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Selecting cases and controls for DNA sequencing studies using family histories of disease.

ABSTRACT: Recent improvements in sequencing technology have enabled the investigation of so-called missing heritability, and a large number of affected subjects have been sequenced in order to detect significant associations between human diseases and rare variants. However, the cost of genome sequencing is still high, and a statistically powerful strategy for selecting informative subjects would be useful. Therefore, in this report, we propose a new statistical method for selecting cases and controls for sequencing studies based on family history. We assume that disease status is determined by unobserved liability scores. Our method consists of two steps: first, the conditional means of liability are estimated with the liability threshold model given the individual's disease status and those of their relatives. Second, the informative subjects are selected with the estimated conditional means. Our simulation studies showed that statistical power is substantially affected by the subject selection strategy chosen, and power is maximized when affected (unaffected) subjects with high (low) risks are selected as cases (controls). The proposed method was successfully applied to genome-wide association studies for type 2 diabetes, and our analysis results reveal the practical value of the proposed methods. Copyright © 2017 John Wiley & Sons, Ltd.

SUBMITTER: Kim W

PROVIDER: S-EPMC5810411 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Selecting cases and controls for DNA sequencing studies using family histories of disease.

Kim Wonji W Qiao Dandi D Cho Michael H MH Kwak Soo Heon SH Park Kyong Soo KS Silverman Edwin K EK Sham Pak P Won Sungho S

Statistics in medicine 20170221 13

Recent improvements in sequencing technology have enabled the investigation of so-called missing heritability, and a large number of affected subjects have been sequenced in order to detect significant associations between human diseases and rare variants. However, the cost of genome sequencing is still high, and a statistically powerful strategy for selecting informative subjects would be useful. Therefore, in this report, we propose a new statistical method for selecting cases and controls for ...[more]

PMID: 28222494

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Project description:Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide; smoking is the main risk factor for COPD, but genetic factors are also relevant contributors. Genome-wide association studies (GWAS) of the lung function measures used in the diagnosis of COPD have identified a number of loci, however association signals are often broad and collectively these loci only explain a small proportion of the heritability. In order to examine the association with COPD risk of genetic variants down to low allele frequencies, to aid fine-mapping of association signals and to explain more of the missing heritability, we undertook a targeted sequencing study in 300 COPD cases and 300 smoking controls for 26 loci previously reported to be associated with lung function. We used a pooled sequencing approach, with 12 pools of 25 individuals each, enabling high depth (30x) coverage per sample to be achieved. This pooled design maximised sample size and therefore power, but led to challenges during variant-calling since sequencing error rates and minor allele frequencies for rare variants can be very similar. For this reason we employed a rigorous quality control pipeline for variant detection which included the use of 3 independent calling algorithms. In order to avoid false positive associations we also developed tests to detect variants with potential batch effects and removed them before undertaking association testing. We tested for the effects of single variants and the combined effect of rare variants within a locus. We followed up the top signals with data available (only 67% of collapsing methods signals) in 4,249 COPD cases and 11,916 smoking controls from UK Biobank. We provide suggestive evidence for the combined effect of rare variants on COPD risk in TNXB and in sliding windows within MECOM and upstream of HHIP. These findings can lead to an improved understanding of the molecular pathways involved in the development of COPD.

Project description:ObjectivesTo evaluate the potential effect of computer support on general practitioners' management of familial breast and ovarian cancer, and to compare the effectiveness of two different types of computer program.DesignCrossover experiment with balanced block design.ParticipantsOf a random sample of 100 general practitioners from Buckinghamshire who were invited, 41 agreed to participate. From these, 36 were selected for a fully balanced study.InterventionsDoctors managed 18 simulated cases: 6 with computerised decision support system Risk Assessment in Genetics (RAGs), 6 with Cyrillic (an established pedigree drawing program designed for clinical geneticists), and 6 with pen and paper.Main outcome measuresNumber of appropriate management decisions made (maximum 6), mean time taken to reach a decision, number of pedigrees accurately drawn (maximum 6). Secondary measures were method of support preferred for particular aspects of managing family histories of cancer; importance of specific information on cancer genetics that might be provided by an "ideal computer program."ResultsRAGs resulted in significantly more appropriate management decisions (median 6) than either Cyrillic (median 3) or pen and paper (median 3); median difference between RAGs and Cyrillic 2.5 (95% confidence interval 2.0 to 3.0; P<0.0001). RAGs also resulted in significantly more accurate pedigrees (median 5) than both Cyrillic (median 3.5) and pen and paper (median 2); median difference between RAGs and Cyrillic 1.5 (1.0 to 2.0; P<0.0001). The time taken to use RAGs (median 178 seconds) was 51 seconds longer per case (95% confidence interval 36 to 65; P<0.0001) than pen and paper (median 124 seconds) but was less than Cyrillic (median 203 seconds; difference 23. (5 to 43; P=0.02)). 33 doctors (92% (78% to 98%)) preferred using RAGs overall. The most important elements of an "ideal computer program" for genetic advice in primary care were referral advice, the capacity to create pedigrees, and provision of evidence and explanations to support advice.ConclusionsRAGs could enable general practitioners to be more effective gatekeepers to genetics services, empowering them to reassure the majority of patients with a family history of breast and ovarian cancer who are not at increased genetic risk.

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Selecting cases and controls for DNA sequencing studies using family histories of disease.

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Selecting cases and controls for DNA sequencing studies using family histories of disease.

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