Elevated sympathetic activity is associated with kidney dysfunction. Here we used twin pairs to probe heritability of GFR and its genetic covariance with other traits.DBH genetic variation on renal disease was tested in the NIDDK-AASK cohort.We evaluated renal and adrenergic phenotypes in twins. GFR was estimated by CKD-EPI algorithm. Heritability and genetic covariance of eGFR and associated risk traits were estimated by variance-components. Meta-analysis probed reproducibility of DBH genetic effects. Effect of 2 = 67.3±4.7% (p = 3.0E-18), as were secretion of norepinephrine  and dopamine , and eGFR displayed genetic co-determination (covariance) with norepinephrine  as well as dopamine . Since dopamine β-hydroxylase (DBH) catalyzes conversion of dopamine to norepinephrine, we studied functional variation at DBH; DBH promoter haplotypes predicted transcriptional activity (p<0.001), plasma DBH (p<0.0001) and norepinephrine (p = 0.0297) secretion; transcriptional activity was inversely (p<0.0001) associated with basal eGFR. Meta-analysis validated DBH haplotype effects on eGFR across 3 samples. In NIDDK-AASK, we established a role for DBH promoter variation in long-term renal decline rate .Norepinephrine secretion rose across eGFR tertiles while eGFR fell (p<0.0001). eGFR was heritable, at hDBH may assist in profiling subjects for rational preventive treatment.The heritable GFR trait shares genetic determination with catecholamines, suggesting new pathophysiologic, diagnostic and therapeutic approaches towards disorders of GFR as well as CKD. Adrenergic activity may play a role in progressive renal decline, and genetic variation at  The autonomic nervous system, and in particular its sympathetic branch, plays a role in physiological control of GFR as well as the development of CKD (chronic kidney disease) and eventually end-stage renal disease (ESRD) 2) is an increasingly recognized syndrome, with substantial elevations in cardiovascular morbidity and mortality CKD , though the association disappeared once adjusted for age. BMI was slightly higher in the middle eGFR tertile.Each estimator of GFR was significantly different by tertile in the age-adjusted model, as was eGFR between individuals with and without CKD (P<0.0001). Urine albumin excretion was elevated in subjects with CKD (p = 0.0164).Individuals in the lower eGFR strata displayed higher plasma norepinephrine concentrations .2 = 67.0±5.0% (p<0.0001); heritability values for other physiological, physical and adrenergic traits are also displayed graphically in 2 = 66.5±5.0%, p<0.0001), plasma epinephrine  and plasma dopamine . Other physical traits such as BMI displayed an even higher rate of heritability, at h2 = 81.3±2.3%, p<0.0001.Inter-individual correlations between eGFR and physiological or adrenergic traits are presented in G = 0.98±0.008, p<0.0001) and Cockroft/Gault eGFR . Two catecholamine traits showed significant genetic covariance with eGFR: plasma norepinephrine  and plasma dopamine . G) versus environmental (ρE) co-determination of eGFR with associated variables of interest, illustrating significant pleiotropy for eGFR with these two adrenergic traits.Several phenotypic traits that correlated with eGFR were analyzed for shared heredity (genetic covariance or pleiotropy) in twin pairs . As expeDBH promoter region DBH transcription (p<0.001).In reference to our previous work in the Across biogeographic ancestries (white versus black), we found that DBH plasma activity substantially increased (p<0.0001) with increasing copy number  for haplotype-2 (C→T), with a directionally opposite effect (p = 0.0007) for haplotype-3 (T→C) . We alsoWe explored directional effects for individual promoter SNP genotypes on DBH activity : the min2, or chi2). Within the overall study population and specifically in the white ethnic subpopulation, we found minimal historical recombination as indicated by high LD values  displayed the most significant , for a total of n = 3063 subjects, indicated allelic effects consistent in magnitude  and direction (sign on slope) across groups; the overall slope of the meta-analysis regression on haplotype-2  was significant, at beta (slope) = −1.321, with SE (of beta) = 0.560, and p = 0.018 . In a siDBH haplotype frequencies were observed in the African-American subjects as compared to other ethnicities in the twins/siblings (Very different DBH promoter haplotypes C→C and C→T displayed significant associations with GFR slope (DBH promoter variant C-970T retained significant association with GFR slope (DBH promoter genetic variation did not influence baseline (pre-study) urinary protein excretion .FR slope , haplotyFR slope . At indi2 (explanatory coefficient) of ∼6.5%, indicating that sympathetic over-activity is not restricted to End-Stage Renal Disease (ESRD) Here we probed the aggregation of renal function with physical, physiological, and adrenergic traits, focusing on the role of heredity in control of GFR . We found plasma norepinephrine to be inversely associated with eGFR  with an DBH, and GFR displays genetic covariance with both norepinephrine and dopamine , C→T (p = 0.0013), and T→C (p<0.0001) – displayed significant effects on the eGFR trait  Well-characterized cohorts with multiple phenotypes profiled; b) Twin study design to allow genetic analyses such as heritability of GFR, as well as and genetic and environmental covariance for associated traits; c) Pleiotropic effects of DBH genetic variation on GFR and adrenergic traits; d) Novel effects of two functional DBH promoter variants on the GFR, shedding light on the adrenergic influence on renal function; and e) Extension of the findings into an African American cohort with progressive renal disease.Particular Caveats to interpretation of these results include the primarily white twin/sibling study sample, which may limit generalization of findings to other populations, particularly other ethnic groups; however, the relatively homogeneous biogeographic ancestry does limit the potential artifactual influence of admixture. In addition, our healthy study subjects (twins/siblings) were predominantly female, and the majority was recruited from southern California. Thus, the twin/sibling results might not be demographically generalizable to the population as a whole. Nonetheless, the NIDDK-AASK results do extend our findings into a minority (and disease) population. Heritability and genetic covariance results emerge from correlations, and it may not be possible to impute causality or directionality from such findings; such findings do however suggest hereditary control of physiological or disease pathways. ∼9% of our twin/sibling subjects reported antihypertensive drug treatment; vasoactive drugs may influence GFR, and this potential confounder is of uncertain significance here.Finally, the phenotype of plasma norepinephrine steady-state concentration is not solely dependent on rate of norepinephrine biosynthesis or secretion; inter-individual variability in the steady-state concentration of plasma catecholamines can also be determined by removal processes including reuptake (by NET1 or OCT3) or metabolism/degradation (by COMT or MAOA/MAOB); indeed, when plasma norepinephrine is elevated in uremia, one causative factor seems to be diminished plasma clearance of the amine DBH genetic variation. During genome wide association studies (GWAS), other loci, such as UMOD, have been documented to have small but reproducible effects on eGFR After initial positive findings, we focused thereafter on the role of DBH, and variants at the DBH locus seem to confer risk in progressive renal disease, and may assist in profiling appropriate subjects for rational preventive treatment. With this knowledge in hand, subjects with an initial decline in renal function could be profiled for risk of future accelerated decline. Furthermore, understanding the linkage between adrenergic  polymorphism and renal decline would suggest rational intervention with agents that interfere with the formation, release, or renal actions of the sympathetic neurotransmitter norepinephrine.We conclude that GFR is a highly heritable trait that shares genetic determination with adrenergic phenotypes, in particular norepinephrine and dopamine secretion. Linking these observations , GFR is Recruitment and characterization of the twin/sibling cohort has been previously described in detail http://irb.ucsd.edu/>), and subjects gave written informed consent.Human studies were approved by the UCSD HRPP  from S-adenosylmethionine during O-methylation, mediated by the enzyme catechol-O-methyltransferase (COMT). Prior to O-methylation, plasma catecholamines were extracted into dilute acetic acid to remove COMT inhibitors in plasma. Assay sensitivities (lower limits of detection) were ∼10 pg for dopamine and norepinephrine, and ∼6 pg for epinephrine. Inter-assay coefficients of variation were ∼10% for urine dopamine, ∼20% for plasma dopamine, ∼10% for norepinephrine and ∼13% for epinephrine.Blood and urine samples were obtained after at least 3 hours of fasting. Seated individuals, with a heparin-lock intravenous cannula previously in place for 5 minutes, had blood drawn into EDTA (for catecholamines or creatinine) or heparin (for DBH activity) tubes. Anticoagulated blood was promptly chilled on ice (at 0°C) prior to centrifugation within 30 min for preparation of plasma, which was then frozen at −70°C prior to assays in batch. In untimed urine specimens, analytes were normalized to endogenous creatinine concentration in the same sample. Urine albumin excretion was measured as previously described 2+, N-ethylmaleimide, and fumarate) to octopamine, which is then oxidized to parahydroxybenzaldehyde by sodium periodate; the oxidation is terminated by sodium metabisulfite, and the parahydroxybenzaldehyde product is then quantified by its absorbance at 330 nm. Results are expressed as international units per liter (IU/L) of plasma. The DBH activity inter-assay coefficient of variation was 4.5%.DBH enzymatic activity was measured in heparinized plasma by a modification of the spectrophotometric procedure An autoanalyzer  measured levels of plasma and urine creatinine, by the Kinetic Jaffe Reaction with traceable isotope dilution-mass spectrometry a×max−1.209×0.993Age×1.018 [if female]×1.159 [if black], where pCr is plasma creatinine (mg/dL), “k” is 0.7 for females and 0.9 for males, “a” is −0.329 for females and −0.411 for males, “min” indicates the minimum of sCr/k or 1, and “max” indicates the maximum of sCr/k or 1. Although the CKD-EPI formula is proposed to be superior to the Modification of Diet in Renal Disease Study (MDRD) and Cockcroft-Gault (C–G) equations in study populations including normal renal function, we estimated GFR by all three