisions:1) Aspects around IBD – Please provide a more thorough description on the IBD methodology used. We'd recommend starting with a more general description of IBD and its role in causal mutation detection – currently the text is drafted in a manner that almost assumes the reader is very familiar with IBD analysis. Please explain specific terms, like "local ancestry".Second, please justify the use of 3cM in IBD mapping. Third, provide more details in "Materials and methods" on how IBD cliques were identified and QCd.2) Aspects around Statistical Methods – Currently, the manuscript lacks clarity on exactly which statistical models were applied to the data.First, be specific about the regression models referred to in paragraph two of subsection “Detecting a Locus Shared Identical-by-Descent Underlying Extreme Short Stature in 172 Puerto Ricans” – have you modelled the zscore as a function of the number of copies  of the haplotype in a specific clique?Second, the initial analysis of cliques relied on permutation to evaluate significance. Then when restricting analysis to Puerto Rican ancestry subjects  and 480 cliques w/ at least 3 people, are you using significance evaluated from asymptotics? Why the change in methodology? Bonferroni thresholds are mentioned, correcting for how many tests?Third, the analysis of ICD9 codes vs. genotype at COL27A1.pG697R – we assume you did 416 different logistic regressions, coding each subject as yes/no for a particular ICD9 diagnosis – this could be made more clear. Was any correction done here for 416 tests? While the significance values are impressive, we concerned about inflation due to case-control imbalance.Fourth, please repeat ICD9 codes vs. genotype at COL27A1.pG697R association testing using Firth test. It appears that many of these dx categories are quite rare, as is the variant. Logistic regression with case-control imbalance results in serious inflation of test statistics, particularly in the case of rare variants .3) Power of IBD mapping – First, currently no power calculations are presented to substantiate the claim that the IBD mapping approach outperforms GWAS in terms of power. Please present these calculations as the p-value yielded through 10,000 permutations is only p=0.0018, which is substantially less than a GWAS significance threshold. Second, please clarify if the COL27A1.pG697R variant is imputable with 1000 Genomes reference. In the Materials and methods section, you provide detailed description on imputation. Furthermore in paragraph two of subsection “Fine-mapping Short Stature Locus Reveals Putative Link to Mendelian Syndrome” it states that there was 96.7% concordance between variant genotype and the associated haplotype – so wouldn't the GWAS have identified the association using the variant?4) Genetic ancestry and COL27A1.pG697R variant – Currently the text is lacking detailed description of the pathogenic variant. Furthermore, more detail is essential to understand how partitioning into ancestry groups was performed.First, in the functional analysis section . Is the variant predicted damaging by popular annotation tools?- polyphen, sift, cadd, gerp, mutation taster, phyloP etc.Second, please provide a confidence interval for the dating of the variant, (around 36 generations).Third, please provide exact criteria how ancestry groups were defined from PCA vectors. Such details are currently missing from the Materials and methods. Furthermore, please provide clear evidence that suggest the populations that are described in subsection “Local Ancestry Estimation” present Native American are not admixed for European ancestry, or at least not significantly so. 1) Aspects around IBD – Please provide a more thorough description on the IBD methodology used. We'd recommend starting with a more general description of IBD and its role in causal mutation detection – currently the text is drafted in a manner that almost assumes the reader is very familiar with IBD analysis. Please explain specific terms, like "local ancestry".Second, please justify the use of 3cM in IBD mapping. Third, provide more details in "Materials and methods" on how IBD cliques were identified and QCd.We have updated the text in several ways to address these points. We added a description of IBD mapping to the Introduction, Results, including references to previously published IBD mapping papers, Discussion, and Materials and methods. We have also added a supplemental figure schematic outlining the IBD mapping pipeline , referen2) Aspects around Statistical Methods – Currently, the manuscript lacks clarity on exactly which statistical models were applied to the data.First, be specific about the regression models referred to in paragraph two of subsection “Detecting a Locus Shared Identical-by-Descent Underlying Extreme Short Stature in 172 Puerto Ricans” – have you modelled the zscore as a function of the number of copies  of the haplotype in a specific clique?Second, the initial analysis of cliques relied on permutation to evaluate significance. Then when restricting analysis to Puerto Rican ancestry subjects  and 480 cliques w/ at least 3 people, are you using significance evaluated from asymptotics? Why the change in methodology? Bonferroni thresholds are mentioned, correcting for how many tests?Third, the analysis of ICD9 codes vs. genotype at COL27A1.pG697R – we assume you did 416 different logistic regressions, coding each subject as yes/no for a particular ICD9 diagnosis – this could be made more clear. Was any correction done here for 416 tests? While the significance values are impressive, we concerned about inflation due to case-control imbalance.Fourth, please repeat ICD9 codes vs. genotype at COL27A1.pG697R association testing using Firth test. It appears that many of these dx categories are quite rare, as is the variant. Logistic regression with case-control imbalance results in serious inflation of test statistics, particularly in the case of rare variants .We have revised the text substantially to update and clarify the details of the analysis, removed discussion of the additive IBD analysis as it does not add substantially to the our findings, and reordered the presentation of the analysis steps for better comprehension. To address the first point, we have explicitly described how we modeled the z scores in the Materials and method section. For the second point, we defined the Bonferonni adjustment in the Results and report Bonferonni adjusted genome-wide significance. To address the third point, we have clarified the description of the PheWAS analysis in the Results and Material and methods sections.Regarding the fourth and fifth point, we agree with the reviewers general concern, however best practices for PheWAS models that jointly account for case:control imbalance, rare variants/ICD9 codes, and elevated distant relatedness remain an open problem for the genomics community. At the reviewers suggestion, and to examine this question, in addition to running an Generalized Linear Model (GLM), we ran three other score based tests; the Firth test that incorporates a bias-reduction for small numbers of observations ; a linear mixed model (GCTA) to account for distant relatedness ; and a test that uses saddlepoint approximation (SPATest) to account for case:control imbalance . GLM and Firth test are each well vetted methods for case:control association, and GLMs are extensively used in PheWAS analysis. We also ran GCTA in binary trait mode and SPATest, which the author have kindly made available for download pre-publication. We added age, sex and the first five PC eigenvectors as covariates to each model. As anticipated, we observed over inflation in the distribution of the PheWAS test statistic, measured by λ (λ), for all four score based models , indicating that none of these models is perfectly calibrated. The top signal we originally reported, the ICD9 code linked to vertebral osteomyelitis (730.08), was the top signal in all four models. The other two signals, cervical spondylosis (721.0) and knee arthropathy (716.98) were each highly ranked, but only significantly in two of the four models. Therefore, to investigate whether there was support for the PheWAS findings, we extended the manual analysis of patient medical records, representing hundreds of hours of manual clinical chart review by