Biology subjects

Alkes L. Price

Publications and source records attributed to Alkes L. Price.

3 recordsLinked to original sources

bioRxiv2016

Multi-ethnic polygenic risk scores improve risk prediction in diverse populations.

Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multi-ethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size and Latino training data in small sample size. We attained a >70% relative improvement in prediction accuracy (from R2=0.027 to R2=0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. Application of our approach to predict T2D in a South Asian UK Biobank cohort attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.\n\nAuthor SummaryThe use of genetic information to predict disease risk is of great interest because of its potential clinical application. Prediction is performed via the construction of polygenic risk scores, which separate individuals into different risk categories. Polygenic risk scores can also be applied to improve our understanding of the genetic architecture of complex diseases. The ideal training data set would be a large cohort from the same population as the target sample, but this is generally unavailable for non-European populations. Thus, we propose a summary statistics based polygenic risk score that leverages both a large European training sample and a training sample from the same population as the target population. This approach produces a substantial relative improvement in prediction accuracy compared to methods that use a single training population when applied to predict type 2 diabetes in a Latino cohort, consistent with simulation results. We observed similar relative improvements in applications to predict type 2 diabetes in a South Asian cohort and height in an African cohort.

Bioinformatics↗

bioRxiv2015

Partitioning heritability by functional category using GWAS summary statistics

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits spanning a total of 1.3 million phenotype measurements. To enable this analysis, we introduce a new method for partitioning heritability from GWAS summary statistics while controlling for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior. These results demonstrate that GWAS can aid in understanding the biological basis of disease and provide direction for functional follow-up.

Genetics↗

bioRxiv2014

Explicit modeling of ancestry improves polygenic risk scores and BLUP prediction

Polygenic prediction using genome-wide SNPs can provide high prediction accuracy for complex traits. Here, we investigate the question of how to account for genetic ancestry when conducting polygenic prediction. We show that the accuracy of polygenic prediction in structured populations may be partly due to genetic ancestry. However, we hypothesized that explicitly modeling ancestry could improve polygenic prediction accuracy. We analyzed three GWAS of hair color, tanning ability and basal cell carcinoma (BCC) in European Americans (sample size from 7,440 to 9,822) and considered two widely used polygenic prediction approaches: polygenic risk scores (PRS) and Best Linear Unbiased Prediction (BLUP). We compared polygenic prediction without correction for ancestry to polygenic prediction with ancestry as a separate component in the model. In 10-fold cross-validation using the PRS approach, the R2 for hair color increased by 66% (0.0456 to 0.0755; p<10-16), the R2 for tanning ability increased by % (0.0154 to 0.0344; p<10-16) and the liability-scale R2 for BCC increased by 68% (0.0138 to 0.0232; p<10-16) when explicitly modeling ancestry, which prevents ancestry effects from entering into each SNP effect and being over-weighted. Surprisingly, explicitly modeling ancestry produces a similar improvement when using the BLUP approach, which fits all SNPs simultaneously in a single variance component and causes ancestry to be under-weighted. We validate our findings via simulations, which show that the differences in prediction accuracy will increase in magnitude as sample sizes increase. In summary, our results show that explicitly modeling ancestry can be important in both PRS and BLUP prediction.

Genetics↗