algorithms. Aside from the C–G formula, both CKD-EPI and MDRD formulas require plasma creatinine, age, sex and ethnicity. Creatinine was measured in plasma in the twin/sibling study, while serum creatinine was used in the Kaiser/replication study, and the NIDDK AASK study.Algorithms to estimate glomerular filtration rate (GFR) included the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) method, with a simplified equation 2) was calculated from weight and height as measure of body size 2) was obtained as a function of weight and height to measure body fat Body surface area  were randomized to one of two blood pressure goal groups , and one of three blinded medication classes: ACE inhibition with ramipril; beta-adrenergic blockade with metoprolol; or calcium channel blockade with amlodipine. Since drugs such as ACE inhibitors influence GFR slope most prominently in patients without substantial proteinuria 125I]-iothalamate twice at baseline, then at 3 and 6 months from baseline, and then every 6 months to end of follow-up. Serum and urine creatinine and protein were measured by a central laboratory (Cleveland Clinic) at 6-month intervals. The primary AASK trial was completed before the inception of ClinicalTrials.gov. Availability of data and samples from the AASK trial is described at <https://www.niddkrepository.org/home/>.Subjects were recruited through a prospective trial involving 21 centers that has been previously described DBH promoter was re-sequenced as described for variant discovery, on an ABI-3100 capillary device, using Sanger-dideoxy chemistry and specific fluorescent base incorporation DBH promoter was excised as a 3050-bp fragment by PCR from a large-insert (BAC) genomic clone spanning the entire DBH locus >), and the 3050 bp promoter was subcloned into the polylinker (promoter insertion site) of a firefly luciferase reporter plasmid as described The human rd and 67th percentiles, used to compare traits of interest while adjusting for age as a covariate. GEE analysis tested for associations between DBH promoter variants or haplotypes with renal and adrenergic traits. Further inferential statistics through chi2 (or Fisher's Exact test) and logistic regression analysis provided information on distribution of haplotypes between/among ethnic groups and in determining the effect of DBH promoter genotype on eGFR. Univariate analyses through general linear models assessed the effect of DBH promoter haplotypes on eGFR and plasma norepinephrine.Descriptive and inferential statistics were obtained at baseline from the entire study population using generalized estimating equations (GEE) implemented in SAS PROC GENMOD in order to account for intra-family correlations between/among twins, siblings and parents http://gump.qimr.edu.au/general/daleN/SNPSpD/>. Haplotypes were imputed from unphased diploid genotypes on the DBH promoter region using an expectation-maximization algorithm in PLINK , http://pngu.mgh.harvard.edu/~purcell/plink/>.In order to maintain the false positive (Type I error) rate at α<0.05 during genetic associations at a single locus, we used the Nyholt's single nucleotide polymorphism spectral decomposition (SNPSpD) correction to determined an experiment-wide significance threshold p-value 2) estimates of physiological and adrenergic traits were obtained through a variance component method in Sequential Oligogenic Linkage Analysis Routines (SOLAR) http://solar.txbiomedgenetics.org/download.html>. Heritability is estimated by the following equation: h2 = VG/VP, where VG is additive genetic variance and VP is total phenotypic variance. Through this method, a normal distribution in twin (monozygotic and dizygotic) pairs is assumed in order to maximize the likelihood with a mean dependent on traits of interest. Genetic covariance (or pleiotropy) of eGFR with other correlated heritable traits was estimated as ρG in SOLAR, while environmental covariance was estimated as parameter ρE.Heritability , SPSS , Nyholt's SNPSpD <2 are included for analyses. Serum creatinine was determined in the Kaiser-Permanente clinical laboratory by spectrophotometric autoanalyzer (Jaffe reaction).Three observational study cohorts were examined, including not only the twins/siblings (previously described) but also two population samples from the Kaiser primary care Caucasian population whose medical information was obtained through routine health visits to Kaiser Permanente Medical Group, a primary healthcare organization in San Diego . Although previously described The genotype effect size , its SE (standard error) and p value were obtained by regression analysis in SPSS-17. Meta-analyses were carried out with the command META, testing fixed effect  models in STATA-12 , after individual study regression analysis in SPSS, to derive significance as well as pooled genotype effect size  and its SE (standard error).File S1Supporting tables. Table S1. eGFR (by CKD-EPI): Stratification into tertiles. Descriptive and inferential statistics for the twin and sibling study population. Values are Mean ± (SEM), or (n%). Inferential statistics were derived from GEEs (PROC GENMOD in SAS) to account for correlations between twins. CKD-EPi eGFR is grouped by tertiles (formed through SAS using 33th and 67th percentile cut off points). Numbers (N) describe the number of individuals analyzed. As a control, we observed CKD-EPi eGFR trait based on CKD-EPI eGFR tertiles to be significant (P<0.0001). BMI indicates body mass index; BSA, body surface area; SBP, systolic blood pressure; DBP, diastolic blood pressure; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration formula; CG, Cockroft-Gault formula; MDRD, Modification of Diet in Renal Disease study formula. Significant differences (P<0.05) are bold. Table S2. Heritability (h2) as well as genetic and environmental covariance of eGFR. Heritability, shared genetic determination  and environmental determination  for traits correlated with CKD-EPI eGFR. ρG and ρE are fractions, scaled from −1 to +1, and determined in SOLAR. Pearson parametric trait on trait correlations is also reported. BMI indicates body mass index; eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration formula; CG, Cockroft-Gault formula; MDRD, Modification of Diet in Renal Disease study formula. Significant differences (P<0.05) are bold. Analysis was undertaken in MZ and DZ twin pairs of European ancestry. Table S3a. Frequencies of DBH promoter variant diploid genotypes across biogeographic ancestry groups. *Fisher's Exact Test p-value. Significant differences (P<0.05) are bold. Table S3b. LD (Linkage Disequilibrium) between DBH promoter variants C-2073T and C-970T in twin/sibling and NIDDK-AASK study populations. DBH, dopamine β-hydroxylase. * Fisher's Exact Test p-values. Significant differences (P<0.05) are bold. Table S3c. Frequency of DBH promoter alleles and haplotypes by ethnicity in the twin/sibling and NIDDK-AASK study populations. Allele and haplotype frequencies of DBH promoter variants in twin/sibling  and the NIDDK-AASK populations . Analysis is restricted to CKD-EPI eGFR>60 in twin/sibling population and with entry urine protein/creatinine ratio ≤0.22 gm/gm in NIDDK-AASK. Significant differences (P<0.05) are bold. Table S4a. DBH promoter haplotypes (C-2073T→C-970T): Renal and adrenergic trait associations in the twin/sibling population. Haplotypes are imputed from PLINK using the twin/sibling population (N = 229). Inferential and descriptive statistics between haplotypes and on trait were obtained through GEE. Significant differences (P<0.05) are bold. Haplotype T→T was not observed. Table S4b. DBH promoter diploid haplotype (C-2073T→C-970T) effects on renal and adrenergic traits in twins/siblings. One-Way ANOVA between DBH diploid haplotype and CKD-EPI eGFR and plasma norepinephrine in an age-adjusted model. P-values are age adjusted. Diploid genotype; No, 0 copies; Yes, 1 copy or more. Significant differences (P<0.05) are bold. Table S4c. Associations of renal and adrenergic traits with DBH promoter variant C-2073T in twins/siblings. DBH, dopamine β-hydroxylase. P-values are obtained through GEEs (PROC GENMOD). Significant differences (P<0.05) are bold. Table S5. Effect of DBH promoter haplotypes on eGFR: Extension to multiple independent groups, by meta-analysis. eGFR: Estimated glomerular filtration rate.CKD-EPI: Chronic Kidney Disease Epidemiology algorithm for GFR. KWE: Kaiser primary-care Caucasian population samples (cohorts 1&2). Table S6a. DBH promoter haplotype effects on progressive renal disease: Longitudinal GFR slope in the NIDDK-AASK cohort. One-Way ANOVA analysis of change in iothalamate GFR overtime with haplotypes adjusted for influential variables. Model I indicates adjustment for Pro/Cr, blood pressure (BP) goal, drug group, and mean baseline glomerular filtration rate (MB GFR). Model II indicates adjustment for Pro/Cr, BP goal, drug group, MB GFR, age at randomization and sex. DBH indicates dopamine β-hydroxylase. In this analysis, we focused on AASK individuals with entry urine protein/creatinine ratio ≤0.22 gm/gm  are bold. 2N = number of chromosomes. Table S6b. DBH SNP genotype effects on longitudinal GFR slope in the NIDDK-AASK population of African Americans with progressive renal disease. ANOVA analysis of change in GFR  over time adjusted for influential variables. Model-I indicates adjustment for Pro/Cr, blood pressure (BP) goal, drug group, and mean baseline glomerular filtration rate (MB GFR). Model-II indicates adjustment for Pro/Cr, BP goal, drug group, MB GFR, Age at randomization and sex. DBH indicates dopamine β-hydroxylase. In this analysis, we focused on AASK individuals with entry urine protein/creatinine ratio ≤0.22 gm/gm  are bold.(DOC)Click here for additional data file.File S2Supporting figures. Figure S1. Adrenergic function and GFR. Trait-on-trait correlation. Association of CKD-EPI eGFR with plasma norepinephrine in the segment of the cohort without CKD . An inverse relationship is seen between plasma norepinephrine and estimated GFR by CKD-EPI. The inverse correlation was maintained when subjects with CKD were included. Figure S2. Heritability h2 and genetic covariance: Shared genetic and environmental co-determination for traits correlated with CKD-EPI eGFR. Covariance estimates (± SEM) are from analyses in SOLAR, and plotted with ρE as a function of ρG, with a diagonal line of identity (Y = X). h2 estimates arose from n = 340  to n = 357 (adrenergic traits) individuals. P-values are given in DBH promoter haplotypes (C-2073T→C-970T): Results for DBH secretion in humans. Haplotype-1 (C→C) association with plasma DBH activity in African Americans, where C→C has a higher haplotype frequency (at ∼76% of chromosomes).(PPTX)Click here for additional data file.