two independent clinical experts. Information from inpatient, outpatient, emergency and private practice settings housed in the Mount Sinai health system since 2004 was reviewed. This data includes laboratory reports, radiological data, pathology results, operative notes and discharge summaries, pharmacy, inpatient results reviews, and nurses reports. The clinical experts examined for clinical features similar to those reported for Steel syndrome cases in Flynn et al., 2010, including developmental dysplasia of the hip , carpal coalition, scoliosis, and joint and spine anomalies. Both clinical experts reviewed patient records independently and compared notes to resolve discrepancies. They reviewed the records of the 34 youngest COL27A1.pG697R carriers (mean age 42), and compared their findings to 31 randomly selected age and sex matched Puerto Rican non-carriers, and also to published reports of population prevalences of key clinical features for similar age groups where available. In general they found increased incidence of strong to moderate spine and joint degradation in carriers compare to noncarriers (p<0.03). Specifically, they found evidence of a significant incidence of cervical stenosis in carriers 5/34 carriers compared to 0/31 non-carriers (Fisher exact test p<0.05). This was especially interesting as cervical stenosis, or narrowing of the upper spinal canal, is thought to be rare in individuals under the age of 50. Although not reaching statistically significance, they also found a trend of 2-fold higher rates of scoliosis , arthritis , and lumbar spine degradation  in carriers compared to non-carriers and previous published reports. We report the new analysis of the detailed chart review and additional PheWAS models in the Results, Discussion, 3) Power of IBD mapping – First, currently no power calculations are presented to substantiate the claim that the IBD mapping approach outperforms GWAS in terms of power. Please present these calculations as the p-value yielded through 10,000 permutations is only p=0.0018, which is substantially less than a GWAS significance threshold. Second, please clarify if the COL27A1.pG697R variant is imputable with 1000 Genomes reference. In the Materials and methods section, you provide detailed description on imputation. Furthermore in paragraph two of subsection “Fine-mapping Short Stature Locus Reveals Putative Link to Mendelian Syndrome” it states that there was 96.7% concordance between variant genotype and the associated haplotype – so wouldn't the GWAS have identified the association using the variant?The specific issue here is that IBD tagged the three true homozygotes perfectly, whereas a standard imputation pipeline (SHAPEIT2/IMPUTE2/1000 Genomes Project phase 3 reference panel) called one of the homozygotes as a heterozygote. Running the same recessive model in both cases results in an difference of the p-value of association from p<2.57x10-11  to p<0.001  comparing IBD mapping with SNP imputation, respectively. In addition, there were over 10 million SNP tests performed in the imputed dataset compared to the 480 IBD-cliques tested in the IBD mapping approach, so the Bonferonni adjusted genome-wide significance testing burden is considerably reduced in the IBD mapping approach. Despite the frequencies and effects sizes being similar for both approaches, the p-values and absolute rank of the test of association are diminished for the imputation approach. Thus, in this case, IBD mapping was better powered compared to the SNP imputation approach for association of the recessive COL27A1.pG697R allele. Recent methods that leverage IBD for imputation, such as EAGLE , have demonstrated improved accuracy of low frequency and rare variant imputation and may have improved imputation of the homozygotes in this study. However, the broader point is that imputation methods are dependent on representative haplotypic diversity in the sequence reference panels. Since rare disease variants are more likely to be population-specific or geographically localized, lacking the relevant diversity in sequence reference panels or sequence discovery efforts means that inference of rare variants within cohorts independent of available reference panels via methods like IBD mapping, which rely on inferring distant familial relationships, are likely to remain important for disease variant discovery.We have updated the text to clarify details of this analysis in subsection “Fine-mapping Short Stature Locus Reveals Putative Link to Mendelian Syndrome”, 4) Genetic ancestry and COL27A1.pG697R variant – Currently the text is lacking detailed description of the pathogenic variant. Furthermore, more detail is essential to understand how partitioning into ancestry groups was performed.First, in the functional analysis section . Is the variant predicted damaging by popular annotation tools?- polyphen, sift, cadd, gerp, mutation taster, phyloP etc.Second, please provide a confidence interval for the dating of the variant, (around 36 generations).Third, please provide exact criteria how ancestry groups were defined from PCA vectors. Such details are currently missing from the Materials and methods. Furthermore, please provide clear evidence that suggest the populations that are described in subsection “Local Ancestry Estimation” present Native American are not admixed for European ancestry, or at least not significantly so.We have added details to the text to clarify the first and third points in the Results and Materials and methods sections,


Due to the advancement in high throughput technology, single nucleotide polymorphism (SNP) is routinely being incorporated along with phenotypic information into genetic evaluation. However, this approach often cannot achieve high accuracy for some complex traits. It is possible that SNP markers are not sufficient to predict these traits due to the missing heritability caused by other genetic variations such as microsatellite and copy number variation (CNV), which have been shown to affect disease and complex traits in humans and other species.In this study, CNVs were included in a SNP based genomic selection framework. A Nellore cattle dataset consisting of 2230 animals genotyped on BovineHD SNP array was used, and 9 weight and carcass traits were analyzed. A total of six models were implemented and compared based on their prediction accuracy. For comparison, three models including only SNPs were implemented: 1) BayesA model, 2) Bayesian mixture model (BayesB), and 3) a GBLUP model without polygenic effects. The other three models incorporating both SNP and CNV included 4) a Bayesian model similar to BayesA (BayesA+CNV), 5) a Bayesian mixture model (BayesB+CNV), and 6) GBLUP with CNVs modeled as a covariable (GBLUP+CNV). Prediction accuracies were assessed based on Pearson’s correlation between de-regressed EBVs (dEBVs) and direct genomic values (DGVs) in the validation dataset. For BayesA, BayesB and GBLUP, accuracy ranged from 0.12 to 0.62 across the nine traits. A minimal increase in prediction accuracy for some traits was noticed when including CNVs in the model .This study presents the first genomic prediction study integrating CNVs and SNPs in livestock. Combining CNV and SNP marker information proved to be beneficial for genomic prediction of some traits in Nellore cattle.The online version of this article (10.1186/s12864-018-4787-6) contains supplementary material, which is available to authorized users. Genomic prediction is the estimation of breeding values using genetic variations such as single nucleotide polymorphism (SNP) . IdeallyIn the last ten years, attention has been drawn to CNVs, as they are deemed to impact phenotypes. CNVs are structural variations larger than 50 bp in the form of insertions, deletions, duplications, inversions and translocations , 12. ForGiven the ubiquity of immunity related genes that coincide with CNVs, there are likely many more immunity traits that are influenced by CNVs. Antimicrobial peptides (AMPs) represent a class of copy number variable genes within livestock species that function as part of the innate immune response to pathogens. The β-defensin class of AMPs appears to be copy number variable in several livestock species, but most notably in cattle , 16. TheThe two 1000 human genome structural variation (SV) papers reported