Exploiting family history in aggregation unit-based genetic association tests

Leveraging family history in genetic association analyses of binary traits

BACKGROUND

Considering relatives' health history in logistic regression for case-control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case-control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS.

RESULTS

In our simulation scenario of trait with modest T2D heritability (h² = 0.28), variant minor allele frequency ranging from 1% to 50%, and 1% of phenotype variance explained by the genetic variants, Fam-meta had the highest overall power, while both methods incorporating family history were more powerful than CC-GWAS. All three methods had controlled type I error rates, while LT-FH was the most conservative with a lower-than-expected error rate. In addition, we observed a substantial increase in power of the two familial history methods compared to CC-GWAS when the prevalence of the phenotype increased with age. Furthermore, we showed that, when only the phenotypes of more distant relatives were available, Fam-meta still remained more powerful than CC-GWAS, confirming that leveraging disease history of both close and distant relatives can increase power of association analyses. Using FHS data, we confirmed the well-known association of TCF7L2 region with T2D at the genome-wide threshold of P-value < 5 × 10^-8, and both familial history methods increased the significance of the region compared to CC-GWAS. We identified two loci at 5q35 (ADAMTS2) and 5q23 (PRR16), not previously reported for T2D using CC-GWAS and Fam-meta; both genes play a role in cardiovascular diseases. Additionally, CC-GWAS detected one more significant locus at 13q31 (GPC6) reported associated with T2D-related traits.

CONCLUSIONS

Overall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies.

A novel heterozygous variant in PANX1 is associated with oocyte death and female infertility

PURPOSE

Oocyte death is a severe clinical phenotype that causes female infertility and recurrent in vitro fertilization and intracytoplasmic sperm injection failure. We aimed to identify pathogenic variants in a female infertility patient with oocyte death phenotype.

METHODS

Sanger sequencing was performed to screen PANX1 variants in the affected patient. Western blot analysis was used to check the effect of the variant on PANX1 glycosylation pattern in vitro.

RESULTS

We identified a novel PANX1 variant (NM_015368.4 c.86G > A, (p. Arg29Gln)) associated with the phenotype of oocyte death in a non-consanguineous family. This variant displayed an autosomal dominant inheritance pattern with reduced penetrance. Western blot analysis confirmed that the missense mutation of PANX1 (c.86G > A) altered the glycosylation pattern in HeLa cells. Moreover, the mutation effects on the function of PANX1 were weaker than recently reported variants.

CONCLUSION

Our findings expand the inheritance pattern of PANX1 variants to an autosomal dominant mode with reduced penetrance and enrich the variational spectrum of PANX1. These results help us to better understand the genetic basis of female infertility with oocyte death.

Family history aggregation unit-based tests to detect rare genetic variant associations with application to the Framingham Heart Study

A challenge in standard genetic studies is maintaining good power to detect associations, especially for low prevalent diseases and rare variants. The traditional methods are most powerful when evaluating the association between variants in balanced study designs. Without accounting for family correlation and unbalanced case-control ratio, these analyses could result in inflated type I error. One cost-effective solution to increase statistical power is exploitation of available family history (FH) that contains valuable information about disease heritability. Here, we develop methods to address the aforementioned type I error issues while providing optimal power to analyze aggregates of rare variants by incorporating additional information from FH. With enhanced power in these methods exploiting FH and accounting for relatedness and unbalanced designs, we successfully detect genes with suggestive associations with Alzheimer disease, dementia, and type 2 diabetes by using the exome chip data from the Framingham Heart Study.