This study is motivated by National Household Surveys that collect genetic data, in which complex samples (e.g. stratified multistage cluster sample), partially from the same family, are selected. In addition to the differential selection probabilities of selecting households and persons within the sampled households, there are two levels of correlations of the collected genetic data in National Genetic Household Surveys (NGHS). The first level of correlation is induced by the hierarchical geographic clustered sampling of households and the second level of correlation is induced by biological inheritances from individuals sampled in the same household.To test for Hardy-Weinberg Equilibrium (HWE) in NGHS, two test statistics, the CCS method [1] and the QS method [2], appear to be the only existing methods that take account of both correlations. In this paper, I evaluate both methods in terms of the test size and power under a variety of complex designs with different weighting schemes and varying magnitudes of the two correlation effects. Both methods are applied to a real data example from the Hispanic Health and Nutrition Examination Survey with simulated genotype data.The QS method maintains the nominal size well and consistently achieves higher power than the CCS method in testing HWE under a variety of sample designs, and therefore is recommended for testing HWE of genetic survey data with complex designs. This study is motivated by population-based family data collected from National Genetic Household Surveys (NGHS), in which complex samples (i.e. sample collected with stratified multistage cluster sampling), partially from the same family, are selected. There are two levels of correlations of the collected genetic data in NGHS. The first level of correlation is induced by the hierarchical geographic clustered sampling of households and the second level of correlation is induced by biological inheritances from individuals sampled in the same household. Moreover, national household surveys often apply differential selection probabilities of selecting households and persons within the sampled households.NGHS from various countries, such as the Canadian Health Measures Survey , Health differential population weights related to genetic variability are associated with participants in the study. The second complication of geographic correlation in genetic variations is induced because race and social-economic status of individuals vary by location of residence, and multi-stage clustered sampling is often geography-based. Lastly, genetic correlation is induced by genetic inheritance from biological parents within the household. Therefore, inflated variances of the estimates for the genetic quantities due to differential population weights, geographical intracluster correlation among households, and the genetic correlation within households are resulted. Consequently, the sample distribution with respect to genetic factors can be considerably different from the underlying population distribution. Researchers who implement complex sample designs should perform analyses with adjustments for the sample design complications. If analysis for simple random samples (SRS), instead, is performed on data collected with complex sample designs, the inference may be invalid.There are at least three complications to analyze data collected in NGHS: 1) differential population weights, 2) hierarchical geographical correlation among families, and 3) genetic correlation within families. As known, genetic variability can differ by race  and sociTesting Hardy-Weinberg Equilibrium (HWE) of marker genotype frequencies has been widely recommended as a crucial step in genetic association studies -13. The A and a) of autosomal genes, there are 10 possible genotype combinations among parents-child triads when ignoring the mating orders of father and mother. If HWE holds, the observed number of families in each of the 10 categories follows a multinomial distribution. Departure from HWE was then tested with the corrected Pearson chi-square statistics, including first-order correction, second-order correction, or a Satterthwaite F-version second-order correction  higher than the power achieved by the r = 0.05.In the first simulation study, the genetic data are correlated among family members, but independent among families within PSUs. Specifically, we select 60 PSU’s by simple random sampling from 2,500 PSU’s. As described above, the genetic information for each pair of parents of the 40 families in the PSU are independently generated by multinomial distributions. Thus, the genetic information among the families within PSUs is independent. Table aa within the family. The 2,500 PSU’s are then formed by grouping every 40 families sequentially in the sorted population. Thus, the genetic information among the families in each PSU is correlated. A simple random sample of 60 PSU’s is then selected from the 2,500 reformed PSU’s. Table QS test consistently achieves higher power than r = 0.1 or 0.2 across different weighting strategies.In the second simulation study, the genetic data are correlated among family members as well as among families within PSUs. In order to introduce the correlation among families within PSUs, we generate a clustered finite population. In detail, we sort all the 100,000 families by the number of genotype w ≡ {1, 3, 5}, it requires r = 0.03 for the QS test to achieve the power of 0.209 when the families are independent fA/ r, (1− r)(1 − fA)/ r) when r ≠ 0. When r = 0, pA takes fA, where r is the fixation coefficient. In step 2, for each parent, two alleles were drawn at random from the binomial distribution Bin . Given parental genotypes, the genotype of a child was randomly generated by the Mendelian law.Since the HHANES did not genotype their sampled individuals, we generated genotype data using a two-step procedure by following . In stepAccording to the within-family sampling design of HHANES, we set the within-family weights to be 2 if the individual is 2–19 years; 1.33 if the person is between 20–44 years; and 1 if the person is 45–74 years. The final sample weight for each sampled individual was provided and calculated by the product of inclusion probabilities at each stage of sampling with nonresponse and postratification adjustments. For construction of family-level weights, we follow  by takinr to be 0, 0.1, 0.2 and 0.3. Table QS for testing Hardy-Weinberg Equilibrium (HWE) with specified fA = 0.3. All the four tests take account of the correlation induced from the selection of the families, and the biological correlation within the family. Consistent with results from the simulation studies, fA are conservative, producing larger p-values than r and w. All the four tests accept HWE when r = 0. The QS starts rejecting null hypothesis of HWE with p-values < 0.05 when r ≥ 0.1; whereas r = 0.1 with p-value = 0.207.We varied the values of the fixation coefficient First, when genotypes among families within PSUs are independent, i.e. there is no first-level correlation from sampling families, the sizes of QS (when r = 0) maintain the nominal level across different weighting strategies and the QS test achieves the greater power under alternative hypothesis of r > 0. Second, when genotypes among families within PSUs are correlated, the sizes are conservative for QS test maintains the nominal level relatively well. In terms of the power, however, the QS test consistently achieves higher power than QS test for testing HWE of NGHS genetic data.In this paper, we compared test statistics recently proposed by She et al.  and Li eWe originally planned for testing HWE using the National Health and Nutrition Examination Survey III (NHANES III) genetic data. However, in order to access NHANES III genetic data, researchers are required to be onsite at NCHS in Hyattsville, Maryland. Being located out of the state of Maryland, we are not able to access the data. Considering the similar survey components in Hispanic Health and Nutrition Examination Survey (HHANES) as in the NHANES III, we decided to apply the developed tests to HHANES with simulated genotype data. Although the genetic data are simulated, all the sampling components, such as the stratification, hierarchical clustering, family size, and family relationships, are real, and thus the analysis can still serve as a useful illustration for testing HWE in NGHS. However, we admit that the fact of simulated genotypes in the real data analysis is one of the limitations in this study.In the simulation studies, the family members are selected by family relationship (i.e. 2P1O). In real surveys, however, individuals could be selected by their phenotypic characteristics, e.g., diseased or disease-free, which are often correlated within certain susceptible genetic variations. The magnitude of this correlation will differ depending on the susceptible genetic variations of interest. In our simulation studies (results not shown), both methods produced biased estimates of allele frequencies and the type I error rate is inflated when within-family selection is highly related to the genotypes. In future research, an extension of the QS estimator will be studied to account for within-family weights that are correlated with genetic variations.The author declares that she has no competing interests.YL designed overall study including designing the sampling schemes, implementing the simulation studies, performing the analysis of HHANES with generated genotypes, and writing the manuscript.


Het<0.05 at 5 out of 37 loci), but no more than expected by chance for LDL-C (1 out of 14 loci), or trigycerides (0 out 24). There were 2 (out of 5) with opposite direction of effect in adolescents compared to adults for HDL-C, but none for LDL-C. The biggest difference in effect size was for LDL-C at rs6511720 near LDLR, adolescents (0.021±0.033 mmol/L) and adults (0.157±0.023 mmol/L), pHet = 0.013; followed by ZNF664 (pHet = 0.018) and PABPC4 (pHet = 0.034) for HDL-C. Our findings suggest that some of the previously identified variants associate differently with lipid traits in adolescents compared to adults, either because of developmental changes or because of greater interactions with environmental differences in adults.A recent meta-analysis of genome-wide association (GWA) studies identified 95 loci that influence lipid traits in the adult population and found that collectively these explained about 25–30% of heritability for each trait. Little is known about how these loci affect lipid levels in early life, but there is evidence that genetic effects on HDL- and LDL-cholesterol  and triglycerides vary with age. We studied Australian adults  and adolescents  who participated in twin and family studies and for whom we have lipid phenotypes and genotype information for 91 of the 95 genetic variants. Heterogeneity tests between effect sizes in adult and adolescent cohorts showed an excess of heterogeneity for HDL-C (p Plasma lipids are important and much-studied risk factors for cardiovascular disease. For clinical use and in epidemiological studies the main focus is on concentrations of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides. These are known to be substantially influenced by genetic variation, with heritabilities of 40%–60% in adults and even higher (70%–80%) in adolescents. Many novel loci affecting lipid risk factors for cardiovascular disease have been discovered through meta-analysis of genome-wide association (GWAS) data However, it is known that mean values for plasma lipids change with age, and there is some evidence that the gene variants which contribute to differences between people also change across the lifespan. Longitudinal studies of twins across adolescence or early adulthood Comparatively little is known about