multiple types of CNVs, including deletions, tandem duplications, novel sequence insertions and mobile element insertions , 23. MilBy comparing deletion genotypes with genotypes of nearby SNP, Mills et al.  found, cBos indicus cattle. We detected 17 CNVs significantly associated with seven growth traits [CNV can function either as causal variants or as tagging markers. A human study found that CNVs captured around 18% of the total variation in gene expression in cultured lymphocytes . Furtherh traits . The objhttp://www.gensys.com.br/home/show_page.php?id=701). Phenotypes used to fit the models comprised records from 542,918 animals born between 1985 and 2011, and raised in 243 grazing-based herds. The evaluated traits included birth weight (BW), post weaning gain (PWG), weaning gain (WG), carcass conformation at weaning (CW), muscling at weaning (MW), carcass finishing precocity at weaning (PW), carcass conformation at yearling (CY), muscling at yearling (MY) and carcass finishing precocity at yearling (PY). Conformation, finishing precocity and muscling traits (CPM) were based on recorded visual scores assigned in a discrete ordered scale, relative to the animals of the same management group  were based on Best Linear Unbiased Predictor (BLUP) estimates of single-trait animal models obtained from routine genetic evaluations using performance and pedigree data from the database . For eaIn this study, genomic prediction analysis was carried out using de-regressed EBV (dEBVs) instead of EBVs as the response variable in order to remove any bias due to double counting phenotypic and pedigree information. De-regressing of EBVs was performed according to the approach proposed by  which reBos indicus) were genotyped for 777,962 SNP markers with the Illumina BovineHD BeadChip assay. This data builds on previously published studies [SNP) < 0.98 and Fisher’s exact test P-value for Hardy-Weinberg Equilibrium (HWE) < 1 × 10− 5.A total of 2230 Nellore animals  was used to detect common CNV events. This is because other traditional CNV discovery methods are not designed to find common CNVs but to report more CNVs . In totan = 55) with frequency > 5%; 2) all deletions (n = 72) and 3) all deletions and biallelic duplication (n = 173) , all 231 CNVs were assigned into 2 categories: CNV events with simple and distinct genotype clusters or CNV with multiallelic and complex genotype clusters. Deletions and biallelic duplications can be genotyped if the clusters representing different genotypes are sufficiently distinct. Based on this classification and event frequency, three different CNV subsets were tested and used in genomic prediction analyses: 1) common deletions (The first three models used to estimate DGVs considering SNP effects only are the following: 1) Bayesian regression model (BayesA), a mixture Bayesian model (Bayes B) and a GBLUP model without polygenic effects (GBLUP). All three models accounted for additive effects only.The first approach to combine SNP marker information and CNV information (BayesA+CNV) is described below. In this approach we assume that SNP effects and CNV effects contribute to the genetic variance. Using this approach the effects of variants will be modeled as the following:yi is the pseudo-phenotype (dEBV) for animal i, μ is the overall mean, xik is the SNP marker genotype for animal i at locus k coded as the number of copies of minor allele, bk is the kth SNP effect, zil is the CNV genotype for animal i, gl is the lth CNV effect and ei is the residual term.Where For CNV effects, a flat prior was assumed since the number of CNVs is several folds smaller than the number of observations therefore allowing the data to drive the inferences of CNV effects.xik is the genotype of the kth marker, coded as the number of copies of the minor allele, bk is the effect of marker k, and Ik is an indicator variable that is equal to 1 if the kth marker has a non-zero effect on the trait and 0 otherwise. A binominal distribution with known probability π = 0.01 was assumed for Ik. As opposed to SNPs, a mixture distribution was not assumed for CNVs, since the number of CNVs is small.The second approach to incorporate SNP markers and CNVs is a mixture model (BayesB+CNV) similar to the model in eq. (1) except for the SNP effects part y is the vector of dEBVs, b is a vector of fixed CNV covariates coded as − 1, 0, 1 for neutral, loss and gain states respectively, a is the vector of random animal additive effects and e is the vector of residual terms. The direct genomic value (DGV) was calculated as:A third approach is GBLUP where CNVs were modeled as a covariate which can be described as:DGV is the vector of direct genomic values, X is the matrix of CNV covariates, b is the vector of CNV effects, Z is the matrix of genotypes and u is the vector of estimated SNP effects.Where Models adopted in this study were compared using the following criteria: Pearson’s correlation between dEBV and DGV, mean squared error of Prediction (MSE) and the regression slope of dEBVs on DGVs for animals in the validation dataset in order to test the inflation/deflation degree of genomic predictions.Gibbs sampler was used with a chain of 90,000 iterations for each parameter, with a burn-in period of 10,000 iterations and a sampling interval of 100 iterations. Convergence testing was performed for all parameters including SNP effects following Geweke’s (1992)  and Heidn = 55) with frequency > 5%; 2) all deletions (n = 72) and 3) all deletions and biallelic duplication (n = 173)  were pure deletions. Within the remaining CNVs, only 12 CNVs (5.19%) have duplication frequency > 5% and all other 124 CNV had duplication frequency < 5%. Based on CNV classification and event frequency (Methods), three different CNV subsets were tested and used in genomic prediction analyses: 1) common deletions , the slope of the regression (b1) of dEBVs on DGV was evaluated. Table A study using the same dataset  revealedIn this study, including copy number variation information into genomic selection proved to be beneficial for some traits. However, their impact varied from model to model and from trait to trait and a universal model is yet to be developed. The small increase in prediction accuracy seen when integrating CNVs could be due to their function either as causal genes or as tagging markers. This might help in the prediction of complex traits and explain part of the missing heritability that SNP markers fail to capture. Future efforts are warranted to better utilize CNV information in genomic evaluation methods.Additional file 1:Table S1. Detail information of the CNVs detected with high confidence. (XLSX 37 kb)


Regional differences in health-related phenotypes have been detected between and within countries. In Scotland, regions differ for a variety of health-related traits and display differences in mean lifespan of up to 7.5 years. Both genetics and lifestyle differences are potential causes of this variation. Using data on obesity-related traits of ~11,000 Scottish individuals with genome-wide genetic information and records of lifestyle and socioeconomic factors, we explored causes of regional variation by using models that incorporate genetic and environmental information jointly. We found that variation between individuals within regions showed substantial influence of both genetic variation and family environment. Regional variation for most obesity traits was associated with lifestyle and socioeconomic variables, such as smoking, diet and deprivation which are potentially modifiable. There was limited evidence that regional differences were of genetic origin. This has important implications for healthcare policies, suggesting that inequalities can be tackled with appropriate social and economic interventions. Health-related traits are known to vary geographically. Here, Amador and colleagues show that regional variation of obesity-related traits in a Scottish population is influenced more by lifestyle differences than it is by genetic differences. Regions in Scotland differ for several health-related traits, and display differences in lifespan of up to 7.5 years in men and 4.9 years in women5. Complex traits related to obesity, such as body size, have a genetic basis with heritabilities between 30 and 60%7. On the other hand, changes in body size and the so-called