Evolutionary triangulation: informing genetic association studies with evolutionary evidence

Estimating prevalence of human traits among populations from polygenic risk scores

The genetic basis of phenotypic variation across populations has not been well explained for most traits. Several factors may cause disparities, from variation in environments to divergent population genetic structure. We hypothesized that a population-level polygenic risk score (PRS) can explain phenotypic variation among geographic populations based solely on risk allele frequencies. We applied a population-specific PRS (psPRS) to 26 populations from the 1000 Genomes to four phenotypes: lactase persistence (LP), melanoma, multiple sclerosis (MS) and height. Our models assumed additive genetic architecture among the polymorphisms in the psPRSs, as is convention. Linear psPRSs explained a significant proportion of trait variance ranging from 0.32 for height in men to 0.88 for melanoma. The best models for LP and height were linear, while those for melanoma and MS were nonlinear. As not all variants in a PRS may confer similar, or even any, risk among diverse populations, we also filtered out SNPs to assess whether variance explained was improved using psPRSs with fewer SNPs. Variance explained usually improved with fewer SNPs in the psPRS and was as high as 0.99 for height in men using only 548 of the initial 4208 SNPs. That reducing SNPs improves psPRSs performance may indicate that missing heritability is partially due to complex architecture that does not mandate additivity, undiscovered variants or spurious associations in the databases. We demonstrated that PRS-based analyses can be used across diverse populations and phenotypes for population prediction and that these comparisons can identify the universal risk variants.

Evolutionarily derived networks to inform disease pathways

Methods to identify genes or pathways associated with complex diseases are often inadequate to elucidate most risk because they make implicit and oversimplified assumptions about underlying models of disease etiology. These can lead to incomplete or inadequate conclusions. To address this, we previously developed human phenotype networks (HPN), linking phenotypes based on shared biology. However, such visualization alone is often uninterpretable, and requires additional filtering. Here, we expand the HPN to include another method, evolutionary triangulation (ET). ET utilizes the hypothesis that alleles affecting disease risk in multiple populations are distributed consistently with differences in disease prevalence and compares allele frequencies among populations and their relationship to phenotype prevalence. We hypothesized that combining these methods will increase our ability to detect genetic patterns of association in complex diseases. We combined HPN and ET to identify network patterns associated with type 2 diabetes mellitus (T2DM), a leading cause of death worldwide. Fasting glucose, a continuous trait, was used as a proxy for T2DM and differs significantly among continental populations. The combined method identified several diabetes-related traits and several phenotypes related to cardiovascular diseases, for which diabetes is a major risk factor. ET-HPN found more phenotypes related to our target and related phenotypes than the application of either method alone. Not only could we detect phenotype connections related to T2DM, but we also identified phenotypes that are distributed in parallel to it, e.g., amyotrophic lateral sclerosis. Our analyses showed that ET-filtered HPN provides information that neither technique can individually.

The transformative potential of an integrative approach to pregnancy

BACKGROUND

Complex traits typically involve diverse biological pathways and are shaped by numerous genetic and environmental factors. Pregnancy-associated traits and pathologies are further complicated by extensive communication across multiple tissues in two individuals, interactions between two genomes-maternal and fetal-that obscure causal variants and lead to genetic conflict, and rapid evolution of pregnancy-associated traits across mammals and in the human lineage. Given the multi-faceted complexity of human pregnancy, integrative approaches that synthesize diverse data types and analyses harbor tremendous promise to identify the genetic architecture and environmental influences underlying pregnancy-associated traits and pathologies.

METHODS

We review current research that addresses the extreme complexities of traits and pathologies associated with human pregnancy.

RESULTS

We find that successful efforts to address the many complexities of pregnancy-associated traits and pathologies often harness the power of many and diverse types of data, including genome-wide association studies, evolutionary analyses, multi-tissue transcriptomic profiles, and environmental conditions.

CONCLUSION

We propose that understanding of pregnancy and its pathologies will be accelerated by computational platforms that provide easy access to integrated data and analyses. By simplifying the integration of diverse data, such platforms will provide a comprehensive synthesis that transcends many of the inherent challenges present in studies of pregnancy.