genetic loci affecting lipid levels in early life, despite higher heritabilities in adolescents than in adults. Practically all the published GWAS data are from adults, often from the older adults at highest risk of cardiovascular disease. While this is logical, atherosclerosis may start early in life, and the question of whether the SNPs and genes already identified affect lipid variation in childhood or adolescence is a relevant one. Practical benefits could ensue if SNPs which affect adult levels of risk factors, but not childhood levels, could be identified; intervention based on a genetic profile could potentially be started well before the risk becomes evident from conventional (phenotypic) risk factor assessments We have analysed lipid GWAS data from adolescents, concentrating on the genes and SNPs shown to be important in adults and testing for heterogeneity of allelic effects between adult  and adolescent (N = 2363) cohorts drawn from the Australian population. The opposite approach, of starting with polymorphisms significantly affecting lipids in adolescence and comparing them against adult results, is limited by the comparative lack of data on young subjects. We first tested for age heterogeneity within the adolescents and the adults separately. After showing a lack of significant heterogeneity by age within these two groups, allelic association tests on each cohort were performed separately. Heterogeneity tests on each lipid trait at 91 loci with genotyping were conducted to determine whether there is any difference in the effect of these significant reported SNPs on plasma lipid concentrations between adolescents and adults.Lipid traits  were measured in serum samples from twins and their families, and genome-wide SNP markers were genotyped. The study participants consist of:Adolescent twins and their non-twin siblings living in south-east Queensland  who had participated in the Brisbane Longitudinal Twin study Adults (twins and their family members) who participated in studies of: (i) alcohol and nicotine use and dependence and metabolic risk for alcoholic liver disease There was no overlap between participation in the adolescent and adult studies. However, some adults participated in more than one study and many of the adolescents had phenotypic measurements on more than one occasion. Combining all these studies, each individual was first categorised into age groups. For the adolescents these were 1 (age 12–13.99 years), 2 (age 14–15.99 years) or 3 (16–17.99 years). Adult groups were 4 (18–29.99 years), 5 (30–44.99 years), 6 (45–59.99 years) or 7 (age 60 years and over). The present study includes 20,634 individuals with HDL-C, LDL-C and triglycerides measurements and 12,514 from 5,424 families  who had both genotype and phenotype data. All participants  gave informed written consent and all studies were approved by the Human Research Ethics Committee of the Queensland Institute of Medical Research.Serum was separated from the blood samples and stored at −70°C until analyzed. Serum cholesterol, HDL cholesterol and triglycerides, were measured using Roche methods on a Roche 917 or Modular P analyzer . LDL-cholesterol was calculated using the Friedewald equation.MACH 1.0 (http://www.sph.umich.edu/csg/abecasis/mach/index.html) DNA was extracted from blood samples using standard methods and genotyped with Illumina 610K, 317K or 370K chips at CIDR or deCODE Genetics. Data cleaning for SNP genotypes included checking the expected relationships between individual family members and resolving Mendelian errors 2), sex, sex×age and sex×age2 and standardized residuals were obtained. All data pre-processing and descriptive analyses were done using STATA version 7.0 SPSS version 17.0.2 . SNP family-based association analysis was performed using “fastassoc” and “inverseNormal” options in MERLIN 1.1.2. METAL 1.1.2Distributions of lipid variables were examined and triglyceride was log-transformed. Individuals whose results for any trait were more than five standard deviations from the mean were excluded for that trait. Before association analysis, the variables were adjusted for the effects of age, squared age (age2<0.3) or violation of Hardy-Weinberg equilibrium (at P<10−6). Most SNPs  associated with lipid concentrations (as the lead trait) in our adult data were directionally consistent with the Global Lipids Consortium meta-analysis. The correlations between effect sizes reported by the Global Lipids Consortium for the SNPs they found significant and our effect sizes were 0.94 for HDL-C, 0.98 for LDL-C and 0.95 for triglycerides. In adolescents, 41 out of the 98 SNPs were significantly (p<0.05) associated with at least one of the lipid traits , ERGIC3 on LDL-C (pHet = 0.014) and SCARB1 on LTG (pHet = 0.031) (UBE2L3 and PINX1 on HDL-C (pHet = 0.020 and 0.031), COBLL1, APOE, ANGPTL3, HFE, MAFB, HMGCR and ZNF664 on LDL-C  and FLJ36070, GALNT2, SLC39A8, PDE3A and PINX1 on triglycerides   in effect sizes between the three age groups (within adolescents) except for = 0.031) , S3, S4., 0.013) , S3, S4.Het = 0.016 at MC4R), LDL-C (significant pHet = 0.013 at LDLR) and triglycerides respectively (LDLR gene) showing a 7-fold difference in association with LDL-C  in adolescents and 0.174±0.051 in SD  in adults). Some SNPs in HDL-C were seen to have opposite direction of effect in adolescents compared to adults. The biggest difference in effect direction was for the A-allele of rs4660293 in PABPC4  in adolescents; 0.061±0.019 in SD unit  in adults). There were also some SNPs showing an increase or decrease in effect size without change in direction from adolescents to adults. In addition to rs7134594 (gene MVK) with HDL-C, for which the effect size of the T-allele was almost 6-fold higher in adolescents than in adults, rs12967135 (MC4R gene) also showed a 3-fold higher in effect size of the G-allele in adolescents compared to adults. In addition, one variant showed a p-value of less than 0.05 in heterogeneity between adolescents and adults in more than one trait. The effect size of the G-allele of rs6511720 in LDLR was differentially associated with both HDL-C  in adolescents; −0.019±0.024 in SD  in adults; pHet = 0.032) and LDL-C  in adolescents; 0.174±0.025 in SD  in adults; pHet = 0.013).Heterogeneity testing between the adolescent and adult cohorts showed there were 5 out of 37), 1 (out of 14) and 0 out of 24) variants showing significant heterogeneity (p<0.05) in effect size for HDL-C  heterogeneity of allelic effects between adolescents and adults  SNPs genome-wide. These did not show genome-wide significant  and adolescent (r2>0.80) results. All SNPs giving significant associations at p<5×10−8 in our adolescent or adult data had been reported previously.Our study examined and compared allelic effects in 2,363 adolescents and 10,151 adults at 91 previously identified loci known to influence high- or low-density lipoprotein cholesterol, or triglycerides, in adults. The allelic associations found in our data are consistent with previous reports, given the smaller number of subjects available. We found a high correlation between effect sizes reported by the Global Lipids Consortium for the SNPs they found significant Het<0.05 for HDL-C (5 out of 37) and possibly for LDL-C (1 out of 14), but none showed significant heterogeneity (pHet<0.05) for triglycerides. The Q-Q plots of observed versus expected distributions of pHet  of CETP gene on chromosome 16, in both adolescents and adults. The effect estimates of rs374261 [C] on HDL-C in adolescents and adults were comparable  and adults: −0.037 mmol/L . Even though the estimates obtained in this study have the same directional effect as previous studies, our estimates were much lower. The effect obtained by Teslovich et al. CETP and rs445925 on chromosome 19 in APOE to have significant time-dependent effects  on HDL-C and LDL-C respectively CETP  confirms the Bogalusa study interaction effect for HDL-C, but we did not find adult-adolescent heterogeneity for LDL-C at APOE.Overall, the largest allelic association effect size (beta) was observed at rs374261  and suggestive interactions (p<10−6) for triglycerides at rs13290397 , rs6726786  and rs12330441 . They also found suggestive  interaction results for LDL-C at rs8073909  and rs11258628 . As none of these loci were reported as significant in the Global Lipids Consortium meta-analysis and were therefore not prioritised in our comparison, we checked them for heterogeneity between our adults and adolescents but only found rs8073909 on chromosome 17 (AKAP1/MSI2) to show significant heterogeneity (pHet<0.05) for LDL. We also examined the allelic effects for these SNPs across the seven age groups in our study. We noted that there is an increasing trend of allelic effects in minor allele across age groups for rs8073909  between this and the other studies (There is evidence of lipid changes during therapy for endometriosis patients  studies . In relaHet<0.05) between effect sizes in adults and adolescents for HDL-C. These loci associate differently with lipid traits in adolescents compared to adults. However, no locus showing strong evidence of heterogeneity was found. The Bogalusa study reported seven possible loci showing significant or suggestive SNP×Age interaction effects but our study found only one locus out of their seven showing a significant heterogeneity p-value<0.05. Larger studies with more detailed characterisation of age-related and pubertal changes across childhood and adolescence will be required to reach firm conclusions about age-dependent variation in allelic effects on plasma lipids.In summary, our study suggests that there is an excess of loci showing nominally significant heterogeneity .(TIF)Click here for additional data file.Figure S2Quantile-quantile plot of observed against expected heterogeneity P-value for allelic associations with LDL-C for all SNP with 95% confidence interval (grey shaded area).(TIF)Click here for additional data file.Figure S3Quantile-quantile plot of observed against expected heterogeneity P-value for allelic associations with triglycerides for all SNP with 95% confidence interval (grey shaded area).(TIF)Click here for additional data file.Figure S4CETP) on HDL-C; (B) rs445925 (APOE) on LDL-C; (C) rs8073909 (AKAP1/MSI2) on LDL-C; (D) rs11258628 (FRMD4A) on LDL-C; (E) rs7890572 (IL1RAPL1) on triglycerides; (F) rs12280753 (CADM1) on triglycerides; (G) rs13290397 (PSAT1/CHCHD9) on triglycerides; (H) rs6726786 (FSH/NRXN1) on triglycerides and (I) rs12330441 (IL20RB/SOX14) on triglycerides.Alleleic effects and Error bars SE for each estimated effect of minoar allele by age group at (A) rs247616 ((PDF)Click here for additional data file.Table S1Association of loci significant with at least one lipid traits in adolescents , comparing results from the Global lipids Consortium  for adult.(PDF)Click here for additional data file.Table S2Heterogeneity P-values within adolescents and adults in HDL-C in 98 SNPs examined.(PDF)Click here for additional data file.Table S3Heterogeneity P-values within adolescents and adults in LDL-C in 98 SNPs examined.(PDF)Click here for additional data file.Table S4Heterogeneity P-values within adolescents and adults in triglycerides in 98 SNPs examined.(PDF)Click here for additional data file.Table S5Heterogeneity test between adolescents and adults in HDL-C in 98 SNPs examined.(PDF)Click here for additional data file.Table S6Heterogeneity test between adolescents and adults in LDL-C in 98 SNPs examined.(PDF)Click here for additional data file.Table S7Heterogeneity test between adolescents and adults in triglycerides in 98 SNPs examined.(PDF)Click here for additional data file.Table S8The locus/phenotype combinations of EN-others heterogeneity tests reported (PDF)Click here for additional data file.