global obesity pandemic are usually associated with environmental changes related to diet, exercise levels and other socioeconomic changes9. From this perspective both genetics and lifestyle differences are potential causal factors for the observed regional differences in health-related traits15. Disentangling the underlying causes of variation in health-related traits has direct implications for the welfare of future generations; however, genetic and environmental variation may be closely associated and therefore difficult to separate.Marked differences in health-related traits and diseases exist between countries and between regions within countriesIn this study, we explored causes of regional variation in data on obesity-related traits as indicators of the health status of ~11,000 Scottish individuals with genotypic records and a variety of measurements of possible causal lifestyle and socioeconomic factors. Our aim was to exploit these high-quality genomic data and high fidelity and deep phenotypic, lifestyle and socioeconomic data to identify the factors contributing to the differences between regions in health-related traits. As expected, obesity-related traits such as body mass index and weight differ significantly between regions. By accounting for both the genomic data and the environmental information in our analyses, we showed that trait variation is substantially influenced by both genetic variation and family environment. However, the regional variation for most obesity traits was associated with lifestyle and socioeconomic variables, such as deprivation, physical activity, etc. rather than the regional genetic structure of the sample. These results imply that although genes and family environment are important determinants of health-related traits, regional differences are attributable mostly to potentially modifiable environmental factors.The objective of this work was to disentangle genetic and environmental components of health-related traits, linked to geographic variation. We explored a Scottish population consisting of ~11,000 individuals with different degrees of kinship, genotyped for ~500K markers, phenotypes for 11 traits (8 anthropometric and 3 metabolic traits), geographic covariates  reflecting the regional genetic structure of the data (gPCs) and a large set of environmental covariates (socioeconomic and lifestyle (SELS)). We fitted jointly genetic and environmental information in a range of statistical models, in an innovative approach to disentangle the causes of regional variation. For more information, see Supplementary Tables In order to illustrate the geographic differences existing in Scotland, in the Basal Model Fig.  we adjusG) together with sex, age and clinic in a mixed model analysis (Family Model). We tested the residuals from this model for remaining regional differences. When including the genomic relationship matrix in the model, the differences between regions disappeared for two traits  (Table α = 0.05) after adjusting for the genetic relatedness and family structure in the sample.To test if the regional differences detected were due to the genetic relatedness of the sample, we adjusted for kinship by fitting a genomic relationship matrix (14 (for more information see Methods). We adjusted for the gPCs  and we used the residuals of the model to test whether the regional differences remained significant (Table α = 0.05) after adjusting for the gPCs, i.e., the genetic differences between regions do not explain the regional differences in the studied traits.We then explored if the regional differences could be explained by the population genetic structure of the sample, i.e., the genetic differences between the regions. To do that we adjusted for ten geographic principal components (gPCs) that represent geographical population genetic structure in the cohort. The gPCs were calculated using a subset of unrelated individuals and unlinked markers and then extrapolated to the rest of the population. They reflect the genetic differences between regions as shown in Amador et al.α = 0.05) between regions and these differences had become non-significant for waist circumference, BMI, WHR and HDL, indicating that the regional differences are explained by the measured SELS variables. We fitted a final model including both the gPCs and the SELS covariates . When we tested the significance of the region in the residuals of this model  to account simultaneously for the genetic sharing among distant and closely related individuals7; and two environmental relationship matrices that represented shared environments between members of a couple (C) and siblings (S)17  and of the pedigree heritability  did not change significantly when including the extended set of covariates in the model, even for those traits where the environmental covariates contributed to regional differences. Furthermore, for most of the traits the estimates of variance due to the shared environments of couples and siblings (C and S) were robust to the inclusion of the extended set of SELS variables (Table The estimates of the genotyped-single-nucleotide polymorphism (SNP) heritability (C) was significant for eight traits although for HDL the significance disappeared after including the full set of environmental covariates. This would suggest that, for HDL, some of the phenotypic similarities observed in couples can be accounted for by the recorded lifestyle or socioeconomic variables. In addition, the variance captured by the sibling environment (matrix S) was detectable only for two traits (BIA fat and TC). For creatinine and HDL, the variance captured by sibling environment was not different from zero in the Family Model, but became significant after including the whole set of covariates. In all the cases the differences in proportion of the variance captured between the Structure and Environment Model (including the whole set of covariates) and the Family Model explored were subtle.The proportion of the variance captured from the couple environment  still explains 0.5% of the variance in the trait  in one index and the associations with SIMD corroborate the described associations between socioeconomic inequalities and health12. The results showed that people living in less deprived areas are taller, with higher levels of HDL, and lower BMI and BIA fat levels. Other environmental variables also explained part of the differences between regions such as years of education, level of activity or alcohol intake. These more specific variables could be also picking up effects of more complex environmental variables such as socioeconomic status .The deprivation index SIMD was the environmental variable significantly affecting the largest number of traits, although the variance accounted for by SIMD was relatively small for all traits  areas and making an impact on the relevant strata of the population will be crucial.The increased prevalence of obesity is a worldwide health concern. Reducing the incidence of obesity by effective intervention policies in affected areas would provide substantial benefits in health and quality of life of individuals concerned and significantly reduce associated healthcare costs to the community22. Ethical approval for the study was given by the NHS Tayside committee on research ethics (ref: 05/s1401/89). Governance of the study, including public engagement, protocol development and access arrangements, was overseen by an independent advisory board, established by the Scottish government. Research participants gave consent to allow both academic and commercial research.We used the data from the Generation Scotland: Scottish Family Health Study (GS:SFHS)23 to exclude single-nucleotide polymorphisms (SNPs) that had a missingness > 2% and a Hardy–Weinberg Equilibrium test P < 10−6. Markers with a minor allele frequency smaller than 0.05 were discarded. Duplicate samples, individuals with gender discrepancies and those with more than 5% missing genotypes were also removed. The resulting data set was merged with the 1092 individuals of the 1000 Genomes population24 and a principal component analysis (PCA) was performed using GCTA18. Individuals more than ix standard deviations away from the mean of principal component 1 and principal component 2 were removed as potentially having African/Asian ancestry as shown in Amador