scholarly journals Combining case-control status and family history of disease increases association power

2019 ◽  
Author(s):  
Margaux L.A. Hujoel ◽  
Steven Gazal ◽  
Po-Ru Loh ◽  
Nick Patterson ◽  
Alkes L. Price
Keyword(s):  
Family History ◽  
Linear Regression ◽  
Threshold Model ◽  
Association Studies ◽  
Case Control ◽  
Posterior Mean ◽  
Genome Wide ◽  
History Of Disease ◽  
History Of ◽  
Control Association

AbstractFamily history of disease can provide valuable information about an individual’s genetic liability for disease in case-control association studies, but it is currently unclear how to best combine case-control status and family history of disease. We developed a new association method based on posterior mean genetic liabilities under a liability threshold model, conditional on both case-control status and family history (LT-FH); association statistics are computed via linear regression of genotypes and posterior mean genetic liabilities, equivalent to a score test. We applied LT-FH to 12 diseases from the UK Biobank (average N=350K). We compared LT-FH to genome-wide association without using family history (GWAS) and a previous proxy-based method for incorporating family history (GWAX). LT-FH was +63% (s.e. 6%) more powerful than GWAS and +36% (s.e. 4%) more powerful than the trait-specific maximum of GWAS and GWAX, based on the number of independent genome-wide significant loci detected across all diseases (e.g. 690 independent loci for LT-FH vs. 423 for GWAS); the second best method was GWAX for lower-prevalence diseases and GWAS for higher-prevalence diseases, consistent with simulations. We also confirmed that LT-FH was well-calibrated (assessed via stratified LD score regression attenuation ratio), consistent with simulations. When using BOLT-LMM (instead of linear regression) to compute association statistics for all three methods (increasing the power of each method), LT-FH was +67% (s.e. 6%) more powerful than GWAS and +39% (s.e. 4%) more powerful than the trait-specific maximum of GWAS and GWAX. In summary, LT-FH greatly increases association power in case-control association studies when family history of disease is available.

scholarly journals Accounting for age-of-onset and family history improves power in genome-wide association studies

2021 ◽  
Author(s):  
Emil M Pedersen ◽  
Esben Agerbo ◽  
Oleguer Plana-Ripoll ◽  
Jakob Grove ◽  
Julie W. Dreier ◽  
...  
Keyword(s):  
Family History ◽  
Age Of Onset ◽  
Threshold Model ◽  
Association Studies ◽  
Case Control ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Large Power ◽  
Standard Case ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age-of-onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), that jointly accounts for age-of-onset and sex, as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields large power gains over both LT-FH and genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data, and to mortality in the UK Biobank, finding 20 genome-wide significant associations with LT-FH++, compared to 10 for LT-FH and 8 for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

scholarly journals Case–control association mapping by proxy using family history of disease

2017 ◽  
Vol 49 (3) ◽  
pp. 325-331 ◽  
Author(s):  
Jimmy Z Liu ◽  
Yaniv Erlich ◽  
Joseph K Pickrell
Keyword(s):  
Family History ◽  
Association Mapping ◽  
Case Control ◽  
History Of Disease ◽  
History Of ◽  
Control Association

scholarly journals Leveraging Family History in Population-Based Case-Control Association Studies

2014 ◽  
Vol 38 (2) ◽  
pp. 114-122 ◽  
Author(s):  
Arpita Ghosh ◽  
Patricia Hartge ◽  
Peter Kraft ◽  
Amit D. Joshi ◽  
Regina G. Ziegler ◽  
...  
Keyword(s):  
Family History ◽  
Association Studies ◽  
Population Based ◽  
Case Control ◽  
Control Association

scholarly journals Robust ranks of true associations in genome-wide case-control association studies

2007 ◽  
Vol 1 (S1) ◽  
Author(s):  
Gang Zheng ◽  
Jungnam Joo ◽  
Jing-Ping Lin ◽  
Mario Stylianou ◽  
Myron A Waclawiw ◽  
...  
Keyword(s):  
Association Studies ◽  
Case Control ◽  
Genome Wide ◽  
Control Association

scholarly journals Incorporating family history of disease improves polygenic risk scores in diverse populations

2021 ◽  
Author(s):  
Margaux L.A. Hujoel ◽  
Po-Ru Loh ◽  
Benjamin M. Neale ◽  
Alkes L. Price
Keyword(s):  
Family History ◽  
Threshold Model ◽  
South Asians ◽  
Target Population ◽  
Risk Scores ◽  
Diverse Populations ◽  
Polygenic Risk ◽  
History Of Disease ◽  
History Of ◽  
Large Improvement

AbstractPolygenic risk scores derived from genotype data (PRS) and family history of disease (FH) both provide valuable information for predicting disease risk, enhancing prospects for clinical utility. PRS perform poorly when applied to diverse populations, but FH does not suffer this limitation. Here, we explore methods for combining both types of information (PRS-FH). We analyzed 10 complex diseases from the UK Biobank for which family history (parental and sibling history) was available for most target samples. PRS were trained using all British individuals (N=409K), and target samples consisted of unrelated non-British Europeans (N=42K), South Asians (N=7K), or Africans (N=7K). We evaluated PRS, FH, and PRS-FH using liability-scale R2, focusing on three well-powered diseases (type 2 diabetes, hypertension, depression) with R2 > 0.05 for PRS and/or FH in each target population. Averaging across these three diseases, PRS attained average prediction R2 of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R2 of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans; for each disease and each target population, the improvement was highly statistically significant. PRS-FH methods based on a logistic model and a liability threshold model performed similarly when covariates were not included in predictions (consistent with simulations), but the logistic model outperformed the liability threshold model when covariates were included. In conclusion, including family history greatly improves the accuracy of polygenic risk scores, particularly in diverse populations.

MAX-rank: a simple and robust genome-wide scan for case-control association studies

2008 ◽  
Vol 123 (6) ◽  
pp. 617-623 ◽  
Author(s):  
Qizhai Li ◽  
Kai Yu ◽  
Zhaohai Li ◽  
Gang Zheng
Keyword(s):  
Association Studies ◽  
Case Control ◽  
Genome Wide ◽  
Control Association ◽  
Genome Wide Scan
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