LPL and TRIB1 gene regions, previously reported in GWAS of triglycerides in Caucasians but as yet unconfirmed in Hispanic populations. We report statistically significant evidence for genetic association in both genes, and we further demonstrate the importance of considering population substructure and genetic heterogeneity in genetic association studies performed in the United States Hispanic population.Using ∼60,000 SNPs selected for minimal linkage disequilibrium, we perform population structure analysis of 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By projection of principal components (PCs) of ancestry to samples from the HapMap phase III and the Human Genome Diversity Panel (HGDP), we show the first two PCs quantify the Caucasian, African, and Native American origins, while the third and fourth PCs bring out an axis that aligns with known South-to-North geographic location of HGDP Native American samples and further separates MESA Mexican versus Central/South American samples along the same axis. Using k-means clustering computed from the first four PCs, we define four subgroups of the MESA Hispanic cohort that show close agreement with self-identification, labeling the clusters as primarily Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. To demonstrate our recommendations for genetic analysis in the MESA Hispanic cohort, we present pooled and stratified association analysis of triglycerides for selected SNPs in the  Using genotype data from about 60,000 distinct genetic markers, we examined population structure in 1,374 unrelated Hispanic individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), with self-identification corresponding to Central America (n = 93), Cuba (n = 50), the Dominican Republic (n = 203), Mexico (n = 708), Puerto Rico (n = 192), and South America (n = 111). By comparing genetic ancestry of MESA Hispanic participants to reference samples representing worldwide diversity, we show major differences in ancestry of MESA Hispanics reflecting their Caucasian, African, and Native American origins, with finer differences corresponding to North-South geographic origins that separate MESA Mexican versus Central/South American samples. Based on our analysis, we define four subgroups of the MESA Hispanic cohort that show close agreement with the following self-identified regions of origin: Dominican/Cuban, Mexican, Central/South American, and Puerto Rican. We examine association of triglycerides with selected genetic markers, and we further demonstrate the importance of considering differences in genetic ancestry (or factors associated with genetic ancestry) when performing genetic studies of the United States Hispanic population. Although epidemiologic studies often regard Hispanics in the United States as a homogenous group, U.S. Hispanics have a complex population structure comprised of many overlapping subgroups, and also vary markedly in environmental and cultural factors linked to country of origin and history of immigration to the United States. A widely recognized distinction from genetic analysis has been between Hispanics carrying primarily Caucasian and African ancestry, versus those having predominantly Caucasian and Native American ancestry The Multi-Ethnic Study of Atherosclerosis (MESA) provides one of the largest and most thoroughly-characterized samples of Hispanic individuals to date. MESA has 1,374 unrelated Hispanic individuals and a total of 2,174 subjects of self-reported Hispanic ethnicity, including pedigrees. Most self-reported Hispanic participants also reported more detailed self-identification corresponding to Central America, Cuba, the Dominican Republic, Mexico, Puerto Rico or South American origin . As MESAUsing the recently available genome-wide genotype data, we perform population structure analysis of an unrelated subset of 1,374 individuals from the MESA Hispanic cohort. By Principal Component Analysis (PCA) LPL) and tribbles homolog 1 (TRIB1) gene regions with triglycerides in the full MESA Hispanic cohort, as well is in stratified analyses to assess evidence for association within each of the four Hispanic subgroups. Our genetic analysis indicates pooled analysis provides the best power when there is only modest heterogeneity in genetic effects, while stratified analysis offers better resolution to detect genetic loci in which SNP effects are limited to or much stronger within a single subgroup of Hispanics.We perform cluster analysis on the first four principal components (PCs) of ancestry to identify four distinct subgroups of the MESA Hispanic cohort. Based on participant self-identification, we find these subgroups represent primarily Central/South America, the Dominican Republic and Cuba, Mexico, and Puerto Rico. To demonstrate a principled approach to genetic association analysis taking into account genetic diversity in the MESA Hispanic cohort, we perform analysis of SNPs in the lipoprotein lipase  of ancestry were computed for 1,374 unrelated individuals from the MESA Hispanic cohort using the program SMARTPCA, which is distributed with the software package EIGENSTRAT The resulting PCs were projected to HapMap phase III and HGDP samples, and the first four principal components of ancestry are displayed for an unrelated set of MESA Hispanic subjects and key reference populations in i.e. less admixed) position on the plot.The first two PCs of ancestry display strong population stratification across the Hispanic cohort. The three predominant sources of ancestry correspond to Caucasian, Native American and African founder populations, with the vast majority of MESA Hispanic individuals lying along two edges of a triangle, corresponding to two major clusters broadly representing individuals reporting Mexican versus Caribbean  origin. Projection of these principal components to all four MESA ethnic groups  as well The plot of the third and fourth PCs reveals additional structure, separating Puerto Rican and Central/South American subjects into two distinct groups that are further separated from the rest of the MESA Hispanic cohort. Interestingly, population structure shown in the plot of PC4 versus PC3 is specific to MESA Hispanic and HGDP Native American samples, with little separation of other worldwide populations  and S2. We did not identify any clear patterns of population substructure in the MESA Hispanic cohort in plots of the higher order PCs  and S2. Using the same set of 1,374 unrelated individuals from the MESA Hispanic cohort and the same 64,199 autosomal SNPs as used for PCA, we performed model-based cluster analysis using the software ADMIXTURE For K = 4 and K = 5, the first two groups correspond to Caucasian and African ancestral populations as seen for K = 3, while additional ancestral populations appear to account for regional differences in Native American ancestry . CompariWe performed k-means clustering using the first four principal components of ancestry, to define four major groups within the Hispanic cohort. The resulting clusters of ancestry showed notably good agreement with self-identified country/region of origin, and were accordingly identified with Central/South America (abbreviated “CSAmer”), the Dominican Republic and Cuba, Mexico, and Puerto Rico .i.e. the Mexican cluster contained 658 of 708 unrelated individuals with Mexico as their self-identified country of origin. In most cases, it was also true that a given cluster carried very few individuals self-identifying with a different country/region of origin, with the Dominican/Cuban cluster being the one notable exception. The Dominican/Cuban cluster is labeled as such because it contains 199 of 203 self-identified Dominican individuals and 49 out of 50 self-identified Cuban individuals from the unrelated subset of individuals reported in Each of the clusters was labeled as such because it carried the vast majority of individuals self-identifying with the corresponding region, LPL) and tribbles homolog 1 (TRIB1) gene regions with triglyceride levels in GWAS of Caucasians Multiple studies have reported association between SNPs in the lipoprotein lipase , as well as stratified analysis within the PCA-based clusters corresponding to Central and South America (N = 204), the Dominican Republic (N = 472), Mexico (N = 913) and Puerto Rico (N = 181).We selected SNPs rs10096633 and rs12678919 reported in previous studies LPL SNPs in MESA Hispanics, we saw statistically significant association of 18 SNPs with triglyceride outcomes (even after conservative Bonferroni correction for multiple testing using the cutoff 0.05/33 = 0.0015), with the strongest association observed for rs325, P = 8.86E-6, and rs328 (Ser474Stop), P = 8.88E-6 , perhaps reflecting the fact that rs328 is a nonsense mutation, and is quite possibly a causal variant underlying the observed association. Still, we keep in mind the test of heterogeneity may be somewhat underpowered given the Central/South American and Puerto Rican subgroups have only ∼200 individuals each.To quantify evidence for heterogeneity in genetic effects of rs328 observed across the four Hispanic subgroups, we performed a test of genetic heterogeneity using the meta-analysis software METAL LPL region, in stratified analyses of each of the four Hispanic subgroups  that exhibited modest to strong linkage disequilibrium (LD) with the P = 1.09E-3) is the only SNP that reaches the Bonferroni cutoff (0.05/45 SNPs = 1.11E-3) .In pooled analysis of the MESA Hispanic cohort, rs4351435 (1.11E-3) . In stra1.11E-3) , and a tP = 0.037). Interestingly, the index SNP rs2954029 originally identified in studies of Caucasians was neither statistically significant in association analysis of the pooled MESA Hispanic cohort (P = 0.134) nor in analysis of the MESA African American cohort (P = 0.748). These results suggest that while the TRIB1 gene plays a role in determining triglycerides in Caucasians as well as African American and Dominican/Cuban individuals, the variants underlying this association vary by genetic ancestry. Another possibility is that the SNP effects interact with an environmental or dietary factor that is correlated with proportion of African ancestry within the MESA Hispanic cohort.The observed differences in genetic effects across the four Hispanic subgroups suggest the strength of genetic association increases with the proportion of African ancestry, seen in higher proportions for the Dominican/Cuban and Puerto Rican subgroups compared to the Central/South American and Mexican subgroups. To quantify this relationship, we added an interaction between the SNP rs4351435 and PC1 in the linear model used to assess genetic association in the pooled Hispanic cohort. The rs4351435-PC1 interaction term is statistically significant (P = 0.019), suggesting heterogeneity in effects of rs4351435 on triglycerides is attributable in part to the proportion of African versus Native American or Caucasian ancestry (as quantified by PC1) within the MESA Hispanic cohort. In validation, we observed statistically significant association of the rs4351435 SNP with triglycerides in analysis 2,067 individuals from the MESA African American cohort (TRIB1 SNPs (P = 8.67E-5) as compared to pooled analysis (P = 1.09E-3). We do not observe any SNPs reaching the threshold for statistical significance in analysis of the Central/South American, Mexican, or Puerto Rican subgroups. These results indicate that when there is considerable heterogeneity in genetic effects observed across the full Hispanic cohort, stratified analysis may provide better resolution to uncover genetic association signals that exhibit stronger effects within a single subgroup of the Hispanic cohort.We went on to examine genetic association in stratified analysis of the four Hispanic subgroups for the full set of 45 IB1 SNPs , S11. WhLPL, we performed genetic association analyses of TRIB1 stratified by self-reported country/region of origin to compare with results of analyses stratified by PCA-based clusters  and statistically significant evidence for genetic heterogeneity at the most strongly associated TRIB1 SNP rs4351435. Furthermore, evidence for the TRIB1 SNP rs4351435 was substantially stronger in stratified analysis of the Dominican and Cuban subgroup alone, as compared to pooled analysis of the full MESA Hispanic cohort. Our genetic association analyses indicate pooled analysis provides good power to detect variants exhibiting little heterogeneity in genetic effects, while stratified analysis will provide an advantage in detecting SNPs with heterogeneity in which the genetic effect