et al.14 After quality control individuals had genotypes for 519,819 common SNP spread over the 22 autosomes. Of the ~24,000 individuals in GS:SFHS, the number of individuals without missing values for any of the covariates used in our study was N = 11,118  so we used this set of samples for all the analyses in order to allow comparisons between the models.Individuals were genotyped with the Illumina HumanOmniExpressExome-8 v1.0 or v1.2. We used PLINK version 1.9b2c2), ABSI25 (Waist/(BMI2/3×Height1/2))) and metabolic traits  were set to missing , waist circumference, hips circumference, WHR(waist/hips); BMI(weight/height20), years of education, household size, vehicle ratio and job status. Lifestyle covariates are alcohol units consumption, smoking status, activity level, fruit units eaten per day and consumption of different foods . A detailed description of these variables is shown in Supplementary Table We explored a large set of covariates representing potential environmental factors influencing differences between individuals in the study. We fitted these factors as putative predictors of trait variation in statistical models as discrete or continuous covariates depending on their nature. The covariates lay in three categories: basic, socioeconomic and lifestyle. Basic covariates were sex, age, and clinic where the phenotypes were measured; socioeconomic covariates were SIMD . We performed a PCA in this subset , we calculated the loadings of the SNPs contributing to each of the first 10 principal components, and we computed the values for these principal components for the whole 11,118 individuals used in subsequent analyses. The resulting set of variables represents well the regional genetic structure of the sample as shown in Amador et al.14 We refer to these 10 PCs as gPCs or geographical population genetic structure.We also calculated a set of variables that represent genomic geographic origin through a PCA. To do so, we created a pruned subset of SNPs in approximate linkage equilibrium with each other and we removed markers from chromosome 6 in the major histocompatibility complex region and markers in the 8p23.1 region17: G is a genomic relationship matrix (GRM) containing relatedness between pairs of individuals based on identity-by-state at the genotyped SNPs26. K is a matrix representing pedigree relationships as in Zaitlen et al.7 This is a modification of G obtained by setting all entries in G lower than 0.025 to 0. C is a matrix representing common environmental effects shared between couples. The matrix contains a value of 1 between pairs of individuals identified as members of a couple as in ref. 17; S is a matrix representing common environmental effects shared between siblings. The matrix contains a value of 1 between pairs of individuals identified as siblings as in ref. 17. G and K were calculated using GCTA18; The environmental matrices (C and S) were created using R version 3.1.116.We used design matrices representing genomic or environmental relationships as in Xia et al.A summary with names of models and analyses undertaken is shown in Fig. 16, we testing the significance of the variable “region where individuals live” (region) in a linear model.Firstly, to illustrate which traits show variation between the different regions in Scotland we explored in a simple linear regression if there were differences in the traits between council areas . Using the statistical package R18. The basic general mixed linear model explored is shown in (1).y is an n × 1 vector of observed phenotypes with n being the number of individuals, β is a vector of fixed effects and X is its design matrix, gg is an n × 1 vector of the total additive genetic effects of the individuals captured by genotyped SNPs with gg~ N. ε is an n × 1 vector for the residuals.To explore if trait variation was due to genetic structure or to the environmental differences, we used variance component analyses. This way the models take appropriately into account the kinship in the sample. All the analyses were implemented in GCTAWe fitted different sets of covariates in this general framework to explore four different models:The Family Model (F) included only sex, age and clinic as fixed effects.The Structure Model (S) included the geographical principal components (gPCs), together with sex, age and clinic as fixed effects.The Environment Model (E) included the SELS covariates, together with sex, age and clinic in the vector of fixed effects.The Structure and Environment Model (S+E) included all gPCs and SELS, together with sex, age and clinic in the vector of fixed effects.G (σ2g) in all these models and the variance explained by covariates included was calculated asb is the effect of the covariate estimated from the GREML analyses.For each model, we predicted by the BLUP (best linear unbiased prediction) method the total genetic  effect of each individual together with their residuals. We tested if the residuals were significantly different between the regions to show if the differences between regions were explained by the models or remained unexplained. We also estimated the variance explained by matrix G) and the gPCs allowed us to test if adding the geographic structure to Model (F) (including only a genetic matrix) would account for the differences between regions. Model (E) including G and the SELS covariates compared with Model (F), allowed us to test if the differences were due to the SELS variables. Model (S+E) included both gPCs and SELS together for comparison with the two previous models.Model (S) including a genetic matrix  and K as shown in (3)gkin is an n × 1 vector of the extra genetic effects associated with the pedigree for relatives with gkin~ N, ec and es are n × 1 vectors representing the common environmental effects shared by couples or siblings, with ec~ N and es~ N.We fitted again the models described above, including in addition another genetic and two environmental matrices fitted in linear mixed models as in Xia et al.Results for the four models (Model (F), Model (G), Model (E) and Model (G+E)) were also explored in the context of Eq. .Data are available from the MRC IGMM Institutional Data Access/Ethics Committee for researchers who meet the criteria for access to confidential data. GS:SFHS data are available to researchers on application to the Generation Scotland Access Committee (access@generationscotland.org). The managed access process ensures that approval is granted only to research which comes under the terms of participant consent which does not allow making participant information publicly available.Supplementary InformationPeer Review File


Litopenaeus vannamei (L. vannamei) showed that GS was feasible in penaeid shrimp. However, the applicability of GS might be influenced by many factors including heritability, marker density and population structure etc. Therefore it is necessary to evaluate the major factors affecting the prediction ability of GS in shrimp. The aim of this study was to evaluate the factors influencing the GS accuracy for growth traits in L. vannamei. Genotype and phenotype data of 200 individuals from 13 full-sib families were used for this analysis.Due to the great advantages in selection accuracy and efficiency, genomic selection (GS) has been widely studied in livestock, crop and aquatic animals. Our previous study based on one full-sib family of L. vannamei was estimated firstly based on the full set of markers (23 K). It was 0.321 for body weight and 0.452 for body length. The estimated heritability increased rapidly with the increase of the marker density from 0.05 K to 3.2 K, and then it tended to be stable for both traits. For genomic prediction on the growth traits in L. vannamei, three statistic models  showed similar performance for the prediction accuracy of genomic estimated breeding value (GEBV). The prediction accuracy was improved with the increasing of marker density. However, the marker density would bring a weak effect on the prediction accuracy after the marker number reached 3.2 K. In addition, the genetic relationship between reference and validation population could influence the GS accuracy significantly. A distant genetic relationship between reference and validation population resulted in a poor performance of genomic prediction for growth traits in L. vannamei.In the