ABSTRACT With the recent advance of biomedical technology, a lot of ‘OMIC’ data from genomic, tra... more ABSTRACT With the recent advance of biomedical technology, a lot of ‘OMIC’ data from genomic, transcriptomic, and proteomic domain can now be collected quickly and cheaply. One such technology is the microarray technology which allows researchers to gather information on expressions of thousands of genes all at the same time. With the large amount of data, a new problem surfaces – how to extract useful information from them. Data mining and machine learning techniques have been applied in many computer applications for some time. It would be natural to use some of these techniques to assist in drawing inference from the volume of information gathered through microarray experiments. This chapter is a survey of common classification techniques and related methods to increase their accuracies for microarray analysis based on data mining methodology. Publicly available datasets are used to evaluate their performance.
Recently, 2 independent studies reported that a rare missense variant, rs75932628 (R47H), in exon... more Recently, 2 independent studies reported that a rare missense variant, rs75932628 (R47H), in exon 2 of the gene encoding the "triggering receptor expressed on myeloid cells 2" (TREM2) significantly increases the risk of Alzheimer disease (AD) with an effect size comparable to that of the APOE ε4 allele. In this study, we attempted to replicate the association between rs75932628 and AD risk by directly genotyping rs75932628 in 2 independent Caucasian family cohorts consisting of 927 families (with 1,777 affected and 1,235 unaffected) and in 2 Caucasian case-control cohorts composed of 1,314 cases and 1,609 controls. In addition, we imputed genotypes in 3 independent Caucasian case-control cohorts containing 1,906 cases and 1,503 controls. Meta-analysis of the 2 family-based and the 5 case-control cohorts yielded a p value of 0.0029, while the overall summary estimate (using case-control data only) resulted in an odds ratio of 1.67 (95% confidence interval 0.95-2.92) for the association between the TREM2 R47H and increased AD risk. While our results serve to confirm the association between R47H and risk of AD, the observed effect on risk was substantially smaller than that previously reported.
DNA methylation may represent an important contributor to the missing heritability described in c... more DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.
The revolution in next-generation sequencing has made obtaining both common and rare high-quality... more The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework-FBAT-v, FBAT-lmm, FBAT-m, and FBAT-lwere used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both setbased and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis.
2010 4th International Conference on Bioinformatics and Biomedical Engineering, 2010
Abstract Genotyping quality is one of the key properties that defines the success chances of any ... more Abstract Genotyping quality is one of the key properties that defines the success chances of any genome-wide association studies. Data cleaning of the genotype data is therefore an important part of the statistical analysis. With family-data the genotype quality of each ...
Genome-wide association studies have been able to identify disease associations with many common ... more Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.
With advent of several treatment options in multiple myeloma (MM), a selection of effective regim... more With advent of several treatment options in multiple myeloma (MM), a selection of effective regimen has become an important issue. Use of gene expression profile (GEP) is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated the ability of GEP to predict complete response (CR) in MM. GEP from pretreatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional data sets from three different studies (n=511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four data sets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56-78% in test data sets and no significant difference with regard to GEP platforms, treatment regimens or in newly diagnosed or relapsed patients. Importantly, permuted P-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.