is strong for one subgroup and close to zero in other subgroups of the Hispanic cohort.To demonstrate our recommendations for genetic association analysis taking into account our documented genetic diversity in the MESA Hispanic cohort, we performed association analysis of triglycerides with SNPs in the e,g, diet) as well as other social and cultural factors associated with diverse regions of origin and diverse histories of migration to the United States.In practice, whether a formal test of heterogeneity is statistically significant or not, examining heterogeneity by effect plots or other tools will be an important step toward identifying the most promising samples for follow-up and replication studies. We do not expect genetic diversity will be the sole cause of heterogeneity in SNP effects. Evidence both from our current work and from previous studies e.g. Caucasian, Asian, or African American).Toward generalizing our results to the United States Hispanic population as a whole, it is important to keep in mind the demographics of our cohort. The MESA Hispanic cohort represent primarily recent immigrants to the United States, with 65% born outside the United States, and another 28% having at least one parent born outside the United States Individuals in the MESA Hispanic cohort were recruited primarily from three sites in the United States, namely New York City, Minneapolis, and Los Angeles. Based on this geographic distribution, the MESA Hispanic cohort cannot be regarded as a fully representative sample of Hispanics from across the United States. Examining the self-identification data for an unrelated subset of 1,374 individuals from the MESA Hispanic cohort  compared to the U.S. Hispanic population  for 8,227 individuals  with 897,981 SNPs passing study specific quality control (QC). Due to differences in allele frequencies across the MESA ethnic groups, there was no filter of minor allele frequency prior to release of the genotype data on dbGaP. Thus, we applied a filter on minor allele frequency at the stage of genetic association analysis .The Multi-Ethnic Study of Atherosclerosis (MESA) is a longitudinal study of subclinical cardiovascular disease and risk factors that predict progression to clinically overt cardiovascular disease or progression of the subclinical disease The country or region of Hispanic origin was coded for individuals in the MESA Hispanic cohort using the following categories: Mexican, Dominican, Puerto Rican, Cuban, Central American, South American or other Hispanic subgroup. Participant self-identification was available for 83% of individuals in the MESA Hispanic cohort. For the remaining 17% where this self-identification was not provided, the data were obtained from the place of birth of the most recent generation  outside of the 50 United States as follows:If the participant reported place of birth outside of the United States, Europe, and Asia, then that place of birth was used.Otherwise, if there was a single reported place of birth outside of the United States, Europe, and Asia for both parents, then that place of birth was used.Otherwise, the place of birth of the grandparents was used. If more than one place of birth outside of the Unites States, Europe, and Asia was specified, then the majority was used.http://hapmap.ncbi.nlm.nih.gov/downloads/ in PLINK To make use of the HapMap phase III release 3 genotypes as a reference panel for our analysis of MESA samples, we began with 1,397 individuals from the following 11 HapMap populations: ASW, CEU, CHB, CHD, GIH, JPT, LWK, MEX, MKK, TSI, and YRI. Genotype data were obtained by the HapMap 3 Consortium using the Affymetrix Human SNP array 6.0 and the Illumina Human1M-single beadchip. Following data merging and cleaning http://hagsc.org/hgdp/files.html for 1,043 individuals on 660,918 SNPs Publicly available genotype data for the Human Genome Diversity Project (HGDP) were downloaded from To allow common analysis across the three sources of genotype data , the first step was to merge the genotype files aligned on a common set of alleles. To avoid any ambiguity in strand alignment, we merged the genotype data files using an approach that does not rely on a priori knowledge of strand direction in annotation files. Briefly, SNPs with alleles A/G or C/T could be merged across genotype files, unambiguously flipping alleles  as necessary. The small proportion of SNPs with alleles A/T or G/C could not be merged using this method, due to ambiguities in strand-flipping, as so were removed. Although this strand-flipping procedure forces us to remove the small proportion of ambiguous SNPs, the resulting set of retained SNPs is less error prone  than if we had relied on strand direction reported in annotation files alone. The allele flipping procedure described as run as currently implemented in the software package KING After file merging and allele flipping, we filtered on SNP level call rate <95% across 12,058 genotyped individuals from the three genotype data sets , resulting in 144,564 autosomal SNPs common to all data sets. We then filtered on individual level call rate <95%, resulting in a combined set of 10,666 individuals across the three genotype data sets with overall genotyping rate 0.998 across the 144,564 autosomal SNPs.rd-degree using genotypes from genome-wide association data, implemented in the freely available software package KING rd-degree are grouped together.To identify an unrelated set of MESA individuals for population structure analysis, we performed relationship inference using a method we developed recently for accurate relationship up to the 3rd degree. While we recognize that some inferred relationships of the 3rd degree may be false positives, we used this stringent criterion to ensure we had a clean set of individuals for population structure analysis. The final list of unrelated individuals generated by this procedure included 6,496 MESA participants. From this group of individuals, we further removed 5 Hispanic individuals identified as outliers according to principal components of ancestry, as computed in SMARTPCA .A list of unrelated individuals from MESA was constructed by selecting one individual from each known family (common family ID reported in the downloaded data), and further thinning to include no more than one individual from each cluster inferred in the KING clustering of individuals with inferred relationships up to the 3st- or 2nd-degree relatives in the unrelated set.Because the HapMap samples were collected with systematic relatedness including father-mother-child trios Details of the data set after SNP QC, individual-level genotype QC, by cohort representation and status of inclusion in the final set of unrelated individuals, are provided in Prior to population structure analysis, we first constructed a subset of typed SNPs, thinned for linkage disequilibrium (LD) among MESA samples self-identified as Hispanic. Based on the assumption that Hispanics have a considerable proportion of Caucasian ancestry, we first removed from consideration SNPs in regions of known long-range linkage disequilibrium among Caucasians Using our LD-thinned subset of 64,199 SNPs, we performed Principal Component Analysis (PCA) as implemented in the program SMARTPCA Following computation of the PCs using the unrelated subset of MESA Hispanic individuals, we used SMARTPCA to project these components to all MESA samples that were not included in the PCA (including non-Hispanic samples), as well as the HapMap and HGDP samples, to assist in interpretation of the strongest PCs .As principal component analysis is known to be sensitive to outliers and undetected family structure, both of which can produce spurious PCs, we undertook a series of QC steps to assess properties of the components reported by SMARTPCA. First, we constructed histograms and QQ-plots to assess symmetry and normality of the distribution of loadings for each principal component. We found the distributions of loadings for the first four PCs closely matched the ideal symmetric, standard normal distribution, with no coefficients more extreme than 4.6. For higher PCs, we observed loadings as extreme as 6.4 . We also performed genome-wide association of each principal component as a quantitative trait among MESA Hispanics, using a method that accounts for familial correlation For comparison with our PCA of the MESA Hispanic cohort with projection to relevant reference panels, we performed PCA for unrelated individuals from the MESA Hispanic cohort pooled with samples from the HapMap and HGDP, using the same set of 64,199 SNPs selected for population structure analysis performed on the MESA Hispanic cohort. The resulting principal components from pooled analysis were subject to the same QC steps as applied for the MESA Hispanic-specific analysis, including examination of the distributions of PC loadings, and genome-wide association analysis of each PC as a quantitative trait to assess potential effects of long range LD.We performed model-based cluster analysis using our LD-thinned subset of 64,199 SNPs using the package ADMIXTURE In order to obtain a clear characterization of the MESA Hispanic cohort, we first performed the ADMIXTURE analysis for the same unrelated subset of 1,374 MESA Hispanic individuals that we used to perform PCA. We ran this analysis for K values 1, …, 10, assessing results for each of these runs in terms of cross validation error, as well as with graphic displays of proportions of ancestry across self-identified Hispanic country/region of origin.Visual inspection of the first four PCs of ancestry  suggesteWe performed k-means clustering in the statistical software R LPL gene region with triglycerides, we began by selecting SNPs of interest in the region, with a focus on the index SNPs rs12678919 and rs10096633 reported in previous GWAS of Caucasians LPL gene region.To assess association of SNPs in the TRIB1 gene region, we used a strategy similar to that for LPL. We began by targeting the SNP rs2954029 previously reported in GWAS of Caucasians For selection of SNPs to be included in association analysis of the Fasting triglycerides were measured in plasma using a glycerol blanked enzymatic method . To select individuals to be included in this analysis, we began with the full set of N = 2,169 individuals with data available from principal component analysis. We then restricted the data set to individuals with triglyceride phenotypes available  and no known use of any lipid lowering medication . To allow study site to be included as a covariate in genetic association analysis, we restricted the data set to individuals from study sites with data available for at least 20 individuals . Outliers were defined as individuals with log triglyceride values more then 3.5 SD from the mean, with the mean and SD calculated separately for each of the five analyses performed .Based on these criteria, we performed pooled analysis of all MESA Hispanics , as well as stratified analysis within the PCA-based clusters corresponding to Central and South America (N = 204), the Dominican Republic and Cuba (N = 472), Mexico (N = 913) and Puerto Rico (N = 181). For comparison, we also performed stratified analysis by self-reported country/region of origin for the following groups: Central America (N = 109), Cuba (N = 34), the Dominican Republic (N = 315), Mexico (N = 961), Puerto Rico (N = 202), and South America (N = 123). Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships as implemented in the package R/GWAF 2 which quantifies the proportion of total variation across studies due to heterogeneity rather than chance To assess genetic heterogeneity seen in stratified analysis of the four Hispanic subgroups, we performed a test of heterogeneity using Cochran's Q and also examined the inconsistency metric ITo validate results seen for SNPs exhibiting the strongest association in the Hispanic cohort, we performed genetic association analysis in the MESA African American cohort. We began with the full set of N = 2,588 consenting individuals from MESA or MESA Family self-identified as African American. There were N = 2,580 individual remaining after removing outliers from principal component analysis. We then restricted the data set to individuals with triglyceride phenotypes  available and no known use of any lipid lowering medication . All study sites had data available for at least 20 African American individuals. After removing outliers were defined as individuals with log triglyceride values more then 3.5 SD from the mean, we performed genetic association analysis of N = 2,067 individuals, using a linear mixed-effects model to account for familial relationships Figure S1Top eight principal components of ancestry computed in an unrelated subset of 1,374 MESA Hispanic individuals, with projection to an unrelated subset of the remaining MESA samples. Individuals are labeled according to group inclusion as indicated.