present study, the heritability of growth traits in L.vannamei population. The genetic relationship between the reference population and the validation population showed significant effects on the accuracy for genomic prediction. Therefore it is very important to optimize the design of the reference population when applying GS to shrimp breeding.For the growth traits with moderate or high heritability, such as body weight and body length, the number of about 3.2 K SNPs distributed evenly along the genome was able to satisfy the need for accurate GS prediction in the investigated  Litopenaeus vannamei (L. vannamei), have been performed in a number of countries since 1993 [L. vannamei contributes approximately 42% of the global shrimp production, so this species is regarded as one of the most important cultured shrimp species in the world [L. vannamei.Selective breeding is recognized as a main propelling force for the development of efficient and sustainable aquaculture production . For mannce 1993 . These phe world . Howeverhe world , limitatL. vannamei accelerated the MAS for the interesting economic traits [L. vannamei have been performed, and several significant QTLs for growth traits were revealed [L. vannamei is controlled by multiple QTLs with small effects, so the traditional MAS tend to be inefficient. Alternatively, genomic selection (GS), one kind of MAS which can use genome-wide markers to calculate genomic estimated breeding value (GEBV) for candidate breeding animals [MAS has become a promising method to speed up the selective progress in both plants and animals. The development of molecular markers in c traits –11. Deterevealed . However animals , 14, has animals . GS has  animals –18. Comp animals –21. Alth animals . More re animals –25. The  animals –26. TherL. vannamei based on one full-sib family [L. vannamei.In a previous study, we found that GS was feasible for growth traits in b family . For witb family . HoweverL. vannamei.In the present study, all samples from 13 families, which derived from different commercial lines were used. Therefore, it is a good material to estimate the impact of potential factors on the accuracy of GS in applied breeding programs. This study will be helpful for the further application of GS in 2 tank for seedlings. After grown to 3 cm, 50 individuals from each family were transferred to a 10 m2 pond for culture. At the harvest, two hundred individuals were randomly collected, and two growth traits, including body weight and body length, were measured.All samples used in this study were from Guangtai Marine Breeding Company in Hainan province, China. Totally, 13 full-sib families (offsprings of 13 dams and 13 sires) were created in July 2015. Each full-sib family was cultured separately in the 5 mGenomic DNA of each sample was isolated from muscles of shrimp using Plant Genomic DNA Kit  following the manual instruction. The purity and integrity of extracted DNA was determined by using a NanoDrop 1000 Spectrophotometer  and electrophoresis on 1% agarose gel. Qualified genomic DNA was stored at −20 °C.All individuals were genotyped using 2b–RAD method which was performed by Oebiotech Company . Briefly, the 2b–RAD libraries were prepared following the standard protocol , and theThe quality control of SNP data was performed using R software . SNPs wi2) of each trait was defined as the ratio of additive genetic variance to the total phenotypic variance (VA/VP), and was estimated using the genetic relationship matrix calculated based on genetic markers. The variance components were estimated using the package rrBLUP [A.mat function in rrBLUP package with the default options. Then, the kin.blup function, taking G matrix as covariance matrix, was used to estimate the variance components. Additionally, 10 Random-distributed marker subsets  were used to investigate the impact of marker density on heritability estimation. To reduce the sampling error, 50 random selections of SNPs for each subset were chosen from the full marker set according to the subset size. The heritability was calculated by averaging the results of the 50 random selections for each marker subset. The method for the heritability estimation based on each marker subset was the same as that calculated using the full set of markers.In this study, the narrow-sense heritability  using the k-means method in R software [A multidimensional scaling (MDS) analysis was performed using the software  to verifsoftware . Then thsoftware  and the software .ij = 0, 1, 2 for genotypes AA, AB and BB, respectively for the i th animals and j th SNP; g is the vector of additive effects of markers estimated by the models.Three statistical models were used to predict the genomic estimated breeding value (GEBV) for two growth traits: ridge regression best linear unbiased prediction (RR-BLUP), Bayesian LASSO (BL), and BayesA (BA). All models estimated the marker effects by incorporating the markers as random effects, and no fixed effects were fitted in the models. The RR-BLUP model was fitted by the R package rrBLUP , and theThe accuracy of genomic prediction was measured as the correlation between the GEBV of shrimp in validation population and their observed phenotypes divided by the square root of trait heritability (estimated using full marker). In order to assess the impact of several factors on the accuracy of genomic prediction, different cross-validation approaches were used.To determine the effect of marker density on the prediction accuracy, a five-fold cross-validation approach was carried out . In brieTo investigate the impact of the genetic relatedness between reference and validation population on prediction accuracy , two groThe phenotypic statistics for body weight and body length were given in Table The estimated heritability was shown in Fig. The multidimensional scaling (MDS) analysis of an Identity By State (IBS) matrix for 200 samples was shown in Fig. The average prediction accuracy for body weight and body length with different marker density was shown in Fig. Across the growth traits, the RAND group showed higher prediction accuracy than the DIST group Fig. . For bodL. vannamei was estimated by using pedigree information, which is time-consuming and expensive [L. vannamei by using genome-wide markers. The estimated heritability based on the full-set of genome-wide markers was 0.321 for body weight and 0.452 for body length. This value estimated in this study was located at the reported range of 0.24–0.515 estimated based on pedigree information for growth traits in L. vannamei [L. vannamei.Narrow-sense heritability is a central parameter in quantitative genetics and represents the proportion of total phenotypic variance that is due to additive genetic effects. Traditionally, the heritability of growth traits in xpensive , 38–40. vannamei –44. Howevannamei . From thL. vannamei might be controlled by a large number of QTLs with small effects.There are already some reports to evaluate the performance of various GS models –47. In tNe) [Ne, the number of independent segments in the genome is expected to be small, and fewer independent segments means that fewer markers are needed to mark all segments [An increase of marker densities generally resulted in raised accuracy predicted by three statistical models for both body length and body weight. However, above a threshold of approximately 3.2 K, the increase of marker density showed minor effects on the improvement of prediction accuracy. Similar phenomenon was found in other species although the threshold might be different. With a SNP density of around 5 K, the prediction accuracy for host resistance to sea lice in farmed Atlantic salmon reached a plateau . For sevNe) , 52. At segments . In the L. vannamei, we designed two different groups (DIST and RAND) according to the population stratification. As a result, relatively lower accuracy was observed across both traits in DIST group than that in RAND group. It suggested that the poor relatedness between the reference and validation population had negative effect on genomic prediction accuracy. This result was crucial for the future application of GS in shrimp. For selective breeding in shrimp, the core germplasm is generally made up of many families, which easily leads to population stratification. Therefore, the composition of the