Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction.

Myocardial infarction (MI), a leading cause of death around the world, displays a complex pattern of inheritance. When MI occurs early in life, genetic inheritance is a major component to risk. Previously, rare mutations in low-density lipoprotein (LDL) genes have been shown to contribute to MI risk in individual families, whereas common variants at more than 45 loci have been associated with MI risk in the population.

Isolated central hypothyroidism in young siblings as a manifestation of PROP1 deficiency: clinical impact of whole exome sequencing.

Background/aims: Central hypothyroidism (CH) in children is rare and may be due to a variety of genetic defects. Most of these defects, but not all, are associated with additional pituitary hormone deficits. In a young child presenting with CH, it is important to determine whether additional pituitary hormone deficiencies are present, but this may be difficult to establish clinically.

Whole exome sequencing reveals a novel mutation in CUL7 in a patient with an undiagnosed growth disorder.

We present the case of a 19-year-old man with a growth disorder, which was undefined, despite extensive evaluation. Whole exome sequencing demonstrated a novel homozygous frameshift mutation in CUL7, one of the causative genes of 3-M syndrome. We discuss the utility of exome sequencing in diagnosing rare disorders.

The mystery of missing heritability: Genetic interactions create phantom heritability.

Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data.

Quantifying the underestimation of relative risks from genome-wide association studies.

Genome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will identify alleles with larger effects on genetic risk than those currently estimated from GWAS replication studies.

Disease model distortion in association studies.

Most findings from genome-wide association studies (GWAS) are consistent with a simple disease model at a single nucleotide polymorphism, in which each additional copy of the risk allele increases risk by the same multiplicative factor, in contrast to dominance or interaction effects. As others have noted, departures from this multiplicative model are difficult to detect. Here, we seek to quantify this both analytically and empirically.