(PDF)Click here for additional data file.Figure S2Top eight principal components of ancestry computed in an unrelated subset of 1,374 MESA Hispanic individuals, with projection to an unrelated subset of individuals from the other MESA ethnic groups, in addition to samples from the HapMap and HGDP. Individuals are labeled according to group inclusion: “MESAHispNOS”="MESA Hispanic, Other or Unspecified country/region or origin”, other labels are self-explanatory. “HapHGDPAfrica” includes LWK and YRI from the HapMap, as well as African samples from the HGDP. “HapHGDPEurope” includes CEU and TSI from the HapMap and European samples from the HGDP. “HapHGDPCSAsia” includes GIH from the HapMap and Central/South Asian samples from the HGDP. “HapHGDPEastAsia” includes CHB, CHD, and JPT from the HapMap and East Asian samples from the HGDP. All other labels are self-explanatory.(PDF)Click here for additional data file.Figure S3Individual-level proportion of ancestry estimates from model-based clustering analysis in ADMIXTURE for 1,374 unrelated individuals of self-reported Hispanic origin from the Multi-Ethnic Study of Atherosclerosis (MESA), shown for K values 2 through 7.(PDF)Click here for additional data file.Figure S4LPL gene region with triglycerides . Strength of association versus SNP position on chromosome 8 based on stratified analyses for self-reported country/region of origin corresponding to (A) Central America, (B) Cuba, (C) the Dominican Republic, (D) Mexico, (E) Puerto Rico, and (F) South America. Genotyped SNPs are indicated as solid black dots, imputed SNPs as solid gray dots, the imputed SNP rs328 as an open gray diamond, and horizontal dashed gray lines indicate a conservative Bonferroni-threshold for statistical significance based on multiple testing of 33 SNPs.Summary of regional association for SNPs in the (PDF)Click here for additional data file.Figure S5TRIB1 gene region with triglycerides . Strength of association versus SNP position on chromosome 8 based on stratified analyses for self-reported country/region of origin corresponding to (A) Central America, (B) Cuba, (C) the Dominican Republic, (D) Mexico, (E) Puerto Rico, and (F) South America. Genotyped SNPs are indicated as solid black dots, imputed SNPs as solid gray dots, the genotyped SNP rs4351435 as an open black diamond, and horizontal dashed gray lines indicate a conservative Bonferroni-threshold for statistical significance based on multiple testing of 45 SNPs.Summary of regional association for SNPs in the (PDF)Click here for additional data file.Figure S6Top eight principal components of ancestry computed in pooled analysis of unrelated MESA Hispanic individuals together with HapMap and HGDP samples, with projection to an unrelated subset of individuals from the other MESA ethnic groups. Individuals are labeled according to group inclusion: “MESAHispNOS”="MESA Hispanic, Other or Unspecified country/region or origin”, other labels are self-explanatory. “HapHGDPAfrica” includes LWK and YRI from the HapMap, as well as African samples from the HGDP. “HapHGDPEurope” includes CEU and TSI from the HapMap and European samples from the HGDP. “HapHGDPCSAsia” includes GIH from the HapMap and Central/South Asian samples from the HGDP. “HapHGDPEastAsia” includes CHB, CHD, and JPT from the HapMap and East Asian samples from the HGDP. All other labels are self-explanatory.(PDF)Click here for additional data file.Figure S7Top eight principal components of ancestry computed in pooled analysis of unrelated MESA Hispanic individuals together with HapMap and HGDP samples. Results are displayed for an unrelated subset of individuals from the MESA Hispanic cohort and key reference populations from the HapMap and HGDP.(PDF)Click here for additional data file.Table S1Representation of worldwide populations in population structure analysis. Counts of unrelated individuals are reported after relationship inference for systematic removal of related individuals, and outlier removal based on principal components of ancestry, as detailed in the (XLS)Click here for additional data file.Table S2LPL gene region with triglycerides in the pooled MESA Hispanic cohort. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 33 SNPs in the (CSV)Click here for additional data file.Table S3LPL gene region with triglycerides in stratified analysis of the Central/South American subgroup. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 33 SNPs in the (CSV)Click here for additional data file.Table S4LPL gene region with triglycerides in stratified analysis of the Dominican and Cuban subgroup. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 33 SNPs in the (CSV)Click here for additional data file.Table S5LPL gene region with triglycerides in stratified analysis of the Mexican subgroup. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 33 SNPs in the (CSV)Click here for additional data file.Table S6LPL gene region with triglycerides in stratified analysis of the Puerto Rican subgroup. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 33 SNPs in the (CSV)Click here for additional data file.Table S7TRIB1 gene region with triglycerides in the pooled MESA Hispanic cohort. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 45 SNPs in the (CSV)Click here for additional data file.Table S8TRIB1 gene region with triglycerides in stratified analysis of the Central/South American subgroup. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 45 SNPs in the (CSV)Click here for additional data file.Table S9TRIB1 gene region with triglycerides in stratified analysis of the Dominican and Cuban subgroup. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 45 SNPs in the (CSV)Click here for additional data file.Table S10TRIB1 gene region with triglycerides in stratified analysis of the Mexican subgroup. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 45 SNPs in the (CSV)Click here for additional data file.Table S11TRIB1 gene region with triglycerides in stratified analysis of the Puerto Rican subgroup. Analysis was performed using an additive model with a linear mixed-effects model to account for familial relationships, and inclusion of basic covariates gender, age, study site, and PCs of ancestry.Results for association analysis of 45 SNPs in the (CSV)Click here for additional data file.Table S12Representation of study sites for the full set of 1,374 unrelated MESA Hispanic individuals used for principal component analysis.(XLS)Click here for additional data file.


Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD).In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods . We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNPs) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset.UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models.Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine  and random forest (cforest) had the best prediction accuracy whereas models derived from naïve Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes  and 5 genetic attributes (Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD. The prevalence of diabetic kidney disease (DKD) is rising in parallel to the growing epidemic of type 2 diabetes and obesity in both developing and industrialized societies . The devIn this post-GWAS era, in addition to conventional statistical methods such as chi-square test or logistic regression, machine learning methods are other tools which can be used to identify novel relationships between genetic variations and disease susceptibility -8. Thesest July 1994 and 30th June of 1998, a consecutive cohort of 1,386 Chinese patients with type 2 diabetes were enrolled into the Hong Kong Diabetes Registry with documentation of risk factors, complications, drug use and clinical outcomes. None of the patients had history of unprovoked ketosis and did not require insulin within the first year of diagnosis. The study protocol was approved by the Clinical Research Ethics Committee of the Chinese University of Hong Kong. All patients gave written informed consent and donated their clinical data and DNA for research and publication purposes. Declaration of Helsinki was adhered to in the study.Between 12[th July 2005. Data were retrieved from the Hospital Authority Central Computer System, using the Hong Kong Identity Card number, which is compulsory for all residents in Hong Kong.All patients underwent a structured 4-hour clinical and biochemical assessment, details of which have been described . In brie2. All pathttp://www.ncbi.nlm.nih.gov/gene?term=gene, accessed 1 June 2007). In addition, we genotyped the ALR2  polymorphisms based on known association between this genetic variant and DKD. The method for genotyping of the ALR2 polymorphism has been described [http://pngu.mgh.harvard.edu/~purcell/plink/download.shtml) in the study population. After excluding SNPs with call rate less than 95%, P value < 0.05 for Hardy-Weinberg equilibrium and/or minor allele frequency < 0.01, 79 SNPs of 55 genes were included in the present analysis. Full details of these SNPs are available in Additional file Genotyping was performed using line arrays from Roche Molecular Systems comprising 107 single nucleotide polymorphisms (SNPs) in 65 candidate genes related to lipid and homocysteine metabolism, inflammation, thrombosis, endothelial function, stress and natriuretic responses. The selection of these genes was based on published studies on their biological plausibility and risk associations with cardiovascular diseases, immune response and inflammation ,13 (httpescribed . Genotyp2 for DKD (n = 119) or more than 65 ml/min/1.73 m2 for non-DKD (n = 554).From the cohort of 1,386 type 2 diabetic patients, we excluded 500 patients due to missing eGFR at baseline or end of follow-up. Those who had normal renal function at baseline but progressed to develop DKD (n = 80) and those who had DKD at baseline but then regressed to have normal renal function (n = 6) were excluded. To reduce confounding effects due to patients with inconclusive renal status, we only included patients with consistent eGFR at baseline and end of follow-up, i.e. less than 55 ml/min/1.73 mWe removed parameters indicative of renal function to discover novel predictors. These included urinary ACR and serum creatinine at baseline. We also excluded drug data due to confounding effects of drug indications, i.e. patients with more risk factors were more likely to need treatment. For variables with close inter-correlations, we only selected one of them for analysis. Finally, we excluded variables with zero- or near zero-variance, leaving 87  attributes for model development. These attributes were then grouped into three categories for input into various machine learning programs: 1) clinical and genetic attributes; 2) genetic attributes only; and 3) clinical attributes only.We imputed the missing values by exploring similarities between cases. Firstly, we identified the 10 most similar cases and calculated the Euclidean distance between the values of cases and used the median value to impute the missing value. To adjust for class imbalance, we applied the Synthetic Minority Over-sampling Technique, which generated new examples of the minority class (those with DKD) using the nearest neighbors of these cases and under-sampled the majority class examples (those without DKD) .All statistical analyses were performed using the SPSS Statistics 17.0 (SPSS Inc. Chicago) unless otherwise specified. The clinical data were expressed as median  or percentages. The Mann–Whitney Two-Sample test and Chi-square test were used as appropriate. A P value less than 0.05 (2-tailed) was considered significant.We applied and compared the following machine learning methods: partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naïve Bayes classification, neural network and support vector machine. All the machine learning methods were performed under the R computing environment. The details of package versions and parameters used for each machine learning method were described in Additional file Seventy-five percent of the data were partitioned into the training set and the remaining, into the testing set. For each machine learning method, ten sets of parameters were tested. For