Despite the numerous and successful applications of genome-wide association studies (GWASs), ther... more Despite the numerous and successful applications of genome-wide association studies (GWASs), there has been a lot of difficulty in discovering disease susceptibility loci (DSLs). This is due to the fact that the GWAS approach is an indirect mapping technique, often identifying markers. For the identification of DSLs, which is required for the understanding of the genetic pathways for complex diseases, sequencing data that examines every genetic locus directly is necessary. Yet, there is currently a lack of methodology targeted at the identification of the DSLs in sequencing data: existing methods localize the causal variant to a region but not to a single variant, and therefore do not allow one to identify unique loci that cause the phenotype association. Here, we have developed such a method to determine if there is evidence that an individual loci affects case/control status with sequencing data. This methodology differs from other rare variant approaches: rather than testing an entire region comprised of many loci for association with the phenotype, we can identify the individual genetic locus that causes the association between the phenotype and the genetic region. For each variant, the test determines if the pattern of linkage disequilibrium (LD) across the other variants coincides with the pattern expected if that variant were a DSL. Power simulations show that the method successfully detects the causal variant, distinguishing it from other nearby variants (in high LD with the causal variant), and outperforms the standard tests. The efficiency of the method is especially apparent with small samples, which are currently realistic for studies due to sequencing data costs. The practical relevance of the approach is illustrated by an application to a sequencing dataset for nonsyndromic cleft lip with or without cleft palate. The proposed method implicated one variant (P 5 0.002, 0.062 after Bonferroni correction), which was not found by standard analyses. Code for implementation is available. Genet. Epidemiol. 35:880-886, 2011.
Between 75 000 and 125 000 U.S. infants are hospitalized for respiratory syncytial virus (RSV) br... more Between 75 000 and 125 000 U.S. infants are hospitalized for respiratory syncytial virus (RSV) bronchiolitis every year. Up to half will be diagnosed with asthma in later childhood. Vitamin D deficiency has been associated with susceptibility to asthma and respiratory infections. Measured vitamin D is largely bound to vitamin D-binding protein (VDBP); VDBP levels are influenced by its gene (GC) haplotype. We assessed the relationship between polymorphisms rs7041 and rs4588, which define haplotypes GC1s, GC1f, and GC2, and RSV bronchiolitis susceptibility and subsequent asthma. We retrospectively recruited 198 otherwise healthy children (93% White) hospitalized for severe RSV bronchiolitis in Boston and 333 parents into a follow-up study to assess asthma diagnosis. Data were analysed using family-based genetic association tests. We independently validated our results in 465 White children hospitalized with RSV bronchiolitis and 930 White population controls from the Netherlands. The rs7041_C allele (denoting haplotype GC1s) was overtransmitted (P = 0.02, additive model) in the entire Boston cohort, in Whites (P = 0.03), and especially in children subsequently diagnosed with asthma (P = 0.006). The GC1f haplotype was undertransmitted in the asthma subgroups (all races and White, both P < 0.05). The rs7041_C allele was also more frequent in the RSV bronchiolitis group compared with controls (OR 1.12, 95% CI 1.02, 1.4, P = 0.03) in the Netherlands, especially in mechanically ventilated patients (P = 0.009). GC1s haplotype carriage may increase the risk of RSV bronchiolitis in infancy and subsequent asthma development. The GC1s haplotype is associated with higher VDBP levels, resulting in less freely available vitamin D. Vitamin D-binding protein (VDBP) haplotypes influence free vitamin D levels. We report an association between a VDBP haplotype and hospitalization for RSV bronchiolitis in infancy in two independent cohorts.
Background: For genetic association studies in designs of unrelated individuals, current statisti... more Background: For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results. Results: In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available. Conclusions: The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.
Background: As Next-Generation Sequencing data becomes available, existing hardware environments ... more Background: As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed. Results: Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs.
Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of... more Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of regression models that include as covariates single-nucleotide polymorphism (SNP) genetic markers which did not achieve genome-wide significance in the original genome-wide association study, but were among the SNPs with the smallest P-value for the selected association test. We compare the results of such regression models to the standard approach which is to include only SNPs that achieve genome-wide significance. Using mean square prediction error as the model metric, our simulation results suggest that by using the coefficient of determination (R 2 ) value as a guideline to increase or reduce the number of SNPs included in the regression model, we can achieve better prediction quality than the standard approach. However, important parameters such as trait heritability, the approximate number of QTLs, etc. have to be determined from previous studies or have to be estimated accurately.
Linkage- and association-based methods have been proposed for mapping disease-causing rare varian... more Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotid...