reference population is very important for the predicted accuracy of GS. The results of the current study also suggested that high accuracy of GS can be achieved by the optimal design of the reference population even if the population stratification exists. To achieve high prediction accuracy of GS, the reference population needs to be more representative for the prediction population in genetic diversity. For GS of shrimp in family-based breeding programs, suitable sampling from different families may be an effective strategy for the design of reference population.The accuracy of genomic prediction can also be affected by the genetic relatedness of the reference and validation population , 55. In L. vannamei. Furthermore, it is especially interesting that high prediction accuracy can be obtained with relatively low marker density (3.2 K), which implies that the genomic selection for growth traits of L. vannamei could be realized with low costs for marker genotyping. Nevertheless, several factors should be taken into consideration in practical application: firstly, different breeding populations may correspond to different Ne, and thus require different number of markers to perform genomic selection; Besides, the expected accuracy of GS will rapidly decay as a result of the decreasing family relationship after several generations of selection. Therefore, higher marker density is expected to capture more LD in the population for multi-generation selection. In addition, although the size of current population is limited, the genetic relationship is complex and its impact on the accuracy of GS is significant. Considering that the shrimp may be from different population in most shrimp breeding projects, the genetic relationship may be more complex than the studied population. Therefore, it is extremely valuable to analyze the population structure firstly and then optimize the design of the reference population to achieve the high accuracy of GS in shrimp breeding program. In genomic selective breeding programs, maximizing the genetic diversity and balancing the contribution of alleles may be an effective strategy for the design of reference population.The previous studies reported that the performance of genomic prediction may depend on many factors, such as the trait heritability, the genetic architecture, the marker density, the training set size, and the relatedness between the training population and validation population –58. HoweL. vannamei breeding programs. All three models appear to be applicable for GS of growth traits in L. vannamei. Relatively low marker density (around 3.2 K) was sufficient for accurate prediction on the breeding value for both traits in the studied population. The relatedness between reference and validation set showed a great effect on the prediction accuracy for the growth traits in shrimp. Therefore, it is necessary to analyze the population structure and then optimize the design of the reference population in the future application.In this study, the heritability estimated using a full set of genome-wide markers was 0.321 and 0.452 for body weight and body length, respectively. The present study showed that genomic selection was an efficient approach in


Associations between traits are prevalent in nature, occurring across a diverse range of taxa and traits. Individual traits may co-evolve with one other, and these correlations can be driven by factors intrinsic or extrinsic to an organism. However, few studies, especially in microbes, have simultaneously investigated both across a broad taxonomic range. Here we quantify pairwise associations among 48 traits across 784 diverse yeast species of the ancient budding yeast subphylum Saccharomycotina, assessing the effects of phylogenetic history, genetics, and ecology.We find extensive negative (traits that tend to not occur together) and positive (traits that tend to co-occur) pairwise associations among traits, as well as between traits and environments. These associations can largely be explained by the biological properties of the traits, such as overlapping biochemical pathways. The isolation environments of the yeasts explain a minor but significant component of the variance, while phylogeny  plays an even more limited role. Positive correlations are pervasive among carbon utilization traits and track with chemical structures  and metabolic pathways, suggesting a molecular basis for the presence of suites of traits. In several cases, characterized genes from model organisms suggest that enzyme promiscuity and overlapping biochemical pathways are likely mechanisms to explain these macroevolutionary trends. Interestingly, fermentation traits are negatively correlated with the utilization of pentose sugars, which are major components of the plant biomass degraded by fungi and present major bottlenecks to the production of cellulosic biofuels. Finally, we show that mammalian pathogenic and commensal yeasts have a suite of traits that includes growth at high temperature and, surprisingly, the utilization of a narrowed panel of carbon sources.These results demonstrate how both intrinsic physiological factors and extrinsic ecological factors drive the distribution of traits present in diverse organisms across macroevolutionary timescales.The online version of this article (10.1186/s12915-018-0498-3) contains supplementary material, which is available to authorized users. Trait correlations are widespread across life. These correlations can be intrinsic to an organism’s biology or the result of extrinsic factors. Intrinsic biological factors that can lead to associations between traits include overlapping biochemical or genetic pathways , 2, promTrait syndromes can include a set of traits that collectively provide a fitness advantage, either additively or non-additively, in the environment and have been described for distinct groups of species that are associated with certain environments –14. For Saccharomyces cerevisiae and the human commensal and pathogen Candida albicans, its more than 1000 known species share a common ancestor approximately half-a-billion years ago. These yeasts display considerable genetic, phenotypic, and ecological diversity and provide a unique opportunity to quantify trait associations and elucidate the mechanisms that drive them  and expected values [the mean of either  or  for the randomized dataset ] for that association   and corrpvclust (v. 2.0–0) [We determined whether traits showed similar patterns of associations via cluster analysis. We calculated a dissimilarity matrix, using a Euclidean distance, for all trait pairs using the difference between observed and expected association values for both the positive and negative associations. All associations that were not statistically significant were set to a value of 0. Traits were clustered using Ward’s method in the R package . 2.0–0) .vegan (v. 2.4–3). This statistic estimates the contributions of the independent variables  to the response variable (trait association). Three matrices were used for the independent variables. For the phylogenetic matrix, D1/D2 sequences from the rDNA locus for 578 Saccharomycotina species in our association analysis and the outgroup basidiomycete Cryptococcus neoformans were used to construct a phylogenetic tree of the subphylum Saccharomycotina [Adjusted bi-multivariate statistics . The biological properties matrix calculated the numbers of each type of carbon source , and the isolation environments matrix was a binary matrix consisting of all species and whether or not they were isolated from an environment. The response variable was a trait association matrix that consisted of whether two traits  were both present (1) or one trait was present while the other was absent (0) in a species.The inferred maximum likelihood tree was then used to make a matrix using the cophenetic.phylo function in the R package D, a measure of dispersion for binary traits [D = 1) and the clumping expected under a Brownian model of evolution (D = 0). It was calculated using the phylo.d function in the R package caper (v.0.5.2) [D was significantly different from random associations and clumping.The D1/D2 maximum likelihood tree was used to detect the phylogenetic signal in our trait data. We determined whether there was a phylogenetic signal for individual traits by