each set of parameters, 10-fold cross validation was performed to obtain an average value of the performance across hold-out predictions. Receiver operator curve (ROC) analysis was used to select the optimal model using the largest value of area under the curve (AUC). We then estimated the performance of the trained models by subjecting them to the testing dataset to predict DKD. The machine learning methods with the best performance were then used in a second stage to select a subset of important variables to develop models with performance comparable to that using the entire dataset. In ranking the importance of variable, we adopted the conditional importance in random forest  and Vari2 for patients with DKD and those without DKD, respectively. The Additional file In this prospective cohort of 673 patients with type 2 diabetes, 41.2% were male, the median age was 57 (IQR: 48 to 65) years and the median duration of diabetes was 9 (IQR 3 to 13) years. Compared to patients without DKD, patients who had DKD were older and more obese. They also had higher BP, urinary ACR and worse lipid profile and were more likely to be treated with angiotensin converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB), anti-hypertensive drugs, lipid lowering drugs and insulin at baseline . All models had comparable performance if drug treatments were included in the dataset. Therefore, we excluded these attributes to identify subjects with hidden patterns for early intervention. We compared the performance of different machine learning methods using different sub-groups of attributes: 1) clinical and genetic; 2) genetic only; 3) clinical only , hepatic lipase (LIPC -514C > T) and apolipoprotein B (APOB Thr71Ile) were most preferentially selected for model building  to build the final models  and 5 genetic attributes  to explore the structure of the dataset which only showed average performance. Random forest performed better than the two tree methods, but required long computation time probably due to the generation of a large number of bootstrapped trees for decision making. Naïve Bayes classifier had the largest variations in performance even during the training stage, indicating that this method was highly sensitive to data input. Neural network had excellent prediction accuracy comparable to those of support vector machine and random forest, but this was not maintained when only clinical attributes were included. Support vector machine was the best performing machine learning method using clinical, genetic or both attributes. The contrast between the performance of neural network and support vector machine using clinical attributes suggested that clinical attributes might generate optimal solutions detected only by a specific method.With rapid advancement of molecular technology, large datasets containing many genotypes and phenotypes are now available. However, there are major challenges in synthesizing these discoveries and translating them to clinical practice. Thus, our second objective was to determine the best combination of genetic and clinical attributes to predict DKD. Indeed, all machine learning methods preferentially selected clinical, notably, age, age of diagnosis and systolic BP, over genetic attributes for model building. These findings suggested at least in subjects with phenotypes predictive of DKD, the predictive value of genetic factors might be attenuated. These findings also reinforced our current understanding that apart from age, early diagnosis and optimal control of BP are the most effective preventive measures to reduce risk of DKD.LIPC -514C > T, APOB Thr71Ile and UGB G38A polymorphisms were also in accord with our previous reports using regression analysis [The third objective of this analysis was to develop a novel method to increase the utility of these predictors. Using the top 10 clinical attributes and 5 genetic attributes selected by the best models, we were able to build models using support vector machine and random forest to generate high-performance models. In keeping with our previous reports regarding the importance of metabolic syndrome -20 in pranalysis ,22. Our UGB, have known immunomodulatory and regulatory roles in the deposition of fibronectin and collagen in mouse glomeruli. Herein, DKD is characterized by glomerulopathy with glomerular sclerosis, thickening of basement membrane and mesangial expansion. In UGB knockout mice, the animal developed severe renal disease due to abnormal deposition of fibronectin and collagen in the glomeruli [UGB G38A polymorphism with progression of IgA nephropathy has been reported [Of note, the steroid-inducible protein, lomeruli . In a Jareported . Taken treported ,26.The strengths of our study included the extensive phenotypes and genotypes and definition of DKD using prospective design. However, our study has several limitations. Firstly, we did not include eGFR and urinary ACR in order to discover novel predictors. Secondly, we excluded drug treatment which was selected by all machine learning methods, likely confounded by drug indications. Thirdly, this was a proof-of-concept study and the results from this exploratory analysis required independent replication in larger cohorts. Lastly, all participants were of Chinese ethnicity and thus the results might not be applicable to non-Chinese population and individuals without diabetes.Using a prospective database, we compared the performance of seven common machine learning methods to build models with the optimal combinations of clinical and genetic predictors for DKD. Amongst them, support vector machine and random forest had the best performance. Age, age of diagnosis and lipid parameters were major clinical predictors while genetic polymorphisms related to inflammation and lipid metabolism were the most selected genetic predictors. Validation of these genetic markers in subjects without clinical evidence of renal disease may provide an opportunity to identify high risk subjects for regular surveillance and individualized treatment including control of inflammation  and dyslACEI: Angiotensin-converting enzyme inhibitor; ACR: Albumin to creatinine ratio; ARB: Angiotensin receptor blockers; AUC: Area under the curve; BP: Blood pressure; cforest: Random forest; DKD: Diabetic kidney disease; eGFR: Estimated glomerular filtration rate; GWAS: Genome wide association studies; IQR: Inter-quartile range; LDL: LDL cholesterol; NB: Naïve bayes classification; NNET: Neural network; PLS: Partial least squares regression; ROC: Receiver operator curve; SNP: Single nucleotide polymorphism; svmRadial: Support vector machine; TC: Total cholesterol; TG: Triglyceride; WBC: White blood cell count; WHR: Waist to hip ratio.The authors declared that they had no competing interests.JCNC, SKWT, RCWM, WYS and MCYN have participated in the study design, acquisition of data and interpretation of data. YW, VL and MCYN have performed the SNPs genotype. RKKL, YW, and AOYL have participated in data analysis and interpretation of data. RKKL, YW, AOYL and JCNC have participated in paper writing and revision. JCNC and SKWT gave final approval of the version to be published. All authors have read and approved the final manuscript.1Hong Kong Bioinformatics Centre, 2Department of Medicine and Therapeutics, 3Li Ka Shing Institute of Health, 4Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2369/14/162/prepubMinor allele frequencies of 79 SNPs of 55 candidate genes related to cardiovascular disease and inflammation analysed in 1386 Chinese patients with type 2 diabetes.Click here for filePackage versions and parameters of machine learning methods used in the present study.Click here for fileComparison of baseline clinical and biochemical characteristics of type 2 diabetic patients included in the machine learning analysis (N = 673) and the excluded patients (N = 713) due to incomplete dataset.Click here for fileRanking of importance of variables by different machine learning models (information unavailable in nnet and nb), using A) clinical and genetic attributes, B) genetic-only attributes, C) clinical-only attributes.Click here for file


Next-generation sequencing and other high-throughput technologies have made it feasible to characterize millions of sequence variations on large numbers of study participants. But when it comes to identifying a small number of these genetic features (or feature sets) that are associated with a disease trait, the investigator is faced with a formidable multiple-testing challenge. It can be thought of as a signal-to-noise problem, where the large number of unrelated genetic features tends to drown out the faint signal of the small number of biologically relevant features.The theoretical underpinnings of an emerging class of statistical methods for genomic studies, two-stage procedures for both gene-gene and gene-environment interactions have recently been described in a remarkable article , but also to (epistatic) interactions between genetic variants (GxG), since in constructing these hypothesis tests, both “gene” and “environment” features are treated analogously as discrete or continuous variables in models designed to identify associations with a disease trait. A notable exception is when the approach depends on the environmental exposure being a randomized treatment, allowing additional assumptions to be made.P[D]) = α + β*1SNP1 + β*2SNP2 + β*3SNP1*SNP2, where β3 is the interaction parameter and D indicates disease. The interaction parameter can be tested alone or in a multi degree-of-freedom test of one or both main effects together with the interaction, an approach that was generally found to be more powerful , as in the case-only interaction analysis  = λ + β*1SNP1 + β*2SNP2 + β*3SNP1*SNP2, where exp (λ) is the baseline risk, the two SNPs have equal relative risks per allele, i.e., β1 = β2, there is a weak interaction , and equal numbers of cases and controls sampled for a moderately rare disease. It is apparent that power to identify the disease related SNPs using a screening approach based on composite LD in the pooled case-control sample can increase with the strength of the main effects and be quite powerful despite a weak interaction  screening-testing approaches are valid if independent statistics are used for the screening and testing stages, and (2) various pairs of statistics are independent. However, there is general way to conceptualize some of these approaches that leads to an intuitive understanding of conditions that preserve type I error, thus allowing investigators to develop novel and complex screening-testing approaches without having to develop new proofs. In the types of studies under discussion, the ultimate objective is often to detect some form of association between the genetic features and the disease trait, conditional on the ascertainment scheme, that is, to find evidence of associations in the study sample that reflect dependencies in the general population. Thus, the corresponding null hypotheses consist of some form of independence between features of interest and the disease trait both in the underlying population and conditional on ascertainment. If we define the global null hypothesis to be independence between the feature vector and the disease trait, then any function of the features is also independent of the disease trait under the null, due to basic properties of independence of random variables (Cassella and Berger, Screening-testing interaction approaches have the potential to be applied across a broad array of study designs, and have already branched out beyond the original case-control application. For instance, there have been applications for studies of nuclear families (Millstein et al., Screening-testing interaction approaches could have even broader applications if we are able to relax the assumption of independence between the screening and testing statistics. The methods could then be applied to other designs, such as cross-sectional and cohort studies. For example, suppose we think that some pair of features may jointly affect disease, having both main effects and interactions. We may want to screen on the interaction effect but jointly test main effects and interactions in the testing stage, since joint tests can be much more powerful (Millstein et al., Given the breadth of previously proposed applications as well as potential new directions and insights discussed here, it seems likely that the use of screening-testing interaction approaches will prove to have a big impact on future identification of multi-locus as well as GxE effects.