ABSTRACT With the recent advance of biomedical technology, a lot of ‘OMIC’ data from genomic, tra... more ABSTRACT With the recent advance of biomedical technology, a lot of ‘OMIC’ data from genomic, transcriptomic, and proteomic domain can now be collected quickly and cheaply. One such technology is the microarray technology which allows researchers to gather information on expressions of thousands of genes all at the same time. With the large amount of data, a new problem surfaces – how to extract useful information from them. Data mining and machine learning techniques have been applied in many computer applications for some time. It would be natural to use some of these techniques to assist in drawing inference from the volume of information gathered through microarray experiments. This chapter is a survey of common classification techniques and related methods to increase their accuracies for microarray analysis based on data mining methodology. Publicly available datasets are used to evaluate their performance.
Recently, 2 independent studies reported that a rare missense variant, rs75932628 (R47H), in exon... more Recently, 2 independent studies reported that a rare missense variant, rs75932628 (R47H), in exon 2 of the gene encoding the &amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot;triggering receptor expressed on myeloid cells 2&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;quot; (TREM2) significantly increases the risk of Alzheimer disease (AD) with an effect size comparable to that of the APOE ε4 allele. In this study, we attempted to replicate the association between rs75932628 and AD risk by directly genotyping rs75932628 in 2 independent Caucasian family cohorts consisting of 927 families (with 1,777 affected and 1,235 unaffected) and in 2 Caucasian case-control cohorts composed of 1,314 cases and 1,609 controls. In addition, we imputed genotypes in 3 independent Caucasian case-control cohorts containing 1,906 cases and 1,503 controls. Meta-analysis of the 2 family-based and the 5 case-control cohorts yielded a p value of 0.0029, while the overall summary estimate (using case-control data only) resulted in an odds ratio of 1.67 (95% confidence interval 0.95-2.92) for the association between the TREM2 R47H and increased AD risk. While our results serve to confirm the association between R47H and risk of AD, the observed effect on risk was substantially smaller than that previously reported.
DNA methylation may represent an important contributor to the missing heritability described in c... more DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.
The revolution in next-generation sequencing has made obtaining both common and rare high-quality... more The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework-FBAT-v, FBAT-lmm, FBAT-m, and FBAT-lwere used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both setbased and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis.
2010 4th International Conference on Bioinformatics and Biomedical Engineering, 2010
Abstract Genotyping quality is one of the key properties that defines the success chances of any ... more Abstract Genotyping quality is one of the key properties that defines the success chances of any genome-wide association studies. Data cleaning of the genotype data is therefore an important part of the statistical analysis. With family-data the genotype quality of each ...
Genome-wide association studies have been able to identify disease associations with many common ... more Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.
With advent of several treatment options in multiple myeloma (MM), a selection of effective regim... more With advent of several treatment options in multiple myeloma (MM), a selection of effective regimen has become an important issue. Use of gene expression profile (GEP) is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated the ability of GEP to predict complete response (CR) in MM. GEP from pretreatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional data sets from three different studies (n=511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four data sets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56-78% in test data sets and no significant difference with regard to GEP platforms, treatment regimens or in newly diagnosed or relapsed patients. Importantly, permuted P-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.