calculating y traits , and tesv.0.5.2) . We usedThe Yeasts: A Taxonomic Study [Isolation environments were manually scored and curated from the “Ecology” section for each species available in ic Study . Isolatin = 4, 11, 26, 47, 76, 147, 217). For each sample size, we randomly drew that number of species from our trait data set and ran the analysis described above. We did this for each sample size 1000 times and calculated the average difference for each sample size.To determine whether the negative and positive associations among traits were indirectly caused by the environment, we calculated the average difference between the observed and expected numbers of trait associations for each environment using a co-occurrence matrix . Any devigraph (v. 1.0.1) [fast.greedy community), an algorithm that maximizes modularity. We determined whether there was enrichment for specific molecular properties or functions within each subnetwork using two-sided Fisher’s exact tests.To quantify whether there was an enrichment of positive associations in our association data, the data were limited to carbon metabolism traits that had at least one significant association, and we used a two-sided Fisher’s exact test. Positive and negative association networks were created in the R package . 1.0.1) , and carS. cerevisiae) versus 79 previously curated genome assemblies [e value cutoff of 10−10. We also collapsed the MAL12 and IMA1–4 genes into a single IMA/MAL group since these genes are closely related paralogs [Χ2 test to detect associations between growth and gene presence and corrected for multiple tests across associations with the Benjamini–Hochberg correction. We also tested whether genes of positively associated traits co-occurred more frequently than those of traits that showed random associations using a test for equal proportions. We included two groups of genes in our analyses; the first group of genes  consisted of GAL1, GAL7, GAL10, LAC12, and MEL1. The second group (Glucosides) comprised MAL11, MAL13, MAL6, the IMA/MAL-collapsed genes, IMA5, and SUC2. We tested whether genes within each group co-occurred more frequently than genes that were from the two different groups. All statistical analyses were done in R.Gene presence was detected using TBLASTX and BLASTN searches using query sequences  from thesemblies , using aparalogs . For eacAdditional file 1:Table S1. Growth validation of 240 yeasts on four carbon sources. Growth on a carbon source was scored as 1, while no growth was scored as 0. Cases where our data did not match The Yeasts: A Taxonomic Study are indicated with an X. A summary, including the counts of mismatched data, proportion of mismatches, and percentage correct, is given below each column. (XLSX 17 kb)Additional file 2:Table S2. All pairwise trait associations. The “Direction” column indicates whether a trait association was classified as a positive, negative, or random association. The expected value is the average value from the randomized data . (XLSX 75 kb)Additional file 3:Figure S1. Hierarchical cluster analysis of traits, using similarities among trait associations. The different colors represent selected significant clusters. Biologically meaningful clusters were named accordingly. Note that the strengths of the positive and negative associations with fermentation and with growth at high temperatures (37 °C) lead to clustering, which, in some cases, obscures the subtler associations among carbon source utilization traits that are readily observed in the network analysis presented in Fig. Additional file 4:Table S3. Over 50% of individual traits do not evolve under a Brownian model of evolution. To test for a phylogenetic signal in our data, we calculated D, a measure of dispersion used for binary traits that tests for a significant departure from both random associations (D = 1) and the clumping expected under a Brownian model of evolution (D = 0). Values that significantly differ from 0 suggest a trait is not evolving under Brownian evolution. The function calculates randomized data sets (n = 1000) for both departures from random distributions and patterns expected under a Brownian model of evolution. To test for phylogenetic randomness, the function randomly shuffles trait values relative to the tips of the phylogeny. To test for evolution under a Brownian model, the function evolves a continuous trait along the phylogeny under a Brownian process, and then it is converted to a binary trait using a threshold that reproduces the relative prevalence of the observed trait. (XLSX 11 kb)Additional file 5:Table S4. Species isolation environment matrix. A value of 1 means the species was isolated from that environment, while a value of 0 means the species was not isolated from that environment. (XLSX 94 kb)Additional file 6:Figure S2. Bar graph of number of species found in each isolation environment. (PDF 185 kb)Additional file 7:Table S5. Pairwise association quantifications among traits and isolation environments. The “Direction” column indicates whether an isolation/trait association was classified as positive, negative, or random. The expected value is the average value from the randomized data  (XLSX 111 kb)Additional file 8:Table S6. Number of positive, negative, and random associations among carbon utilization traits. (XLSX 10 kb)Additional file 9:Table S7. Assigned functional categories used statistically to analyze the similarities between carbon sources. (XLSX 11 kb)Additional file 10:Table S8. Gene and trait presence for 79 species with curated genome sequences [S. cerevisiae) versus the curated genome assemblies [e value cutoff of 10− 10. (XLSX 15 kb)equences . Gene prsemblies , using aAdditional file 11:Figure S3. Genes show pleiotropic functions for carbon utilization. We determined gene presence for 79 species for which we had trait data and curated genome sequences [a Bar graph of growth on carbon sources  when genes are present (gray) or absent (white). Significant associations are denoted with an asterisk. It is well established that galactose utilization is associated with the presence of the GAL1, GAL7, and GAL10 genes [MEL1 and LAC12 are galactosidases that cleave the disaccharides melibiose and lactose, respectively, and were also significantly associated with galactose utilization across macroevolutionary timescales. The melibiose and lactose utilization data are consistent (but not significant) with this trend; however, there are few species that can use them. b Bar graph of growth on carbon sources (Glucosides) when associated genes are present (gray) or absent (white). We found significant associations (asterisk) between glucoside utilization genes and multiple carbon sources. These results suggest that pleiotropic genes could be responsible for the utilization of multiple carbon sources. c We quantified whether genes associated with traits that show positive trait associations co-occur more frequently than those that show random associations. Genes associated with positively associated traits  co-occurred 81.1% of the time (red), while genes associated with randomly associated traits co-occurred 60.2% of the time (gray). The significant co-occurrence of genes associated with the utilization of positively associated carbon sources provides further support that biological properties contribute to positive trait associations. (PDF 521 kb)equences . We quan10 genes ; this reAdditional file 12:Table S9. Χ2 associations between gene presence and carbon traits. The “Same” column represents the number of times a gene was present and there was growth on the carbon source. The “Different” column represents the count of gene absence and growth plus gene presence and no growth. We corrected for multiple tests across associations with the Benjamini–Hochberg correction (q < 0.05 considered significant), which is represented by the column labeled “pAdj”. (XLSX 11 kb)Additional file 13:Table S10. Species trait matrix, scored with growth (+), no growth (−), variable (v), or data unavailable (n), curated from The Yeasts: A Taxonomic Study [ic Study . Additional file 15:Table S12. Proportion of correctly predicted traits across each method implemented to handle missing data. (XLSX 12 kb)Additional file 16:Table S13. Newick file of D1/D2 sequences generated for 578 yeast species in RAxML-HPC BlackBox. (XLSX 23 kb)