Despite the numerous and successful applications of genome-wide association studies (GWASs), ther... more Despite the numerous and successful applications of genome-wide association studies (GWASs), there has been a lot of difficulty in discovering disease susceptibility loci (DSLs). This is due to the fact that the GWAS approach is an indirect mapping technique, often identifying markers. For the identification of DSLs, which is required for the understanding of the genetic pathways for complex diseases, sequencing data that examines every genetic locus directly is necessary. Yet, there is currently a lack of methodology targeted at the identification of the DSLs in sequencing data: existing methods localize the causal variant to a region but not to a single variant, and therefore do not allow one to identify unique loci that cause the phenotype association. Here, we have developed such a method to determine if there is evidence that an individual loci affects case/control status with sequencing data. This methodology differs from other rare variant approaches: rather than testing an entire region comprised of many loci for association with the phenotype, we can identify the individual genetic locus that causes the association between the phenotype and the genetic region. For each variant, the test determines if the pattern of linkage disequilibrium (LD) across the other variants coincides with the pattern expected if that variant were a DSL. Power simulations show that the method successfully detects the causal variant, distinguishing it from other nearby variants (in high LD with the causal variant), and outperforms the standard tests. The efficiency of the method is especially apparent with small samples, which are currently realistic for studies due to sequencing data costs. The practical relevance of the approach is illustrated by an application to a sequencing dataset for nonsyndromic cleft lip with or without cleft palate. The proposed method implicated one variant (P 5 0.002, 0.062 after Bonferroni correction), which was not found by standard analyses. Code for implementation is available. Genet. Epidemiol. 35:880-886, 2011.
Between 75 000 and 125 000 U.S. infants are hospitalized for respiratory syncytial virus (RSV) br... more Between 75 000 and 125 000 U.S. infants are hospitalized for respiratory syncytial virus (RSV) bronchiolitis every year. Up to half will be diagnosed with asthma in later childhood. Vitamin D deficiency has been associated with susceptibility to asthma and respiratory infections. Measured vitamin D is largely bound to vitamin D-binding protein (VDBP); VDBP levels are influenced by its gene (GC) haplotype. We assessed the relationship between polymorphisms rs7041 and rs4588, which define haplotypes GC1s, GC1f, and GC2, and RSV bronchiolitis susceptibility and subsequent asthma. We retrospectively recruited 198 otherwise healthy children (93% White) hospitalized for severe RSV bronchiolitis in Boston and 333 parents into a follow-up study to assess asthma diagnosis. Data were analysed using family-based genetic association tests. We independently validated our results in 465 White children hospitalized with RSV bronchiolitis and 930 White population controls from the Netherlands. The rs7041_C allele (denoting haplotype GC1s) was overtransmitted (P = 0.02, additive model) in the entire Boston cohort, in Whites (P = 0.03), and especially in children subsequently diagnosed with asthma (P = 0.006). The GC1f haplotype was undertransmitted in the asthma subgroups (all races and White, both P &lt; 0.05). The rs7041_C allele was also more frequent in the RSV bronchiolitis group compared with controls (OR 1.12, 95% CI 1.02, 1.4, P = 0.03) in the Netherlands, especially in mechanically ventilated patients (P = 0.009). GC1s haplotype carriage may increase the risk of RSV bronchiolitis in infancy and subsequent asthma development. The GC1s haplotype is associated with higher VDBP levels, resulting in less freely available vitamin D. Vitamin D-binding protein (VDBP) haplotypes influence free vitamin D levels. We report an association between a VDBP haplotype and hospitalization for RSV bronchiolitis in infancy in two independent cohorts.
Background: For genetic association studies in designs of unrelated individuals, current statisti... more Background: For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results. Results: In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available. Conclusions: The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.
Background: As Next-Generation Sequencing data becomes available, existing hardware environments ... more Background: As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed. Results: Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs.
Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of... more Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of regression models that include as covariates single-nucleotide polymorphism (SNP) genetic markers which did not achieve genome-wide significance in the original genome-wide association study, but were among the SNPs with the smallest P-value for the selected association test. We compare the results of such regression models to the standard approach which is to include only SNPs that achieve genome-wide significance. Using mean square prediction error as the model metric, our simulation results suggest that by using the coefficient of determination (R 2 ) value as a guideline to increase or reduce the number of SNPs included in the regression model, we can achieve better prediction quality than the standard approach. However, important parameters such as trait heritability, the approximate number of QTLs, etc. have to be determined from previous studies or have to be estimated accurately.
Linkage- and association-based methods have been proposed for mapping disease-causing rare varian... more Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotid...
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